From a900b90a373080ea5c73eda0615703e2e555501d Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 25 Oct 2022 15:09:13 +0200 Subject: [PATCH 01/81] unify CLI actions --- cc_cli.py | 155 ----------------- cli.py | 194 +++++++++++++++++++++ docs/quickstart.md | 4 +- examples/fips_oop_demo.py | 41 ----- fips_cli.py | 246 --------------------------- sec_certs/dataset/common_criteria.py | 59 +------ sec_certs/dataset/dataset.py | 49 +++++- sec_certs/dataset/fips.py | 22 ++- setup.py | 2 +- tests/test_fips_oop.py | 2 +- 10 files changed, 268 insertions(+), 506 deletions(-) delete mode 100755 cc_cli.py create mode 100644 cli.py delete mode 100644 examples/fips_oop_demo.py delete mode 100755 fips_cli.py diff --git a/cc_cli.py b/cc_cli.py deleted file mode 100755 index db3458d0..00000000 --- a/cc_cli.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -import logging -import sys -from datetime import datetime -from pathlib import Path -from typing import List, Optional - -import click - -from sec_certs.config.configuration import config -from sec_certs.dataset import CCDataset -from sec_certs.utils.helpers import warn_if_missing_poppler, warn_if_missing_tesseract - -logger = logging.getLogger(__name__) - - -@click.command() -@click.argument( - "actions", - required=True, - nargs=-1, - type=click.Choice(["all", "build", "download", "convert", "analyze", "maintenances"], case_sensitive=False), -) -@click.option( - "-o", - "--output", - type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True), - help="Path where the output of the experiment will be stored. May overwrite existing content.", - default=Path("./cc_dset/"), - show_default=True, -) -@click.option( - "-c", - "--config", - "configpath", - default=None, - type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), - help="Path to your own config yaml file that will override the default one.", -) -@click.option( - "-i", - "--input", - "inputpath", - type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), - help="If set, the actions will be performed on a CC dataset loaded from JSON from the input path.", -) -@click.option("-s", "--silent", is_flag=True, help="If set, will not print to stdout") -def main( - configpath: Optional[str], actions: List[str], inputpath: Optional[Path], output: Optional[Path], silent: bool -): - """ - Specify actions, sequence of one or more strings from the following list: [all, build, download, convert, analyze] - If 'all' is specified, all actions run against the dataset. Otherwise, only selected actions will run in the correct order. - """ - file_handler = logging.FileHandler(config.log_filepath) - stream_handler = logging.StreamHandler(sys.stderr) - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - stream_handler.setFormatter(formatter) - handlers: List[logging.StreamHandler] = [file_handler] - - if output: - output = Path(output) - - if not inputpath and not output: - print( - "Error: You did not specify path to load the dataset from, nor did you specify where dataset can be stored." - ) - sys.exit(1) - - if not silent: - handlers.append(stream_handler) - - logging.basicConfig(level=logging.INFO, handlers=handlers) - start = datetime.now() - - if configpath: - try: - config.load(Path(configpath)) - except FileNotFoundError: - print("Error: Bad path to configuration file") - sys.exit(1) - except ValueError as e: - print(f"Error: Bad format of configuration file: {e}") - - actions_set = {"build", "download", "convert", "analyze", "maintenances"} if "all" in actions else set(actions) - - if inputpath and "build" not in actions_set: - dset: CCDataset = CCDataset.from_json(Path(inputpath)) - if output and dset.root_dir != output: - print( - "Warning: you provided both input and output paths. The dataset from input path will get copied to output path." - ) - dset.root_dir = output - - if inputpath and "build" in actions_set: - print( - f"Warning: you wanted to build a dataset but you provided one in JSON -- that will be ignored. New one will be constructed at: {output}" - ) - - if "build" in actions_set: - if output is None: - raise RuntimeError("Output path was not provided.") - dset = CCDataset( - certs={}, - root_dir=output, - name="CommonCriteria_dataset", - description=f"Full CommonCriteria dataset snapshot {datetime.now().date()}", - ) - dset.get_certs_from_web() - dset.process_protection_profiles() - elif "build" not in actions_set and not inputpath: - print("Error: If you do not provide input parameter, you must use 'build' action to build dataset first.") - sys.exit(1) - - if "download" in actions_set: - if not dset.state.meta_sources_parsed: - print( - "Error: You want to download all pdfs, but the data from commoncriteria.org was not parsed. You must use 'build' action first." - ) - sys.exit(1) - dset.download_all_pdfs() - - if "convert" in actions_set: - if not dset.state.pdfs_downloaded: - print( - "Error: You want to convert pdfs -> txt, but the pdfs were not downloaded. You must use 'download' action first." - ) - sys.exit(1) - warn_if_missing_poppler() - warn_if_missing_tesseract() - dset.convert_all_pdfs() - - if "analyze" in actions_set: - if not dset.state.pdfs_converted: - print( - "Error: You want to process txt documents of certificates, but pdfs were not converted. You must use 'convert' action first." - ) - sys.exit(1) - dset.analyze_certificates() - - if "maintenances" in actions_set: - if not dset.state.meta_sources_parsed: - print( - "Error: You want to process maintenance updates, but the data from commoncriteria.org was not parsed. You must use 'build' action first." - ) - sys.exit(1) - dset.process_maintenance_updates() - - end = datetime.now() - logger.info(f"The computation took {(end-start)} seconds.") - - -if __name__ == "__main__": - main() diff --git a/cli.py b/cli.py new file mode 100644 index 00000000..3d361719 --- /dev/null +++ b/cli.py @@ -0,0 +1,194 @@ +#!/usr/bin/env python3 +import logging +import sys +from dataclasses import dataclass, field +from datetime import datetime +from pathlib import Path +from typing import Callable, List, Optional, Type, Union + +import click + +from sec_certs.config.configuration import config +from sec_certs.dataset import CCDataset, FIPSDataset +from sec_certs.utils.helpers import warn_if_missing_graphviz, warn_if_missing_poppler, warn_if_missing_tesseract + +logger = logging.getLogger(__name__) + + +@dataclass +class ProcessingStep: + name: str + processing_function_name: str + precondition: Optional[str] = field(default=None) + precondition_error_msg: Optional[str] = field(default=None) + pre_callback_func: Optional[Callable] = field(default=None) + + def run(self, dset: Union[CCDataset, FIPSDataset]) -> None: + if self.precondition and not getattr(dset.state, self.precondition): + err_msg = ( + self.precondition_error_msg + if self.precondition_error_msg + else f"Error, precondition to run {self.name} not met, exiting." + ) + print(err_msg) + sys.exit(1) + if self.pre_callback_func: + self.pre_callback_func() + + getattr(dset, self.processing_function_name)() + + +def warn_missing_libs(): + warn_if_missing_poppler() + warn_if_missing_tesseract() + + +def build_or_load_dataset( + framework: str, inputpath: Optional[Path], outputpath: Optional[Path], to_build: bool +) -> Union[CCDataset, FIPSDataset]: + constructor: Union[Type[CCDataset], Type[FIPSDataset]] = CCDataset if framework == "cc" else FIPSDataset + dset: Union[CCDataset, FIPSDataset] + + if to_build: + if inputpath: + print( + f"Warning: you wanted to build a dataset but you provided one in JSON -- that will be ignored. New one will be constructed at: {outputpath}" + ) + dset = constructor( + certs={}, + root_dir=outputpath, + name=framework + "_dataset", + description=f"Full {framework} dataset snapshot {datetime.now().date()}", + ) + dset.get_certs_from_web() + else: + if inputpath: + dset = constructor.from_json(inputpath) + if outputpath and dset.root_dir != outputpath: + print( + "Warning: you provided both input and output paths. The dataset from input path will get copied to output path." + ) + dset.root_dir = outputpath + else: + print( + "Error: If you do not use 'build' action, you must provide --input parameter to point to an existing dataset." + ) + sys.exit(1) + + return dset + + +@click.command() +@click.argument( + "framework", + required=True, + nargs=1, + type=click.Choice(["cc", "fips"], case_sensitive=False), +) +@click.argument( + "actions", + required=True, + nargs=-1, + type=click.Choice(["all", "build", "process-aux-dsets", "download", "convert", "analyze"], case_sensitive=False), +) +@click.option( + "-o", + "--output", + type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True), + help="Path where the output of the experiment will be stored. May overwrite existing content.", + default=Path("./cc_dset/"), + show_default=True, +) +@click.option( + "-c", + "--config", + "configpath", + default=None, + type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), + help="Path to your own config yaml file that will override the default one.", +) +@click.option( + "-i", + "--input", + "inputpath", + type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), + help="If set, the actions will be performed on a CC dataset loaded from JSON from the input path.", +) +@click.option("-s", "--silent", is_flag=True, help="If set, will not print to stdout") +def main( + framework: str, + actions: List[str], + outputpath: Path, + configpath: Optional[str], + inputpath: Optional[Path], + silent: bool, +): + file_handler = logging.FileHandler(config.log_filepath) + stream_handler = logging.StreamHandler(sys.stderr) + formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + file_handler.setFormatter(formatter) + stream_handler.setFormatter(formatter) + handlers: List[logging.StreamHandler] = [file_handler] if silent else [file_handler, stream_handler] + logging.basicConfig(level=logging.INFO, handlers=handlers) + start = datetime.now() + + if configpath: + try: + config.load(Path(configpath)) + except FileNotFoundError: + print("Error: Bad path to configuration file") + sys.exit(1) + except ValueError as e: + print(f"Error: Bad format of configuration file: {e}") + + actions_set = ( + {"build", "process-aux-dsets", "download", "convert", "analyze", "maintenances"} + if "all" in actions + else set(actions) + ) + + dset = build_or_load_dataset(framework, inputpath, outputpath, "build" in actions_set) + aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" + analysis_pre_callback = None if framework == "cc" else warn_if_missing_graphviz + + steps = [ + ProcessingStep( + "process-aux-dsets", + "process_auxillary_datasets", + precondition="meta_sources_parsed", + precondition_error_msg=f"Error: You want to process the auxillary datasets: {aux_dsets_to_handle} , but the data from cert. framework website was not parsed. You must use 'build' action first.", + pre_callback_func=None, + ), + ProcessingStep( + "download", + "download_all_pdfs", + precondition="meta_sources_parsed", + precondition_error_msg="Error: You want to download all pdfs, but the data from the cert. framework website was not parsed. You must use 'build' action first.", + pre_callback_func=None, + ), + ProcessingStep( + "convert", + "convert_all_pdfs", + precondition="pdfs_downloaded", + precondition_error_msg="Error: You want to convert pdfs -> txt, but the pdfs were not downloaded. You must use 'download' action first.", + pre_callback_func=warn_missing_libs, + ), + ProcessingStep( + "analyze", + "analyze_certificates", + precondition="pdfs_converted", + precondition_error_msg="Error: You want to process txt documents of certificates, but pdfs were not converted. You must use 'convert' action first.", + pre_callback_func=analysis_pre_callback, + ), + ] + + processing_step: ProcessingStep + for processing_step in [x for x in steps if x in actions_set]: + processing_step.run(dset) + + end = datetime.now() + logger.info(f"The computation took {(end-start)} seconds.") + + +if __name__ == "__main__": + main() diff --git a/docs/quickstart.md b/docs/quickstart.md index 88bfac42..9b2f9ed7 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -38,13 +38,13 @@ If you insist on processing the whole certificates pipeline, make sure that you ::::{tab-set} :::{tab-item} Common Criteria ```bash -$ cc-certs all +$ sec-certs cc all ``` ::: :::{tab-item} FIPS 140 ```bash -$ fips-certs new-run +$ sec-certs fips all ``` ::: :::: diff --git a/examples/fips_oop_demo.py b/examples/fips_oop_demo.py deleted file mode 100644 index 73d5c5c5..00000000 --- a/examples/fips_oop_demo.py +++ /dev/null @@ -1,41 +0,0 @@ -import logging -from datetime import datetime - -from sec_certs.config.configuration import config -from sec_certs.dataset import FIPSDataset - -logger = logging.getLogger(__name__) - - -def main(): - file_handler = logging.FileHandler(config.log_filepath) - stream_handler = logging.StreamHandler() - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - stream_handler.setFormatter(formatter) - logging.basicConfig(level=logging.INFO, handlers=[file_handler, stream_handler]) - start = datetime.now() - - # Full processing of FIPS dataset, fresh run - dset = FIPSDataset() - dset.get_certs_from_web() - dset.convert_all_pdfs() - dset.pdf_scan() - dset.extract_certs_from_tables() - dset.finalize_results() - - # TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/210 and refactor this - # if not no_download_algs: - # aset.get_certs_from_web() - # logger.info(f"Finished parsing. Have algorithm dataset with {len(aset)} algorithm numbers.") - # dset.algorithms = aset - - # TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/211 and uncomment - # dset.plot_graphs(show=False) - - end = datetime.now() - logger.info(f"The computation took {(end - start)} seconds.") - - -if __name__ == "__main__": - main() diff --git a/fips_cli.py b/fips_cli.py deleted file mode 100755 index a01fde08..00000000 --- a/fips_cli.py +++ /dev/null @@ -1,246 +0,0 @@ -#!/usr/bin/env python3 -import logging -import sys -from datetime import datetime -from pathlib import Path -from typing import List, Optional, Set - -import click - -from sec_certs.config.configuration import DEFAULT_CONFIG_PATH, config -from sec_certs.dataset import FIPSDataset -from sec_certs.utils.helpers import warn_if_missing_graphviz, warn_if_missing_poppler, warn_if_missing_tesseract - -logger = logging.getLogger(__name__) - - -@click.command() -@click.argument( - "actions", - required=True, - nargs=-1, - type=click.Choice( - [ - "new-run", - "all", - "build", - "convert", - "algorithms", - "update", - "pdf-scan", - "table-search", - "analysis", - "graphs", - ], - case_sensitive=False, - ), -) -@click.option( - "-o", - "--output", - type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True), - help="Path to the directory where the output of the 'build' or 'new-run' actions will be stored.", - default=Path("./fips_dset/"), - show_default=True, -) -@click.option( - "-c", - "--config", - "configpath", - default=None, - type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), - help="Path to your own config yaml file that will override the default one.", -) -@click.option( - "-i", - "--input", - "inputpath", - type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), - help="If set, the actions will be performed on a FIPS dataset loaded from JSON from the input path.", -) -@click.option( - "-n", - "--name", - "json_name", - default=str(datetime.now().strftime("%d-%n-%Y-%H:%M:%S")) + ".json", - type=str, - help="Name of the json object to be created in the <> directory. Defaults to <>.json.", -) -@click.option("--redo-web-scan", "redo_web_scan", help="Redo HTML webpage scan from scratch", is_flag=True) -@click.option("--redo-keyword-scan", "redo_keyword_scan", help="Redo PDF keyword scan from scratch", is_flag=True) -@click.option( - "--higher-precision-results", - "higher_precision_results", - help="Redo table search for certificates with high error rate. Behaviour undefined if used on a newly instantiated dataset.", - is_flag=True, -) -@click.option("-s", "--silent", is_flag=True, help="If set, will not print to stdout") -def main( - configpath: Optional[str], - actions: Set[str], - inputpath: Optional[Path], - output: Optional[Path], - silent: bool, - no_download_algs: bool, - redo_web_scan: bool, - redo_keyword_scan: bool, - higher_precision_results: bool, - json_name: str, -): - """ - Specify actions, sequence of one or more strings from the following list: - - ["new-run", "all", "build", "convert", "update", "pdf-scan", "table-search", "analysis", "graphs"] - - If 'new-run' is specified, a new dataset will be created and all the actions will be run. - If 'all' is specified, dataset will be updated and all actions run against the dataset. - Otherwise, only selected actions will run in the correct order. - - Dataset loading: - - 'build' Create a skeleton of a new dataset from NIST pages. - - 'update' Load a previously used dataset (created by 'build') and update it with nonprocessed entries from NIST pages. - - 'algorithms' Download the FIPS algorithms from NIST pages. - - These options download the files needed for analysis. - - Analysis preparation: - - 'convert' Convert all downloaded PDFs. - - 'pdf-scan' Perform a scan of downloaded CMVP security policy documents - Keyword extraction. - - 'table-search' Analyze algorithm implementation entries in tables in security policy documents. - - Analysis preparation actions are by default done only for certificates, where each corresponding action failed. - This behaviour can be changed using '--redo-*' options. - These actions are also independent of each other. - - Analysis: - - 'analysis' Merge results from analysis preparation and find dependencies between certificates. - - 'graphs' Plot dependency graphs. - """ - file_handler = logging.FileHandler(config.log_filepath) - stream_handler = logging.StreamHandler(sys.stderr) - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - stream_handler.setFormatter(formatter) - handlers: List[logging.StreamHandler] = [file_handler] - - if output: - output = Path(output) - - if not inputpath and not output: - logger.error( - "You did not specify path to load the dataset from, nor did you specify where dataset can be stored." - ) - sys.exit(1) - - if not silent: - handlers.append(stream_handler) - - logging.basicConfig(level=logging.INFO, handlers=handlers) - start = datetime.now() - - # load config - if configpath: - try: - config.load(Path(configpath)) - except FileNotFoundError: - logger.error("Bad path to configuration file") - sys.exit(1) - except ValueError as e: - logger.error(f"Bad format of configuration file: {e}") - else: - logger.info(f"Using default configuration file at {DEFAULT_CONFIG_PATH}.") - - if "all" in actions and "new-run" in actions: - logger.error("Only one of 'new-run' and 'all' can be specified.") - sys.exit(1) - - r_actions = ( - {"algorithms", "convert", "pdf-scan", "table-search", "analysis", "graphs"} - if "all" in actions or "new-run" in actions - else set(actions) - ) - - r_actions |= {"build"} if "new-run" in actions else {"update"} if "all" in actions else set() - - actions = r_actions - - if "build" in actions and "update" in actions: - logger.error( - "'build' and 'update' cannot be specified at once. Use 'build' to create dataset from scratch, 'update' to update existing dataset." - ) - - if "build" in actions: - assert output - if inputpath: - logger.warning( - "Both 'build' and 'inputpath' specified. 'build' creates new dataset, 'inputpath' will be ignored." - ) - dset: FIPSDataset = FIPSDataset( - certs={}, - root_dir=output, - name=json_name, - description=f"Full FIPS dataset snapshot {datetime.now().date()}", - ) - dset.get_certs_from_web() - inputpath = dset.json_path - output = None - - # only 'build' can work without inputpath - else: - if not inputpath: - logger.error("You must provide inputpath to previously generated dataset with 'build'") - sys.exit(1) - - assert inputpath - dset = FIPSDataset.from_json(inputpath) - - logger.info(f"Have dataset with {len(dset)} certs.") - if output: - logger.warning( - "You provided both inputpath and outputpath, dataset will be copied to outputpath (without data)" - ) - dset.root_dir = output - dset.to_json(output) - - if "update" in actions: - dset.get_certs_from_web(update=True, redo_web_scan=redo_web_scan) - - if "algorithms" in actions: - dset.process_algorithms() - - if "convert" in actions or "update" in actions: - warn_if_missing_poppler() - warn_if_missing_tesseract() - dset.convert_all_pdfs() - - if "pdf-scan" in actions or "update" in actions: - dset.pdf_scan(redo=redo_keyword_scan) - - if "table-search" in actions or "update" in actions: - if not higher_precision_results: - logger.info( - "You are using table search without higher precision results. It is advised to use the switch in the next run." - ) - dset.extract_certs_from_tables(high_precision=higher_precision_results) - - if "analysis" in actions: - dset.finalize_results() - - if "graphs" in actions: - warn_if_missing_graphviz() - dset.plot_graphs(show=True) - - end = datetime.now() - logger.info(f"The computation took {(end-start)} seconds.") - - -if __name__ == "__main__": - main() diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 565f1022..7bd56357 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -1,15 +1,13 @@ from __future__ import annotations -import copy import itertools import json import locale import shutil import tempfile -from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Callable, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Type, Union +from typing import Callable, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union import numpy as np import pandas as pd @@ -41,38 +39,15 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType): and dataset transformations. Many private methods that perform internal operations, feel free to exploit them. """ - @dataclass - class DatasetInternalState(ComplexSerializableType): - meta_sources_parsed: bool = False - pdfs_downloaded: bool = False - pdfs_converted: bool = False - certs_analyzed: bool = False - - def __bool__(self): - return any(vars(self)) - def __init__( self, certs: Dict[str, CommonCriteriaCert] = dict(), root_dir: Optional[Path] = None, name: str = "Common Criteria Dataset", description: str = "No description", - state: Optional[DatasetInternalState] = None, + state: Optional[Dataset.DatasetInternalState] = None, ): - super().__init__(certs, root_dir, name, description) - - if state is None: - state = self.DatasetInternalState() - self.state = state - - def __call__(self, certs): - return copy.deepcopy(self) - - def to_dict(self) -> Dict[str, Any]: - """ - Returns self serialized into dictionary - """ - return {**{"state": self.state}, **super().to_dict()} + super().__init__(certs, root_dir, name, description, state) def to_pandas(self) -> pd.DataFrame: """ @@ -106,28 +81,7 @@ def get_st_keywords_df(self) -> pd.DataFrame: data = [dict({"dgst": x.dgst}, **x.get_keywords_df_row()) for x in self] return pd.DataFrame(data).set_index("dgst") - @classmethod - def from_dict(cls: Type[CCDataset], dct: Dict[str, Any]) -> "CCDataset": - """ - Reconstructs the CCDataset object from a dictionary - """ - dset = super().from_dict(dct) - dset.state = copy.deepcopy(dct["state"]) - return dset - - # for type ignore explanation, see: https://github.com/python/mypy/issues/5936 - @Dataset.root_dir.setter # type: ignore - def root_dir(self, new_dir: Union[str, Path]) -> None: - old_dset = copy.deepcopy(self) - Dataset.root_dir.fset(self, new_dir) # type: ignore - self._set_local_paths() - - if self.state and old_dset.root_dir != Path(".."): - logger.info(f"Changing root dir of partially processed dataset. All contents will get copied to {new_dir}") - self._copy_dataset_contents(old_dset) - self.to_json() - - def _copy_dataset_contents(self, old_dset: "CCDataset") -> None: + def _copy_dataset_contents(self, old_dset: CCDataset) -> None: if old_dset.state.meta_sources_parsed: try: shutil.copytree(old_dset.web_dir, self.web_dir) @@ -350,6 +304,7 @@ def get_certs_from_web( """ Downloads CSV and HTML files that hold lists of certificates from common criteria website. Parses these files and constructs CommonCriteriaCert objects, fills the dataset with those. + Also downloads protection profiles snapshot :param bool to_download: If CSV and HTML files shall be downloaded (or existing files utilized), defaults to True :param bool keep_metadata: If CSV and HTML files shall be kept on disk after download, defaults to True @@ -375,6 +330,7 @@ def get_certs_from_web( self._set_local_paths() self.state.meta_sources_parsed = True + self.process_protection_profiles() def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> Dict[str, "CommonCriteriaCert"]: """ @@ -930,6 +886,9 @@ def _compute_heuristics(self, download_fresh_cpes: bool = False) -> None: def compute_related_cves(self, download_fresh_cves: bool = False) -> None: raise NotImplementedError + def process_auxillary_datasets(self) -> None: + raise NotImplementedError + @classmethod def from_json(cls, input_path: Union[str, Path]) -> "CCDatasetMaintenanceUpdates": input_path = Path(input_path) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index eeec1e85..0ee3c216 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -1,8 +1,10 @@ +import copy import itertools import json import logging import re from abc import ABC, abstractmethod +from dataclasses import dataclass from datetime import datetime from pathlib import Path from typing import ( @@ -42,13 +44,24 @@ DatasetSubType = TypeVar("DatasetSubType", bound="Dataset") -class Dataset(Generic[CertSubType], ABC): +class Dataset(Generic[CertSubType], ComplexSerializableType, ABC): + @dataclass + class DatasetInternalState(ComplexSerializableType): + meta_sources_parsed: bool = False + pdfs_downloaded: bool = False + pdfs_converted: bool = False + certs_analyzed: bool = False + + def __bool__(self): + return any(vars(self)) + def __init__( self, certs: Dict[str, CertSubType] = dict(), root_dir: Optional[Path] = None, name: str = "dataset name", description: str = "dataset_description", + state: Optional[DatasetInternalState] = None, ): if not root_dir: root_dir = Path.cwd() / (type(self).__name__).lower() @@ -59,15 +72,26 @@ def __init__( self.description = description self.certs = certs + if state is None: + state = self.DatasetInternalState() + self.state = state + @property def root_dir(self) -> Path: return self._root_dir @root_dir.setter def root_dir(self, new_dir: Union[str, Path]) -> None: - new_dir = Path(new_dir) - new_dir.mkdir(exist_ok=True) - self._root_dir = new_dir + old_dset = copy.deepcopy(self) + self._root_dir = Path(new_dir) + self.root_dir.mkdir(exist_ok=True) + + self._set_local_paths() + + if self.state and old_dset.root_dir != Path(".."): + logger.info(f"Changing root dir of partially processed dataset. All contents will get copied to {new_dir}") + self._copy_dataset_contents(old_dset) + self.to_json() @property def web_dir(self) -> Path: @@ -122,6 +146,7 @@ def __str__(self) -> str: def to_dict(self) -> Dict[str, Any]: return { + "state": self.state, "timestamp": self.timestamp, "sha256_digest": self.sha256_digest, "name": self.name, @@ -133,7 +158,7 @@ def to_dict(self) -> Dict[str, Any]: @classmethod def from_dict(cls: Type[DatasetSubType], dct: Dict) -> DatasetSubType: certs = {x.dgst: x for x in dct["certs"]} - dset = cls(certs, Path("../"), dct["name"], dct["description"]) + dset = cls(certs, Path("../"), dct["name"], dct["description"], dct["state"]) if len(dset) != (claimed := dct["n_certs"]): logger.error( f"The actual number of certs in dataset ({len(dset)}) does not match the claimed number ({claimed})." @@ -150,18 +175,30 @@ def from_json(cls: Type[DatasetSubType], input_path: Union[str, Path]) -> Datase def _set_local_paths(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + # Workaround from https://peps.python.org/pep-0673/ applied. + def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + @abstractmethod def get_certs_from_web(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def convert_all_pdfs(self) -> None: + def process_auxillary_datasets(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + @abstractmethod + def convert_all_pdfs(self) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + + @abstractmethod + def analyze_certificates(self) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + @staticmethod def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: exit_codes = cert_processing.process_parallel( diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 6ac89aa4..41bedd57 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -1,7 +1,7 @@ import logging import tempfile from pathlib import Path -from typing import Any, Dict, List, Optional, Set +from typing import Any, Dict, List, Optional, Set, Union import numpy as np import pandas as pd @@ -33,8 +33,9 @@ def __init__( root_dir: Optional[Path] = None, name: str = "FIPS Dataset", description: str = "No description", + state: Optional[Dataset.DatasetInternalState] = None, ): - super().__init__(certs, root_dir, name, description) + super().__init__(certs, root_dir, name, description, state) self.keywords: Dict[str, Dict] = {} self.algorithms: Optional[FIPSAlgorithmDataset] = None @@ -46,6 +47,12 @@ def _policies_dir(self) -> Path: def _algs_dir(self) -> Path: return self.web_dir / "algorithms" + # for type ignore explanation, see: https://github.com/python/mypy/issues/5936 + @Dataset.root_dir.setter # type: ignore + def root_dir(self, new_dir: Union[str, Path]) -> None: + # TODO: Implement me, also implement this for Algorithm Dataset? + pass + def _get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]: """ Returns list of certificates that match given name. @@ -265,7 +272,10 @@ def get_certs_from_web( self.web_scan(cert_ids, redo=redo_web_scan, update_json=False) @serialize - def process_algorithms(self): + def process_auxillary_datasets(self) -> None: + self._process_algorithms() + + def _process_algorithms(self): logger.info("Processing FIPS algorithms.") self.algorithms = FIPSAlgorithmDataset( {}, Path(self.root_dir / "web" / "algorithms"), "algorithms", "sample algs" @@ -425,7 +435,7 @@ def web_lookup(cert): ) @serialize - def finalize_results(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_heuristics: bool = True): + def analyze_certificates(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_heuristics: bool = True): """ Performs processing of extracted data. Computes all heuristics. @@ -433,6 +443,9 @@ def finalize_results(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_ :param bool perform_cpe_heuristics: If CPE heuristics shall be computed, defaults to True """ logger.info("Entering 'analysis' and building connections between certificates.") + self.pdf_scan(redo=False) + self.extract_certs_from_tables(high_precision=True) + self._extract_metadata() self._unify_algorithms() self._compute_heuristics_clean_ids() @@ -440,6 +453,7 @@ def finalize_results(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_ if perform_cpe_heuristics: _, _, cve_dset = self.compute_cpe_heuristics() self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict, cve_dset=cve_dset) + self.plot_graphs(show=True) def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: current_cert = self.certs[current_dgst] diff --git a/setup.py b/setup.py index de8d33b3..84c56760 100644 --- a/setup.py +++ b/setup.py @@ -29,7 +29,7 @@ }, include_package_data=True, package_data={"sec_certs": ["settings.yaml", "settings-schema.json", "rulles.yaml"]}, - entry_points={"console_scripts": ["cc-certs=cc_cli:main", "fips-certs=fips_cli:main"]}, + entry_points={"console_scripts": ["sec-certs=cli:main"]}, project_urls={ "Project homepage": "https://seccerts.org", "GitHub repository": "https://github.com/crocs-muni/sec-certs/", diff --git a/tests/test_fips_oop.py b/tests/test_fips_oop.py index df5eb253..07035077 100644 --- a/tests/test_fips_oop.py +++ b/tests/test_fips_oop.py @@ -33,7 +33,7 @@ def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path dataset.pdf_scan() dataset.extract_certs_from_tables(high_precision=True) dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") - dataset.finalize_results(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) + dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) return dataset From 0e12ba4a07497b53a84c2d61fd6352f415e24b69 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 25 Oct 2022 16:26:20 +0200 Subject: [PATCH 02/81] merge dataset constructors, from_web_latest code --- sec_certs/dataset/common_criteria.py | 18 +----- sec_certs/dataset/dataset.py | 34 ++++++++--- sec_certs/dataset/fips.py | 88 +++------------------------- tests/test_fips_oop.py | 2 +- 4 files changed, 38 insertions(+), 104 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 7bd56357..7570b01d 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -39,16 +39,6 @@ class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType): and dataset transformations. Many private methods that perform internal operations, feel free to exploit them. """ - def __init__( - self, - certs: Dict[str, CommonCriteriaCert] = dict(), - root_dir: Optional[Path] = None, - name: str = "Common Criteria Dataset", - description: str = "No description", - state: Optional[Dataset.DatasetInternalState] = None, - ): - super().__init__(certs, root_dir, name, description, state) - def to_pandas(self) -> pd.DataFrame: """ Return self serialized into pandas DataFrame @@ -225,14 +215,8 @@ def archived_csv_tuples(self) -> List[Tuple[str, Path]]: def from_web_latest(cls) -> "CCDataset": """ Fetches the fresh snapshot of CCDataset from seccerts.org - :return CCDataset: returns the CCDataset object downloaded from seccerts.org """ - with tempfile.TemporaryDirectory() as tmp_dir: - dset_path = Path(tmp_dir) / "cc_latest_dataset.json" - helpers.download_file( - config.cc_latest_snapshot, dset_path, show_progress_bar=True, progress_bar_desc="Downloading CC Dataset" - ) - return cls.from_json(dset_path) + return cls.from_web(config.cc_latest_snapshot, "Downloading CC Dataset", "cc_latest_dataset.json") def _set_local_paths(self): for cert in self: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 0ee3c216..a0e5ee73 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -3,6 +3,7 @@ import json import logging import re +import tempfile from abc import ABC, abstractmethod from dataclasses import dataclass from datetime import datetime @@ -59,22 +60,34 @@ def __init__( self, certs: Dict[str, CertSubType] = dict(), root_dir: Optional[Path] = None, - name: str = "dataset name", - description: str = "dataset_description", + name: Optional[str] = None, + description: str = None, state: Optional[DatasetInternalState] = None, + auxillary_datasets: Optional[Any] = None, ): + if state is None: + state = self.DatasetInternalState() + self.state = state + if not root_dir: root_dir = Path.cwd() / (type(self).__name__).lower() self._root_dir = root_dir self.timestamp = datetime.now() self.sha256_digest = "not implemented" + + if not name: + name = type(self).__name__ + " dataset" self.name = name + + if not description: + description = datetime.now().strftime("%d/%m/%Y %H:%M:%S") self.description = description - self.certs = certs - if state is None: - state = self.DatasetInternalState() - self.state = state + self.name = name + self.description = description + + self.certs = certs + self.auxillary_datasets = auxillary_datasets @property def root_dir(self) -> Path: @@ -88,7 +101,7 @@ def root_dir(self, new_dir: Union[str, Path]) -> None: self._set_local_paths() - if self.state and old_dset.root_dir != Path(".."): + if old_dset.root_dir != self.root_dir: logger.info(f"Changing root dir of partially processed dataset. All contents will get copied to {new_dir}") self._copy_dataset_contents(old_dset) self.to_json() @@ -144,6 +157,13 @@ def __eq__(self, other: object) -> bool: def __str__(self) -> str: return str(type(self).__name__) + ":" + self.name + ", " + str(len(self)) + " certificates" + @classmethod + def from_web(cls: Type[DatasetSubType], url: str, progress_bar_desc: str, filename: str) -> DatasetSubType: + with tempfile.TemporaryDirectory() as tmp_dir: + dset_path = Path(tmp_dir) / filename + helpers.download_file(url, dset_path, show_progress_bar=True, progress_bar_desc=progress_bar_desc) + return cls.from_json(dset_path) + def to_dict(self) -> Dict[str, Any]: return { "state": self.state, diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 41bedd57..ce46bcaa 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -1,7 +1,6 @@ import logging -import tempfile from pathlib import Path -from typing import Any, Dict, List, Optional, Set, Union +from typing import List, Optional, Set import numpy as np import pandas as pd @@ -27,31 +26,13 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ - def __init__( - self, - certs: Dict[str, FIPSCertificate] = dict(), - root_dir: Optional[Path] = None, - name: str = "FIPS Dataset", - description: str = "No description", - state: Optional[Dataset.DatasetInternalState] = None, - ): - super().__init__(certs, root_dir, name, description, state) - self.keywords: Dict[str, Dict] = {} - self.algorithms: Optional[FIPSAlgorithmDataset] = None - @property def _policies_dir(self) -> Path: return self.root_dir / "security_policies" @property - def _algs_dir(self) -> Path: - return self.web_dir / "algorithms" - - # for type ignore explanation, see: https://github.com/python/mypy/issues/5936 - @Dataset.root_dir.setter # type: ignore - def root_dir(self, new_dir: Union[str, Path]) -> None: - # TODO: Implement me, also implement this for Algorithm Dataset? - pass + def _algorithms_dir(self) -> Path: + return self.auxillary_datasets_dir / "algorithms" def _get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]: """ @@ -63,11 +44,9 @@ def _get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]: return [crt for crt in self if crt.web_data.module_name == module_name] @serialize - def pdf_scan(self, redo: bool = False) -> None: + def _extract_data(self, redo: bool = False) -> None: """ - pdf_scan() Extracts data from pdf files - :param bool redo: Whether to try again with failed files, defaults to False """ logger.info("Entering PDF scan.") @@ -217,22 +196,7 @@ def from_web_latest(cls) -> "FIPSDataset": """ Fetches the fresh snapshot of FIPSDataset from mirror. """ - with tempfile.TemporaryDirectory() as tmp_dir: - dset_path = Path(tmp_dir) / "fips_latest_dataset.json" - logger.info("Downloading the latest FIPS dataset.") - helpers.download_file( - config.fips_latest_snapshot, - dset_path, - show_progress_bar=True, - progress_bar_desc="Downloading FIPS dataset", - ) - dset: FIPSDataset = cls.from_json(dset_path) - logger.info( - "The dataset with %s certs and %s algorithms.", - len(dset), - len(dset.algorithms) if dset.algorithms is not None else 0, - ) - return dset + return cls.from_web(config.cc_latest_snapshot, "Downloading FIPS Dataset", "fips_latest_dataset.json") def _set_local_paths(self) -> None: cert: FIPSCertificate @@ -260,7 +224,7 @@ def get_certs_from_web( self.web_dir.mkdir(parents=True, exist_ok=True) self._policies_dir.mkdir(exist_ok=True) - self._algs_dir.mkdir(exist_ok=True) + self._algorithms_dir.mkdir(exist_ok=True) # Download files containing all available module certs (always) cert_ids = self._prepare_dataset(test, update) @@ -443,7 +407,7 @@ def analyze_certificates(self, use_nist_cpe_matching_dict: bool = True, perform_ :param bool perform_cpe_heuristics: If CPE heuristics shall be computed, defaults to True """ logger.info("Entering 'analysis' and building connections between certificates.") - self.pdf_scan(redo=False) + self._extract_data(redo=False) self.extract_certs_from_tables(high_precision=True) self._extract_metadata() @@ -549,39 +513,6 @@ def _create_dot_graph( dot.render(self.root_dir / (str(output_file_name) + "_connections"), view=show) single_dot.render(self.root_dir / (str(output_file_name) + "_single"), view=show) - def to_dict(self) -> Dict[str, Any]: - """ - Serializes dataset into a dictionary - - :return Dict[str, Any]: Dictionary that holds the whole dataset. - """ - return { - "timestamp": self.timestamp, - "sha256_digest": self.sha256_digest, - "name": self.name, - "description": self.description, - "n_certs": len(self), - "certs": self.certs, - "algs": self.algorithms, - } - - @classmethod - def from_dict(cls, dct: Dict[str, Any]) -> "FIPSDataset": - """ - Reconstructs the original dataset from a dictionary - - :param Dict[str, Any] dct: Dictionary that holds the serialized dataset - :return FIPSDataset: Deserialized FIPSDataset that corresponds to `dct` contents - """ - certs = dct["certs"] - dset = cls(certs, Path("./"), dct["name"], dct["description"]) - dset.algorithms = dct["algs"] - if len(dset) != (claimed := dct["n_certs"]): - logger.error( - f"The actual number of certs in dataset ({len(dset)}) does not match the claimed number ({claimed})." - ) - return dset - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame([x.pandas_tuple for x in self.certs.values()], columns=FIPSCertificate.pandas_columns) df = df.set_index("dgst") @@ -589,9 +520,8 @@ def to_pandas(self) -> pd.DataFrame: df.date_validation = pd.to_datetime(df.date_validation, infer_datetime_format=True) df.date_sunset = pd.to_datetime(df.date_sunset, infer_datetime_format=True) - df = df.loc[ - ~(df.embodiment == "*") - ] # Manually delete one certificate with bad embodiment (seems to have many blank fields) + # Manually delete one certificate with bad embodiment (seems to have many blank fields) + df = df.loc[~(df.embodiment == "*")] df = df.astype( {"type": "category", "status": "category", "standard": "category", "embodiment": "category"} diff --git a/tests/test_fips_oop.py b/tests/test_fips_oop.py index 07035077..b58e9972 100644 --- a/tests/test_fips_oop.py +++ b/tests/test_fips_oop.py @@ -30,7 +30,7 @@ def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path dataset.web_scan(set(certs)) dataset.download_all_pdfs(set(certs)) dataset.convert_all_pdfs() - dataset.pdf_scan() + dataset._extract_data() dataset.extract_certs_from_tables(high_precision=True) dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) From 7aff8bf5cab494bac57467ca2ae9e62db5572fe9 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 25 Oct 2022 16:49:44 +0200 Subject: [PATCH 03/81] unify _get_certs_by_name methods --- sec_certs/dataset/common_criteria.py | 3 --- sec_certs/dataset/dataset.py | 28 ++++++++-------------------- sec_certs/dataset/fips.py | 9 --------- sec_certs/dataset/fips_algorithm.py | 2 +- 4 files changed, 9 insertions(+), 33 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 7570b01d..50cebc50 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -812,9 +812,6 @@ def analyze_certificates(self, fresh: bool = True) -> None: self.state.certs_analyzed = True - def _get_certs_from_name(self, cert_name: str) -> List[CommonCriteriaCert]: - return [crt for crt in self if crt.name == cert_name] - def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: """ Creates and fully processes (download, convert, analyze) a dataset of maintenance updates that are related diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index a0e5ee73..63e1e921 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -8,22 +8,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import ( - Any, - Collection, - Dict, - Generic, - Iterator, - List, - Optional, - Pattern, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, -) +from typing import Any, Collection, Dict, Generic, Iterator, Optional, Pattern, Set, Tuple, Type, TypeVar, Union, cast import requests @@ -199,6 +184,12 @@ def _set_local_paths(self) -> None: def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + def _get_certs_by_name(self, name: str) -> Set[CertSubType]: + """ + Returns list of certificates that match given name. + """ + return {crt for crt in self if crt.name and crt.name == name} + @abstractmethod def get_certs_from_web(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @@ -382,7 +373,7 @@ def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: else: cert_name = annotation["text"] - certs = self._get_certs_from_name(cert_name) + certs = self._get_certs_by_name(cert_name) labeled_cert_digests.update({x.dgst for x in certs}) for c in certs: @@ -390,9 +381,6 @@ def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: return labeled_cert_digests - def _get_certs_from_name(self, name: str) -> List[CertSubType]: - raise NotImplementedError("Not meant to be implemented by the base class.") - def enrich_automated_cpes_with_manual_labels(self) -> None: """ Prior to CVE matching, it is wise to expand the database of automatic CPE matches with those that were manually assigned. diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index ce46bcaa..a4c57168 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -34,15 +34,6 @@ def _policies_dir(self) -> Path: def _algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" - def _get_certs_from_name(self, module_name: str) -> List[FIPSCertificate]: - """ - Returns list of certificates that match given name. - - :param str module_name: name to search for - :return List[FIPSCertificate]: List of certificates with web_data.module_name == module_name - """ - return [crt for crt in self if crt.web_data.module_name == module_name] - @serialize def _extract_data(self, redo: bool = False) -> None: """ diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index eb70d112..98feb9e5 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -111,7 +111,7 @@ def _build_id_map(self): self._id_map.setdefault(cert.cert_id, []) self._id_map[cert.cert_id].append(cert.dgst) - def _get_certs_from_name(self, name: str) -> List[FIPSAlgorithm]: + def _get_certs_by_name(self, name: str) -> Set[FIPSAlgorithm]: raise NotImplementedError("Not meant to be implemented") def _set_local_paths(self) -> None: From d0d9f916600a5ce5b92037b13c9be7e991c59d99 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 25 Oct 2022 17:21:40 +0200 Subject: [PATCH 04/81] unify get_keywords_df method --- notebooks/cc/temporal_trends.ipynb | 69813 +------------------------ notebooks/fips/temporal_trends.ipynb | 2 +- sec_certs/dataset/common_criteria.py | 7 - sec_certs/dataset/dataset.py | 8 + sec_certs/dataset/fips.py | 7 - sec_certs/sample/certificate.py | 23 +- sec_certs/sample/common_criteria.py | 24 +- sec_certs/sample/fips.py | 17 +- 8 files changed, 53 insertions(+), 69848 deletions(-) diff --git a/notebooks/cc/temporal_trends.ipynb b/notebooks/cc/temporal_trends.ipynb index de4d6ee6..5f2a4306 100644 --- a/notebooks/cc/temporal_trends.ipynb +++ b/notebooks/cc/temporal_trends.ipynb @@ -54,3848 +54,7 @@ { "data": { "image/png": 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:02.108087\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" ] @@ -3947,1692 +106,7 @@ { "data": { "image/png": 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nnsizpHbv/xGdTkdsbCxGo5FXXnmFV199FcgJ8lIoFDg6OtrkPTlkyBCWLFmCv7+/2TGPGDGCzp07s3v3bubMmUNwcHCeh4DiPte7x6agzyQwMJBLly6xd+9exo0bx+jRoxkwYEC+86zJ5qtX+DXkXKneIymDW5HWrVvj6enJokWLMBqNAOzfv59169ZRr149Zs+ezYYNGxg0aBBTpkwxO6uCkMlkXLt2jWXLlpm/IGJjYzEYDHlmS0Vx/Phx2rRpw7Bhw6hXrx4HDx4023U3SqUSg8GQ77ivry+dOnXi888/N+8jXLhwgR9//JH69euXuP327duzfft2srOzSUlJYf/+/QD89ttvfPvtt/Tq1YvPPvuMxMRE0tLSzO9LTk4mPDzcHGFXWPv39pX7RRgcHMyNGzfynWM0Gpk3bx6JiYkAGAwGwsLCaNKkSZFt349dCoWiwNfeeOMNFi1aRHh4OJDjEGbNmkW1atVK3H7Dhg1JSEgwB35s2bIFAB8fHzZs2MCGDRvyObhc3nvvPU6fPs3u3btp3rw5Z86cISwsDIDvvvuO2bNn5zn/4MGDPP/88/Tv3x+ZTMalS5cwmUx57p22bdvyzz//kJCQYH5IW758OY899hhnz54lJiYGk8lkzpEtjuDgYHbs2MEzzzxDnTp1SElJ4cSJEwCsXbvW7Fx8fHyoVq0aS5cupX///nnacHZ2pmrVqhw8eBCADRs28M0339CuXTs2bNhARkYGBoOBUaNGsWPHDpu9J1u3bk10dDRHjx6lW7duQM6+W0ZGBiNGjGDEiBHm7xOFQoHBYCjR51oYBoOB7t274+7uzsiRIxkwYAAXL14s0XstxZarV1l+rnQODqSZnFWRyWR89913zJw5k759+6JUKnF3d2fp0qVUqVKFl156iY8++oj169ejUCj47LPPAPjmm2/w9vZm2LBhedqbP38+M2fOpGvXrtjb2+Ps7My8efPyLBEVRe/evRk9ejT9+vVDpVLRsGFDbt68me88T09PfH19Cww9njFjBnPnzmXAgAGo1Wrs7e2ZM2cODRo0wNXVtUTtd+vWjXPnztG3b1+qVKliXl4bOHAgH374If369UOpVDJ69Ghz7VMANzc3nn32Wfr06YOTkxMtWrRAq9WSmZlZ6DWPHTuWCRMm0KdPH+rUqVPg0tCQIUNISkpi2LBh5gChPn368Mwzz5RoXEtj15NPPslnn33GV199RatWrczHn3rqKT744AM++OADjEYjBoOBnj17Mnr0aORyeYnaV6lUzJ8/n3HjxqFUKkvspCHnM3/zzTeZPXs2mzZtYsaMGbz//vuYTCZ8fHzMAQ25vPLKK0ydOpWffvoJR0dHWrZsyc2bN2ndunWee2f06NG88sormEwmGjduzFtvvYVGoyEwMJARI0Zgb29fZKRkYGAgDg4OyGQy7O3tWbBggTki9JtvvuHLL79Ep9Ph5OTEV199ZX7fgAEDWLBgQYGRrHPmzGHq1KnMnj0bd3d3Zs+ejbe3N5cuXeK5557DaDTy5JNPMmjQIHPgiS3ek08//TTJycnmVYYPP/yQCRMmoFQq0Wg0TJs2DYCuXbsyYMAA1q1bV+znWhhKpZKxY8fy6quvYmdnh4uLS57xtjbbrl3jl3PB9/VemShqI8CGuHnzJl27dmXnzp0PvJ7chQsXOH36tKSELiEhkQ8hBHq9nldffZVJkybdV85tZWLH9Wv8ePZsoa/7GIz8ExhYqG+QlittkNjYWPr27VvRZkhISNggcXFxdOzYkebNmz8EDu46y4pwcCVBWq60QTp16lTRJkhISNgo3t7eHD9+vKLNsDr/hF1n2dkzlHWpUZrJSUhISEjYFP+GhfG/M2V3cCA5OQkJCQkJG2JneDhLz5y2iIMDyclJSEhISNgIuyPC+eH0KYs5OJCcnISEhISEDbAnIoIlpyzr4EBychISEhISFczeGzf47tRJizs4eECjK0173rdKu/JOXxd7Tm4+3/PPP8/06dPNxy9evMjAgQOZOXNmkbppudpSQgiWLFnCjBkzitTCulcXDGD06NE8/fTTpbu4Qti1axcRERG8+uqr/PnnnwD5ktRLy4QJEzhy5Aiurq7m6i2vvfYagwYNKnVbMTExBAYGFlrJQ0JCwrY5EBnJ4pMnrOLg4AF1chWNm5sb+/fvx2g0muvSbd26FQ8PjxK3ERUVRWRkJFC8FtbdumCW5vz58+bfy+rc7mbs2LFmZx8ZGcnw4cPx8fEptfiqj4+P5OAkJCop52JjWWRFBwdWXq785ptv6N27N3369DEXHp44cSLdu3dnwIABDBgwgH/++QeAQ4cO0a9fP7p3786CBQusaZbVcXR0pHHjxnlyWQ4ePJjnC/xuLbF169YxYcKEPG188cUXhISEMG3aNI4ePcpLL71UKhvubfOll17i6NGjHD16lNdee413332XHj16MHbsWLKzs4GcQss9evSgd+/ezJkzh6tXr7Jy5UpWrlzJ2rVr+fbbb/n222+BHJ2uAQMG0K9fP959911zcdwuXbrw9ddf88wzz9CnTx9CQoqvFl69enVefvll/vjjDwDzzHHQoEEMGzaMCxcukJSURMeOHdHr9QBcvnyZfv36cfPmTbp06QLArVu3ePnll+nbty/PPPOMuYZjUFAQgwYNYsCAAUyaNAmdToder2fcuHEMHDiQgQMHsmrVqlKNr4SERNmISElhztEjGK1cdMtqTu7YsWMcOXKEjRs3snbtWlasWMH169cJCQnht99+MxeMffrpp9FqtUyaNInvvvuOrVu3EhISwt69e61lWrnQq1cvduzYAeQUYW3YsGG+CuhFERgYSLNmzcz1LIvirbfeMj80lERF+vTp00yZMoVt27YRFRXFgQMHCA4O5o8//mDNmjVs3LiR8+fPo9VqGTp0KEOHDmXIkCHm9yckJDBlyhQWL17Mpk2baNWqVZ6lWTc3N9asWcPQoUP54YcfSnS9DRo0MAuEjh8/nnHjxrF+/Xo+//xzPvjgA9zd3Xn00Uc5cOAAkFN8+N7iu9OmTaNHjx5s3ryZMWPGsGTJEq5cucKqVatYuXIlGzZswNPTk2XLlnH69GlSUlIICgri559/5tSpUyWyU0JCouwkZGUx8/AhsgooBG9prLZc2aZNG3799VeUSiUxMTEYjUY0Gg1RUVFMnjyZqKgonn76aUaPHk1wcDA1a9Y0Fyrt168f27dvJyAgwFrmWZ3OnTvz9ddfYzKZ2LZtG7169SpxtfXSUtrlyvr161O1alUA6tatS0pKCmFhYXTu3BlnZ2cgZ1YHOTO2ewkODubRRx819/n888+zdOlS8+tPPvmkuZ+///67xHbZ2dmRkZFBSEgIEydONB/PzMwkKSnJrBPWuXNntm3bxq+//ppHLeH48ePMnz8fgICAAAICAvjtt98K1DQbNmwYYWFhvP766zz11FN55FEkJCSsR6Zez8xDh0jIyiqX/qy6J6dSqVi4cCE//fQTPXv2xGg00q5dO6ZPn46DgwMjR45kzZo1ODg44OXlZX6ft7c3MTEx1jTN6jg5OdGoUSNOnjzJkSNH+Oijj/I5uVyduYJkbQqiOF2wu7lXsyp3mQ/Io6+Ve969+lExMTHY29sX2PbdWmC513H3NRSkoVUcd+uEqdVqsxQJQHR0NG5ubnTp0oWZM2dy/PhxqlatStWqVfOoHNx9DUIIrl27VqimmYuLC1u2bOHgwYPs3buXQYMGsWXLljwV5iUkJCyLwWRi7tGjRKSmlFufxS5XZmVlmVV1ly9fzsSJE4mKiipxB2PHjuXw4cPcvn2bw4cPs3jxYjw9PbG3t+ell15i7969BSoil+YL0lbp1asX8+bNo1mzZvmciLu7O1euXEEIwa5du/K9N1cD6m5Kogt2d/vXrl1DCEFkZCShoaFFnt+6dWv27dtn1tP66KOPCAkJKdCO5s2bc/bsWbOD+euvvwqUNCkp4eHh/PHHHwwbNgxnZ2dq1apldnIHDx40qzGo1WqefPJJZsyYkW+pMvcacjXUDh06xOTJkwvVNNu5cycff/wxnTp1Msu53L59+76vQUJConi+P32Kc3Gx5dpnsTO5iRMnUr16deRyOb/++isDBw5k8uTJLFu2rMj3Xbt2jezsbBo3boy9vT3du3dn69atuLm50aNHDwDzDMLHx8ccuAA5Vfi9vb3v+6JKEupfHnTu3JlPP/2U9957L99rH330EW+//TZVqlThscceIykpKc/rdevWJS0tjXHjxpVY1+xuOnTowNq1a+nZsye1a9fmscceK/L8pk2b8uKLLzJ06FBMJhNPP/00HTp0QKVSMX78eKpUqWI+t0qVKkyfPp3Ro0ej1+vx9fUtML2hKBYuXMjy5cuRyWQoFArGjx9v1lfL1fv68ccfUalULFiwwPzQM2DAADZu3EjPnj3ztTllyhQCAwP5448/sLe354svvqBevXoFaprJ5XJ27NhBnz590Gg0dO/ePU8wkISEhGX56+IF9hYgEGttitWTGzJkCGvXruWbb75BoVAwevRo87Gi2Lt3LwsXLjTnVr399tu0atWK1atXs3HjRhwcHHj77bcZNGgQTz/9NN27d+fXX3/F39+fkSNHMmTIEHr16mVu72HSk5OQkJB4kNgZHs73p60T3FWcnlyxM7ncvZwDBw4wfvx4jEZjkaq3uQQEBHD27FkGDhyIQqGge/fujB49Gnd3d4YNG2aWU8/VTZs1axZjxoxBp9MREBBQ4JO6hISEhETl4kxMDP87c7rC+i/WybVq1YrevXujUCho1aoVr7zySokTdseOHcvYsWPzHHvhhRcKVLxu3749GzduLKHZEhISEhK2TlhyMvOPHbV6LlxRFOvkJk+ezOnTp2nYsCFyudwcci0hISEhIVEYcZmZ5ZYLVxTFRlcqFAri4+P58ccfycrKIj09HblcqussISEhIVEwGdnZzDh0kCSttqJNKd7JLV26lD///JPt27ej1WpZtGgRixcvLg/bJCQkJCQqGXqTiTlHj3AzLa2iTQFK4OS2bNnC//73P+zt7XF3d2fVqlVs3ry5PGyTkJCQkKhECCH47uRJzt+VElbRFOvklEolarXa/LeLi0u+xGbbY7eVforn5s2bNGzYkIMHD+Y53qVLlzzVOQqitEWYS2JLbvFia5Fb+PnefivbGLz00ks8/fTT5hqgXbt2ZcSIEXnyNwt7373XX9A5paGwgtwTJkygU6dO5sLY/fr1Y/369aVqO5eYmBjefPPN+3qvtSlsTLdv387gwYPp378//fr148cff7yv9idOnMitW7fKaqZEAfxx4TwHbkZWtBl5KNbJVatWjT179iCTycjOzmbJkiX4+fmVh22VFpVKxeTJk0lPTy/V+44dO2Yli8qfyjgGX3zxhbmizD///IOTk5NZPaMsWPKaxo4dy4YNG9i0aRPfffcd8+fP59ChQ6Vup7JJFMXExPDVV1+xbNkyNm7cyMqVK9m6dSs7d+4sdVtHjx4tsMpSZSFDrycsOZmjt27xT9h19PeU2aso/g67TtDlyxVtRj5KFF35ySefEBoaSosWLWjevDnz5s0rD9sqLd7e3nTo0IGvvvqKzz//PN/rS5cuZdu2bRiNRp544gnGjRtnrhjy7LPP8uijj1K3bl2GDx/OqlWr+Pnnn9m2bRt6vZ5u3brx77//cuDAAXMB6OrVqzN9+nSqVKlCly5dePTRR7l48SJz5swx97ljxw4WL17ML7/8kkfX7vLly3z++edkZmaSmJjIq6++yssvv8y3335LTEwMERER3Lp1i2effZZ33nmH7OxsPv30U0JCQvDz88tXqaUyjkFB5BaFfvTRR4GcotQzZ85Eq9Xi7u7OtGnTzAXFAQwGA1OnTuXKlSvEx8dTu3ZtFi1axNy5c83XtHr1avbt28fChQsxGAz4+/vz+eef4+7uzoEDB5g5cyYajYbatWsXaVsud0sUdejQgYiICKZOnUpycjJ2dnZMnjyZatWq0bdvX/bs2YNKpeLy5ct89NFHLFmyhJdffpldu3Zx69YtJk6cSGJiInZ2dnzxxRc0atSIoKAgli9fjslkomnTpnz22WfI5XImTZrElStXABg+fLi5+HUu1rinkpKS0Ov1aO8EMjg6OjJr1iw0Gg2HDx/mm2++YeXKlQCsX7+eM2fOMGzYMKZMmYLBYECj0TBz5kz+/vtvYmNjeeutt/j999+JjIws8HN96aWXaNy4MYcPH0ar1RIYGMiKFSu4evUqI0aMYMSIERw+fNh8f7m6ujJv3rxSaUYWht5oJDYzk9jMDGIzcv6NycjIOZaRQcZddWgBDt68ybi27XC8a8WtvDl5+zbLzp6tsP6LokRhksuXL+fkyZMcO3aMP//8s0TJ4A87EyZM4MCBA/mW7Pbt20dISAhr1qwhKCiImJgYNm7caC4gvHr1agICAjhy5AgAhw8fJiUlhfj4eE6ePEmLFi1ITU0tUurmqaeeYseOHeb/cAcOHGDx4sX89NNP+f4Trl69mnfffZe1a9fy66+/5tHyCw0NZdmyZaxevZqlS5eSmprKihUrANi2bRuBgYHcKKJMT2UZg1wCAwPp378/TzzxBM8//zwdOnRgxIgRZGdnExgYyLx581i/fj2vvvoqkydPzvPe06dPo1Kp+Ouvv/jnn3/Q6XTs3bs3zzUlJiYyb948li1bRlBQEE888QRz584lOzubCRMmsHDhQtatW4ednV2hY3ovtihRZI17qlGjRnTt2pVu3brxzDPPMGfOHEwmEzVr1qRdu3bExcWZ37d+/XoGDx7M8uXLefXVV1m3bh0vvfQSZ86c4a233sLb25ulS5fi6OhY7Oe6adMmBgwYwBdffMG3337L77//bg68++6775g6dSrr1q2jc+fOXLhwocSfWy5nY2JYdfECi06cYPK+vYzctpUXNm7g/X//YcahQ/x49gwbr1zhaFQUYcnJ+RwcwPn4eCbv20dcBX0vX0tKYsHxY5hsdHZc6EwuOTkZgDfffJMVK1aYK+bHx8fz7rvvlkpC5WHEycmJzz//nMmTJ+dJcj98+DDBwcFmVWytVouvr2+e97Zt25YpU6ZgNBq5fv06vXv35vjx45w7d47OnTsXK3XTvHlz8+9JSUmMGTOGMWPG5Kk/mcuECRPYv38/P/zwA6GhoXkeYNq2bYtarcbT0xM3NzfS0tI4duwYzz//PAC1atWiZcuWlX4Mcvniiy9o27Ytp06dYuzYsQQEBKBWq7l8+TKRkZG888475nPvXYZ9/PHHcXNz4/fff+f69euEh4fnexg8e/Yst2/f5uWXXwZy1BxcXV0JDQ3F29ubunXrAjBo0CC++eabQu28F1uTKLLWPTVt2jTeffddDhw4wIEDB3juueeYO3cu3bt3Z9CgQWzcuJHBgweTkJBA8+bNuX37NtOnT2f//v107tzZXDM3l/Dw8CI/19x8YF9fX5o3b469vT1+fn6kpqYC0LVrV0aPHk23bt3o2rUrHTt2LMGn9R9XkxKZefiQRRKlI9NS+XTvHia270BtN7cyt1dSTkbfZvHJk+iMxnLrs7QU6uQ++ugj8xP43RXmlUol3bp1s75lDwBPPPGEeckuF6PRyCuvvMKrr74KQGpqKgqFIs/7NBoNjRo1YtOmTdSpU4e2bdty+PBhTp48yRtvvMHp03lL5BQmdQM5ag6LFy/m448/pk+fPvj4+OR57/vvv4+LiwudO3emd+/e5ir+BbWT+6Bzt9ROcUFItjoGAwYMML9+t6wP5FT5eemllxg/fjwbNmzAZDLh7+9vPs9oNOYLSNm5cycLFy7k5ZdfZvDgwSQlJeXb9zEajbRq1Yrvv/8eAJ1OR0ZGBlFRUXnG9N6xKApblCiyxj21Z88eMjMz6d27N0OGDGHIkCGsWrWKNWvWmJ3cG2+8gVqtNn+2PXv2pGXLluzevZvly5ezd+9evvjiC3ObxX2ud4scF2TTiBEj6Ny5M7t372bOnDkEBwfncZhFkWUw8M3x4xatBJKk1fLZ/n181KYtze/5f25pso1Gfg05x447qwi2TKHLlcuWLePSpUsMGjSIS5cumX9CQkL4+uuvy9HEyk3ukl1sbI68RLt27diwYYNZ0mbUqFFmBfG7ZW0CAgJYvHgxbdq0oU2bNuzcuRN7e3s8PDxKJXXj5uZG+/btGTZsWJ7/4LkcPHiQsWPH0q1bN44fPw7k/GcvjPbt27N582ZMJhO3bt0qkaK2LY5BboDJvQ4ul1dffZWsrCxWrlxJnTp1SElJ4cSJEwCsXbs23wzm8OHD9OrViyFDhlClShWOHz9uHsfca2revDlnzpwhLCwMyFnumj17Ng0bNiQhIYFLly4B5HEKRWGrEkXWuKfs7OyYN2+e+fMWQnD16lUaN24MgJ+fH1WrVmXlypVmJ/f+++8THBzM0KFDee+998zLiQqFAqPRWKLPtSieffZZMjIyzHt0pVmu/PnsWaIzMkp8fknJMhiYefgQu8LDLd52LhEpKUzYs7tSODgoQeDJzJkzSU5OJisrCyEERqORGzdulHpq/rCSu2T3+uuvAzlh9JcuXeK5557DaDTy5JNPMmjQICBn+WPAgAGsW7eOTp06MXXqVNq0aYOrqyuenp506tQJuD+pm7feeov+/fuzc+dOunbtaj4+ZswYhg8fjouLC7Vr18bPz6/IMP/hw4dz5coVevXqhZ+fHw0aNKj0Y1AQarWa999/nxkzZjBgwAC++eYbvvzyS3Q6HU5OTnlmppDzhffxxx+zfft21Go1LVq0MI/j3dc0Y8YM3n//fUwmEz4+PsyZMweVSsX8+fMZN24cSqWSJk2aFGpXZZAossY91a5dO0aPHs3bb79tLhr/5JNPMmrUKPM5vXv35u+//zavVrz99tt8+umnfPfddygUCiZMmABAp06deOutt/jxxx+L/VyL4sMPP2TChAkolUo0Gg3Tpk0r0fsO37rJ7hsRJe6ntBiFYMnpU8RlZfJ848Lvpfth67Wr/BYSYjMRnSWhWKmdhQsX8sMPPwA5T0B6vZ569eqxadOmcjEwF0lqR0JCojAMBgOffPIJPXv2pHv37hVtTqHEZ2by8a6dBQaQWINONWowsmUrlGUsxZii07L45ElOx8RYyDLLUZzUTrFXHhQUxO7du+nRowd///03s2bNol69elYxVkJCQqK0CCF48sknkclkNh0vYBKChSdOlJuDA9hz40ZOkeQy9Hk6OpqPdu60SQdXEopdrvTw8MDb25s6depw6dIlBgwYwPLly8vDNgkJCYlikclkHD58uKLNKJagy6FcTCj/clfBsbFM3r+PSe074GFvX+L36Y1GVpwPYfu1a9hmckDJKFFZrxs3blCnTh1OnDiBwWAwh9BKSEhISBTPlcREVl28WGH9R6SkMGnvHm6kppTo/MjUVCbs2c22SuDgqjppiny9WCc3cuRIJk+eTKdOnfjnn3/o1KkT7dq1s5iBEhISEg8yWQYD35ywbLrA/ZCQlcXkffsIiYsr8rzt168xfvcubtjwZEYhk9HG151XWyh4zK/gqku5FLtc2blzZx5//HEcHBwICgri8uXL5lJHEhISEhJF89PZM8RYIV3gfsjU6/ny0EHebdWKJ6vXyPNaik7HklMnORkdXUHWFY+9UklbP2fqe8ajVuVUuMkqRtGn2Jnc1q1bzZUpoqOjefvtt9m9u2QV+b/55ht69+5Nnz59zIVuDx06RL9+/ejevXuecj8XL15kyJAh9OjRg08//TRPYq+EhIREZeTQzZvsKaL0XUVgMJn49sQJ1oVeMh87GxPDxzv/tVkH52FnR696rrzcIp2mVW+gVpW8hFmxTu7777/n119/BaB27dqsW7eOb7/9ttiGjx07xpEjR9i4cSNr165lxYoVXLp0iUmTJvHdd9+xdetWQkJC2Lt3LwDjxo1j8uTJ7NixAyEEq1atKvFFSEhIVC6yjUZORt8u/sRKTFxmJkvPnC7+xApAAH9euMDS06f5JTiYLw8dJFmnq2iz8lHd2ZHBjZx5/pEE6njeRKkofZRoscuVJpOJqlWrmv+uVq1anhI8hdGmTRt+/fVXlEolMTExGI1GUlNTqVmzprl6e79+/di+fTv16tVDq9XSokULAAYPHszChQsZPnx4qS8IYE+UdaI/O/m+UqLzMjIymDt3LgcOHMDe3h4nJyfGjBlD+/btrWIX5FSUeOutt4iOjmbKlCn5qn+kp6czdOhQvv/++zy5JOPHj6dt27bm2XpJ+Ouvv3B0dKRv375MmDCBNm3alOr9lmLixImMHj3aqtJPn376KUOHDuWRRx6xWh8PI0tPn+ZUTDSLuvfA4a7yWQ8KJiH4tpzTBe6Hf8LDKtqEAmns6UqLatl4OJZ9ZlnsTM7Dw4OVK1diMBgwGo2sWbOmyCK3d6NSqVi4cCF9+vShffv2xMbG4uXlZX7d29ubmJiYfMe9vLyIqaQ5GUII3n77bVQqFVu2bDFX1x83blyx4pplISYmhtDQULZs2ZLPwZ09e5Zhw4YRflepn5iYGN5++222b99e6r5Onz5NdnZ2WU0uM+WhC/bll19KDs7C7Lh+jb2RN0jLzmbjFdvTH7ME6ysoXaAyo5DJeLyaOyOaK+lS7yYejrEWabfYmdy0adP46KOPmD59OjKZjKZNm5o1skrC2LFjefPNN3n77bfzfMnmkluktaDjlZFjx44RFRXFr7/+ar6GJk2a8M477/Ddd9/Rtm1bXnrpJR555BFOnjxJYmIigYGBBAQEEB8fz5QpU4iOjkYmk/HRRx/RoUOHPO1nZWURGBhIaGgoMpmM119/nYEDBzJy5EiSk5MZPHgw69aty/OeVatW8dlnn/HJJ5+Yj23atImuXbviVkTF8jNnzphLHrm7uzN9+nRu3brFrl27OHLkiPnBZM+ePfzxxx8kJCTw9ttv8/zzz5ORkcH06dO5cuUKRqORN998k759+7Ju3TrWr19PcnIynTt35sMPPzT3V5iu2YIFC8xyO+7u7nz77besX7++RLpgly9fZsKECRiNRlq3bs2+ffv4559/iI+P59NPPyUqKgqlUskHH3zAU089xbfffsuZM2e4ffs2L7zwAtu3b2f06NG0bdu2QA28jIwMPvzwQ3Nh31GjRhVbMuxhJjQhgZ+Dg81/b756lR516uJeCnkhW+dKYiKrKzBdoLJhr1TSxteZBl4JqJWW378s1snl7sOlpKSgUChwcnIqUcPXrl0jOzubxo0bY29vT/fu3dm+fXueCuuxsbF4e3vj4+OTp/p3XFwc3t7e93E5Fc+5c+do1qxZPif9+OOP5xGb1ev1/PXXX+zatYtvvvmGgIAAvvzyS4YMGULXrl2JjY1l+PDhBAUF5Rnzb7/9Fnd3dzZv3kxiYiLPPvssjRo1Motg3uvggAJrOr7xxhsAnDx5ssDryM7O5sMPP+Trr7/m0UcfZdu2bXz44YesXbuWLl260KZNG5588km2bNlCdnY2q1ev5sqVK7z88ss8//zzLFmyhKZNm/LVV1+Zl0pz5W9iYmLYunVrvsruubpmL7zwAnv37mXJkiV8+OGHXL9+nZUrVyKXy/nkk0/YtGkTb731FitXrsyjC/b999/j6+vL/v37mTx5Mr/88gsTJkzgvffeIyAggF9++cVcKPjzzz+nXbt2vPrqq0RGRjJs2DCCgoLM175161YA80z3bg08mUzGuHHj2LhxIyaTCT8/P5YuXcq1a9dYs2aN5OQKIUmrZd6xo3lC6XVGI6svXeStFoVLNlUmsvR6m0gXqCw4q9U82zQbe7X1gnMKdXL/+9//ePPNN/n8888LnFXlSnAUxs2bN1m4cCF//vknkCNFMnToUGbPnk1ERAT+/v5s3ryZIUOG4Ofnh0aj4eTJkzz22GMEBQWZtZwqGzKZrMCK6/p71uaffPJJAOrXr2/W7jt06BDXr19n4cKFQE49vsjISHOldYAjR44wY8YMIGcpuWvXrhw7dowuXbpY9DrCw8NxcXExp4v06tWLKVOmkJaWP163a9euyGQy6tevb1Z1PnToEFqtlrVr1wI52ma5atJNmjQpULqkIF0zyNk3XL16NWFhYZw5c4YaNfKGPhemC5acnMytW7fM7QwZMsQcRHXkyBGzIkH16tXNqgZAgSkyhWngDRkyhPnz5xMTE0OnTp3yFAyW+A+DycT8Y0dJuqPsfTe7wsPpV68+1Ur4AG3LLAs+azPpAraORqFgUGOBvbqYHIAyUqiTc3Z2BsDd3f2+Gg4ICODs2bMMHDgQhUJB9+7d6dOnDx4eHowZMwadTkdAQIC5KvrcuXMJDAwkIyODJk2amIUlKxvNmzdnxYoV6PX6PHpUZ86cybO3k6urdfcDhMlkYvny5eYlxJiYmHz7n/cu7eYqQ1iagoKLCusrd3Z+77XMmTOHpk2bAhAfH4+rqyubNm0qVPm6IF0zrVbLRx99xIgRI+jRowdyuTzfGBSmC6ZQKArdsytqHAuyrzANPEdHR7Zt28b+/fvZvXs3P/30E9u2bau0y+3W4teQc1xKSCjwNaMQ/HH+PB8VIpVUWTh48yZ7bSxdwFZRyGQMbqzG2c4y+25FUWjgSe4TeFZWFqNHj873UxLGjh3L1q1b2bRpE2PGjAFytKM2btzIjh07mDRpkvnLoFGjRqxZs4Zt27Yxb9481Gp1Wa+tQmjdujX16tVjxowZ5tlbSEgIS5Ys4d133y3yve3ateOPP/4A4OrVq/Tv35+srKx856xZswaAxMREdu7cSZs2bSx+HXXq1CE5OZngO/snW7duxdfXFzc3N7MeV3HXkjuLj42NpX///vl0x+6lIF2z48eP06ZNG4YNG0a9evU4ePBgHp22onTBnJ2dqVGjhjlN5W7ljLvHMTIyklOnTpmjewu7noI08H777Te+/fZbevXqxWeffUZiYmKBs92Hmf2RN9h27VqR5xyJusWVxMRyssjyxGVm8j8bTRewNWTAwIZOFgssKY5CZ3IJCQl8//33bN68ucBoytwnWlukpKH+1mLRokUsWLCAvn37olAocHV1Zc6cOYWKeuYSGBjIlClT6NevHwCzZ8/Otwc6atQopk6dSr9+/TAajbz99ts0bdq0SL2u+0GtVrNgwQI+//xzsrKycHV1NSfvd+jQgfnz55tn+wUxevRopk6dSt++fTEajYwbN44aNWqYHVFBFKRr5uzszOjRo+nXrx8qlYqGDRuar7UkumBfffUVkyZN4uuvv6Zhw4bmWdqnn37KlClTzHuYX3zxRZH7wIVp4OUGnvTr1w+lUsno0aPzqGQ/7ISnJPPD6ZJ9+f9+PoSpT1a+bQqjEHx74rjNpwvYCr3quVHVNbLc+itUT+7gwYNs2bKFnTt3FrjfM3PmTKsbdzeSnpzE/bBo0SKee+45vL29+fvvv9m0aVOJihlIlJ307Gwm7Nldqj2qSe070PKuvNzKwNpLl1h5seSq4A8znWq607SqZZd0s+Lh/WdnFOobCp3JdezYkY4dO7Js2TKzorOERGXD19eX1157DaVSiYuLS7Hq4RKWIUc77XipgzB+P3+e5j4+yCvJnublxERWX5LSBUrC49XcaeJT/nuWxUZXRkVFmaPQ7qa46EoJCVtg8ODBFVKN5WFn1cWL9yWyGZGawv7ISALuiaC1RWIzMvjm+LEKTReQgc1L4QA0qeJKa/+bVMSzi9WiKyUkJB5OTty+naf4b2n56+IFOvj5oborp9bWCE1IYM7RI6SUc71HN42GWm72+DmDp2MGDqp0tl32JCI1vVztKA21XZ15qnY0cnnx5SCtQaFObujQoUBOLta9NSSXLl1qXaskJCQqJbfT0/n25IkyzS7iMjPZfv06/erXt5hdluRAZCTfnTqJvgQ1fMuCXCbD39mRGq4qfJz0uNunoVHlLxXWvV4Kq8872GSBZR9HB56un4hCXnGqMoU6uT///BOtVssvv/yC7q7B0+v1rFixgrfeeqtcDJSQkKgcaA0G5hw9QqYFogzXXw6lS61aONpY8eZVFy+w+tL9z1KLwl6ppLabI37Ocrwcs3CxT0YhL164VK3KpH8jO/48J7e64y0NrhoN/RpmoFLkLwBQnhTq5JRKJZcvX0ar1XL58n9FVBUKBZMnTy4X4yQkJCoPS06dItJCatJp2dlsuBzK8KbNLNJeWck2Gvnu1EkOWjhVp5mXK3XcBZ4OGdirk5HJila5Lgxnu0QGNPRlzUXbyNG0VyoZ1NiARlXxy6iFOrlnn32WZ599ln///Zdu3bqVp01l5tn1+es3WoLVg0oWwFCc1I61pGq+/fZbVq5cSZUqVRBCoNfrGTx4MG+++eZ9tTdgwABzFRFborAxO3r0KPPnzycrKwuj0UhAQAAfffRRnnqpJWHhwoV06NCB1q1bW9LsB5pNV65w6JZlHcDWa9foWacuHvb2Fm23tKTotMw+coTLFk5Wf6yqG21rRFosGMPHJYqutWqwM/z+HKWlUMnlDGmsxFETV6F25FKs1M7d6t0SxVMSqR1rStUMHTqUDRs2sHHjRn7//Xc2btzI6tWr76stW3RwhZGdnc1HH33E3Llz2bhxI+vXr+f69ev8/vvvpW7r+PHjVimV9qByPi6O38+HWLxdndHIqgoOz7+RmsLEPXss7uAae7rSpvoti0cbNvS+QXMfN8s2WgpkwMBGDrg62IaDgxKoEDRo0IAlS5bQunVrHBwczMdzaxJK5KU4qR2j0Wg1qZp78fDw4J133uF///sfzz77bIFSPm3atKFTp04EBQVRpUoVkpOT6du3L7t376ZZs2aEhoaSnJzMp59+yvXr11Gr1UyYMIH27duzb98+Fi5ciMFgwN/fn88//xx3d3e++uorDh48iEKhoGvXrvnKwMXExDBp0iTS0tKIi4ujT58+fPzxx6xbt479+/eTkpJCZGQkHTt2ZOrUqQghmDVrFnv27MHb2xuj0ZivlFlWVhbp6enmMmhqtZpPP/2UjIwMIiIieOWVV9i1axdyuZxjx46xdOlSvvjiCz7++GMyMzORy+UEBgYSHh5OSEgIgYGBLFq0CDs7O6ZOnUpycjJ2dnZMnjyZJk2aMGHCBOzt7Tl58iRpaWlMmjSJDRs2cOnSJbp168aECRO4dOkSU6ZMwWAwoNFomDlzJrVq1bLg3VbxJGRlssCKYfS7IyLoV68+fkVU17EWp6OjWXD8GFkGywZN1HR1IqCOdaINZTJoX+M2SVle3KiAiMu+9V3xdrbsjL6sFOvkzp49y9mzZ/PMBmQyGTt37rSqYZWV4qR2OnToYDWpmoJo0KAB169fByhUyqdnz55s376dF198kb///ptu3brlKS79zTffUKNGDRYvXkxoaChTpkyhYcOGzJs3j19//RVXV1dWrlzJ3Llzeffdd9m3bx9btmxBp9Px6aefotPpzAWpATZv3kzfvn0ZNGgQaWlpBAQE8NprrwE5s9zNmzejUCjo2bMnw4YNIywsjAsXLrB582bS0tLo379/vut0dXVl5MiRDB48mNq1a9O2bVt69uxpXnL09/fn6NGjtG/fnvXr1zN48GDWrFlDp06deOONNzh69CgnT57k9ddfZ+3atYwePZqGDRsydOhQpkyZQpMmTbh69aq5ZiXk1OTMnTVOnDiRHTt2oNFoeOqppxg1ahTLly/n1VdfpVevXmzdupUzZ848UE5ObzIx7+hRq4bRm+4Ubx7Xrp3V+iiIbdeu8cu5YEwWdt4+jvb0qJ9k1WhDhdxA9/qprA6xI0VXfuLGXWu7U8PD9gpUF/stuWvXrvKw44GhpFI7d2MpqZrC7Mmt11iYlM+AAQOYMWMGL774Ips3b+b999/P08bx48fNQrkNGzbkr7/+Yvfu3dy+fdusFmEymXB1dcXHxweNRsPQoUPp3Lkz77//fh4HB/D6669z5MgRli1bxpUrV9Dr9eYZWMuWLc31OqtXr05KSgrHjh2je/fuqFQqPDw8CpVheuedd3j++ec5dOgQBw8e5M033+S9995jxIgRDBkyhI0bN9KiRQuOHDnCtGnTOHfuHGPGjOHixYsEBATw4osv5mkvIyODkJAQJk6caD6WmZlp/oxy7fD19aV+/fp4enoC4ObmRkpKCgEBAUyfPp39+/fTuXNnevToUaLPrLLw89mzXEmy/v7PsdtRXE5MpIGHh9X7MgrBz8Fn2XHnwdCSuGrU9G2YWS7RhhplBv0b2rEypHwiLtv7udPI2/YcHJTAyWVkZDBv3jyuXbvGN998w/z58xk/fjyOjo7lYV+lo6RSO3djKamagggNDaVu3brmNguS8lEoFKSkpBAcHExMTAytWrXK08a9DvXatWsYjUZatWrF999/D4BOpyMjIwOlUsnq1as5duwY+/btY+jQoaxYsYLatWub3z9r1iwiIyPp27cv3bp149ChQ2bpm7sdYq5qvEwmyyP9U5CDP3PmDOfPn+eFF16gb9++5p8ZM2YwYsQIevbsyYIFC9ixYwdPPfUUarWaxx57jC1btrBnzx62bt3K+vXr+fnnn/N8Bmq1Os/eZHR0tHn87v58C7KpZ8+etGzZkt27d7N8+XL27t1bYPWgysihmzf5Jzys3Pr7LSSE6VbWmMzU61lw/Bhn7qNSS3HkRBuasCvHaEMX+wT6NfBl3SXrRlw+6u1GC7/yK7hcWooNPMmtBJ+QkIBGoyE9PZ0pU6aUh22VkpJI7VhLquZeYmNj+f7773nhhRfMbRYm5dOvXz8+++wzevfuXeA15SplX7t2jTfffJNHH32UM2fOEBaW80X33XffMXv2bC5cuMCLL77I448/zvjx46lbt675nFwOHjzI66+/Tq9evbh9+zYxMTEF6tfl0r59e7Zv3052djYpKSns378/3zmurq4sWrSIS3flMF25csUsOGtvb89TTz3F/PnzzVGZs2fPZsOGDQwaNIgpU6Zw4UJOkd3cz8fZ2ZlatWqZndzBgwfNY1kS3n//fYKDgxk6dCjvvfeeuf3KTmJWFv87c6Zc+7yYEM/JUt7/pSE2I4PAvXut4uCUcjmDGytx1JR/1GM11yg617RO1Sp7pZLe9dzoWPMWcpntFhcrdiZ38eJFZs6cyd69e7G3t2fu3Ln07du3PGyrtBQntWMtqRqAlStX8u+//5pnQc8//zx9+vQBipby6d+/v3mmfi9jx44lMDCQ/v37o1QqmT17Nt7e3syYMYP3338fk8mEj48Pc+bMwd3dnRYtWtC3b1/s7e1p3LhxvuXFkSNH8sknn+Di4oKnpyfNmjUrUiqoW7dunDt3jr59+1KlShXzzPRuateuzaxZs5g0aRLp6enIZDKaN2+e54GsT58+nDp1yry3+dJLL/HRRx+xfv16FAoFn332GZCj2v7ZZ5/x1VdfMWfOHKZOncqPP/6ISqViwYIFJRZEffvtt/n000/57rvvUCgUTJgwoUTvs2WEECw+dZJ0ffnt9eTy+4XztKxa1eLFm61ZoitHO80BNwfrOejiaOwTSXyWP+diky3Sngxo4+fOo1VjUSsrNl2hJBQqtZPL4MGDWbduHQMHDiQoKAi9Xs+gQYPYvHlzedkISFI7EmXDaDSyYMECPD09bVoL0dbZeu0qP98R0q0I3m31GJ1r1rRYe9Yu0dWvgSs13Cs+2tBoUrL5UhVuppVOFeJe6rg582TNLJzsbMe53bfUTi6PP/44c+bMQavVsn//fn777bdixT9zWbRoEdu2bQMgICCATz75hIkTJ3Ly5Ens7yR4jh49mqeffppDhw4xc+ZMdDodvXr14oMPPijNdUpIFMmQIUNwd3dnyZIlFW1KpSUyNZXfQiyfD1ca/rp4gY7+/qjLULw5Izub47dvcyTqFiejoy1oXV661nKnhrttBGMo5AZ61k9jVYgdqfeRo+uq0dCllppqrlEVoiRQFop1ch9//DFLly7F2dmZBQsW8OSTT5r3lori0KFDHDhwgPXr1yOTyXjjjTf4559/CAkJ4bfffsujwqzVapk0aRIrVqygWrVqjBw5kr179xIQEFC2q5OQuENQUFBFm1CpMZhMfHvieIXXRkzIymL79Wv0r9+gVO9L1ek4FhXFkahbhMTFWV0ep52fO40qQDutKDSqDAY0suPPEDmGEn6OCpmMJ2u40cj7Ngp55VQ+L9bJqVQq2rRpw6hRo0hOTubEiRP5QsILwsvLiwkTJqBWqwGoW7cuUVFRREVFMXnyZKKionj66acZPXo0wcHB1KxZk+rVqwM5QRDbt2+XnJyEhI3w18ULhKWkVLQZAKwPvUzXmrVwvPPdUhhJ2iyORkVx5FYUFxPiLZ7zVhiPeLvR0kajDV3sE+hX34/1ocXXGG1axZW21VOwV9uWsy4txTq5BQsWcOrUKVasWIFWq2Xp0qVcvny52Nlc/btkMsLDw9m6dSt//PEHx44dY/r06Tg4ODBy5EjWrFmDg4ODufoHgLe3NzFWiHKSkJAoPRfj49lwV5H2iiZdn836y5d5sVn+4s1xmZkcjbrFkVtRXE5MKHdB0bruzjxRK8qmow193W7RqWYN9kQUvK/m7WBP59oyqjhV/F5iSZAVkyRQrJPbuXMn69evB6Bq1ar89ttvDB48uERLlpATxj1y5EjGjx9PnTp1WLx4sfm1l156yVxxI5/hlW3hV0LiASRLry+zPpw12Hb9Gr3q1sXT3p7b6ek5ji0qimvlkJxeGL5OjnSrm4BcZvt1T5v4RBKf6UdI3H+zc41CQedaztT2uFVhAqelQSW3w9/RB2HMKvK8Yp3cvUnNKpWqxA7o5MmTjB07lkmTJtGnTx9CQ0MJDw83V34QQqBUKvHx8SE+/j8xwNjY2Dx7dhISEhXDT8FnicvMtHi7Srmceu5OXEq4P2mebKORBceOojUaibCBZVQPOzv6NEhFqbA94dKCkMkEHWvGkJjlSVR6Bq2rudOyWjxqlW0us96Ng9IZf8cqVHUwIZcJbqYU/QhWrJNr1aoVH330Ec888wwymYygoCBznlFR3L59m1GjRrFgwQKzxIwQghkzZtCuXTscHBz466+/GDRoEM2bNycsLIyIiAj8/f3ZvHkzQ4YMKeElS0hIWIOjt26x54bl92M0CgWDG6txd7iFp30NDt68v9lXqIWVAe4XR5WKAY11qFWWfxiwJkqFnl4N0sk2aHCxt/19N1e1J9WdXPHU6JGVYrZcrJObPHkyCxcuZObMmSiVStq3b5+vqnxBLFu2DJ1Ox6xZs8zHhg4dyltvvcWwYcMwGAx0797dnFg+a9YsxowZg06nIyAgoMAlTAkJifIhSZvFD2dOW7xde6WSZ5rIcbGPBaCF3w3UihrsLmR/yNZRKxQMbizDQW0Zsdjyxk6VXq6lxkqPDC87H6o7OeCiNgAGctLRS9FCccngtoKUDC4hUX58eeigxUtcOavVDG5iwqmA8lZX46uz41qyRfuzNs5qNX3qK/F0koLkLI1cpqCqfVWqO6mxVxY9a7t5M4uuXT+4/2RwCQmJh4sd169Z3MG522kY2Di70BlPvSqRqBX+bL6cYnNBLvfi42hPWz8Vfm7RlSLIpDKhkmvwc6yKn6MMldwElH18JScnISFh5lZaGr9auKqJl4M9/RtlFrssVsP9JoMa+RIUml5uOW2loa6bM4/5mqjiFIPMhlMEKiP2Cif8nbyo6mBEIRNgwUcdyclJSEgA/1U1yS5GIaM0+Do50KdhKmpl0WHeuVRzjeKZxlVZdymrxFU5rImMHCmZR6tm4mIfVdHmPJDUcq5JTSdTqYJJSkOhTq64MkgDBw60sCkSEhIVyZpLF7mWnGyx9mq6ONGzQWKpw+q9nKN5tok36y5mo7Ogwy0NKrmcx31daeSVhL3a9sPqKyv2CidqOAmr5kUX6uS2b98OQFxcHNevX6ddu3YolUqOHj1K48aNJScnIfEAEZqQwHoLVjWp5+5C17qxKBX3V+/QwzGW55p6svaCjEyDwWJ2FYeTWkU7PyfqeMaiUth+WH1lp7aLN3KZdT/fQp1cruLzW2+9xYIFC6hRowaAufakhITEg0GWwcC3J09YbB+sSRVXnqodjUJeti8vF/sEnm3mzroLatLuo3J+afB2sKetnxo/t2gUctvIv3vQcVa54WWnp7QpAaWl2D2527dvmx0cgK+vL9FWlKeQkJAoX5YHBxOTUTadsVxa+LjRvoblykI5aZJ4tqkL6y9qSNJavppIbTdnWvsKvJyipWCScqaOiwcyK8/ioAROzsvLi4ULFzJo0CAA/vrrL7NagISEROXmeFQUOyPCLdJWG193HvOPtHhxYnt1KoObOLHxkgNxmSULYCkMuUxGHTdn6nnIqeqchqNGCiapCNw1XrhrymcZulgnN2vWLKZNm8aAAQOQy+U8+eSTzJgxozxsk5CwSc7FxfJbSAg+jo54Ozji7eiAt4MjPo6OVHFwQCUvuir6/aI1GEjSaknWaknLzsZZrcbNzg53OzvslKUPlE7Wavn+tGWqmjxR3Z1Hq92wmqCmnSqdgY2NbAl1JSq9dLNOJ5WKRlWcqOmmp4pjIkpF5axO8iBRx8WZnOol1qfY/xne3t4sXryYlJQUXF1dy8MmCQmbxSQEy4PPEZGawvUCIhFlgIe9vdn5+Tg44u3oiLeDA96OjnjY2eWLJEvLziZZqyXpzk/u78m6vMeyigjAsFcqzQ7P3c4ON82df+8+ZmeH810abEtOnSI1u+xLgF1qudO4HARC1cos+jUysf2yBxGpRefcVXd2pL6nGj+XDJzsEpHLpH02W8HbvhrOqvILJirWyV2/fp0xY8aQmprKmjVrGDFiBIsWLaJu3brlYZ+EhE2xMzyciNTCq94LctSrE7KyuJiQ/3WVXI6XgwOOKhXJOh3JWq1F1LazDAay0tO5nV70l79KLsfNzg4Hlcoi1ft71nWjbpXyi0JUKnT0bBDPrmveXEn6b0amUSho4OlMbTcTPs7JqJVS3IAtIkNObWc7LFHJpKQU6+S++OILJk2axJw5c/Dx8eHFF19kypQp/P777+Vhn4SEzZCp1/PXxQtlakNvMhFVjCOyJnqTySLSOTKgbwNXariXfw6ZUqGnW/1oXCP9sFNCdVcdbvbxyOXJ5W5LWanlXJO07CwSdfEIKj753dr4OvoWW4vS0hS7eZCcnEzHjh3Nf7/wwgukV+B/UgmJimJdaCgpusqhF2ZtutZ2o4Z7xSlHy2VG2ta4QXPfG3g4xiCXV74akv6O/tRyFjziaUeHqjWo71oLF5VHRZtlNRQyFTWdyr/IVol61Ol05n2EuLg4TDZQbkdCojyJychg67WrFW2GTdCyqhsNvKQqIGXBSeVGHZf/5hgquQk/R/BzdCLL4EpMlonYrEQyDWkVaKVlqe7ki1pR/r6jWCc3fPhwXn/9dRISEpg3bx5btmzhjTfeKA/bJCRshhUh5yyyd1bZqenqRLsaUVaLonwYUMiUNHF3L1TBwF5ppJYz1HJ2Jy3bi5gsPbFZcWSbtOVsqeVQy+2o7lgxfRfr5J555hlq1KjB3r17MRgMTJ8+nSeeeKI8bJOQsAnOx8dxNErKp3LTaOheN0WSlykjDVxr4KAsWXShs9qAs1pGXRdvknRKYrK0xGtjMYryi060BDWdq6KwUIGA0lKi5co2bdrQpk0ba9siIWFzmIRg+blzFW1GhaOSy+nfyIRaVfaglYcZH3tffBxK76BkMvCwM+Bhp8Ro8iVeqyAmK50kXTzCxhX47BVOVHOouFWQYp1cy5YtC6wQferUKasYJCFhS+yJiCDMgpX5KysDGjribCfNZsuCvcKJBq4qyqqVppCDj4MRHwd7dMaaXErOcXa2SnkUYS6KYp3c5s2bzb/r9Xr+/vtvFApFiRpftGgR27ZtAyAgIIBPPvmEQ4cOMXPmTHQ6Hb169eKDDz4A4OLFiwQGBpKenk7r1q2ZNm0ayvuo4iAhYSmyDAb+vHC+os2ocLrWcsfHRarIXxZkyGni4YXCwlGgGoWJRz3siUivSXjaDSwpNmoJnFXueNtX7NJqsSkEfn5+5p9atWrx1ltvmWV4iuLQoUMcOHCA9evXExQUxPnz59m8eTOTJk3iu+++Y+vWrYSEhLB3714Axo0bx+TJk9mxYwdCCFatWlX2q5OQKAPrQ0NJfshTBlr4uNHQW3JwZaWuS02cVdbZy5TJZNRyFrTwrI1abm+VPu6XOi7uFW1C8U7uXq5du0ZCQgGlHO7By8uLCRMmoFarUalU1K1bl/DwcGrWrEn16tVRKpX069eP7du3c+vWLbRaLS1atABg8ODBJXKkEhLWIi4zk81Xr1S0GRVKDRcn2tW4LUVSlhFPjTf+TtYP1nHTGGjt5YO7xsvqfZUEj3IswlwUpdqTE0Kg1+sZN25csQ3Xr1/f/Ht4eDhbt27lpZdewsvrvw/A29ubmJgYYmNj8xz38vIiJiamVBciIWFJfgsJeahTBlw1anrUTy2zJtzDjlpuTyN3ByinaiZqhYlHPey4kV6TsApdvpRRx8WJ8izfVRil2pOTyWS4uLjg5ORU4g6uXLnCyJEjGT9+PEqlkrCwsDyvy2QyRAFijdaUQ5eQKIrQhAQO3bJeNQ9HlYp2/k7EZwpupWqJz7Kt/CeVXE7/hqBWWkZj7uFFRhP3aqjK+UFBJpNR01ngqq7NhaTbZJvKJk90P/jYV8XJSsuzpaVYJ+fh4UFoaCgtWrRg+fLlXLp0iTFjxuDr61ts4ydPnmTs2LFMmjSJPn36cOzYMeLj/4sCio2NxdvbGx8fnzzH4+Li8Pb2vs9LkpC4f4QQ/Hwu2Grtu6jVDG5ixFHz3z6XwaghTetCUpYdMRlwK01HbEZWhT2D92vgiIu9FElZVmo6VcetApfrcpcvLyVnkKiLK7d+K6IIc1EU6+QmTpxI9erVkcvl/PrrrwwcOJDJkyezbNmyIt93+/ZtRo0axYIFC2jfvj0AzZs3JywsjIiICPz9/dm8eTNDhgzBz88PjUbDyZMneeyxxwgKCuKpp56yzBVKSJSCfZGRXEtKskrbnvZ2DGikxV6dt1STUqHD3TEOd0eoUyXnmNGkIl3nQlKWPbEZcqLSsrmdnompgFUPS9K5pjvVXKVAk7LiqvaklnNFW5GzfPnIneXL8LQb5ZJT5+foh105F2EuimKdXGRkJF9//TXffPMNgwYNYvTo0QwZMqTYhpctW4ZOp2PWrFnmY0OHDmXWrFmMGTMGnU5HQEAAPXv2BGDu3LkEBgaSkZFBkyZNePnll8twWRISpUdnMPDH+RCrtO3j6EC/RuloSrgEqJDrcbVPwNUeat2p2WsSCtK1riRrHYhJlxMSl06mXm8xGx/1dqOxj1STsqwoZWoauzsjk9nGnm7u8qXbneVLnRWXLxUyFTWdFZTXHmRJKNbJ6e/8Jzpw4ADjx4/HaDSSWQKpjsDAQAIDAwt8bePGjfmONWrUiDVr1hTbroSEtQi6cplEreX3x/ydHenVIBm1smxty2VGXOwTcbFPpIY7POYnJz7Dm8sJKs7HpWIoQ6CMv7MjHWreRiazrTyrykgjN3/sFLYXsOOqMdDa24eLSZkk6mKt0kcNJ19UFVS+qzCKdXKtWrWid+/eKBQKWrVqxSuvvEKHDh3KwzYJiXIjISuTTVcsnzJQx82Zp+vFo1RkW7xtudyEt3M03s7Qrrqa6DQvLsTC1aTUUi1KuajV9KyfJkVSWgA/R3+qVHDyc1Go5CYe8dAQmV6LsLQIiy5fquV2+Dva3kNSsU5u8uTJnD59moYNGyKXy3n99del/TKJB47fz59HZ7TsPkJDTxc614kpF+ehVGTj73YLfzcI0DtyM8WD4Bg9UelFL48q5XIGNAKNSoqkLCuOSlfquiiwtaoj9yKTyajhbMJVU5sLSdHojJapR1rLuZrFK7pYgmKdXHZ2NkqlEmdnZ3N0ZYMGDUoUXSkhURm4kpjIgUjL7kU94uXKE7VuV4iYp0aVQd0qGdStAhlad8KSnQmOySRJm796S7/6TrjY3yp3Gx805DIlTdw9KpVCg6vaQGsvb2KzZKTpdaRnZ5BhSLsvhXJ7pTNVHSrm2oUouqaJ1aIrJSQqC7+cC7bos/djVd1oU/0WchvYm3C0S6JZ1SSa+shIzqzCtUR7gmPTyDIYCKjhjq+bFElpCeq7VsfRRvLCSkOuWGuOK3DFJFzJ0CtI00O6Pps0fSYZ+lRMxaQD1HH2smoRZiEAoQKDDLQ6REYKpMRCYiSkFF1L2WrRlRISlYEDkZFcTky0WHvt/dxp6RdpcwEcMpnA3TGO1o7Qyl9BUkYVPBylSEpL4G1fjWoVNIuxNHIZOKuNOKsBFIAzJuFMpkFBuh7S9HrS9Zmk61PNmnbOKne8LLQPmePM1KAXOc4sPRlSonOcmaGwwC3XItu0WnSlhIStozMa+d2CKQMBNdxpVs32Z0ZymRFPp4e7bJ4cBVXsvdEolBhMJgwmIwaTAYMwoDcZMJiyMQg9xe2v2SmcaOCqLva8yoxcBk4qI04qqIoccEIIR7KMStKywVEFZUn8FloVIi4Kkm9DUiSYLDsjLFHtSim6UuJBZPOVK8RnWSZn6Ona7jSQqvXbPI5KV6o5uuNjL1DJcx2T4s6POs+5QgiMQo7BJMMg5OhNYDCBQQgMJoHBZKKKvQKlDQZbWBuZTIaD0ohDGdXQhFGNOLbe4o7tboo1ccqUKfmiKwMCAqxmkIREeZCkzWL95dAytyMDetd3pZaH5OBsFYVMhbe9N9UcNLioDZQ0UVkmk6GUCZRyUcR7Hj4HZymEkCFCz1jVwUERTu7w4cO0b9+enTt3mv/O5Z9//qF79+5WNUxCwppYImVABgxo6IKfm/WKOUvcPy5qD6o5uOJtZ0QhB7Dd/LWHkhQDxFpfzqpQJ7dlyxbat2/PihUr8r0mk8kkJydRabkQH8++G2WbeSlkMgY1csTHRQq/tyVUcg0+9t5Uc1DeiXaUZlq2iDCqEcHry6WvQp3cF198AVCgk5OQqIwkZmWx8sIF9tyIKFOYgEouZ0gTDZ6Oty1mm0RZkOGuqUI1B2eq2BmQy0BybrZLeS1T5lLGbUMJCdsny2Bgw+VQNl+9apGqJr3qOeHpKM3gKhqNwoGq9lWo5iDHTmlCWo6sJJTTMmUukpOTeGAxCsHO8DBWXbxIii5/tY/74XFfd6q7S0EmFYmTypXqjh542RvvzNoqPuleomQIowYRvK5c+yzUyf3zzz88/fTTZGdno1arCztNQsImOXH7Nr+dD+FWWlrxJ5cQP2dHHvOTxEQrCg+NF9WdnHDXSHttlREhZIhLp8ttmTKXQot+LVy4EIDnn3++3IyRkCgr15OTmLp/P18dOWxRB2evVNKjXoZUqb+ckSHHx96X1l61edTT/o6Dk6iUpOghrvyWKXMpdCbn6OhIjx49iImJoV+/fvle37Rpk1UNk5AoDXGZmfx54TwHIiOtUnuibwM77NVSoEl5oZCp8HWoir+TEo1CmrlVdnKWKbdUSN+FOrkff/yRixcv8umnnzJ58uTytElCosRk6PWsDw1l67Wr6MsgGloUT9Zwx9tZ2ocrDzRye/ydfKjmkJuE/eA6NyFkiLAb4OyJzN0dFDpksoq2yvLkLFOeKvdlylwKdXJOTk48/vjj/PDDD3h7e3P+/HkMBgOPPvooTk5OJe4gPT2doUOH8v333+Pv78/EiRM5efIk9vb2AIwePZqnn36aQ4cOMXPmTHQ6Hb169eKDDz4o+9VJPLAYTCb+CQtj9aWLpGVbXpA0l7ruzjTzkZK9rY2j0pXqTh542xuRyx6SQJJUA9w4BdypfOlYBVGjOTIPT1A+QA4vRQ9xVyus+2KjK9PS0njppZeoUqUKRqORmJgYvv/+e1q1alVs42fPniUwMJDw8HDzsZCQEH777Te8vb3Nx7RaLZMmTWLFihVUq1aNkSNHsnfvXql8mESBnImJ4aezZ7mdkW7VflzUarrUTrYJyZwHFXdNFao7uuBhZ+BBnrXdizCpEWc35D2YEQ8Xd+Y4PHs3RI2WyDyrgEpvc6oWJSUn6btililzKdbJffXVV8ydO5d27doBOeW9Zs2axapVq4ptfNWqVXz22Wd88sknAGRmZhIVFcXkyZOJiori6aefZvTo0QQHB1OzZk2qV68OQL9+/di+fbvk5CTykaTVMv/YUbIM1l36kAF9G8hRqyTFDUvjpHLDU+OCl70CJ5WRhy2/TQgQVy+BqYgViKxkCN2d4/A0zoiaLZF5+oDagKySzHSFkCEuVtwyZS7FOrn09HSzgwNo3749M2bMKFHjX375ZZ6/ExISaNeuHdOnT8fBwYGRI0eyZs0aHBwc8PLyMp/n7e1NTMzDLQUiUTC/nw+xuoMDeLqOG+6S3ppFUMiUuGs88bRzwEMj0Chyv6QfnplbHjLlEHWu5Ofr0uDyvhyHp3LImeF5VQON0aYdnkjOhvhrFW1G8U5OLpdz69Yt/Pz8ALh58yYKRdFKrIVRvXp1Fi9ebP77pZdeIigoiJ49e+Y7V/bALEhLWIoriYllrjlZEppWcaVeFcnBlQV7pTOeGjc87VS4qqVSW7kIoUKc3X7/Degz4dpBxDVAqbnj8PzATiCT2c74CqMGzlXsMmUuxTq5UaNG8fzzz9O+fXsADh48yGeffXZfnYWGhhIeHk6PHj2AHL0mpVKJj48P8fHx5vNiY2Pz7NlJSAgh+Dk42OrSlJ72dnSsFffgbPqXEzLkuGk88dQ44WkH9srcL9yHaymyOETkTci20F6yQQfXjyCuA3I1ovqjyHxqgD0V6vBylilPVvgyZS7FOrlu3bpRp04djhw5ghCCt99+m7p1695XZ0IIZsyYQbt27XBwcOCvv/5i0KBBNG/enLCwMCIiIvD392fz5s0MGTLkvvqQeDDZFxnJlaREq/ahlMvp28CASmGZEmAPOmq5PZ52HnhqNLhrjCge8JD/siJ0Krh+xDqNm7Ih4gQi4gTIFAj/R5FVrQUOcmSy8nU2trJMmUuJalfWqVOHOnXqlLmzRo0a8dZbbzFs2DAMBgPdu3enb9++AMyaNYsxY8ag0+kICAgocAlT4uEky2Dg9/MhVu+nV11nnOykdIHicFS6UMu5Cl72uV+etvHEbssIoUAE7ymnzowQeRoReRqQIfyaIataF5yUyGR6K3etgeDNVu2jtJRLgeZdu3aZf3/hhRd44YUX8p3Tvn17Nm7cWB7mSFQy1odeIkmrtWofj1V1o4aHtA9XFHYKJ2o7e+Ntry/32UFlR8Qk5aQIlH/PcOsc4tY5QIao1ghZtQbgpEImt6zDMy9TCtuazUsqBBI2TUxGBpuvWjeR1NfJkcerSyW7CkMjt6emc1WqOhiRywzkJFhIlBRh0MClPRVtBiDg9kXE7Ys5f3nXR+bXCBztQaEvc6SmrS1T5lKsk/vkk0+YPXt2edgiIZGP5eeCrVauC8BOoaBn/Uyp8HIBqOQaajhVw89RILehyL3KhBByxPnDYPWQqfsg9grCrOsmR3jWBE9/ZM4eYK8BhaHEASy2uEyZS7FO7tKlSwghpJB+iXLnXGwsx29bd4bVt4G9VHj5HpQyFdWdfPF3BIWNVnsRQoWISQCFCpmTM2jkIMu2uahYkaSFpMqwDG6ChDBICLvLHcsQ7v7gUQOZq2eO41Ma8zm+nGXKEza3TJlLsU7Oy8uLPn360Lx5cxwdHc3HAwMDrWqYxMONUQh+Phds1T6eqO6Oj4tUeDkXuUyJv6Mv1Z1kqOQ2OPMgp1oI6SDObYXsnGo0ZkuVdgivOuDui8zJBTQKkFdcSaycXDHbnN2UDJHjoJPyKnsIl6pQpRYylyrgYI9ITYX46xVmZXEU6+RatmxJy5Yty8MWCQkzf1+/TmRqqtXar+PmzCNVpUhKyMlx83X0paaTArVCYJNLa9yRawk9DbGFaJIZtHD7Aty+8N8VyNWIKrXBwxeZsxvYqe44PuvOUIUAcTnYZmc3ZSI1GlKjLXaXGEz+mLIMyO0UyNUgUxhAaJEZMkCUfRuhWCc3evRotFotERER1K9fn+zsbOzs7MrcsYREYaRlZ7Pq4kWrte+kVtGlbspDX3hZhoyqDr7UdFZhpzBhs85NKBBxqXBhI6W20ZQNsaEQG/rfO2WKO/tP1ZF5+yCzRl5kOhBzyfLtPmCYVNVI/mHnnSl6fuRuzii8PVB4uqBwc0ThpEHuoESmBrnCCOhAW6j2N1ACJ3f27FlGjRqFUqlk5cqVDBgwgCVLlpRIhUBC4n7468IF0vWWl8/RKBS093ehQZU4VMosi7dfmfCyq0ptF3sclEbAdp290KoQ5/ZYNvxeGHOW1+KvIy4rEE27IfN0sNjsTphUiLO2UdLKplE6kLIuuFAHB2BKTsOUnEZRyQ6pXn5FdlO0CyRHheCXX37Bzc2NqlWrMnv27HyFlyUkLEVESgr/hIdZtE17pZIutdwZ0TKLplVvPNQOzlXtSasqtWnqob7j4GwTYVJjunYDcWSNdfPLhBFCdiBOHULo1JZpMvw6GB7ee6ykZIaCMTbB6v0U6+S0Wi316tUz/x0QEIDRaLv/OSQqN7+cC8ZUxJNdaXBQqehW242XW2TQ2OcGyoe4XJe9womm7nVoWcURF7Xt/v8VQoZINiIOboTI0+XXcVoM4vBqTOG3EUJ1382ILKVZCFWicAwmPzL3lM84FbtcqVQqSUlJMacQXL9uu1E0EpWbo7duERIXV+Z2nNVqOlZ3oKZ7NEqFdetd2joquYaaztXwdTDdSeS2XYReg7hwBJIqMOI1/Bji5jl49GlwlpcqMlMIJeLsTisa92Ag1B4k/3Kw3Por1sm98847vPjii8TFxfHhhx9y8OBBpk+fXh62STxEZBuN/BpSCo2tAnDVaOhY3Y4a7tEo5NZfBrFl5Cjwd/KjhpMMpY0H2AihRETFwpX9FW1KDoYsxKmNUKUuNHoMmbJkKwDiVgxok61rW2VHpiRtTzRkWbdM390U6+Q6d+5MnTp1OHjwICaTiXfffTfP8qWEhCXYdPUKsZn3p8LtYWdHh+oaqrvdRi633aW48sLH3pfazmrslLYbMQl34g0y5Yjgf0BnvXSR+yb+GuLAdUSjTsh83Ius/iGy1XD1QDkaVznRJXmSfdH6xdbvpkS1Kw0GAyaTCaVSiUp1/+vVEhIFkZCVxfrQ0FK/r4q9HR2qq/Fzvf3QpwMAuKmrUNfFGWe17UZMCqGCbBCZmRB9DWJK/7mXLwIu7UbccIdHuiCzz7/kK4Qccc5GZqE2jElVjbR1e8q932Kd3Nq1a5k3bx5PPvkkJpOJRYsWMXnyZLPwqYREWfn9fAi6UgQzeTvY07G6imquURVWzcKWcFA6U8fFiyp2Bkqq5yZEzh67tcZPCECoIVsgMtIhOQbiwyAr2WJ9GIU/clUyMoOFREiLIjMJcXQtwr85sjoNkMn/S3ER8emQFm19GyozJUgXsFrXxZ3wyy+/EBQUZFbqjoqKYuTIkZKTk7AIoQkJHIgseW2/lj5utKsZJRUMBlRyO2o7V6Oag7FU0jfCpEZcOAnxYQgHN3DwAAdXsHcGjQMytQZUSlDIQS5AZigyh8zs0HQmREYaJEVD3HXLKWDnQ4421p30Tf8id3PG9Zm2KEQ5Va+5eRYRdR7xSHdkbhowquD8/UqEyTEKX+TqdGT6ZEtaaXOUV7pAQRTr5FQqldnBAfj6+kpLlhIWQdypT1nSZ7sutdxp5H3D5orwlgW5TIEcBXKZPOd32Z3fkd85JkcukyGXyVHk/o4MtUJBNQeBspR7kEKnRpzc9p8DykzK+bn7nILep3EGRw9wcAM7Z7BzBJMRkqIgLqz88sLkSjKv2ZG5Kyc6z5ScRtKP/2L/RHMcmtkh06dY3waTAc5uRbj5k3Mz3sfsRK4k86qGzN3/gkyGXdtm2D/ijULEWKSUlS2Rky5QcVGnhTq58+fPA9CwYUOmT5/O888/j0KhYN26dVK1EwmLsOdGBNeSkoo9TymX06+BI76ulb+YshwFNZz9qe4IijIVQS7dnpsQIJKyIfg+SmMB6NJyfhIjSv9eS6GwI/20Hu3xE/leyjpwFu0ZB1yfaY9SEUW5BNwk3+fs0XwdJ3P+FgLtkXNoj4DcwwXHTi1Q+xiRZVf+COHyThcoiEKd3JgxY/L8vWfPHvPvMplMUiGQKBNZej1/3HmQKgontYqBjeS42keVg1XWxV3jRQNXJ+zLudKIEErE9evlm1xtYYTSibT9SWRfKFyUU6RnkvzLTuxaN8GxtRsyffEPUOWNUDqRui8B/cWCq/qYElNJW7cPAE2Lhji08kehiMupwVnZqIB0gYIo1Mnt2rXLIh2kp6czdOhQvv/+e/z9/Tl06BAzZ85Ep9PRq1cvPvjgAwAuXrxIYGAg6enptG7dmmnTpqFUSsLlDyprQi+RrCs6/6iaowO9G2Rip04rJ6usg1puRz1XX7ztSx4YYimEUYM4s69SB0YIlRsp229gCC/Zg472xAW05zS4PvcEKvVtbCXSVKjcSdkSjiGyZPqFujOh6M6EInNywLFTCzT+SmT6WCtbaTkqIl2gIIr1InFxcaxfv57k5OQ8xz/55JNiGz979iyBgYGEh4cDOSXCJk2axIoVK6hWrRojR45k7969BAQEMG7cOL744gtatGjBpEmTWLVqFcOHD7+vi5KwbW6np7P1WuFP5ACNPF3pVDsWhaISPsGakeHr4EcdFyXKClAeFxlyxMmgyjkLuINQe5G05jymuFJWrtHpSFmxE82j9XHq6IMs24r1L0uAUHuRtDoEU3zpZ5ciPZP0zYdIB1QNa+HYri5KTRIY7y+vtDyoqHSBgihRxZOqVatSvXr1Uje+atUqPvvsM7NDDA4OpmbNmua2+vXrx/bt26lXrx5arZYWLVoAMHjwYBYuXCg5uQeU5eeCMZgKf7pu7+dOS7/ISp0e4KRyo4GrBy5qA+U9kxBCjohOhNC95dqvpTGpqpL823FMaRn33YYu+Aq6C2G4PPMEaqf4CgnqMKmqkbTiGCL9/q8jF31oOMmh4aBW4RjQEk0dJ2RyA0LIQchAyHLSQ0wgTIARhFHk/BhMYDQh9EaE3gQGA0JvROnnhsolzXKpGBWYLlAQxTo5vV7PokWL7qvxe9UKYmNj8fLyMv/t7e1NTExMvuNeXl7ExMTcV58Sts2Wq1c5GV3w0pkM6FXPjdqelTfARCFTUtvZHz9HU6nC+i2FMKkQl84WLixaSTAp/Ej8eR/oihJZKSEGA6kr96CqXxOXbjWRZZffkp9R4UfST/sg2wLXcTfZejL+OUbZ3eYdlEocuzyGXR07ZPqyffdmXhQVli5QEMU6uaZNm3L58mUaNGhQ5s5EAZ5dJpMVelziweJcXCwrCqlPaadQMKCRhipOJc+ZszW87KpSz9UOjaJi9oBEthpxagdobbBEVikwmvxJ+mnXnamI5dBfiSDhaiTOg59E45EIJgs7nnswGP1I/mm3xa/DKhgMZPx9lAxA06wuDm1roZDHlnqMDEY/MvfZVpHqYp1cq1atGDhwIF5eXnkCQXbuLP2F+Pj4EB//39p4bGws3t7e+Y7HxcXlyc2TqPzEZGSw4NgxjAU80HjY2dG/oQFHu8o5e7dTOFLf1QdPu/JfmoQ7q0IpBsSZ+0wPsCH0WdVI+eNf63UgTKSt3UtWTV9cejVEri9ZEEhpyc6sRuqftvVlX1J0IdfQhVxD7uGCU9eWqDy1Jco/FGpPkn+yvfqdxTq5RYsWMXfuXGrUqFHmzpo3b05YWBgRERH4+/uzefNmhgwZgp+fHxqNhpMnT/LYY48RFBTEU089Veb+JGwDrcHAnCNHSMvOHwBRy9WJp+slo66EQqYy5FR38qOmswxFBcnYCKFAhN+AiPy5Y5ULGbqEKqQF7S6X3gwRUSR+H4W6SV3sW9ZA6aJDlm0JWSYZ2nhP0jeUz3VYE1NiKqmr94JMjkNAS+wauRT+UCBTkronCoqJmK4IinVyrq6u9O7d2yKdaTQaZs2axZgxY9DpdAQEBNCzZ08A5s6dS2BgIBkZGTRp0oSXX37ZIn1KVDzfnTpJRGr+J8HKXKLLVe1JA1cXHFUVZ7swahDBByClkucQyhRkhTuS8W/5FznOvnDNnHunql8T+8fqoHLX3180pkxJVpg9GTttbzZTJoSJzD0nydwDqvo1cOzYAKUqDkz/OTRdkgd6G0gXKAiZKGhD7C4WLFhAdnY23bt3R63+Tx6+adOmVjfubm7evEnXrl3ZuXMn/v7+5dq3xP2zLvQSf164kO94ZS3RJUdBHdfq+DkY73vfWGTIEZePgkwOSjUo1KBQgkJ150eZ8yNXgkIBcgUyuQLk8v9+DAbEub/BUPonZ6H2IvNsKoaYBOTOTsidNMgd7JA7qJFpFMjUCuQqkCkB+Z0AGqFHZtKBUYtFl0TlGtJDTGgPl01L0NKoavlh36Y+Kk9TyQJVFBrSz5rQHrWt67AWMidHnJ5uhbqqESHsSPxhd4VFU8Z5+TF4y4FCfUOxM7lNmzYBsGPHDvMxmUx2X3tyEg8XJ6Nvs/IeByeXyRjQwAlft8oXQemodKGxuydOKiM5saClQwgVIjwMIk6W/r2lfkcBbahc0IbLyNhx4P6/kGQy5C6OyF2cUbg6IHN0QOGkQWavRmanQq5RIFPJcn5yHaTM8J+DFHfNfJWOpB1ORRdse5Gg+vBb6MNvAaD0r4p92waovWV3HN49Y6d0JO1gMrqQonM/HyREegZp6/fn1O6009hMukBBFOvkLFX5ROLh4lZaGt8cP57vy7lvfRd83cqpYrwF8XP0p46LHMV9Lq2KLCXizN8VIw6qsCM72Y3UDYfLvmciBKaUdEwp6RjuJxDW3g6FqxNyF0dMyXE2FWpeGIab0aTdzEl7UVStgkO7RqiqKpHrYxEqF1K3R5od4kOHEBVetqs4inVyP//8c4HHX331VYsbI/FgkKHXM/vIYbIMeYMxOtd0p7p75ZrBqeQaGrn53YmcLD1CKBE3o+DaYQtbVgJkCgyGqqQGncSUkFz+/RdElhZjlhZjdMVWILlfjNHxpAXl7LnJq7iDiLKdsZUokGKd3OXLl82/Z2dnc/LkSdq2bWtVoyQqLyYhWHj8OFHpeasnPFbVjcY+lcvBeWi8aOTmiFpxnw5Op0ac3QmZlojaKx0mhR9p/4aiv158EWyJ++N+SnRJlD/FOrmZM2fm+TsxMbFEdSslHk5WXrjAqZi8FU0aeLjQpvqtShNkIkNOXZca+DkaixQLLQwhFIjohAopqyXU3mQcj0N7Qtozl5CAEji5e/Hw8ODWrYd0/VmiSA7fusn6y6F5jvk6OdKlThxyeSWo+gA4KF1oUpbgEr0GEVz+Vf+Fyg3tNRMZ/+wr134lJGydUu3JCSEICQnB09PTqkZJVD4iUlJYfDJv1KCbRkPvBmmVRknA18Gfuq73F1wihBwRlwYXyrnqiMIeXYIzaZsOW74+ooTEA0Cp9uQAqlWrJi1XSuQhTafjqyOH0Rn/cw52CgUDGxvRqCxWQtZqqOQaGrr5UeV+g0sMGsT5I5BUjnuOMiWGbB9S1x7DlFy59fYkJKxJqffkJCTuxmgyMf/4MeIy/9O2UshkDGqswVFj+7Uo3e8El2juI7hECBkiSQfBmyhTzUq5GqHQgEwNQpUzKzTKEXqBKduE0BoQWgOmTB2mjCyMaVkY4xIxJdpmhQkJCVuiUCc3ceLEQt8kk8mYMWOGVQySqFz8GnKOkLi4PMf6NXDGw9G2921lyKnjUgP/+w0uMaoRF09BfOkTgI0KP9L3XMWYmJozCzNUTN1LCYmHgUKdXP369fMdS0pKYvny5fj5+VnVKInKwe6IiHwK311ru+Nn49VMHJQuNHavgrPKQGmDS4RJjYi7I0hqKqVzUtiTFa4m428p8lFCorwo1Mm99tpref4+dOgQ48ePp1+/fgQGBlrdMAnb5kpiIv87czrPsTa+OfUobRUZMqo7VaeWswx5KVQDhACyVYjIK3Dz7H31bVL6krIhuNImQUtIVFaK3ZMzGAzMmzeP9evXM3XqVLNqgMTDS5JWy9yjR9Cb/lvma+TpymN+tluuy0nlRiM3D5xUBkoa/SiEElKzEVeP339KgFyDNtqJ9E1SeTwJiYqgSCcXERHBBx98gIODA+vXr6datWrlZZeEjaI3Gpl79AiJ2v/q1fk7OxJQO8Ymc+HkKKjp7E91J1Hi2ZswahAx0XDtKBjvv9ajUFUlZdtlDBH3N/uTkJAoO4U6uTVr1jB79mxeffVV3nnnnfK0ScIGSdJq+TcsjH/Cw0i6y8G522noVT8VpcL2crRc1R40dHPFQVl83psQMtAqEBEXIPpi2TqWK9ElepC2bq9NV2eXkHgYKNTJBQYGIpfLWbp0Kf/73//Mx4UQyGQyTp06VS4GSlQsoQkJbLt+jSO3bmG85wvbXqlkYCMDalVmIe+uGBQyJXVcquPrYEBWTGK3ECpEciZcOWKRGpNC7UXqvzfQX5HC+yUkbIFCnZykF/fwkm00ciAyku1h1wlLTi7wHIVMxqBGShw0cQW+XlG4a7xo6OqEnbLoslzCoEHcvgnXj+bVOLtfZAqyM7xJXb4PDJVP6VxC4kGlUCdnzTSBl19+mYSEBJTKnO6nT5/OjRs3WLJkCXq9nhEjRvDCCy9YrX+JgonLzGTH9evsiggnLbvoUlz9Gzrh7hhVTpYVj1Kmpp6rH1UdjEDhTkYIFeLaZbgZbLG+hdqT9AMx6M7ttlibEhISlqHUBZrLihCC69evs2fPHrOTi4mJ4YMPPmDdunWo1WqGDh1K27ZtqVevXnmb91ASHBvL9uvXOHH7doniDrvXccPX9X4UM61DFTsfGrjao1YUszSpVSFO7wCdpcpgyTBk+5L82z7Q2d6epISERAU4uevXryOTyXjzzTdJSEjgueeew9HRkXbt2uHm5gZAjx492L59O6NHjy5v8x4asgwG9t6IYPv169xKK/pLXyWXU9PVieouCnycdHg62YaDU8ntaODqi5e9gaLKagkhR0QnWlT6RqjcyDiZhva4tKwvIWHLlLuTS01NpX379kydOhWtVsvLL79Mr1698PLyMp/j7e1NcLDllpMk/iM6PZ0t166y98aNfMrdubhqNNRxs6eaM3g6ZOCkSUYuTylnS4vGx96Xeq4qVPKi0wKEUYM4dwiSLZTDJ1ejz6pC6l9HEOm2FXAjISGRn3J3ci1btqRly5YAODg48MwzzzBz5kzefvvtPOfJKovCZiVid0Q4y86ezaMWIAP8XRyp4aKmqrMBd/tUNCrbrMqhltvjaeeBt70Gd03RSd1CAKkmxNl1pS+/VRAyJQa9D6lbT2OKkx7AJCQqC+Xu5E6cOIFer6d9+/ZAzh6dn58f8fH/fbHGxsbi7e1d3qY9sGgNBn48c4a9kTewUyhoUsUVP2c5Xo5ZuNgno5DbqlSLDBeVGx52LnjayXFSGu48/BQze7NkcIlMjtHkS9qOcxhuSmkBEhKVjXJ3cmlpaSxcuJCVK1ei1+tZv349c+bMYdy4cSQmJmJvb8/ff//N559/Xt6mPZBEpqYy79hRbqWl0cDDhU61E1EpkyvarEJRylS4azzxtLPHQyNQK0zkzNhKptQtdCrE6b9Bm1pGS2QYZb6k77yEPuxCGduSkJCoKMrdyXXu3JmzZ88ycOBATCYTw4cP57HHHuODDz7g5ZdfRq/X88wzz/Doo4+Wt2kPHLvCw/kpOGd5MqCGO02rRiKT2V4FDgelC552rnhqVLioDchlUFQaQEHkBJckQ2jZw/hNCj/S9l1Hf0kKKpGQqOzIhKgcdYdu3rxJ165d2blzJ/7+/hVtjk2jNRj435kz7LuzPNm3gT0+Lrcr2iwzcpkCN7UnnnaOeGq4k7h9/wijBhFyGJLKFvVpUlUj4/AtdGcvl6kdCQmJ8iPOy4/BWw4U6hvKfSYnYV1upKYw/+gxbqWnUc3RgZ4NsnBQ246Dq+bgRz0XBQo5lHa2di9CAGkCcaZswSVC7UPGyXi0x6RkbgmJBw3JyT1A7AwP46fgYLKNRlr4uNGuRjQKuW0kKctRUN+tBtUcLFPyKie45CrcPHP/bai9yApJJ3O/5fLnJCQkbAvJyT0AZBkM/O/MafZHRiKXyehV1406VWwjYRvATuFIUw9vnFVld3BCKEArQ5z9F7TJpW9ArkEo3NBezSRj5wFJJUBC4gFHcnKVnIiUFOYfO0pUejouajX9Gspxc7AdB+eh8aKxuwMqeekdnBBK0MshKwuREg8JEZBSwqVXhT1C7oIpW4khxYAhJhl92G0Mt2JLbYeEhETlRXJylZh/w8L4+VzO8mQdN2e61km2Kdmbmk41qOUsShTRKYQKsmWIzAxIiYP4cEgvXuFAKJ0QMmdMWjnG5Gz0t5PIvh6FKa7ssjkSEhKVH8nJVUKyDAaWnj7NgZs5M7YnqrvzSNWbNqPMrZSpaezuj6edgYJy24RJDdkix6Elx0B8GGQmFduuUDphMrqij9ViuJVI9rWbmJJtNZFdQkLCFpCcXCUjIiWFeceOcjs9HbVCQd/6DlRzvVHRZplxUrrS1MMde2X+aEdh0CDCL5c8WERhhwkP9HF6tCE30F+5ZFljJSQkHngkJ2ejZOj13EpL42ZqKjfT0riZlvNvfGYmAvB2sKd3g2wcNbaj6eZj70sDNyUKWd4ZpTBqEJFhEH686AbkKkyKKhgSBbpLt9CdCwZhG7NTCQmJyonk5CqYVJ2OyLTUOw4tzezQkrTaQt/ziJcrHWvGolAULWx6L3YKR1zUzqTrM8k0lLXs1X/IkFPPtQZ+jvc4N5MaERUFVw9RYDFlmQKhqoIhRYHuSgzas5cg2zZSHiQkJB4MJCdXjqTrE4nJiCc6XcvtDC0x6VrSsvMu66kVUMfNDrArsA0fJ6jlEUlJRBrUcjvcNO64q+1w04C9ubKImmyjB0k6GUnZOpJ1KWiNGfd1TRq5PU09quGi/u86hFAhouPh8n4QeaMqhcodY6YD2WGJZJ0ORaSfv69+JSQkJEqC5OSsTGp2PHHaCOKzIsgy3gmSkEMV55wfS6KUqXDVeOCutsddI8PRnJdmRAgQRhVCJ5DZgVphwMcBfByUgCdZBm+SdJCcrSVJl4TeVPhMMhc3dRWauDuhVuQ4OCGUiLgUuLQPTP/NMoXaE0OKmsxT4egvHbbsRUtISEgUgeTkLIwQgtTsOOK0EcRpI9Dd5wypJMhlClzV7ripHXHXKHBW3ZlNGRWY0o1kx2VgjIwl+3IY2SGhiJQ7S5RyOZrHH0XTpjmqOlWRu6uxU2Tj6wi+jirAmwy9kiSdICk7g2RdEkaRdxmxulN16jjLkMlMCKFAJGbCxR1gyHGOQu2FPlFB1omr6K9JASMSEhIVg+TkLIAQJpKzY4jLiiBee4NsU5ZV+lHK1DionHBXO+GmVuIik0GGAeOtVAwR0aSGXkcXEgpZxfRvMqE7egbd0TM5f6tVaB5ril2rJijr+CJ3d8BBqcdRZcIfO4SoRppeQZLORHJ2Jr4OznjZG3Iq/yfrERd2QnYGQu1FdoorWcevYIiQHJuEhETFIzm5+8QkTCTrbucsRWojS7S8VxxymQI7hQN2CnvsFGrs5ErsjAJ1tglNpg55cgaGW5FkX7qG/kIoCXoLKF4DZOvRHT6D7vAZAGRODti1aoi6aX2Utaohd3fC2U6Gi9pITewRwoRIA3FhL8KgJjvGnsyjNzBGXbSMPRISEhIWQnJypcAkjCTqoojLiiBBG4lBlC66EUBzx4nZKzTYKVRoTGCnF6i1ehRpGYjoJAw3LqK/FoEhIhKEIBsofU/3j0jPJGvfabL2nQZA7umGfcv6qOr6ovD3QSTEkB2WQNbhUIyxCeVomYSEhETpkJxcMRhNBhJ1t4jTRpCgvZlvb6oo5DIFLio3XJQOOGWDc5oWWUwihps30IdFor8SBjodesCWA+dNCclk/Hsc/q1oSyQkJCRKh+TkCsBg0pOgjSROe4NE3S1MomTLghqFAy4KZ5y04JSUhV1UPPqQ02QHXwSTCakAlYSEhET5Ijm5O+hNuhzHlhVBoi4KQdGVNmTIcJQ746xV4BificPNGLhwAsOlqyAERsB6cZUSEhISEiXBppzcpk2bWLJkCXq9nhEjRvDCCy9Ytb9so5Z47Q3itBEk66KLcGwy7LDHXivDIS4Tu/Bo7EIuI8Jvms+wUAiIhISEhIQFsRknFxMTw4IFC1i3bh1qtZqhQ4fStm1b6tWrZ9F+dMbMHMeWFUFydgx3l5uSIcde2GGXBXaJWahvJaC8Hony0jXkWl2ediSpTQkJCQnbx2ac3KFDh2jXrh1ubm4A9OjRg+3btzN69GgAjMac6h1XboSQpIvCJIyYMCGEEaMw5fwtjAj++90kTAgMmIQJE0YMJj0ZhiTkKLAzadBkCdRJmaijE1GGR6EIj0RmMCCArDs/EhISEhK2S2JmTvpWro+4F5txcrGxsXh5eZn/9vb2Jjg42Px3XFyOgObbr75X7rZJSEhISNgqOdtGcXFx1KxZM9+rNuPkhMi/ACi7qwpxs2bN+P333/Hy8kKhUJSnaRISEhISNorRaCQuLo5mzZoV+LrNODkfHx9OnDhh/js2NhZvb2/z33Z2drRu3boiTJOQkJCQsGEKmsHlIi9HO4qkQ4cOHD58mMTERLKysvj777956qmnKtosCQkJCYlKjM04OR8fHz744ANefvllBg4cSN++fXn00Ucr2qwSsWjRIvr06UOfPn2YPXs2kBNI069fP7p3786CBQvM5168eJEhQ4bQo0cPPv30UwyGnOSDqKgoXnjhBXr27Mk777xDRsbDl2VniXEMCgriiSeeYMCAAQwYMCDPex4GSjOGuYwfP55169aZ/37Y70VLjOHDfh9C6cbx33//ZcCAAfTv3593332XlJQUwEL3opAoEwcPHhTPP/+80Ol0Ijs7W7z88sti06ZNIiAgQNy4cUPo9Xrx2muviT179gghhOjTp484ffq0EEKIiRMnit9//10IIcRbb70lNm/eLIQQYtGiRWL27NkVcj0VhaXGcfr06WLTpk0VdRkVSmnHMDo6WowcOVI8+uijYu3ateZ2HuZ70VJj+DDfh0KUbhzT0tJEx44dRXR0tBBCiK+//lp8/vnnQgjL3Is2M5OrrHh5eTFhwgTUajUqlYq6desSHh5OzZo1qV69Okqlkn79+rF9+3Zu3bqFVqulRYsWAAwePJjt27ej1+s5fvw4PXr0yHP8YcIS4whw7tw5goKC6N+/Px9//LH5ifBhoDRjCDnFF7p27UqvXr3MbTzs96IlxhAe7vsQSjeOer2eqVOn4uPjA0DDhg25ffu2xe5FycmVkfr165u/bMPDw9m6dSsymSxfOkRMTEy+NAkvLy9iYmJISkrCyckJpVKZ5/jDhCXGMff3MWPGsGHDBqpVq8b06dPL9ToqktKMIcAbb7zBs88+m6eNh/1etMQYwsN9H0LpxtHd3Z1u3boBoNVqWbp0Kd26dbPYvSg5OQtx5coVXnvtNcaPH0+NGjXyvS6TyQpNkyjs+MNIWcYRYPHixTRv3hyZTMYbb7zBvn37rG6zrVGSMSwM6V7MoSxjCNJ9mEtpxjEtLY0333yTRo0aMWjQIIvdi5KTswAnT55kxIgRfPTRRwwaNAgfHx/i4+PNr+emQ9x7PC4uDm9vbzw8PEhPTzdn7Ocef9go6zimpaXxyy+/mI8LIcxPgQ8LJR3DwpDuxbKPoXQf5lCacYyNjWX48OE0atSIL7/8ErDcvSg5uTJy+/ZtRo0axdy5c+nTpw8AzZs3JywsjIiICIxGI5s3b+app57Cz88PjUbDyZMngZwIrKeeegqVSkXr1q3ZunVrnuMPE5YYRwcHB3788UfOnj0LwG+//cbTTz9dYddU3pRmDAvjYb8XLTGGD/t9CKUbR6PRyNtvv02vXr349NNPzbM1S92LMlHQnFCixHzxxResXbs2z1R86NCh1KpVi5kzZ6LT6QgICGDixInIZDIuXbpEYGAgGRkZNGnShJkzZ6JWq7l16xYTJkw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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:07.173806\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" ] @@ -12939,1446 +228,7 @@ { "data": { "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" ] @@ -16844,3212 +375,7 @@ { "data": { "image/png": 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:20.988806\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -36392,16321 +397,7 @@ { "data": { "image/png": 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:22.818610\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -52756,3668 +447,7 @@ { "data": { "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" ] @@ -56428,3066 +458,7 @@ { "data": { "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -70411,7 +628,7 @@ ], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", - "st_keywords_df = dset.get_st_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", + "st_keywords_df = dset.get_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", "st_keywords_df = st_keywords_df.rename(columns={x: x.split(\".\")[-1] for x in st_keywords_df.columns}).fillna(0).applymap(lambda x: x > 0)\n", "\n", "df_keywords = df.loc[:, [\"category\", \"eal\", \"not_valid_before\", \"not_valid_after\", \"year_from\"]].copy()\n", diff --git a/notebooks/fips/temporal_trends.ipynb b/notebooks/fips/temporal_trends.ipynb index 10d60ab4..850c01a5 100644 --- a/notebooks/fips/temporal_trends.ipynb +++ b/notebooks/fips/temporal_trends.ipynb @@ -181,7 +181,7 @@ "outputs": [], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", - "st_keywords_df = dset.get_st_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", + "st_keywords_df = dset.get_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", "st_keywords_df = st_keywords_df.rename(columns={x: x.split(\".\")[-1] for x in st_keywords_df.columns}).fillna(0).applymap(lambda x: x > 0)\n", "\n", "df_keywords = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\"]].copy()\n", diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 50cebc50..c68b93e1 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -64,13 +64,6 @@ def to_pandas(self) -> pd.DataFrame: return df - def get_st_keywords_df(self) -> pd.DataFrame: - """ - Get dataframe of keyword hits in security target. - """ - data = [dict({"dgst": x.dgst}, **x.get_keywords_df_row()) for x in self] - return pd.DataFrame(data).set_index("dgst") - def _copy_dataset_contents(self, old_dset: CCDataset) -> None: if old_dset.state.meta_sources_parsed: try: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 63e1e921..f6b26d31 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -10,6 +10,7 @@ from pathlib import Path from typing import Any, Collection, Dict, Generic, Iterator, Optional, Pattern, Set, Tuple, Type, TypeVar, Union, cast +import pandas as pd import requests import sec_certs.constants as constants @@ -433,3 +434,10 @@ def compute_related_cves( logger.info( f"In total, we identified {n_vulnerabilities} vulnerabilities in {n_vulnerable} vulnerable certificates." ) + + def get_keywords_df(self) -> pd.DataFrame: + """ + Get dataframe of keyword hits. + """ + data = [dict({"dgst": x.dgst}, **x.keywords_df_data) for x in self] + return pd.DataFrame(data).set_index("dgst") diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index a4c57168..d21e6f02 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -526,13 +526,6 @@ def to_pandas(self) -> pd.DataFrame: return df - def get_st_keywords_df(self) -> pd.DataFrame: - """ - Get dataframe of keyword hits in security target. - """ - data = [dict({"dgst": x.dgst}, **x.get_keywords_df_row()) for x in self] - return pd.DataFrame(data).set_index("dgst") - def plot_graphs(self, show: bool = False) -> None: """ Plots FIPS graphs. diff --git a/sec_certs/sample/certificate.py b/sec_certs/sample/certificate.py index 2a442224..b403a88f 100644 --- a/sec_certs/sample/certificate.py +++ b/sec_certs/sample/certificate.py @@ -2,10 +2,13 @@ import json import logging from abc import ABC, abstractmethod +from collections import ChainMap from dataclasses import dataclass, field from pathlib import Path from typing import Any, Dict, Generic, Optional, Set, Type, TypeVar, Union +import sec_certs.utils.extract +from sec_certs.cert_rules import PANDAS_KEYWORDS_CATEGORIES from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder logger = logging.getLogger(__name__) @@ -51,6 +54,20 @@ def dgst(self): def label_studio_title(self): raise NotImplementedError("Not meant to be implemented") + @property + def keywords_df_data(self) -> Dict[str, float]: + """ + Returns dictionary of keywords divided by categories, used to generate dataframe. + """ + return dict( + ChainMap( + *[ + sec_certs.utils.extract.get_sums_for_rules_subset(self._get_keyword_data_input(), cat) + for cat in PANDAS_KEYWORDS_CATEGORIES + ] + ) + ) + def __eq__(self, other: object) -> bool: if not isinstance(other, Certificate): return False @@ -63,7 +80,7 @@ def to_dict(self) -> Dict[str, Any]: } @classmethod - def from_dict(cls: Type[T], dct: dict) -> T: + def from_dict(cls: Type[T], dct: Dict) -> T: dct.pop("dgst") return cls(**dct) @@ -75,3 +92,7 @@ def from_json(cls: Type[T], input_path: Union[Path, str]) -> T: @abstractmethod def compute_heuristics_version(self) -> None: raise NotImplementedError("Not meant to be implemented") + + @abstractmethod + def _get_keyword_data_input(self) -> Optional[Dict]: + raise NotImplementedError("Not meant to be implemented") diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py index 2900970b..d4305d35 100644 --- a/sec_certs/sample/common_criteria.py +++ b/sec_certs/sample/common_criteria.py @@ -2,7 +2,7 @@ import copy import re -from collections import ChainMap, Counter, defaultdict +from collections import Counter, defaultdict from dataclasses import dataclass, field from datetime import date, datetime from enum import Enum @@ -18,13 +18,7 @@ import sec_certs.utils.pdf import sec_certs.utils.sanitization from sec_certs import constants as constants -from sec_certs.cert_rules import ( - PANDAS_KEYWORDS_CATEGORIES, - SARS_IMPLIED_FROM_EAL, - cc_rules, - rules, - security_level_csv_scan, -) +from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL, cc_rules, rules, security_level_csv_scan from sec_certs.sample.cc_certificate_id import canonicalize from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics @@ -596,18 +590,8 @@ def merge(self, other: CommonCriteriaCert, other_source: Optional[str] = None) - f"When merging certificates with dgst {self.dgst}, the following mismatch occured: Attribute={att}, self[{att}]={getattr(self, att)}, other[{att}]={getattr(other, att)}" ) - def get_keywords_df_row(self) -> dict[str, float]: - """ - Returns dictionary of sums of matches of keywords in ST. Iterates over all categories - """ - return dict( - ChainMap( - *[ - sec_certs.utils.extract.get_sums_for_rules_subset(self.pdf_data.st_keywords, cat) - for cat in PANDAS_KEYWORDS_CATEGORIES - ] - ) - ) + def _get_keyword_data_input(self) -> Optional[Dict]: + return self.pdf_data.st_keywords @classmethod def from_dict(cls, dct: Dict) -> CommonCriteriaCert: diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 18ad6e21..617c00f2 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -2,7 +2,6 @@ import copy import re -from collections import ChainMap from dataclasses import dataclass, field from datetime import datetime from pathlib import Path @@ -19,7 +18,7 @@ import sec_certs.utils.helpers as helpers import sec_certs.utils.pdf import sec_certs.utils.tables -from sec_certs.cert_rules import PANDAS_KEYWORDS_CATEGORIES, fips_rules +from sec_certs.cert_rules import fips_rules from sec_certs.config.configuration import config from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics @@ -437,18 +436,8 @@ def pandas_tuple(self) -> Tuple: self.heuristics.st_references.indirectly_referencing, ) - def get_keywords_df_row(self) -> dict[str, float]: - """ - Returns dictionary of sums of matches of keywords in ST. Iterates over all categories - """ - return dict( - ChainMap( - *[ - sec_certs.utils.extract.get_sums_for_rules_subset(self.pdf_data.keywords, cat) - for cat in PANDAS_KEYWORDS_CATEGORIES - ] - ) - ) + def _get_keyword_data_input(self) -> Dict: + return self.pdf_data.keywords @classmethod def from_dict(cls, dct: Dict) -> FIPSCertificate: From 1b150aa58b1ff8effb35649201ad590a41d3cc7e Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 27 Oct 2022 12:24:55 +0200 Subject: [PATCH 05/81] unify and generalize dataset method get_keywords_df() --- notebooks/cc/temporal_trends.ipynb | 4 ++-- notebooks/fips/temporal_trends.ipynb | 11 ++++++--- sec_certs/dataset/dataset.py | 6 ++--- sec_certs/sample/certificate.py | 35 +++++++++++++--------------- sec_certs/sample/common_criteria.py | 8 +++---- sec_certs/sample/fips.py | 10 ++++---- 6 files changed, 37 insertions(+), 37 deletions(-) diff --git a/notebooks/cc/temporal_trends.ipynb b/notebooks/cc/temporal_trends.ipynb index 5f2a4306..83786e5c 100644 --- a/notebooks/cc/temporal_trends.ipynb +++ b/notebooks/cc/temporal_trends.ipynb @@ -628,7 +628,7 @@ ], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", - "st_keywords_df = dset.get_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", + "st_keywords_df = dset.get_keywords_df(\"st_keywords\").drop(columns=[\"crypto_scheme.MAC\"])\n", "st_keywords_df = st_keywords_df.rename(columns={x: x.split(\".\")[-1] for x in st_keywords_df.columns}).fillna(0).applymap(lambda x: x > 0)\n", "\n", "df_keywords = df.loc[:, [\"category\", \"eal\", \"not_valid_before\", \"not_valid_after\", \"year_from\"]].copy()\n", @@ -725,7 +725,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.7" + "version": "3.8.13" }, "vscode": { "interpreter": { diff --git a/notebooks/fips/temporal_trends.ipynb b/notebooks/fips/temporal_trends.ipynb index 850c01a5..24e009c4 100644 --- a/notebooks/fips/temporal_trends.ipynb +++ b/notebooks/fips/temporal_trends.ipynb @@ -181,7 +181,7 @@ "outputs": [], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", - "st_keywords_df = dset.get_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", + "st_keywords_df = dset.get_keywords_df(\"keywords\").drop(columns=[\"crypto_scheme.MAC\"])\n", "st_keywords_df = st_keywords_df.rename(columns={x: x.split(\".\")[-1] for x in st_keywords_df.columns}).fillna(0).applymap(lambda x: x > 0)\n", "\n", "df_keywords = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\"]].copy()\n", @@ -261,7 +261,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -275,7 +275,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.7" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index f6b26d31..3dd59cd3 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -435,9 +435,9 @@ def compute_related_cves( f"In total, we identified {n_vulnerabilities} vulnerabilities in {n_vulnerable} vulnerable certificates." ) - def get_keywords_df(self) -> pd.DataFrame: + def get_keywords_df(self, var: str) -> pd.DataFrame: """ - Get dataframe of keyword hits. + Get dataframe of keyword hits for attribute (var) that is member of PdfData class. """ - data = [dict({"dgst": x.dgst}, **x.keywords_df_data) for x in self] + data = [dict({"dgst": x.dgst}, **x.pdf_data.get_keywords_df_data(var)) for x in self] return pd.DataFrame(data).set_index("dgst") diff --git a/sec_certs/sample/certificate.py b/sec_certs/sample/certificate.py index b403a88f..843e7776 100644 --- a/sec_certs/sample/certificate.py +++ b/sec_certs/sample/certificate.py @@ -15,6 +15,7 @@ T = TypeVar("T", bound="Certificate") H = TypeVar("H", bound="Heuristics") +P = TypeVar("P", bound="PdfData") @dataclass @@ -30,9 +31,23 @@ class Heuristics: related_cves: Optional[Set[str]] -class Certificate(Generic[T, H], ABC, ComplexSerializableType): +class PdfData: + def get_keywords_df_data(self, var: str) -> Dict[str, float]: + data_dct = getattr(self, var) + return dict( + ChainMap( + *[ + sec_certs.utils.extract.get_sums_for_rules_subset(data_dct, cat) + for cat in PANDAS_KEYWORDS_CATEGORIES + ] + ) + ) + + +class Certificate(Generic[T, H, P], ABC, ComplexSerializableType): manufacturer: Optional[str] name: Optional[str] + pdf_data: P heuristics: H def __init__(self, *args, **kwargs): @@ -54,20 +69,6 @@ def dgst(self): def label_studio_title(self): raise NotImplementedError("Not meant to be implemented") - @property - def keywords_df_data(self) -> Dict[str, float]: - """ - Returns dictionary of keywords divided by categories, used to generate dataframe. - """ - return dict( - ChainMap( - *[ - sec_certs.utils.extract.get_sums_for_rules_subset(self._get_keyword_data_input(), cat) - for cat in PANDAS_KEYWORDS_CATEGORIES - ] - ) - ) - def __eq__(self, other: object) -> bool: if not isinstance(other, Certificate): return False @@ -92,7 +93,3 @@ def from_json(cls: Type[T], input_path: Union[Path, str]) -> T: @abstractmethod def compute_heuristics_version(self) -> None: raise NotImplementedError("Not meant to be implemented") - - @abstractmethod - def _get_keyword_data_input(self) -> Optional[Dict]: - raise NotImplementedError("Not meant to be implemented") diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py index d4305d35..4b21be19 100644 --- a/sec_certs/sample/common_criteria.py +++ b/sec_certs/sample/common_criteria.py @@ -22,6 +22,7 @@ from sec_certs.sample.cc_certificate_id import canonicalize from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics +from sec_certs.sample.certificate import PdfData as BasePdfData from sec_certs.sample.certificate import References, logger from sec_certs.sample.protection_profile import ProtectionProfile from sec_certs.sample.sar import SAR @@ -45,7 +46,7 @@ class DependencyType(Enum): class CommonCriteriaCert( - Certificate["CommonCriteriaCert", "CommonCriteriaCert.Heuristics"], + Certificate["CommonCriteriaCert", "CommonCriteriaCert.Heuristics", "CommonCriteriaCert.PdfData"], PandasSerializableType, ComplexSerializableType, ): @@ -197,7 +198,7 @@ def st_is_ok_to_analyze(self, fresh: bool = True) -> bool: return self.st_download_ok and self.st_convert_ok and not self.st_extract_ok @dataclass - class PdfData(ComplexSerializableType): + class PdfData(BasePdfData, ComplexSerializableType): """ Class that holds data extracted from pdf files. """ @@ -590,9 +591,6 @@ def merge(self, other: CommonCriteriaCert, other_source: Optional[str] = None) - f"When merging certificates with dgst {self.dgst}, the following mismatch occured: Attribute={att}, self[{att}]={getattr(self, att)}, other[{att}]={getattr(other, att)}" ) - def _get_keyword_data_input(self) -> Optional[Dict]: - return self.pdf_data.st_keywords - @classmethod def from_dict(cls, dct: Dict) -> CommonCriteriaCert: """ diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 617c00f2..4ea73ed9 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -22,6 +22,7 @@ from sec_certs.config.configuration import config from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics +from sec_certs.sample.certificate import PdfData as BasePdfData from sec_certs.sample.certificate import References, logger from sec_certs.sample.cpe import CPE from sec_certs.sample.fips_algorithm import FIPSAlgorithm @@ -199,7 +200,9 @@ def extract_algorithm_certificates(current_text: str) -> Set[str]: class FIPSCertificate( - Certificate["FIPSCertificate", "FIPSCertificate.Heuristics"], PandasSerializableType, ComplexSerializableType + Certificate["FIPSCertificate", "FIPSCertificate.Heuristics", "FIPSCertificate.PdfData"], + PandasSerializableType, + ComplexSerializableType, ): """ Data structure for common FIPS 140 certificate. Contains several inner classes that layer the data logic. @@ -318,7 +321,7 @@ def __str__(self) -> str: return repr(self) @dataclass(eq=True) - class PdfData(ComplexSerializableType): + class PdfData(BasePdfData, ComplexSerializableType): """ Data structure that holds data obtained from scanning pdf files (or their converted txt documents). """ @@ -436,9 +439,6 @@ def pandas_tuple(self) -> Tuple: self.heuristics.st_references.indirectly_referencing, ) - def _get_keyword_data_input(self) -> Dict: - return self.pdf_data.keywords - @classmethod def from_dict(cls, dct: Dict) -> FIPSCertificate: """ From 5110b523d84f65427195fe5d25414f22783a2e66 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 27 Oct 2022 13:44:13 +0200 Subject: [PATCH 06/81] root_dir setter for FIPSDataset --- sec_certs/dataset/common_criteria.py | 20 +++----------------- sec_certs/dataset/dataset.py | 9 ++++++--- sec_certs/dataset/fips.py | 22 +++++++++++----------- sec_certs/sample/fips.py | 12 +++--------- 4 files changed, 23 insertions(+), 40 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index c68b93e1..db73c102 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -64,23 +64,6 @@ def to_pandas(self) -> pd.DataFrame: return df - def _copy_dataset_contents(self, old_dset: CCDataset) -> None: - if old_dset.state.meta_sources_parsed: - try: - shutil.copytree(old_dset.web_dir, self.web_dir) - except FileNotFoundError as e: - logger.warning(f"Attempted to copy non-existing file: {e}") - if old_dset.state.pdfs_downloaded: - try: - shutil.copytree(old_dset.certs_dir, self.certs_dir) - except FileNotFoundError as e: - logger.warning(f"Attempted to copy non-existing file: {e}") - if old_dset.state.certs_analyzed: - try: - shutil.copytree(old_dset.auxillary_datasets_dir, self.auxillary_datasets_dir) - except FileNotFoundError as e: - logger.warning(f"Attempted to copy non-existing file: {e}") - @property def certs_dir(self) -> Path: """ @@ -827,6 +810,9 @@ def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: return update_dset + def process_auxillary_datasets(self) -> None: + raise NotImplementedError + class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): """ diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 3dd59cd3..46fd937a 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -3,6 +3,7 @@ import json import logging import re +import shutil import tempfile from abc import ABC, abstractmethod from dataclasses import dataclass @@ -84,7 +85,6 @@ def root_dir(self, new_dir: Union[str, Path]) -> None: old_dset = copy.deepcopy(self) self._root_dir = Path(new_dir) self.root_dir.mkdir(exist_ok=True) - self._set_local_paths() if old_dset.root_dir != self.root_dir: @@ -174,7 +174,7 @@ def from_dict(cls: Type[DatasetSubType], dct: Dict) -> DatasetSubType: @classmethod def from_json(cls: Type[DatasetSubType], input_path: Union[str, Path]) -> DatasetSubType: dset = cast("DatasetSubType", ComplexSerializableType.from_json(input_path)) - dset.root_dir = Path(input_path).parent.absolute() + dset._root_dir = Path(input_path).parent.absolute() dset._set_local_paths() return dset @@ -183,7 +183,10 @@ def _set_local_paths(self) -> None: # Workaround from https://peps.python.org/pep-0673/ applied. def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") + try: + shutil.copytree(old_dset.root_dir, self.root_dir, dirs_exist_ok=True) + except FileNotFoundError as e: + logger.error(f"Attempted to copy dataset from {old_dset.root_dir}, but it's not there ({e}).") def _get_certs_by_name(self, name: str) -> Set[CertSubType]: """ diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index d21e6f02..26ddb2df 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -27,11 +27,11 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): """ @property - def _policies_dir(self) -> Path: + def policies_dir(self) -> Path: return self.root_dir / "security_policies" @property - def _algorithms_dir(self) -> Path: + def algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" @serialize @@ -60,15 +60,15 @@ def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None: :raises RuntimeError: If no cert_ids are specified, raises. """ sp_paths, sp_urls = [], [] - self._policies_dir.mkdir(exist_ok=True) + self.policies_dir.mkdir(exist_ok=True) if cert_ids is None: raise RuntimeError("You need to provide cert ids to FIPS download PDFs functionality.") for cert_id in cert_ids: - if not (self._policies_dir / f"{cert_id}.pdf").exists() or ( + if not (self.policies_dir / f"{cert_id}.pdf").exists() or ( fips_dgst(cert_id) in self.certs and not self.certs[fips_dgst(cert_id)].state.txt_state ): sp_urls.append(constants.FIPS_SP_URL.format(cert_id)) - sp_paths.append(self._policies_dir / f"{cert_id}.pdf") + sp_paths.append(self.policies_dir / f"{cert_id}.pdf") logger.info(f"downloading {len(sp_urls)} module pdf files") cert_processing.process_parallel( FIPSCertificate.download_security_policy, @@ -110,9 +110,9 @@ def convert_all_pdfs(self) -> None: """ logger.info("Converting FIPS sample reports to .txt") tuples = [ - (cert, self._policies_dir / f"{cert.cert_id}.pdf", self._policies_dir / f"{cert.cert_id}.pdf.txt") + (cert, self.policies_dir / f"{cert.cert_id}.pdf", self.policies_dir / f"{cert.cert_id}.pdf.txt") for cert in self.certs.values() - if not cert.state.txt_state and (self._policies_dir / f"{cert.cert_id}.pdf").exists() + if not cert.state.txt_state and (self.policies_dir / f"{cert.cert_id}.pdf").exists() ] cert_processing.process_parallel( FIPSCertificate.convert_pdf_file, tuples, config.n_threads, progress_bar_desc="Converting to txt" @@ -172,7 +172,7 @@ def web_scan(self, cert_ids: Set[str], redo: bool = False) -> None: self.certs[dgst] = FIPSCertificate.from_html_file( self.web_dir / f"{cert_id}.html", FIPSCertificate.InternalState( - (self._policies_dir / str(cert_id)).with_suffix(".pdf"), + (self.policies_dir / str(cert_id)).with_suffix(".pdf"), (self.web_dir / str(cert_id)).with_suffix(".html"), False, None, @@ -192,7 +192,7 @@ def from_web_latest(cls) -> "FIPSDataset": def _set_local_paths(self) -> None: cert: FIPSCertificate for cert in self.certs.values(): - cert.set_local_paths(self._policies_dir, self.web_dir) + cert.set_local_paths(self.policies_dir, self.web_dir) @serialize def get_certs_from_web( @@ -214,8 +214,8 @@ def get_certs_from_web( logger.info("Downloading required html files") self.web_dir.mkdir(parents=True, exist_ok=True) - self._policies_dir.mkdir(exist_ok=True) - self._algorithms_dir.mkdir(exist_ok=True) + self.policies_dir.mkdir(exist_ok=True) + self.algorithms_dir.mkdir(exist_ok=True) # Download files containing all available module certs (always) cert_ids = self._prepare_dataset(test, update) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 4ea73ed9..a3819eb5 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -264,15 +264,9 @@ def __init__( self.file_status = file_status self.txt_state = txt_state - def set_local_paths( - self, - sp_dir: Optional[Union[str, Path]], - html_dir: Optional[Union[str, Path]], - ) -> None: - if sp_dir is not None: - self.state.sp_path = (Path(sp_dir) / (str(self.cert_id))).with_suffix(".pdf") - if html_dir is not None: - self.state.html_path = (Path(html_dir) / (str(self.cert_id))).with_suffix(".html") + def set_local_paths(self, sp_dir: Path, web_dir: Path) -> None: + self.state.sp_path = (sp_dir / str(self.cert_id)).with_suffix(".pdf") + self.state.html_path = (web_dir / str(self.cert_id)).with_suffix(".html") @dataclass(eq=True) class WebData(ComplexSerializableType): From 39c89c16ad45beb4ccd6dbf5318de656a2d5c98f Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 27 Oct 2022 16:21:55 +0200 Subject: [PATCH 07/81] WiP: refactor FIPS get_certs_from_web() --- cli.py | 4 +- sec_certs/dataset/common_criteria.py | 10 +- sec_certs/dataset/dataset.py | 6 +- sec_certs/dataset/fips.py | 105 ++++++++--------- sec_certs/dataset/fips_algorithm.py | 2 +- sec_certs/sample/certificate.py | 3 + sec_certs/sample/fips.py | 166 +++++++++++++-------------- 7 files changed, 144 insertions(+), 152 deletions(-) diff --git a/cli.py b/cli.py index 3d361719..9057e1e2 100644 --- a/cli.py +++ b/cli.py @@ -161,9 +161,9 @@ def main( ), ProcessingStep( "download", - "download_all_pdfs", + "download_artifacts", precondition="meta_sources_parsed", - precondition_error_msg="Error: You want to download all pdfs, but the data from the cert. framework website was not parsed. You must use 'build' action first.", + precondition_error_msg="Error: You want to download all artifacts, but the data from the cert. framework website was not parsed. You must use 'build' action first.", pre_callback_func=None, ), ProcessingStep( diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index db73c102..696e08c1 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -278,7 +278,7 @@ def get_certs_from_web( csv_certs = self._get_all_certs_from_csv(get_active, get_archived) self._merge_certs(csv_certs, cert_source="csv") - # TODO: Someway along the way, 3 certificates get lost. Investigate and fix. + # Someway along the way, 3 certificates get lost. logger.info("Adding HTML certificates to CommonCriteria dataset.") html_certs = self._get_all_certs_from_html(get_active, get_archived) self._merge_certs(html_certs, cert_source="html") @@ -290,7 +290,6 @@ def get_certs_from_web( self._set_local_paths() self.state.meta_sources_parsed = True - self.process_protection_profiles() def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> Dict[str, "CommonCriteriaCert"]: """ @@ -538,7 +537,7 @@ def _download_targets(self, fresh: bool = True) -> None: ) @serialize - def download_all_pdfs(self, fresh: bool = True) -> None: + def download_all_artifacts(self, fresh: bool = True) -> None: """ Downloads all pdf files associated with certificates of the datset. @@ -804,14 +803,15 @@ def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: update_dset: CCDatasetMaintenanceUpdates = CCDatasetMaintenanceUpdates( {x.dgst: x for x in updates}, root_dir=self.mu_dataset_path, name="Maintenance updates" ) - update_dset.download_all_pdfs() + update_dset.download_all_artifacts() update_dset.convert_all_pdfs() update_dset._extract_data() return update_dset def process_auxillary_datasets(self) -> None: - raise NotImplementedError + self.process_protection_profiles() + # TODO: Also process MUs class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 46fd937a..d5c3afaa 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -46,7 +46,7 @@ def __bool__(self): def __init__( self, certs: Dict[str, CertSubType] = dict(), - root_dir: Optional[Path] = None, + root_dir: Optional[Union[str, Path]] = None, name: Optional[str] = None, description: str = None, state: Optional[DatasetInternalState] = None, @@ -58,7 +58,7 @@ def __init__( if not root_dir: root_dir = Path.cwd() / (type(self).__name__).lower() - self._root_dir = root_dir + self._root_dir = Path(root_dir) self.timestamp = datetime.now() self.sha256_digest = "not implemented" @@ -203,7 +203,7 @@ def process_auxillary_datasets(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None: + def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 26ddb2df..24550de6 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -1,6 +1,8 @@ +import itertools import logging +import shutil from pathlib import Path -from typing import List, Optional, Set +from typing import Dict, Final, List, Optional, Set import numpy as np import pandas as pd @@ -14,7 +16,6 @@ from sec_certs.model.dependency_finder import DependencyFinder from sec_certs.sample.fips import FIPSCertificate from sec_certs.serialization.json import ComplexSerializableType, serialize -from sec_certs.utils import helpers as helpers from sec_certs.utils import parallel_processing as cert_processing from sec_certs.utils.helpers import fips_dgst @@ -26,6 +27,12 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ + LIST_OF_CERTS_HTML: Final[Dict[str, str]] = { + "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, + "fips_modules_historical.html": constants.FIPS_HISTORICAL_MODULES_URL, + "fips_modules_revoked.html": constants.FIPS_REVOKED_MODULES_URL, + } + @property def policies_dir(self) -> Path: return self.root_dir / "security_policies" @@ -52,13 +59,21 @@ def _extract_data(self, redo: bool = False) -> None: for keyword, cert in keywords: self.certs[cert.dgst].pdf_data.keywords = keyword - def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None: + def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: """ Downloads all pdf files related to the certificates specified with cert_ids. :param Optional[Set[str]] cert_ids: cert_ids to download the pdfs foor, defaults to None :raises RuntimeError: If no cert_ids are specified, raises. """ + # TODO: The code below was migrated here from get_certs_web() + # self.policies_dir.mkdir(exist_ok=True) + # self.algorithms_dir.mkdir(exist_ok=True) + # logger.info("Downloading certificate html and security policies") + # self._download_all_htmls(cert_ids) + # self.download_all_pdfs(cert_ids) + # self.web_scan(cert_ids, redo=redo_web_scan, update_json=False) + sp_paths, sp_urls = [], [] self.policies_dir.mkdir(exist_ok=True) if cert_ids is None: @@ -118,45 +133,36 @@ def convert_all_pdfs(self) -> None: FIPSCertificate.convert_pdf_file, tuples, config.n_threads, progress_bar_desc="Converting to txt" ) - # TODO: this "test" parameter is nasty. - def _prepare_dataset(self, test: Optional[Path] = None, update: bool = False) -> Set[str]: - if test: - html_files = [test] - else: - html_files = [ - Path("fips_modules_active.html"), - Path("fips_modules_historical.html"), - Path("fips_modules_revoked.html"), - ] - helpers.download_file(constants.FIPS_ACTIVE_MODULES_URL, Path(self.web_dir / "fips_modules_active.html")) - helpers.download_file( - constants.FIPS_HISTORICAL_MODULES_URL, Path(self.web_dir / "fips_modules_historical.html") - ) - helpers.download_file(constants.FIPS_REVOKED_MODULES_URL, Path(self.web_dir / "fips_modules_revoked.html")) + def _download_html_resources(self) -> None: + logger.info("Downloading HTML files that list FIPS certificates.") + html_urls = list(FIPSDataset.LIST_OF_CERTS_HTML.values()) + html_paths = [self.web_dir / x for x in FIPSDataset.LIST_OF_CERTS_HTML.keys()] + self._download_parallel(html_urls, html_paths) - # Parse those files and get list of currently processable files (always) - cert_ids: Set[str] = set() - for f in html_files: - cert_ids |= self._get_certificates_from_html(self.web_dir / f, update) + def _get_all_certs_from_html_sources(self) -> Set[FIPSCertificate]: + return set( + itertools.chain.from_iterable( + [self._get_certificates_from_html(self.web_dir / x) for x in self.LIST_OF_CERTS_HTML.keys()] + ) + ) - return cert_ids + def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: + logger.debug(f"Getting certificate ids from {html_file}") - def _get_certificates_from_html(self, html_file: Path, update: bool = False) -> Set[str]: - logger.info(f"Getting certificate ids from {html_file}") with open(html_file, "r", encoding="utf-8") as handle: html = BeautifulSoup(handle.read(), "html5lib") table = [x for x in html.find(id="searchResultsTable").tbody.contents if x != "\n"] - entries: Set[str] = set() + cert_ids: Set[int] = set() for entry in table: if isinstance(entry, NavigableString): continue cert_id = entry.find("a").text - if cert_id not in entries: - entries.add(cert_id) + if cert_id not in cert_ids: + cert_ids.add(int(cert_id)) - return entries + return {FIPSCertificate(cert_id) for cert_id in cert_ids} @serialize def web_scan(self, cert_ids: Set[str], redo: bool = False) -> None: @@ -172,10 +178,8 @@ def web_scan(self, cert_ids: Set[str], redo: bool = False) -> None: self.certs[dgst] = FIPSCertificate.from_html_file( self.web_dir / f"{cert_id}.html", FIPSCertificate.InternalState( - (self.policies_dir / str(cert_id)).with_suffix(".pdf"), - (self.web_dir / str(cert_id)).with_suffix(".html"), False, - None, + False, False, ), self.certs.get(dgst), @@ -195,36 +199,21 @@ def _set_local_paths(self) -> None: cert.set_local_paths(self.policies_dir, self.web_dir) @serialize - def get_certs_from_web( - self, - # TODO: REMOVE THIS TEST ARGUMENT, OMG! - test: Optional[Path] = None, - update: bool = False, - redo_web_scan=False, - ) -> None: - """Downloads HTML search pages, parses them, populates the dataset, - and performs `web-scan` - extracting information from CMVP pages for - each certificate. - - Args: - test (Optional[Path], optional): Path to dataset used in testing. Defaults to None. - update (bool, optional): Whether to update dataset with new entries. Defaults to False. - redo_web_scan (bool, optional): Whether to redo the `web-scan` functionality. Defaults to False. - """ - logger.info("Downloading required html files") - + def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = True) -> None: self.web_dir.mkdir(parents=True, exist_ok=True) - self.policies_dir.mkdir(exist_ok=True) - self.algorithms_dir.mkdir(exist_ok=True) - # Download files containing all available module certs (always) - cert_ids = self._prepare_dataset(test, update) + if to_download: + self._download_html_resources() + + logger.info("Adding empty FIPS certificates into FIPSDataset.") + self.certs = {x.dgst: x for x in self._get_all_certs_from_html_sources()} + logger.info(f"The dataset now contains {len(self)} certificates.") - logger.info("Downloading certificate html and security policies") - self._download_all_htmls(cert_ids) - self.download_all_pdfs(cert_ids) + if not keep_metadata: + shutil.rmtree(self.web_dir) - self.web_scan(cert_ids, redo=redo_web_scan, update_json=False) + self._set_local_paths() + self.state.meta_sources_parsed = True @serialize def process_auxillary_datasets(self) -> None: diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index 98feb9e5..aa367be5 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -126,7 +126,7 @@ def from_dict(cls, dct: Dict[str, Any]) -> "FIPSAlgorithmDataset": def convert_all_pdfs(self): raise NotImplementedError("Not meant to be implemented") - def download_all_pdfs(self, cert_ids: Optional[Set[str]] = None) -> None: + def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: raise NotImplementedError("Not meant to be implemented") def __getitem__(self, item: str) -> FIPSAlgorithm: diff --git a/sec_certs/sample/certificate.py b/sec_certs/sample/certificate.py index 843e7776..bfc63e56 100644 --- a/sec_certs/sample/certificate.py +++ b/sec_certs/sample/certificate.py @@ -74,6 +74,9 @@ def __eq__(self, other: object) -> bool: return False return self.dgst == other.dgst + def __hash__(self) -> int: + return hash(self.dgst) + def to_dict(self) -> Dict[str, Any]: return { **{"dgst": self.dgst}, diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index a3819eb5..bfd0e685 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -244,29 +244,26 @@ class InternalState(ComplexSerializableType): Holds state of the `FIPSCertificate` """ - sp_path: Path - html_path: Path tables_done: bool - file_status: Optional[bool] + file_status: bool txt_state: bool + sp_path: Path + html_path: Path + def __init__( self, - sp_path: Union[str, Path], - html_path: Union[str, Path], - tables_done: bool, - file_status: Optional[bool], - txt_state: bool, + tables_done: bool = False, + file_status: bool = True, # TODO: Check if this is correct + txt_state: bool = True, # TODO: Check if this is correct ): - self.sp_path = Path(sp_path) - self.html_path = Path(html_path) self.tables_done = tables_done self.file_status = file_status self.txt_state = txt_state def set_local_paths(self, sp_dir: Path, web_dir: Path) -> None: - self.state.sp_path = (sp_dir / str(self.cert_id)).with_suffix(".pdf") - self.state.html_path = (web_dir / str(self.cert_id)).with_suffix(".html") + self.state.sp_path = (sp_dir / str(self.dgst)).with_suffix(".pdf") + self.state.html_path = (web_dir / str(self.dgst)).with_suffix(".html") @dataclass(eq=True) class WebData(ComplexSerializableType): @@ -274,33 +271,33 @@ class WebData(ComplexSerializableType): Data structure for data obtained from scanning certificate webpage at NIST.gov """ - module_name: Optional[str] - standard: Optional[str] - status: Optional[str] - date_sunset: Optional[datetime] - date_validation: Optional[List[datetime]] - level: Optional[str] - caveat: Optional[str] - exceptions: Optional[List[str]] - module_type: Optional[str] - embodiment: Optional[str] - algorithms: Optional[Set[FIPSAlgorithm]] - tested_conf: Optional[List[str]] - description: Optional[str] - mentioned_certs: Optional[Dict[str, Dict[str, int]]] - vendor: Optional[str] - vendor_www: Optional[str] - lab: Optional[str] - lab_nvlap: Optional[str] - historical_reason: Optional[str] - security_policy_www: Optional[str] - certificate_www: Optional[str] - hw_version: Optional[str] - fw_version: Optional[str] - revoked_reason: Optional[str] - revoked_link: Optional[str] - sw_versions: Optional[str] - product_url: Optional[str] + module_name: Optional[str] = field(default=None) + standard: Optional[str] = field(default=None) + status: Optional[str] = field(default=None) + date_sunset: Optional[datetime] = field(default=None) + date_validation: Optional[List[datetime]] = field(default=None) + level: Optional[str] = field(default=None) + caveat: Optional[str] = field(default=None) + exceptions: Optional[List[str]] = field(default=None) + module_type: Optional[str] = field(default=None) + embodiment: Optional[str] = field(default=None) + algorithms: Optional[Set[FIPSAlgorithm]] = field(default=None) + tested_conf: Optional[List[str]] = field(default=None) + description: Optional[str] = field(default=None) + mentioned_certs: Optional[Dict[str, Dict[str, int]]] = field(default=None) + vendor: Optional[str] = field(default=None) + vendor_www: Optional[str] = field(default=None) + lab: Optional[str] = field(default=None) + lab_nvlap: Optional[str] = field(default=None) + historical_reason: Optional[str] = field(default=None) + security_policy_www: Optional[str] = field(default=None) + certificate_www: Optional[str] = field(default=None) + hw_version: Optional[str] = field(default=None) + fw_version: Optional[str] = field(default=None) + revoked_reason: Optional[str] = field(default=None) + revoked_link: Optional[str] = field(default=None) + sw_versions: Optional[str] = field(default=None) + product_url: Optional[str] = field(default=None) def __repr__(self) -> str: return ( @@ -320,17 +317,17 @@ class PdfData(BasePdfData, ComplexSerializableType): Data structure that holds data obtained from scanning pdf files (or their converted txt documents). """ - cert_id: int - keywords: Dict - algorithms: Set[FIPSAlgorithm] - clean_cert_ids: Dict[str, int] - st_metadata: Optional[Dict[str, Any]] = field(default=None) + keywords: Dict = field(default_factory=dict) + algorithms: Set[FIPSAlgorithm] = field(default_factory=set) + clean_cert_ids: Dict[str, int] = field(default_factory=dict) + st_metadata: Dict[str, Any] = field(default_factory=dict) - def __repr__(self) -> str: - return str(self.cert_id) + # TODO: Is this meaningful? Cert id attribute got deleted. + # def __repr__(self) -> str: + # return str(self.cert_id) - def __str__(self) -> str: - return str(self.cert_id) + # def __str__(self) -> str: + # return str(self.cert_id) @dataclass(eq=True) class Heuristics(BaseHeuristics, ComplexSerializableType): @@ -338,12 +335,13 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): Data structure that holds data obtained by processing the certificate and applying various heuristics. """ - keywords: Dict[str, Dict] - algorithms: Set[FIPSAlgorithm] - unmatched_algs: int - clean_cert_ids: Dict[str, int] + # TODO: How are keywords, clean_cert_ids and algorithms attributes different from those in pdf data? + keywords: Dict[str, Dict] = field(default_factory=dict) + algorithms: Set[FIPSAlgorithm] = field(default_factory=set) + unmatched_algs: Optional[int] = field(default=None) + clean_cert_ids: Optional[Dict[str, int]] = field(default=None) - extracted_versions: Optional[Set[str]] = field(default=None) + extracted_versions: Set[str] = field(default_factory=set) cpe_matches: Optional[Set[str]] = field(default=None) verified_cpe_matches: Optional[Set[CPE]] = field(default=None) related_cves: Optional[Set[str]] = field(default=None) @@ -393,17 +391,18 @@ def label_studio_title(self) -> str: def __init__( self, cert_id: int, - web_data: FIPSCertificate.WebData, - pdf_data: FIPSCertificate.PdfData, - heuristics: FIPSCertificate.Heuristics, - state: InternalState, + web_data: Optional[FIPSCertificate.WebData] = None, + pdf_data: Optional[FIPSCertificate.PdfData] = None, + heuristics: Optional[FIPSCertificate.Heuristics] = None, + state: Optional[InternalState] = None, ): super().__init__() + self.cert_id = cert_id - self.web_data = web_data - self.pdf_data = pdf_data - self.heuristics = heuristics - self.state = state + self.web_data = web_data if web_data else FIPSCertificate.WebData() + self.pdf_data = pdf_data if pdf_data else FIPSCertificate.PdfData() + self.heuristics = heuristics if heuristics else FIPSCertificate.Heuristics() + self.state = state if state else FIPSCertificate.InternalState() @property def pandas_tuple(self) -> Tuple: @@ -533,7 +532,6 @@ def from_html_file( items_found["product_url"] if "product_url" in items_found else None, ), FIPSCertificate.PdfData( - items_found["cert_id"], {} if not initialized else initialized.pdf_data.keywords, set() if not initialized else initialized.pdf_data.algorithms, {} if not initialized else initialized.pdf_data.clean_cert_ids, @@ -568,7 +566,8 @@ def download_security_policy(cert: Tuple[str, Path]) -> None: @staticmethod def extract_sp_metadata(cert: FIPSCertificate) -> FIPSCertificate: """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" - response, cert.pdf_data.st_metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.sp_path) + _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.sp_path) + cert.pdf_data.st_metadata = metadata if metadata else dict() return cert @staticmethod @@ -686,26 +685,27 @@ def extract_algorithm_certificates(current_text): return True, cert, lst def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: - if "Certlike" in self.pdf_data.keywords["fips_certlike"]: - for found in self.pdf_data.keywords["fips_certlike"]["Certlike"]: - match_in_found = reg_to_match.search(found) - match_in_cert = reg_to_match.search(cert) - if ( - match_in_found is not None - and match_in_cert is not None - and match_in_found.group("id") == match_in_cert.group("id") - ): - to_pop.add(cert) - - this_id = int("".join(filter(str.isdigit, cert))) - - for algo in self.heuristics.algorithms: - try: - algo_id = int("".join(filter(str.isdigit, algo.cert_id))) - if algo_id == this_id: - to_pop.add(cert) - except ValueError: - continue + pass + + # def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: + # for found in self.pdf_data.keywords["fips_certlike"]["Certlike"]: + # match_in_found = reg_to_match.search(found) + # match_in_cert = reg_to_match.search(cert) + # if ( + # match_in_found is not None + # and match_in_cert is not None + # and match_in_found.group("id") == match_in_cert.group("id") + # ): + # to_pop.add(cert) + + # this_id = int("".join(filter(str.isdigit, cert))) + # for algo in self.heuristics.algorithms: + # try: + # algo_id = int("".join(filter(str.isdigit, algo.cert_id))) + # if algo_id == this_id: + # to_pop.add(cert) + # except ValueError: + # continue def clean_cert_ids(self) -> None: """ From 9433658b55c6025567fe4101dae017afc9b4f8a0 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 27 Oct 2022 21:48:38 +0200 Subject: [PATCH 08/81] implement artifact download FIPS --- cli.py | 2 +- sec_certs/dataset/common_criteria.py | 49 ++++--------- sec_certs/dataset/dataset.py | 49 +++++++++++-- sec_certs/dataset/fips.py | 102 ++++++++++++--------------- sec_certs/sample/fips.py | 75 ++++++++++++++++---- 5 files changed, 165 insertions(+), 112 deletions(-) diff --git a/cli.py b/cli.py index 9057e1e2..eaa0f276 100644 --- a/cli.py +++ b/cli.py @@ -161,7 +161,7 @@ def main( ), ProcessingStep( "download", - "download_artifacts", + "download_all_artifacts", precondition="meta_sources_parsed", precondition_error_msg="Error: You want to download all artifacts, but the data from the cert. framework website was not parsed. You must use 'build' action first.", pre_callback_func=None, diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 696e08c1..9e022a5b 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -64,13 +64,6 @@ def to_pandas(self) -> pd.DataFrame: return df - @property - def certs_dir(self) -> Path: - """ - Returns directory that holds files associated with certificates - """ - return self.root_dir / "certs" - @property def reports_dir(self) -> Path: """ @@ -137,6 +130,10 @@ def mu_dataset(self) -> "CCDatasetMaintenanceUpdates": return CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path / "Maintenance updates.json") + @property + def artifact_download_methods(self) -> List[Callable]: + return [self._download_reports, self._download_targets] + BASE_URL: ClassVar[str] = "https://www.commoncriteriaportal.org" HTML_PRODUCTS_URL = { @@ -517,6 +514,11 @@ def _parse_table( return certs def _download_reports(self, fresh: bool = True) -> None: + if fresh: + logger.info("Downloading PDFs of CC certification reports.") + else: + logger.info("Attempting to re-download failed PDFs of CC certification reports.") + self.reports_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh) and x.report_link] cert_processing.process_parallel( @@ -527,6 +529,11 @@ def _download_reports(self, fresh: bool = True) -> None: ) def _download_targets(self, fresh: bool = True) -> None: + if fresh: + logger.info("Downloading PDFs of CC security targets.") + else: + logger.info("Attempting to re-download failed PDFs of CC security targets.") + self.targets_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh)] cert_processing.process_parallel( @@ -536,32 +543,6 @@ def _download_targets(self, fresh: bool = True) -> None: progress_bar_desc="Downloading targets", ) - @serialize - def download_all_artifacts(self, fresh: bool = True) -> None: - """ - Downloads all pdf files associated with certificates of the datset. - - :param bool fresh: whether all (true) or only failed (false) pdfs shall be downloaded, defaults to True - """ - if self.state.meta_sources_parsed is False: - logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") - return - - logger.info("Downloading CC sample reports") - self._download_reports(fresh) - - logger.info("Downloading CC security targets") - self._download_targets(fresh) - - if fresh is True: - logger.info("Attempting to re-download failed report links.") - self._download_reports(False) - - logger.info("Attempting to re-download failed security target links.") - self._download_targets(False) - - self.state.pdfs_downloaded = True - def _convert_reports_to_txt(self, fresh: bool = True) -> None: self.reports_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_convert(fresh)] @@ -589,7 +570,7 @@ def convert_all_pdfs(self, fresh: bool = True) -> None: :param bool fresh: whether all (true) or only failed (false) pdfs shall be converted, defaults to True """ - if self.state.pdfs_downloaded is False: + if self.state.artifacts_downloaded is False: logger.info("Attempting to convert pdf while not having them downloaded. Returning.") return diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index d5c3afaa..4b1c8c7b 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -9,7 +9,23 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Collection, Dict, Generic, Iterator, Optional, Pattern, Set, Tuple, Type, TypeVar, Union, cast +from typing import ( + Any, + Callable, + Collection, + Dict, + Generic, + Iterator, + List, + Optional, + Pattern, + Set, + Tuple, + Type, + TypeVar, + Union, + cast, +) import pandas as pd import requests @@ -36,7 +52,7 @@ class Dataset(Generic[CertSubType], ComplexSerializableType, ABC): @dataclass class DatasetInternalState(ComplexSerializableType): meta_sources_parsed: bool = False - pdfs_downloaded: bool = False + artifacts_downloaded: bool = False pdfs_converted: bool = False certs_analyzed: bool = False @@ -100,6 +116,13 @@ def web_dir(self) -> Path: def auxillary_datasets_dir(self) -> Path: return self.root_dir / "auxillary_datasets" + @property + def certs_dir(self) -> Path: + """ + Returns directory that holds files associated with certificates + """ + return self.root_dir / "certs" + @property def cpe_dataset_path(self) -> Path: return self.auxillary_datasets_dir / "cpe_dataset.json" @@ -116,6 +139,11 @@ def nist_cve_cpe_matching_dset_path(self) -> Path: def json_path(self) -> Path: return self.root_dir / (self.name + ".json") + @property + @abstractmethod + def artifact_download_methods(self) -> List[Callable]: + raise NotImplementedError("Not meant to be implemented by the base class.") + def __contains__(self, item: object) -> bool: if not isinstance(item, Certificate): raise TypeError( @@ -202,9 +230,20 @@ def get_certs_from_web(self) -> None: def process_auxillary_datasets(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - @abstractmethod - def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") + @serialize + def download_all_artifacts(self, fresh: bool = True) -> None: + if self.state.meta_sources_parsed is False: + logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") + return + + for method in self.artifact_download_methods: + method(fresh) + + if fresh: + for method in self.artifact_download_methods: + method(False) + + self.state.artifacts_downloaded = True @abstractmethod def convert_all_pdfs(self) -> None: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 24550de6..c0d4a68a 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -2,7 +2,7 @@ import logging import shutil from pathlib import Path -from typing import Dict, Final, List, Optional, Set +from typing import Callable, Dict, Final, List, Set import numpy as np import pandas as pd @@ -35,12 +35,28 @@ class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): @property def policies_dir(self) -> Path: - return self.root_dir / "security_policies" + return self.certs_dir / "policies" + + @property + def policies_pdf_dir(self) -> Path: + return self.policies_dir / "pdf" + + @property + def policies_txt_dir(self) -> Path: + return self.policies_dir / "txt" + + @property + def module_dir(self) -> Path: + return self.certs_dir / "modules" @property def algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" + @property + def artifact_download_methods(self) -> List[Callable]: + return [self._download_modules, self._download_policies] + @serialize def _extract_data(self, redo: bool = False) -> None: """ @@ -59,64 +75,37 @@ def _extract_data(self, redo: bool = False) -> None: for keyword, cert in keywords: self.certs[cert.dgst].pdf_data.keywords = keyword - def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: - """ - Downloads all pdf files related to the certificates specified with cert_ids. + def _download_modules(self, fresh: bool = True) -> None: + self.module_dir.mkdir(exist_ok=True) - :param Optional[Set[str]] cert_ids: cert_ids to download the pdfs foor, defaults to None - :raises RuntimeError: If no cert_ids are specified, raises. - """ - # TODO: The code below was migrated here from get_certs_web() - # self.policies_dir.mkdir(exist_ok=True) - # self.algorithms_dir.mkdir(exist_ok=True) - # logger.info("Downloading certificate html and security policies") - # self._download_all_htmls(cert_ids) - # self.download_all_pdfs(cert_ids) - # self.web_scan(cert_ids, redo=redo_web_scan, update_json=False) - - sp_paths, sp_urls = [], [] - self.policies_dir.mkdir(exist_ok=True) - if cert_ids is None: - raise RuntimeError("You need to provide cert ids to FIPS download PDFs functionality.") - for cert_id in cert_ids: - if not (self.policies_dir / f"{cert_id}.pdf").exists() or ( - fips_dgst(cert_id) in self.certs and not self.certs[fips_dgst(cert_id)].state.txt_state - ): - sp_urls.append(constants.FIPS_SP_URL.format(cert_id)) - sp_paths.append(self.policies_dir / f"{cert_id}.pdf") - logger.info(f"downloading {len(sp_urls)} module pdf files") + if fresh: + logger.info("Downloading HTML cryptographic modules.") + else: + logger.info("Attempting re-download of failed HTML cryptographic modules.") + + certs_to_process = [x for x in self if x.state.module_is_ok_to_download(fresh)] cert_processing.process_parallel( - FIPSCertificate.download_security_policy, - list(zip(sp_urls, sp_paths)), + FIPSCertificate.download_module, + certs_to_process, config.n_threads, - progress_bar_desc="Downloading PDF files", + progress_bar_desc="Downloading HTML modules", ) - def _download_all_htmls(self, cert_ids: Set[str]) -> None: - html_paths, html_urls = [], [] - self.web_dir.mkdir(exist_ok=True) - for cert_id in cert_ids: - if not (self.web_dir / f"{cert_id}.html").exists(): - html_urls.append(constants.FIPS_MODULE_URL.format(cert_id)) - html_paths.append(self.web_dir / f"{cert_id}.html") - - logger.info(f"downloading {len(html_urls)} module html files") - failed = cert_processing.process_parallel( - FIPSCertificate.download_html_page, - list(zip(html_urls, html_paths)), + def _download_policies(self, fresh: bool = True) -> None: + self.policies_pdf_dir.mkdir(exist_ok=True) + + if fresh: + logger.info("Downloading PDF security policies.") + else: + logger.info("Attempting re-download of failed PDF security policies.") + + certs_to_process = [x for x in self if x.state.policy_is_ok_to_download(fresh)] + cert_processing.process_parallel( + FIPSCertificate.download_policy, + certs_to_process, config.n_threads, - progress_bar_desc="Downloading HTML files", + progress_bar_desc="Downloading PDF security policies", ) - failed = [c for c in failed if c] - - if len(failed) != 0: - logger.info(f"Download failed for {len(failed)} files. Retrying...") - cert_processing.process_parallel( - FIPSCertificate.download_html_page, - failed, - config.n_threads, - progress_bar_desc="Downloading HTML files again", - ) @serialize def convert_all_pdfs(self) -> None: @@ -196,7 +185,7 @@ def from_web_latest(cls) -> "FIPSDataset": def _set_local_paths(self) -> None: cert: FIPSCertificate for cert in self.certs.values(): - cert.set_local_paths(self.policies_dir, self.web_dir) + cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.web_dir) @serialize def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = True) -> None: @@ -221,9 +210,8 @@ def process_auxillary_datasets(self) -> None: def _process_algorithms(self): logger.info("Processing FIPS algorithms.") - self.algorithms = FIPSAlgorithmDataset( - {}, Path(self.root_dir / "web" / "algorithms"), "algorithms", "sample algs" - ) + self.algorithms_dir.mkdir(parernts=True, exist_ok=True) + self.algorithms = FIPSAlgorithmDataset({}, self.algorithms_dir, "algorithms", "sample algs") self.algorithms.get_certs_from_web() logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index bfd0e685..9a122646 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -248,22 +248,46 @@ class InternalState(ComplexSerializableType): file_status: bool txt_state: bool - sp_path: Path - html_path: Path + policy_pdf_path: Path + policy_txt_path: Path + module_html_path: Path + + module_download_ok: bool + policy_download_ok: bool + + errors: List[str] def __init__( self, tables_done: bool = False, file_status: bool = True, # TODO: Check if this is correct txt_state: bool = True, # TODO: Check if this is correct + module_download_ok: bool = False, + policy_download_ok: bool = False, + errors: Optional[List[str]] = None, ): self.tables_done = tables_done self.file_status = file_status self.txt_state = txt_state + self.module_download_ok = module_download_ok + self.policy_download_ok = policy_download_ok + self.errors = errors if errors else [] + + @property + def serialized_attributes(self) -> List[str]: + # TODO: Fix me, add other variables + return ["tables_done", "file_status", "txt_state"] + + def module_is_ok_to_download(self, fresh: bool = True) -> bool: + return True if fresh else not self.module_download_ok + + def policy_is_ok_to_download(self, fresh: bool = True) -> bool: + return True if fresh else not self.policy_download_ok - def set_local_paths(self, sp_dir: Path, web_dir: Path) -> None: - self.state.sp_path = (sp_dir / str(self.dgst)).with_suffix(".pdf") - self.state.html_path = (web_dir / str(self.dgst)).with_suffix(".html") + def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, web_dir: Path) -> None: + self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") + self.state.policy_txt_path = (policies_txt_dir / str(self.dgst)).with_suffix(".txt") + self.state.module_html_path = (web_dir / str(self.dgst)).with_suffix(".html") @dataclass(eq=True) class WebData(ComplexSerializableType): @@ -368,6 +392,14 @@ def dgst(self) -> str: def manufacturer(self) -> Optional[str]: # type: ignore return self.web_data.vendor + @property + def module_html_url(self) -> str: + return constants.FIPS_MODULE_URL.format(self.dgst) + + @property + def policy_pdf_url(self) -> str: + return constants.FIPS_SP_URL.format(self.dgst) + @property def name(self) -> Optional[str]: # type: ignore return self.web_data.module_name @@ -555,18 +587,29 @@ def download_html_page(cert: Tuple[str, Path]) -> Optional[Tuple[str, Path]]: return None @staticmethod - def download_security_policy(cert: Tuple[str, Path]) -> None: - """ - Downloads security policy file from web. Staticmethod to allow for parametrization. - """ - exit_code = helpers.download_file(*cert, delay=constants.FIPS_DOWNLOAD_DELAY) - if exit_code != requests.codes.ok: - logger.error(f"Failed to download security policy from {cert[0]}, code: {exit_code}") + def download_module(cert: FIPSCertificate) -> FIPSCertificate: + if (exit_code := helpers.download_file(cert.module_html_url, cert.state.module_html_path)) != requests.codes.ok: + error_msg = f"failed to download html module from {cert.module_html_url}, code {exit_code}" + logger.error(f"Cert dgst: {cert.dgst} " + error_msg) + cert.state.module_download_ok = False + else: + cert.state.module_download_ok = True + return cert + + @staticmethod + def download_policy(cert: FIPSCertificate) -> FIPSCertificate: + if (exit_code := helpers.download_file(cert.policy_pdf_url, cert.state.policy_pdf_path)) != requests.codes.ok: + error_msg = f"failed to download pdf policy from {cert.policy_pdf_url}, code {exit_code}" + logger.error(f"Cert dgst: {cert.dgst} " + error_msg) + cert.state.policy_download_ok = False + else: + cert.state.policy_download_ok = True + return cert @staticmethod def extract_sp_metadata(cert: FIPSCertificate) -> FIPSCertificate: """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" - _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.sp_path) + _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) cert.pdf_data.st_metadata = metadata if metadata else dict() return cert @@ -626,7 +669,9 @@ def find_keywords(cert: FIPSCertificate) -> Tuple[Optional[Dict], FIPSCertificat if not cert.state.txt_state: return None, cert - keywords = sec_certs.utils.extract.extract_keywords(cert.state.sp_path.with_suffix(".pdf.txt"), fips_rules) + keywords = sec_certs.utils.extract.extract_keywords( + cert.state.policy_pdf_path.with_suffix(".pdf.txt"), fips_rules + ) return keywords, cert @staticmethod @@ -653,7 +698,7 @@ def extract_algorithm_certificates(current_text): ): return cert.state.tables_done, cert, set() - cert_file = cert.state.sp_path + cert_file = cert.state.policy_pdf_path txt_file = cert_file.with_suffix(".pdf.txt") with open(txt_file, "r", encoding="utf-8") as f: tables = sec_certs.utils.tables.find_tables(f.read(), txt_file) From 933b46985e73d01dae95c637e904950cb78c9782 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 4 Nov 2022 16:33:19 +0100 Subject: [PATCH 09/81] refactor tests unittest -> pytest --- tests/cc/test_cc_analysis.py | 258 + tests/cc/test_cc_certificate.py | 135 + tests/cc/test_cc_dataset.py | 156 + .../__init__.py => cc/test_cc_identifiers.py} | 0 tests/cc/test_cc_misc.py | 47 + tests/cc/test_cc_schemes.py | 84 + tests/data/cc/analysis/__init__.py | 0 .../auxillary_datasets/cpe_dataset.json | 0 .../auxillary_datasets/cve_dataset.json | 0 .../auxillary_datasets/pp_dataset.json | 76952 ++++++++++++++++ tests/data/cc/analysis/cc_full_dataset.json | 729 + .../certs/reports/pdf/ebd276cca70fd723.pdf | Bin 0 -> 1174271 bytes .../certs/reports/txt/ebd276cca70fd723.txt | 1068 + .../certs/targets/pdf/ebd276cca70fd723.pdf | Bin 0 -> 615034 bytes .../certs/targets/txt/ebd276cca70fd723.txt | 2168 + .../analysis}/dependency_dataset.json | 4 +- .../dependency_vulnerability_dataset.json | 4 +- .../data/cc/analysis/vulnerable_dataset.json | 73 + .../certificate}/fictional_cert.json | 0 .../dataset}/cc_products_active.csv | 0 .../dataset}/cc_products_active.html | 0 .../dataset}/report_309ac2fd7f2dcf17.txt | 0 .../dataset}/target_309ac2fd7f2dcf17.txt | 0 .../dataset}/toy_dataset.json | 4 +- tests/data/certs.csv | 15 - .../{test_fips_oop => fips}/algorithms.json | 0 .../iut}/fips_iut_2020-10-25T06+01:00.html | 0 .../iut}/fips_iut_2021-12-10T06+01:00.html | 0 .../mip}/fips_mip_2020-10-24T06+02:00.html | 0 .../mip}/fips_mip_2021-02-19T06+01:00.html | 0 .../mip}/fips_mip_2021-12-19T06+01:00.html | 0 tests/data/settings_test.yaml | 59 - .../vulnerable_dataset.json | 71 - tests/fips/test_fips_algorithm_dataset.py | 16 + tests/fips/test_fips_certificate.py | 0 tests/fips/test_fips_dataset.py | 301 + tests/fips/test_fips_iut.py | 39 + tests/fips/test_fips_mip.py | 39 + tests/fips_test_utils.py | 52 - tests/test_cc_heuristics.py | 376 - tests/test_cc_oop.py | 250 - tests/test_cc_schemes.py | 72 - tests/test_cc_txt_processing.py | 62 - tests/test_common.py | 9 + tests/test_cpe.py | 136 +- tests/test_cve.py | 128 +- tests/test_fips_iut.py | 32 - tests/test_fips_mip.py | 32 - tests/test_fips_oop.py | 352 - tests/test_rules.py | 12 - 50 files changed, 82288 insertions(+), 1447 deletions(-) create mode 100644 tests/cc/test_cc_analysis.py create mode 100644 tests/cc/test_cc_certificate.py create mode 100644 tests/cc/test_cc_dataset.py rename tests/{data/test_cc_heuristics/__init__.py => cc/test_cc_identifiers.py} (100%) create mode 100644 tests/cc/test_cc_misc.py create mode 100644 tests/cc/test_cc_schemes.py create mode 100644 tests/data/cc/analysis/__init__.py rename tests/data/{test_cc_heuristics => cc/analysis}/auxillary_datasets/cpe_dataset.json (100%) rename tests/data/{test_cc_heuristics => cc/analysis}/auxillary_datasets/cve_dataset.json (100%) create mode 100644 tests/data/cc/analysis/auxillary_datasets/pp_dataset.json create mode 100644 tests/data/cc/analysis/cc_full_dataset.json create mode 100644 tests/data/cc/analysis/certs/reports/pdf/ebd276cca70fd723.pdf create mode 100644 tests/data/cc/analysis/certs/reports/txt/ebd276cca70fd723.txt create mode 100644 tests/data/cc/analysis/certs/targets/pdf/ebd276cca70fd723.pdf create mode 100644 tests/data/cc/analysis/certs/targets/txt/ebd276cca70fd723.txt rename tests/data/{test_cc_heuristics => cc/analysis}/dependency_dataset.json (99%) rename tests/data/{test_cc_heuristics => cc/analysis}/dependency_vulnerability_dataset.json (99%) create mode 100644 tests/data/cc/analysis/vulnerable_dataset.json rename tests/data/{test_cc_oop => cc/certificate}/fictional_cert.json (100%) rename tests/data/{test_cc_oop => cc/dataset}/cc_products_active.csv (100%) rename tests/data/{test_cc_oop => cc/dataset}/cc_products_active.html (100%) rename tests/data/{test_cc_oop => cc/dataset}/report_309ac2fd7f2dcf17.txt (100%) rename tests/data/{test_cc_oop => cc/dataset}/target_309ac2fd7f2dcf17.txt (100%) rename tests/data/{test_cc_oop => cc/dataset}/toy_dataset.json (98%) delete mode 100644 tests/data/certs.csv rename tests/data/{test_fips_oop => fips}/algorithms.json (100%) rename tests/data/{test_fips_iut => fips/iut}/fips_iut_2020-10-25T06+01:00.html (100%) rename tests/data/{test_fips_iut => fips/iut}/fips_iut_2021-12-10T06+01:00.html (100%) rename tests/data/{test_fips_mip => fips/mip}/fips_mip_2020-10-24T06+02:00.html (100%) rename tests/data/{test_fips_mip => fips/mip}/fips_mip_2021-02-19T06+01:00.html (100%) rename tests/data/{test_fips_mip => fips/mip}/fips_mip_2021-12-19T06+01:00.html (100%) delete mode 100644 tests/data/settings_test.yaml delete mode 100644 tests/data/test_cc_heuristics/vulnerable_dataset.json create mode 100644 tests/fips/test_fips_algorithm_dataset.py create mode 100644 tests/fips/test_fips_certificate.py create mode 100644 tests/fips/test_fips_dataset.py create mode 100644 tests/fips/test_fips_iut.py create mode 100644 tests/fips/test_fips_mip.py delete mode 100644 tests/fips_test_utils.py delete mode 100644 tests/test_cc_heuristics.py delete mode 100644 tests/test_cc_oop.py delete mode 100644 tests/test_cc_schemes.py delete mode 100644 tests/test_cc_txt_processing.py create mode 100644 tests/test_common.py delete mode 100644 tests/test_fips_iut.py delete mode 100644 tests/test_fips_mip.py delete mode 100644 tests/test_fips_oop.py delete mode 100644 tests/test_rules.py diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py new file mode 100644 index 00000000..1d3ca657 --- /dev/null +++ b/tests/cc/test_cc_analysis.py @@ -0,0 +1,258 @@ +from pathlib import Path +from typing import Dict, Set + +import pytest + +import tests.data.cc.analysis +from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL +from sec_certs.dataset import CCDataset +from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cve import CVEDataset +from sec_certs.sample.common_criteria import CommonCriteriaCert +from sec_certs.sample.cpe import CPE +from sec_certs.sample.cve import CVE +from sec_certs.sample.protection_profile import ProtectionProfile +from sec_certs.sample.sar import SAR + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.cc.analysis.__path__[0]) + + +@pytest.fixture(scope="module") +def cpe_single_sign_on() -> CPE: + return CPE( + "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", + "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", + ) + + +@pytest.fixture(scope="module") +def cpes(cpe_single_sign_on: CPE) -> Set[CPE]: + return { + cpe_single_sign_on, + CPE( + "cpe:2.3:a:ibm:security_key_lifecycle_manager:2.6.0.1:*:*:*:*:*:*:*", + "IBM Security Key Lifecycle Manager 2.6.0.1", + ), + CPE( + "cpe:2.3:a:semperplugins:all_in_one_seo_pack:1.3.6.4:*:*:*:*:wordpress:*:*", + "Semper Plugins All in One SEO Pack 1.3.6.4 for WordPress", + ), + CPE( + "cpe:2.3:a:tracker-software:pdf-xchange_lite_printer:6.0.320.0:*:*:*:*:*:*:*", + "Tracker Software PDF-XChange Lite Printer 6.0.320.0", + ), + } + + +@pytest.fixture(scope="module") +def cpe_dset(cpes: Set[CPE]) -> CPEDataset: + return CPEDataset(True, Path("../"), {x.uri: x for x in cpes}) + + +@pytest.fixture(scope="module") +def cves(cpe_single_sign_on) -> Set[CVE]: + return { + CVE( + "CVE-2017-1732", + [cpe_single_sign_on], + CVE.Impact(5.3, "MEDIUM", 3.9, 1.4), + "2021-05-26T04:15Z", + {"CWE-200"}, + ), + CVE( + "CVE-2019-4513", + [cpe_single_sign_on], + CVE.Impact(8.2, "HIGH", 3.9, 4.2), + "2000-05-26T04:15Z", + {"CVE-611"}, + ), + } + + +@pytest.fixture(scope="module") +def cve_dset(cves: Set[CVE]) -> CVEDataset: + cve_dset = CVEDataset({x.cve_id: x for x in cves}) + cve_dset.build_lookup_dict(use_nist_mapping=False) + return cve_dset + + +@pytest.fixture(scope="module") +def cc_dset(data_dir: Path) -> CCDataset: + cc_dset = CCDataset.from_json(data_dir / "vulnerable_dataset.json") + cc_dset.process_protection_profiles() + cc_dset.download_all_artifacts() + cc_dset.convert_all_pdfs() + cc_dset._extract_data() + cc_dset._compute_heuristics(use_nist_cpe_matching_dict=False) + return cc_dset + + +@pytest.fixture +def dependency_dataset(data_dir) -> CCDataset: + return CCDataset.from_json(data_dir / "dependency_dataset.json") + + +@pytest.fixture +def dependency_vulnerability_dataset(data_dir) -> CCDataset: + return CCDataset.from_json(data_dir / "dependency_vulnerability_dataset.json") + + +@pytest.fixture +def random_certificate(cc_dset: CCDataset) -> CommonCriteriaCert: + return cc_dset["ebd276cca70fd723"] + + +def test_match_cpe(cpe_single_sign_on: CPE, random_certificate: CommonCriteriaCert): + assert {cpe_single_sign_on.uri} == random_certificate.heuristics.cpe_matches + + +def test_find_related_cves( + cc_dset: CCDataset, cpe_single_sign_on: CPE, cves: Set[CVE], random_certificate: CommonCriteriaCert +): + random_certificate.heuristics.cpe_matches = {cpe_single_sign_on.uri} + cc_dset.compute_related_cves(use_nist_cpe_matching_dict=False) + assert {x.cve_id for x in cves} == random_certificate.heuristics.related_cves + + +def test_version_extraction(random_certificate: CommonCriteriaCert): + assert random_certificate.heuristics.extracted_versions == {"8.2"} + + new_cert = CommonCriteriaCert( + "", + "", + "IDOneClassIC Card : ID-One Cosmo 64 RSA v5.4 and applet IDOneClassIC v1.0 embedded on P5CT072VOP", + "", + "", + "", + None, + None, + "", + "", + "", + "", + set(), + set(), + None, + None, + None, + ) + new_cert.compute_heuristics_version() + assert new_cert.heuristics.extracted_versions == {"5.4", "1.0"} + + +def test_cert_lab_heuristics(random_certificate: CommonCriteriaCert): + assert random_certificate.heuristics.cert_lab == ["BSI"] + + +def test_cert_id_heuristics(random_certificate: CommonCriteriaCert): + assert random_certificate.heuristics.cert_id == "BSI-DSZ-CC-0683-2014" + + +def test_keywords_heuristics(random_certificate: CommonCriteriaCert): + assert random_certificate.pdf_data.st_keywords + extracted_keywords: Dict = random_certificate.pdf_data.st_keywords + + assert "cc_security_level" in extracted_keywords + assert extracted_keywords["cc_security_level"]["EAL"]["EAL3"] == 1 + assert "cc_sar" in extracted_keywords + + assert extracted_keywords["cc_sar"]["ADV"]["ADV_ARC.1"] == 1 + assert extracted_keywords["cc_sar"]["ADV"]["ADV_FSP.3"] == 1 + assert extracted_keywords["cc_sar"]["ADV"]["ADV_TDS.2"] == 1 + + assert "symmetric_crypto" in extracted_keywords + assert extracted_keywords["symmetric_crypto"]["AES_competition"]["AES"]["AES"] == 2 + + assert "cipher_mode" in extracted_keywords + assert extracted_keywords["cipher_mode"]["CBC"]["CBC"] == 2 + + +def test_protection_profile_matching(cc_dset: CCDataset, random_certificate: CommonCriteriaCert): + artificial_pp: ProtectionProfile = ProtectionProfile( + "Korean National Protection Profile for Single Sign On V1.0", + "EAL1+", + pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", + ) + + random_certificate.protection_profiles = {artificial_pp} + + expected_pp: ProtectionProfile = ProtectionProfile( + "Korean National Protection Profile for Single Sign On V1.0", + "EAL1+", + pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", + pp_ids=frozenset(["KECS-PP-0822-2017 SSO V1.0"]), + ) + + cc_dset.process_protection_profiles(to_download=False) + assert random_certificate.protection_profiles == {expected_pp} + + +def test_single_record_dependency_heuristics(random_certificate: CommonCriteriaCert): + # Single record in daset is not affecting nor affected by other records + assert not random_certificate.heuristics.report_references.directly_referenced_by + assert not random_certificate.heuristics.report_references.indirectly_referenced_by + assert not random_certificate.heuristics.report_references.directly_referencing + assert not random_certificate.heuristics.report_references.indirectly_referencing + + +def test_dependency_dataset(dependency_dataset: CCDataset): + dependency_dataset._compute_dependencies() + test_cert = dependency_dataset["692e91451741ef49"] + + assert test_cert.heuristics.report_references.directly_referenced_by == {"BSI-DSZ-CC-0370-2006"} + assert test_cert.heuristics.report_references.indirectly_referenced_by == { + "BSI-DSZ-CC-0370-2006", + "BSI-DSZ-CC-0517-2009", + } + assert not test_cert.heuristics.report_references.directly_referencing + assert not test_cert.heuristics.report_references.indirectly_referencing + + +def test_direct_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): + dependency_vulnerability_dataset._compute_dependency_vulnerabilities() + assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.direct_dependency_cves == {"CVE-2013-5385"} + + +def test_indirect_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): + dependency_vulnerability_dataset._compute_dependency_vulnerabilities() + assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.indirect_dependency_cves == {"CVE-2013-5385"} + + +def test_sar_object(): + alc_flr_one = SAR("ALC_FLR", 1) + alc_flr_one_copy = SAR("ALC_FLR", 1) + alc_flr_two = SAR("ALC_FLR", 2) + + assert alc_flr_one == alc_flr_one_copy + assert alc_flr_one != alc_flr_two + + with pytest.raises(ValueError): + # does not contain level + SAR.from_string("ALC_FLR") + + with pytest.raises(ValueError): + # unknown family + SAR.from_string("XALC_FLR") + + +def test_sar_transformation(random_certificate: CommonCriteriaCert): + assert random_certificate.heuristics.extracted_sars + + # This one should be taken from security level and not overwritten by stronger SARs in ST + assert SAR("ALC_FLR", 1) in random_certificate.heuristics.extracted_sars + assert SAR("ALC_FLR", 2) not in random_certificate.heuristics.extracted_sars + + # This one should be taken from ST and not overwritten by stronger SAR in report + assert SAR("ADV_FSP", 3) in random_certificate.heuristics.extracted_sars + assert SAR("ADV_FSP", 6) not in random_certificate.heuristics.extracted_sars + + +def test_eal_implied_sar_inference(random_certificate: CommonCriteriaCert): + assert random_certificate.actual_sars + + actual_sars = random_certificate.actual_sars + eal_3_sars = {SAR(x[0], x[1]) for x in SARS_IMPLIED_FROM_EAL["EAL3"]} + assert eal_3_sars.issubset(actual_sars) diff --git a/tests/cc/test_cc_certificate.py b/tests/cc/test_cc_certificate.py new file mode 100644 index 00000000..e11e5f43 --- /dev/null +++ b/tests/cc/test_cc_certificate.py @@ -0,0 +1,135 @@ +import json +import shutil +from datetime import date +from pathlib import Path + +import pytest + +import tests.data.cc.analysis +import tests.data.cc.certificate +from sec_certs.dataset import CCDataset +from sec_certs.sample import CommonCriteriaCert +from sec_certs.sample.protection_profile import ProtectionProfile + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.cc.certificate.__path__[0]) + + +@pytest.fixture(scope="module") +def vulnerable_certificate(tmp_path_factory) -> CommonCriteriaCert: + tmp_dir = tmp_path_factory.mktemp("dset") + dset_json_path = Path(tests.data.cc.analysis.__path__[0]) / "vulnerable_dataset.json" + data_dir_path = dset_json_path.parent + shutil.copytree(data_dir_path, tmp_dir, dirs_exist_ok=True) + cc_dset = CCDataset.from_json(tmp_dir / "vulnerable_dataset.json") + cc_dset.download_all_artifacts() + cc_dset.convert_all_pdfs() + + return list(cc_dset.certs.values())[0] + + +@pytest.fixture(scope="module") +def cert_one() -> CommonCriteriaCert: + return CommonCriteriaCert( + "active", + "Access Control Devices and Systems", + "NetIQ Identity Manager 4.7", + "NetIQ Corporation", + "SE", + {"ALC_FLR.2", "EAL3+"}, + date(2020, 6, 15), + date(2025, 6, 15), + "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", + "https://www.netiq.com/", + set(), + set(), + None, + None, + None, + ) + + +@pytest.fixture(scope="module") +def cert_two() -> CommonCriteriaCert: + pp = ProtectionProfile("sample_pp", None, pp_link="https://sample.pp") + update = CommonCriteriaCert.MaintenanceReport( + date(1900, 1, 1), "Sample maintenance", "https://maintenance.up", "https://maintenance.up" + ) + + return CommonCriteriaCert( + "archived", + "Sample category", + "Sample certificate name", + "Sample manufacturer", + "Sample scheme", + {"Sample security level"}, + date(1900, 1, 2), + date(1900, 1, 3), + "https://path.to/report/link", + "https://path.to/st/link", + "https://path.to/cert/link", + "https://path.to/manufacturer/web", + {pp}, + {update}, + None, + None, + None, + ) + + +def test_extract_metadata(vulnerable_certificate: CommonCriteriaCert): + vulnerable_certificate.state.st_extract_ok = True + CommonCriteriaCert.extract_st_pdf_metadata(vulnerable_certificate) + assert vulnerable_certificate.state.st_extract_ok + + vulnerable_certificate.state.report_extract_ok = True + CommonCriteriaCert.extract_report_pdf_metadata(vulnerable_certificate) + assert vulnerable_certificate.state.report_extract_ok + + +def test_extract_frontpage(vulnerable_certificate: CommonCriteriaCert): + vulnerable_certificate.state.st_extract_ok = True + CommonCriteriaCert.extract_st_pdf_frontpage(vulnerable_certificate) + assert vulnerable_certificate.state.st_extract_ok + + vulnerable_certificate.state.report_extract_ok = True + CommonCriteriaCert.extract_report_pdf_frontpage(vulnerable_certificate) + assert vulnerable_certificate.state.report_extract_ok + + +def test_keyword_extraction(vulnerable_certificate: CommonCriteriaCert): + vulnerable_certificate.state.st_extract_ok = True + CommonCriteriaCert.extract_st_pdf_keywords(vulnerable_certificate) + assert vulnerable_certificate.state.st_extract_ok + + vulnerable_certificate.state.report_extract_ok = True + CommonCriteriaCert.extract_report_pdf_keywords(vulnerable_certificate) + assert vulnerable_certificate.state.report_extract_ok + + +def test_cert_link_escaping(cert_one: CommonCriteriaCert): + assert ( + cert_one.report_link + == "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf" + ) + + +def test_cert_to_json(cert_two: CommonCriteriaCert, tmp_path: Path, data_dir: Path): + cert_two.to_json(tmp_path / "crt_two.json") + + with (tmp_path / "crt_two.json").open("r") as handle: + data = json.load(handle) + + with (data_dir / "fictional_cert.json").open("r") as handle: + template_data = json.load(handle) + + assert data == template_data + + +def test_cert_from_json(cert_two: CommonCriteriaCert, data_dir: Path): + crt = CommonCriteriaCert.from_json(data_dir / "fictional_cert.json") + assert cert_two == crt diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py new file mode 100644 index 00000000..fa8c5acf --- /dev/null +++ b/tests/cc/test_cc_dataset.py @@ -0,0 +1,156 @@ +import json +import shutil +from datetime import date +from pathlib import Path +from tempfile import TemporaryDirectory + +import pytest + +import tests.data.cc.dataset +from sec_certs import constants +from sec_certs.dataset import CCDataset +from sec_certs.sample.common_criteria import CommonCriteriaCert + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.cc.dataset.__path__[0]) + + +@pytest.fixture(scope="module") +def crt() -> CommonCriteriaCert: + return CommonCriteriaCert( + "active", + "Access Control Devices and Systems", + "NetIQ Identity Manager 4.7", + "NetIQ Corporation", + "SE", + {"ALC_FLR.2", "EAL3+"}, + date(2020, 6, 15), + date(2025, 6, 15), + "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", + "https://www.commoncriteriaportal.org/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", + "https://www.netiq.com/", + set(), + set(), + None, + None, + None, + ) + + +@pytest.fixture +def toy_dataset(data_dir: Path) -> CCDataset: + return CCDataset.from_json(data_dir / "toy_dataset.json") + + +def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): + template_report_pdf_hashes = { + "309ac2fd7f2dcf17": "774c41fbba980191ca40ae610b2f61484c5997417b3325b6fd68b345173bde52", + "8cf86948f02f047d": "533a5995ef8b736cc48cfda30e8aafec77d285511471e0e5a9e8007c8750203a", + } + + template_st_pdf_hashes = { + "309ac2fd7f2dcf17": "b9a45995d9e40b2515506bbf5945e806ef021861820426c6d0a6a074090b47a9", + "8cf86948f02f047d": "3c8614338899d956e9e56f1aa88d90e37df86f3310b875d9d14ec0f71e4759be", + } + + with TemporaryDirectory() as td: + toy_dataset.root_dir = Path(td) + toy_dataset.download_all_artifacts() + toy_dataset.convert_all_pdfs() + + for cert in toy_dataset: + assert cert.state.report_pdf_hash == template_report_pdf_hashes[cert.dgst] + assert cert.state.st_pdf_hash == template_st_pdf_hashes[cert.dgst] + + assert not toy_dataset["309ac2fd7f2dcf17"].state.report_convert_garbage + assert not toy_dataset["309ac2fd7f2dcf17"].state.st_convert_garbage + assert toy_dataset["309ac2fd7f2dcf17"].state.report_convert_ok + assert toy_dataset["309ac2fd7f2dcf17"].state.st_convert_ok + + assert toy_dataset["309ac2fd7f2dcf17"].state.report_txt_path.exists() + assert toy_dataset["309ac2fd7f2dcf17"].state.st_txt_path.exists() + + template_report_txt_path = data_dir / "report_309ac2fd7f2dcf17.txt" + template_st_txt_path = data_dir / "target_309ac2fd7f2dcf17.txt" + assert ( + abs(toy_dataset["309ac2fd7f2dcf17"].state.st_txt_path.stat().st_size - template_st_txt_path.stat().st_size) + < 1000 + ) + assert ( + abs( + toy_dataset["309ac2fd7f2dcf17"].state.report_txt_path.stat().st_size + - template_report_txt_path.stat().st_size + ) + < 1000 + ) + + +def test_dataset_to_json(toy_dataset: CCDataset, data_dir: Path, tmp_path: Path): + toy_dataset.to_json(tmp_path / "dset.json") + + with (tmp_path / "dset.json").open("r") as handle: + data = json.load(handle) + + with (data_dir / "toy_dataset.json").open("r") as handle: + template_data = json.load(handle) + + del data["timestamp"] + del template_data["timestamp"] + assert data == template_data + + +def test_dataset_from_json(toy_dataset: CCDataset, data_dir: Path): + assert toy_dataset == CCDataset.from_json(data_dir / "toy_dataset.json") + + +def test_build_empty_dataset(): + with TemporaryDirectory() as tmp_dir: + dset = CCDataset({}, Path(tmp_dir), "sample_dataset", "sample dataset description") + dset.get_certs_from_web(to_download=False, get_archived=False, get_active=False) + assert len(dset) == 0 + assert dset.state.meta_sources_parsed + assert not dset.state.artifacts_downloaded + assert not dset.state.pdfs_converted + assert not dset.state.certs_analyzed + + +def test_build_dataset(data_dir: Path, crt: CommonCriteriaCert, toy_dataset: CCDataset): + with TemporaryDirectory() as tmp_dir: + dataset_path = Path(tmp_dir) + (dataset_path / "web").mkdir() + shutil.copyfile(data_dir / "cc_products_active.csv", dataset_path / "web" / "cc_products_active.csv") + shutil.copyfile(data_dir / "cc_products_active.html", dataset_path / "web" / "cc_products_active.html") + + dset = CCDataset({}, dataset_path, "sample_dataset", "sample dataset description") + dset.get_certs_from_web( + keep_metadata=False, to_download=False, get_archived=False, get_active=True, update_json=False + ) + + assert len(list(dataset_path.iterdir())) == 0 + assert len(dset) == 2 + assert crt in dset + assert dset == toy_dataset + + +def test_process_pp_dataset(toy_dataset: CCDataset): + with TemporaryDirectory() as tmp_dir: + toy_dataset.root_dir = tmp_dir # type: ignore + toy_dataset.process_protection_profiles() + assert toy_dataset.pp_dataset_path.exists() + assert toy_dataset.pp_dataset_path.stat().st_size > constants.MIN_CC_PP_DATASET_SIZE + + +def test_download_csv_html_files(): + with TemporaryDirectory() as tmp_dir: + dset = CCDataset({}, Path(tmp_dir), "sample_dataset", "sample dataset description") + dset._download_csv_html_resources(get_active=True, get_archived=False) + + for x in dset.active_html_tuples: + assert x[1].exists() + assert x[1].stat().st_size >= constants.MIN_CC_HTML_SIZE + for x in dset.active_csv_tuples: + assert x[1].exists() + assert x[1].stat().st_size >= constants.MIN_CC_CSV_SIZE diff --git a/tests/data/test_cc_heuristics/__init__.py b/tests/cc/test_cc_identifiers.py similarity index 100% rename from tests/data/test_cc_heuristics/__init__.py rename to tests/cc/test_cc_identifiers.py diff --git a/tests/cc/test_cc_misc.py b/tests/cc/test_cc_misc.py new file mode 100644 index 00000000..b0932482 --- /dev/null +++ b/tests/cc/test_cc_misc.py @@ -0,0 +1,47 @@ +from sec_certs.sample.cc_certificate_id import canonicalize + + +def test_canonicalize_fr(): + assert canonicalize("Rapport de certification 2001/02v2", "FR") == "ANSSI-CC-2001/02v2" + assert canonicalize("ANSSI-CC 2001/02-R01", "FR") == "ANSSI-CC-2001/02-R01" + + +def test_canonicalize_de(): + assert canonicalize("BSI-DSZ-CC-0420-2007", "DE") == "BSI-DSZ-CC-0420-2007" + + +def test_canonicalize_es(): + assert canonicalize("2011-14-INF-1095-v1", "ES") == "2011-14-INF-1095" + + +def test_canonicalize_it(): + assert canonicalize("OCSI/CERT/SYS/10/2016", "IT") == "OCSI/CERT/SYS/10/2016/RC" + + +def test_canonicalize_in(): + assert canonicalize("IC3S/KOL01/ADVA/EAL2/0520/0021 /CR", "IN") == "IC3S/KOL01/ADVA/EAL2/0520/0021/CR" + + +def test_canonicalize_se(): + assert canonicalize("CSEC2017020", "SE") == "CSEC2017020" + assert canonicalize("CSEC 2017020", "SE") == "CSEC2017020" + + +def test_canonicalize_uk(): + assert canonicalize("CERTIFICATION REPORT No. P123", "UK") == "CRP123" + assert canonicalize("CRP123A", "UK") == "CRP123A" + + +def test_canonicalize_ca(): + assert canonicalize("383-4-123-CR", "CA") == "383-4-123" + assert canonicalize("383-4-123P", "CA") == "383-4-123" + + +def test_canonicalize_jp(): + assert canonicalize("Certification No. C01234", "JP") == "C01234" + assert canonicalize("CRP-C01234-01", "JP") == "C01234" + assert canonicalize("JISEC-CC-CRP-C0689-01-2020", "JP") == "C0689" + + +def test_canonicalize_no(): + assert canonicalize("SERTIT-12", "NO") == "SERTIT-012" diff --git a/tests/cc/test_cc_schemes.py b/tests/cc/test_cc_schemes.py new file mode 100644 index 00000000..d67d31f1 --- /dev/null +++ b/tests/cc/test_cc_schemes.py @@ -0,0 +1,84 @@ +import pytest + +from sec_certs.dataset.common_criteria import CCSchemeDataset + + +def test_australia(): + assert len(CCSchemeDataset.get_australia_in_evaluation()) != 0 + + +def test_canada(): + assert len(CCSchemeDataset.get_canada_certified()) != 0 + assert len(CCSchemeDataset.get_canada_in_evaluation()) != 0 + + +def test_anssi(): + assert len(CCSchemeDataset.get_france_certified()) != 0 + + +def test_bsi(): + assert len(CCSchemeDataset.get_germany_certified()) != 0 + + +@pytest.mark.xfail +def test_india(): + assert len(CCSchemeDataset.get_india_certified()) != 0 + assert len(CCSchemeDataset.get_india_archived()) != 0 + + +def test_italy(): + assert len(CCSchemeDataset.get_italy_certified()) != 0 + assert len(CCSchemeDataset.get_italy_in_evaluation()) != 0 + + +def test_japan(): + assert len(CCSchemeDataset.get_japan_certified()) != 0 + assert len(CCSchemeDataset.get_japan_archived()) != 0 + assert len(CCSchemeDataset.get_japan_in_evaluation()) != 0 + + +def test_malaysia(): + assert len(CCSchemeDataset.get_malaysia_certified()) != 0 + assert len(CCSchemeDataset.get_malaysia_in_evaluation()) != 0 + + +def test_netherlands(): + assert len(CCSchemeDataset.get_netherlands_certified()) != 0 + assert len(CCSchemeDataset.get_netherlands_in_evaluation()) != 0 + + +def test_norway(): + assert len(CCSchemeDataset.get_norway_certified()) != 0 + assert len(CCSchemeDataset.get_norway_archived()) != 0 + + +def test_korea(): + assert len(CCSchemeDataset.get_korea_certified()) != 0 + CCSchemeDataset.get_korea_suspended() + assert len(CCSchemeDataset.get_korea_archived()) != 0 + + +def test_singapore(): + assert len(CCSchemeDataset.get_singapore_certified()) != 0 + assert len(CCSchemeDataset.get_singapore_archived()) != 0 + assert len(CCSchemeDataset.get_singapore_in_evaluation()) != 0 + + +def test_spain(): + assert len(CCSchemeDataset.get_spain_certified()) != 0 + + +def test_sweden(): + assert len(CCSchemeDataset.get_sweden_certified()) != 0 + assert len(CCSchemeDataset.get_sweden_in_evaluation()) != 0 + assert len(CCSchemeDataset.get_sweden_archived()) != 0 + + +def test_turkey(): + assert len(CCSchemeDataset.get_turkey_certified()) != 0 + + +def test_usa(): + assert len(CCSchemeDataset.get_usa_certified()) != 0 + assert len(CCSchemeDataset.get_usa_in_evaluation()) != 0 + assert len(CCSchemeDataset.get_usa_archived()) != 0 diff --git a/tests/data/cc/analysis/__init__.py b/tests/data/cc/analysis/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/data/test_cc_heuristics/auxillary_datasets/cpe_dataset.json b/tests/data/cc/analysis/auxillary_datasets/cpe_dataset.json similarity index 100% rename from tests/data/test_cc_heuristics/auxillary_datasets/cpe_dataset.json rename to tests/data/cc/analysis/auxillary_datasets/cpe_dataset.json diff --git a/tests/data/test_cc_heuristics/auxillary_datasets/cve_dataset.json b/tests/data/cc/analysis/auxillary_datasets/cve_dataset.json similarity index 100% rename from tests/data/test_cc_heuristics/auxillary_datasets/cve_dataset.json rename to tests/data/cc/analysis/auxillary_datasets/cve_dataset.json diff --git a/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json b/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json new file mode 100644 index 00000000..1cd1969f --- /dev/null +++ b/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json @@ -0,0 +1,76952 @@ +{ + "20081013_BeveiligingsprofielBCTV1.0.pdf__178": { + "csv_scan": { + "cc_archived_date": "02/15/2010", + "cc_category": "Other Devices and Systems", + "cc_certification_date": "12/02/2008", + "cc_pp_name": "Beveilingsprofiel Boordcomputer Taxi", + "cc_pp_version": "1.0", + "cc_security_level": "EAL3", + "cert_status": "archived", + "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/20081013_BeveiligingsprofielBCTV1.0.pdf", + "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/Certification Report NSCIB-PP-08-10381-CR.pdf", + "maintainance_updates": [], + "scheme": "NL" + }, + "pp_analysis": { + "separate_profiles": [] + }, + "processed": { + "cc_pp_csvid": [], + "cert_id": "" + } + }, + "20110221143918.pdf__93": { + "csv_scan": { + "cc_archived_date": "", + "cc_category": "ICs, Smart Cards and Smart Card-Related Devices and Systems", + "cc_certification_date": "05/06/2010", + "cc_pp_name": "ePassport Protection Profile V2.1, Version 2.1", + "cc_pp_version": "2.1", + "cc_security_level": "EAL4+", + "cert_status": "active", + "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/20110221143918.pdf", + "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/20110221144244.pdf", + "maintainance_updates": [], + "scheme": "KR" + }, + "frontpage_scan": [ + { + 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Server +Interim Fix 4 and AccessAgent Fix Pack 22 +Security Target +1.19 +Version: +Released +Status: +2014-03-05 +Last Update: + Trademarks +IBM and the IBM logo are trademarks or registered trademarks of International Business Machines +Corporation in the United States, other countries, or both. +The following terms are trademarks of International Business Machines Corporation in the United +States, other countries, or both: +â— DB2® +â— WebSphere® +The following terms are trademarks of Oracle Corporation: +â— Java® +â— Oracle® +The following terms are trademarks of Microsoft Corporation: +â— Active Directory® +â— SQL Server® +â— Windows® +â— Windows Server® +Legal Notice +This document is provided AS IS with no express or implied warranties. Use the information in this +document at your own risk. +This document may be reproduced or distributed in any form without prior permission provided the +copyright notice is retained on all copies. Modified versions of this document may be freely distributed +provided that they are clearly identified as such, and this copyright is included intact. +Revision History +Changes to Previous Revision +Author(s) +Date +Revision +Initial ST. +Scott Chapman +2014-03-05 +1.19 +Page 2 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Table of Contents +1 Introduction .................................................................................................... 7 +1.1 Security Target Identification ......................................................................................... 7 +1.2 TOE Identification .......................................................................................................... 7 +1.3 TOE Type ....................................................................................................................... 7 +1.4 TOE Overview ................................................................................................................ 7 +1.4.1 Required non-TOE software .................................................................................. 7 +1.4.2 Intended method of use ........................................................................................ 8 +1.4.3 Major security features ......................................................................................... 8 +1.5 TOE Description ............................................................................................................. 8 +1.5.1 Introduction ........................................................................................................... 8 +1.5.2 Architecture .......................................................................................................... 9 +1.5.2.1 Communications ........................................................................................... 9 +1.5.2.2 The IMS Server ........................................................................................... 10 +1.5.2.3 AccessAgent ............................................................................................... 11 +1.5.2.4 AccessAdmin .............................................................................................. 12 +1.5.2.5 AccessAssistant .......................................................................................... 12 +1.5.2.6 IMS Configuration Utility ............................................................................. 13 +1.5.3 TOE boundaries ................................................................................................... 13 +1.5.3.1 Physical ...................................................................................................... 13 +1.5.3.2 Logical ........................................................................................................ 14 +1.5.3.3 Evaluated configuration ............................................................................. 14 +1.5.4 Security policy model .......................................................................................... 15 +2 CC Conformance Claim ................................................................................... 16 +3 Security Problem Definition ............................................................................ 17 +3.1 Threat Environment ..................................................................................................... 17 +3.1.1 Threats countered by the TOE ............................................................................ 17 +3.2 Assumptions ................................................................................................................ 18 +3.2.1 Environment of use of the TOE ........................................................................... 18 +3.2.1.1 Physical ...................................................................................................... 18 +3.2.1.2 Personnel .................................................................................................... 18 +3.2.1.3 Logical ........................................................................................................ 18 +3.3 Organizational Security Policies ................................................................................... 19 +4 Security Objectives ........................................................................................ 20 +4.1 Objectives for the TOE ................................................................................................. 20 +4.2 Objectives for the Operational Environment ................................................................ 20 +4.3 Security Objectives Rationale ...................................................................................... 21 +4.3.1 Security objectives coverage .............................................................................. 21 +4.3.2 Security objectives sufficiency ............................................................................ 22 +5 Extended Components Definition .................................................................... 26 +6 Security Requirements ................................................................................... 27 +6.1 TOE Security Functional Requirements ........................................................................ 27 +Page 3 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 6.1.1 Security audit (FAU) ............................................................................................ 28 +6.1.1.1 Audit data generation (FAU_GEN.1) .......................................................... 28 +6.1.1.2 User identity association (FAU_GEN.2) ...................................................... 28 +6.1.1.3 Audit review (FAU_SAR.1) .......................................................................... 28 +6.1.1.4 Restricted audit review (FAU_SAR.2) ......................................................... 28 +6.1.1.5 Protected audit trail storage (FAU_STG.1) ................................................. 29 +6.1.2 User data protection (FDP) .................................................................................. 29 +6.1.2.1 Subset access control (FDP_ACC.2) ........................................................... 29 +6.1.2.2 Security attribute based access control (FDP_ACF.1) ................................ 30 +6.1.3 Identification and authentication (FIA) ................................................................ 30 +6.1.3.1 User attribute definition (FIA_ATD.1) ......................................................... 30 +6.1.3.2 Verification of secrets (FIA_SOS.1) ............................................................ 30 +6.1.3.3 User authentication before any action (FIA_UAU.2) ................................... 31 +6.1.3.4 User identification before any action (FIA_UID.2) ...................................... 31 +6.1.3.5 User-subject binding (FIA_USB.1) .............................................................. 31 +6.1.4 Security management (FMT) ............................................................................... 31 +6.1.4.1 Management of security attributes (FMT_MSA.1) ...................................... 31 +6.1.4.2 Static attribute initialisation (FMT_MSA.3) ................................................. 32 +6.1.4.3 Management of TSF data (FMT_MTD.1) ..................................................... 32 +6.1.4.4 Specification of management functions (FMT_SMF.1) ............................... 32 +6.1.4.5 Security roles (FMT_SMR.1) ....................................................................... 32 +6.2 Security Functional Requirements Rationale ............................................................... 33 +6.2.1 Security requirements coverage ......................................................................... 33 +6.2.2 Security requirements sufficiency ....................................................................... 33 +6.2.3 Security requirements dependency analysis ...................................................... 34 +6.3 Security Assurance Requirements ............................................................................... 36 +6.4 Security Assurance Requirements Rationale ............................................................... 37 +7 TOE Summary Specification ............................................................................ 38 +7.1 TOE Security Functionality ........................................................................................... 38 +7.1.1 Audit .................................................................................................................... 38 +7.1.2 Identification and authentication (I&A) ............................................................... 39 +7.1.2.1 AccessAgent ............................................................................................... 39 +7.1.2.2 AccessAssistant .......................................................................................... 40 +7.1.2.3 AccessAdmin .............................................................................................. 40 +7.1.2.4 IMS Configuration Utility ............................................................................. 40 +7.1.3 User data protection ........................................................................................... 41 +7.1.4 Security management ......................................................................................... 42 +8 Abbreviations, Terminology and References .................................................... 44 +8.1 Abbreviations ............................................................................................................... 44 +8.2 Terminology ................................................................................................................. 45 +8.3 References ................................................................................................................... 46 +Page 4 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + List of Tables +Table 1: Required Fix Packs .................................................................................................. 13 +Table 2: Mapping of security objectives to threats and policies ........................................... 22 +Table 3: Mapping of security objectives for the Operational Environment to assumptions, +threats and policies ........................................................................................................ 22 +Table 4: Sufficiency of objectives countering threats ........................................................... 23 +Table 5: Sufficiency of objectives holding assumptions ....................................................... 23 +Table 6: Sufficiency of objectives enforcing Organizational Security Policies ....................... 25 +Table 7: Security functional requirements for the TOE ......................................................... 27 +Table 8: Wallet Access Control Policy ................................................................................... 29 +Table 9: Wallet Access Control Policy management ............................................................. 31 +Table 10: TSF data management ......................................................................................... 32 +Table 11: Mapping of security functional requirements to security objectives ..................... 33 +Table 12: Security objectives for the TOE rationale ............................................................. 34 +Table 13: TOE SFR dependency analysis .............................................................................. 35 +Table 14: Security assurance requirements ......................................................................... 36 +Page 5 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + List of Figures +Figure 1: TOE Component Relationships ................................................................................ 9 +Figure 2: TOE Dataflow ......................................................................................................... 10 +Page 6 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 1 Introduction +1.1 Security Target Identification +IBM Security Access Manager for Enterprise Single Sign-On Version 8.2 with +IMS Server Interim Fix 4 and AccessAgent Fix Pack 22 Security Target +Title: +1.19 +Version: +Released +Status: +2014-03-05 +Date: +International Business Machines, Corporation +Sponsor: +International Business Machines, Corporation +Developer: +BSI +Certification Body: +BSI-DSZ-CC-0683 +Certification ID: +Security Access Manager, Enterprise Single Sign-On +Keywords: +1.2 TOE Identification +The TOE is IBM Security Access Manager for Enterprise Single Sign-On Version 8.2 with IMS Server +Interim Fix 4 and AccessAgent Fix Pack 22. +1.3 TOE Type +The TOE type is an enterprise single sign-on product for Microsoft Windows-based systems and +consists of software and guidance documentation. +1.4 TOE Overview +The Target of Evaluation (TOE) provides enterprises with single sign-on services, coupled with +strong authentication and comprehensive auditing capabilities. The TOE keeps track of each user's +application credentials (ID/password) in a credential Wallet, and helps users log onto the various +applications. This is achieved through a client-side software "agent" called the AccessAgent that, +in addition to automating application logons, provides application access audit logging. An important +characteristic of the TOE is that this agent emulates the logon actions of the user using the +application native logon interfaces – as such, it will work with applications as-is, without requiring +any applications to be re-programmed or re-configured. The TOE includes a central server called +the IMS (Integrated Management System) Server for storing user Wallets and for providing centralized +management of users and policies. +1.4.1 Required non-TOE software +The Operational Environment for the TOE consists of the following required software products: +â— Microsoft Windows XP and Windows 7 for the AccessAgents (32-bit and 64-bit Windows) +â— Microsoft Windows Server 2008 (32-bit/64-bit) for the IMS Server +â— Microsoft Active Directory (AD) +â— WebSphere Application Server (WAS) 7.0 (32-bit only) required by the IMS Server +â— One of the following SQL-based databases required by the IMS Server: +â IBM DB2 +Page 7 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + â Microsoft SQL Server +â Oracle Database +â— Java Runtime Environment (IBM Java 6 for WAS 7.0) +â— IBM Global Security Kit (GSKit) version 8.0.14.21 required by the AccessAgent. +1.4.2 Intended method of use +The TOE is intended to be used in a distributed Microsoft Windows environment for the automatic +authentication of users to applications. The TOE components use network security protocols (i.e., +SSL, TLS) to protect network data from disclosure and modification when communicating between +one another, so network eavesdropping attacks on TOE communication data is significantly +diminished. (The network security protocols and all cryptographic operations used by the protocols +including random number generation are provided by the Operational Environment.) +1.4.3 Major security features +The main security functions of the TOE are: +â— Audit - Generation and review of audit records +â— I&A - The identification and authentication of all users (regardless of their role) +â— User data protection - Protection of user data stored within Wallets +â— Security management - Role-based management of security behavior of the TOE +1.5 TOE Description +1.5.1 Introduction +The TOE is an enterprise single sign-on product for Microsoft Windows-based systems and consists +of software and guidance documentation. +The TOE automatically enters user credentials into credential-requesting applications on behalf of +the user once the user has successfully authenticated to the TOE. +For every user, the TOE maintains a set of application-to-user-credential mappings in a secure +Wallet. Each Wallet is encrypted with a Common Symmetric Key (CSK) which is uniquely generated +for each user. (The encryption operations and CSK key generation are performed by the Operational +Environment at the request of the TOE. The TOE controls the flow of the cryptographic operations +performed by the Operational Environment.) A more detailed description of Wallets and CSKs is +provided in section 7.1.3. +The TOE contains several types of access policies that are used as configuration data by the TOE. +These access policies are grouped into the following major categories: +â— Machine policies - policies that affect a specific machine/computer (e.g., audit logging +policies, desktop inactivity policies, some Wallet policies). +â— System policies - policies applicable to all users and machines (e.g., auditing policies, +password strength policies, some Wallet policies). +â— User policies - policies that affect a specific user (e.g., Log on/Log off policies, some Wallet +policies). +Of the policies referred to above, only the password strength policies are evaluated by this evaluation. +Page 8 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + The TOE supports user role-based access control. Role-based access control is used to restrict user +access to certain TOE operations. The TOE implements the following user roles which are referred +to in the follow-on sections (for more detailed role descriptions, see section 7.1.4): +â— Administrator +â— Help desk +â— User +1.5.2 Architecture +The TOE consists of multiple components executing in a distributed environment and communicating +using the network. Figure 1 depicts the different components forming the product. Each of the +green shaded components will be described in the subsequent sections. These green shaded +components together form the TOE. +Figure 1: TOE Component Relationships +1.5.2.1 Communications +The relevant communication relationships for the TOE are shown in figure 2. +Page 9 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Figure 2: TOE Dataflow +1.5.2.2 The IMS Server +The IMS (Integrated Management System) Server serves as the central repository and management +point for all system and user data consumed by the AccessAgents. The IMS Server performs the +following functions: +â— Serves as a central repository and distribution point for AccessProfiles and other system +data. +â— Serves as a central repository for all user data, including his credential Wallet and various +authentication and access policies. +â— Provides an internal SOAP API for AccessAgents, as well as AccessAssistant servers, to +authenticate users, and to retrieve and synchronize system and user data. +â— Provides Directory Connector SPI (not externalized) to enable integration with various user +repositories. Refer to the IBM Security Access Manager for Enterprise Single Sign-On 8.2 +RCS for the list of out of the box connectors that are provided. +â— Provides a web-based UI (called "AccessAdmin") for managing users, machines and system +policies, as well as to query audit logs. +â— Creates a user's initial Wallet (including a CSK generated by the Operational Environment +at the TOE's request) when a user account is created via the IMS Server. +Page 10 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Please note that the IMS DB (database) specifically marked in the illustration stores the data +managed by the IMS Server, but the database system is not part of the TOE. +The TOE's IMS Server requires the use of the Microsoft Active Directory directory server in the +evaluated configuration. The TOE can be configured to support password synchronization with +Active Directory. When password synchronization with Active Directory is enabled, the Active +Directory's password quality requirements are enforced by Active Directory, not the TOE. When +password synchronization with Active Directory is disabled, the TOE's password quality requirements +are enforced. +1.5.2.3 AccessAgent +The AccessAgent (AA) is the client software that is installed onto all Windows workstations at the +TOE deployment site, and configured to connect to the designated IMS Server. The AccessAgent +performs the following actions: +â— Authenticates the end-user. Optionally, the AccessAgent protects access to the Windows +desktop by replacing the native Microsoft Graphical Identification and Authentication (GINA) +with the IBM Security Access Manager for Enterprise Single Sign-On GINA. +â— Performs automated sign-on and sign-off to various applications. The AccessAgent has an +Observer module that is hooked into various applications, and which consults the appropriate +AccessProfile to perform the necessary logon/logoff and automation actions. The +AccessAgent will log onto applications using the appropriate application credential retrieved +from the user's credential Wallet. +â— Tracks user application access activities and submits such audit events to the IMS Server. +â— Synchronizes AccessProfiles, user's credential Wallet, and various Policy settings with the +IMS Server. +â— Provides a UI for end-users to manage the application credentials stored in their credential +Wallet. +â— Provides a UI for end-users to manage their own ISAM E-SSO Password. +â— Creates a user's initial Wallet (including a CSK generated by the Operational Environment) +when a user sign's up through the AccessAgent. +The AccessAgent can be deployed to Windows workstations with or without GINA replacement (i.e., +in GINA mode or in GINA-less mode). In GINA mode, a user logs on to the IBM Security Access +Manager for Enterprise Single Sign-On (ISAM E-SSO) GINA using his ISAM E-SSO username and +password, whereupon the AccessAgent auto-logons to the Microsoft GINA with the user's Windows +username and password. In GINA-less mode, a user will log on to Windows through the regular +Microsoft GINA with his operating system username and password first, and the AccessAgent will +then use the same credentials to log on the user to his cached Wallet and to the IMS Server. +The AccessAgent will contact the IMS Server upon start up, upon each user logon, as well as on +periodic intervals, to synchronize system and user data changes with the server. However, the +AccessAgent could cache data locally onto the hard disk. As such, it is able to perform most of its +functions even if it is offline to the IMS Server at any point in time. +The AccessAgent relies on its Observer sub-module to perform its single sign-on and workflow +automation features. +The Observer module is composed of an Observer Core module and a number of Observer agent +instances that are hooked (through Windows APIs) into every Windows application (e.g. Lotus Notes, +Outlook, Internet Explorer, etc) launched. +Page 11 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + The behavior of the Observer agent within each specific Application is driven by a set of behavioral +specifications called an AccessProfile. Each AccessProfile is an XML structure (based on a custom +XML language) that provides a declarative set of preconditions (such as the signature of an +application – e.g. name and version of the EXE file), a set of behavioral states (e.g. pre-logon, +post-logon), and with each state, a set of triggers (e.g. when a specific screen is displayed) and +actions (e.g. auto-fill username into a certain field) for the Observer agent to watch for and execute +accordingly. +The Observer agents retrieve the required AccessProfiles as well as user credentials from the +Observer Core module, which in turn communicates with the rest of the AccessAgent for data +synchronization and session management services. +The AccessAgent controls access to a user's Wallet credentials so that only the application specified +by the AccessProfile obtains the user's credentials specified for that application. This Wallet Access +Control Policy is modeled in section 6.1.2.1 and is unique to the AccessAgent. The IMS Server and +other TOE components provide only a supporting role for this policy. +The AccessAgent creates the initial Wallet for a new user when the user sign's up through the +AccessAgent. The Wallet is used to hold the user's credentials for accessing other applications. The +contents of the Wallet are encrypted by the Operational Environment at the request of the TOE +using a 128 bit Common Symmetric Key (CSK). The generation of the CSK is also performed by the +Operational Environment at the request of the TOE. The CSK is encrypted by the Operational +Environment at the request of the TOE with the user's ISAM E-SSO Password and then stored with +the Wallet in a file called a Cryptobox. +AccessAgent allows each user to change his own ISAM E-SSO Password. The TOE's password strength +policies are enforced only when the AccessAgent is in GINA mode with Active Directory password +synchronization disabled. When in GINA-less mode or when Active Directory password synchronization +is enabled, the Active Directory's password strength policy is enforced by Active Directory, not the +TOE. +1.5.2.4 AccessAdmin +The AccessAdmin application is the web-based management console used to administer the IMS +Server, and to manage users (including modifying the role of each user) and policies. +The AccessAdmin application is installed with the IMS Server and allows the Administrator role to: +â— Manage user authentication factors. (Only password authentication is allowed in the +evaluated configuration.) +â— Manage system, machine, and user policies. +â— Manage machine and user groupings. +â— Query audit logs; or generate out of the box audit reports +The AccessAdmin application is also used by the Help desk role to: +â— Manage user authentication factors. +â— Manage user policies. +1.5.2.5 AccessAssistant +The AccessAssistant is the web-based interface used to provide password self-help to end-users. +In the evaluated configuration, it allows a user to: +Page 12 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + â— View and manage passwords for the various application accounts stored in the user's +Wallet. +The AccessAssistant is implemented as a set of servlets that is by default deployed on the IMS +Server. +1.5.2.6 IMS Configuration Utility +The IMS Configuration Utility is the web-based interface used to manage the configuration of the +IMS Server. The IMS Configuration Utility application is installed with the IMS Server and allows the +Administrator role to: +â— Manage AccessProfiles +â— Manage directory server configurations +â— Manage database configurations +1.5.3 TOE boundaries +1.5.3.1 Physical +The TOE is comprised of the following components: +â— IMS Server +â— AccessAgent +â— AccessAdmin +â— AccessAssistant +â— IMS Configuration Utility +The TOE is software only and is available in download format only from the IBM website. The TOE +installation must include the Fix Packs specified in Table 1. +Name +Type +Component +8.2.0-ISS-SAMESSO-AA-FP0022 +Fix Pack +AccessAgent +8.2.0-ISS-SAMESSO-IMS-IF0004 +Interim Fix +IMS Server +Table 1: Required Fix Packs +The TOE guidance is available online through the Tivoli Software Information Center, except for the +Common Criteria Guide which is available through the software download website. The relevant +guidance documents for the secure operation of the TOE are: +â— IBM Security Access Manager for Enterprise Single Sign-On Administrator Guide +â— IBM Security Access Manager for Enterprise Single Sign-On User Guide +â— IBM Security Access Manager for Enterprise Single Sign-On Quick Start Guide +â— IBM Security Access Manager for Enterprise Single Sign-On Common Criteria Guide +Section 1.4.1 lists the Operational Environment software products required by the TOE. +Page 13 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 1.5.3.2 Logical +Due to the nature of the TOE storing, providing, and operating on user credentials for other +applications, the TOE provides a number of security functions. Each security functional complex is +identified and described below: +â— Audit - The TOE has the ability to audit the user actions performed by all roles and to store +the records in an audit trail using an external database. The Administrator role has the +ability to review the audit trail using the AccessAdmin component or by using the provided +database views. +â— Identification and authentication - The TOE performs the identification and authentication +of all users, regardless of their role, before it allows the user to perform any other actions. +â— User credential protection - The TOE stores the user's credentials for third-party applications +in the user's personal Wallet. A Wallet contains the user's arbitrary credentials which are +treated as opaque user data by the TOE. By using encryption mechanisms in the Operational +Environment, the TOE ensures the following: +â Only owners have access to credential data. +â The Administrator role can reset credential access data and restore use to a Wallet. +â A third-party application is given only the credentials which are intended for this +application and stored in the user's Wallet, preventing applications to "impersonate" +as a different application to illicitly obtain credentials. +â— Security Management - Role-based access control is used to protect access to operations +in the AccessAdmin and AccessAssistant applications. +All cryptographic operations are performed by the Operational Environment. +1.5.3.3 Evaluated configuration +The following configuration specifics apply to the evaluated configuration of the TOE: +â— The use of personal secrets must be disabled. +â— Only the AccessAgent plugins provided with the TOE are allowed. +â— Only the ISAM E-SSO Password authentication factor is allowed. +â— Second factor authentication is disallowed. +â— Self-service policies: +â Self-service password reset must be disabled. +â Self-service authorization code issuance must be disabled. +â Self-service registration and bypass of 2nd factor must be disabled. +â Self-service registering of additional secrets during sign-up must be disabled. +â— The IMS Server's master secret must be protected to only allow the Administrator role +access to it. +â— One-Time Passwords (OTPs) must be disabled. +â— Mobile ActiveCode (MAC) must be disabled. +â— Roaming Desktops (i.e., the use of Microsoft Windows Terminal Server and Citrix +Presentation Server) must be disabled. +â— RADIUS authentication must be disabled. +â— Windows Fast User Switching must be disabled on Windows 7 systems running AccessAgent. +â— Private Desktop must be disabled on Windows XP systems running AccessAgent. +Page 14 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + â— Single sign-on to AccessAdmin when using Microsoft Internet Explorer must be disabled. +â— The IMS Server/application must be the only application running in the WebSphere +Application Server (WAS). +â— The TOE's password synchronization option with Active Directory affects the security of +the TOE. Specifically, enabling password synchronization with Active Directory will disable +the TOE's ability to enforce password quality requirements. +1.5.4 Security policy model +The security policy for the TOE is defined by the security functional requirements in chapter 6. The +following is a list of the subjects and objects participating in the policy. +Subjects: +â— Users +Objects: +â— Wallets +â— Target applications +TSF data: +â— AccessProfiles +â— System policies, machine policies, and user policies +â— User accounts, including the security attributes defined by FIA_ATD.1 +â— Audit records +User data: +â— User credential data stored within Wallets. +Page 15 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 2 CC Conformance Claim +This Security Target is CC Part 2 conformant and CC Part 3 conformant, with a claimed Evaluation +Assurance Level of EAL3, augmented by ALC_FLR.1. +This Security Target does not claim conformance to any Protection Profile. +Common Criteria [CC] version 3.1 revision 3 is the basis for this conformance claim. +Page 16 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 3 Security Problem Definition +3.1 Threat Environment +This section describes the threat model for the TOE and identifies the individual threats that are +assumed to exist in the TOE environment. +The assets to be protected by the TOE are the following: +â— The IMS Server stores all its data within a relational database. The IMS Server's database +contains essentially these classes of data: +â System data - AccessProfiles, system policies, machine policies, user policies, and +other system configuration data +â User data - credentials +â Audit logs - user activities, administrative activities, SOAP event logs +â— Additionally, the AccessAgent caches a subset of system and user data, so that it can +continue to function even when disconnected with the IMS Server. The AccessAgent caches +these data (in encrypted files) on workstations: +â System/machine data - AccessProfiles, system policies, machine policies, and user +policies +â User data - application credentials +â Audit logs - accessing of Wallet, using credentials out of the Wallet to provide to +an application based on the AccessProfiles +The threat agents having an interest in manipulating the data model can be categorized as either: +â— Unauthorized individuals ("attackers") which are unknown to the TOE and its runtime +environment +â— Authorized users of the TOE who try to manipulate data that they are not authorized to +access +Threat agents originate from a well managed user community within an organizations internal +network. Hence, only inadvertent or casual attempts to breach system security are expected from +this community. +TOE administrators, including administrators of the TOE environment, are assumed to be trustworthy, +trained and to follow the instructions provided to them with respect to the secure configuration +and operation of the systems under their responsibility. Hence, only inadvertent attempts to +manipulate the safe operation of the TOE are expected from this community. +3.1.1 Threats countered by the TOE +T.Manage +A threat agent gains access to the management facilities of the TOE allowing the modification +of the security-relevant configuration of the TOE. +T.UserCredentials +A threat agent gains access to user credentials stored for a user by the TOE. +Page 17 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 3.2 Assumptions +3.2.1 Environment of use of the TOE +3.2.1.1 Physical +A.Physical +It is assumed that the Operational Environment provides the TOE with appropriate physical +security, commensurate with the value of the IT assets protected by the TOE. +3.2.1.2 Personnel +A.AuthUser +Authorized users are expected to act in a cooperating manner in a benign environment. +Users are sufficiently trained and trusted to accomplish some task or group of tasks within +the secure Operational Environment by exercising complete control over their user data. +A.Manage +The TOE security functionality as well as the TOE's underlying systems and of the systems +in the TOE's Operational Environment that are involved in safeguarding TSF data or providing +functionality that the TOE depends on is managed by one or more competent individuals. +The system administrative personnel are not careless, willfully negligent, or hostile, and will +follow and abide by the instructions provided by the guidance documentation. +3.2.1.3 Logical +A.CryptoOps +The cryptographic operations performed by the Operational Environment are performed +according to the specified algorithm standards and the random number generation provides +sufficient randomness and entropy for key generation. +A.Remote +Threat agents are unable to violate the integrity, authenticity, and confidentiality of data +exchanged between the components of the TOE distributed in the deployment infrastructure. +A.Repositories +Threat agents are unable to gain access to TSF data or user data stored in the Operational +Environment, thus bypassing the TSF. +A.Runtime +The machines providing the runtime environment for the IMS Server are assumed to be +used solely for this purpose and not to run other application software except as required +for the support of the TOE and for the management and maintenance of the underlying +system and hardware. Especially, it is assumed that the underlying systems are configured +in a way that prevents unauthorized access to security functions provided by or protected +by the runtime environment either locally or via any network based connections. +Page 18 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + A.System +Threat agents are unable to circumvent the TSF by penetrating or manipulating the runtime +environment of the TOE and gain access to TSF or user data. +3.3 Organizational Security Policies +P.Accountability +Administrators and users are to be held accountable for security-relevant actions with the +TOE. +P.PasswordQuality +The quality of the ISAM E-SSO Password protecting the Common Symmetric Key (CSK) must +possess the strength to prevent credential guessing from threat agents. +P.User +Any user is trusted to perform the actions for which they have been authorized to perform. +Authorized users possess the necessary authorization to perform at least one operation on +the information managed by the TOE. +Page 19 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 4 Security Objectives +4.1 Objectives for the TOE +O.AccessProfiles +The TOE must only releases a subset of the credential information stored in a user's Wallet +to the target application the user intends to identify and authenticate to based on +AccessProfiles. +O.Audit +The TOE shall offer an audit mechanism that can be used to hold users of each role +accountable for security-relevant actions performed with the TSF. Security-relevant actions +audited by the audit mechanism shall include password changes and the accessing of Wallets. +Each audit record generated by the audit mechanism shall contain the time and date that +the event occurred. The TOE shall provide a mechanism for authorized users to review the +generated audit records. +O.Authentication +The TOE must ensure that only authorized users gain access to the TOE and its resources. +O.Manage +The TSF must provide all the functions and facilities necessary to support the authorized +users that are responsible for the management of TOE security mechanisms and must ensure +that only authorized users are able to access such functionality. +O.Role +The TOE must assign a role to each user after successful identification and authentication +to the management facility. This role limits the management actions the user is allowed to +perform. +O.PasswordQuality +When in GINA mode with Active Directory password synchronization disabled, the TOE must +ensure that the quality of the ISAM E-SSO Password protecting the Common Symmetric Key +(CSK) must possess the strength to prevent credential guessing from threat agents. +O.WalletAccess +The TOE must ensure that users can only access the contents of the Wallet assigned to +them. +4.2 Objectives for the Operational Environment +OE.CryptoOps +The cryptographic operations performed by the Operational Environment shall perform +according to the specified algorithm standards and the random number generation shall +provide sufficient randomness and entropy for key generation. +Page 20 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + OE.InfoProtect +Those responsible for the TOE must be competent individuals, not careless, willfully negligent, +or hostile. They must follow and abide by the instructions provided by the guidance +documentation. They must establish and implement procedures to ensure that information +is protected in an appropriate manner. In particular: +â— All network communication channels and peripheral link must be approved for the +transmittal of the most sensitive data held by the system. Such links are assumed +to be adequately protected against threats to the authenticity, confidentiality and +integrity of the data transmitted. +â— All repositories holding TSF data and user data stored in the Operational Environment +are access protected to restrict access to the TSF only. +OE.PasswordQuality +When either in GINA mode with Active Directory password synchronization enabled or when +in GINA-less mode, the runtime environment must ensure that the quality of the ISAM E-SSO +Password protecting the Common Symmetric Key (CSK) must possess the strength to prevent +credential guessing from threat agents. +OE.Physical +Those responsible for the TOE must ensure that those parts of the TOE critical to security +policy are protected from physical attack which might compromise IT security objectives. +The protection must commensurate with the value of the IT assets protected by the TOE. +OE.Runtime +The runtime environment used for all components of the TOE must be properly administered +and protected from interference by unauthorized entities. This includes the requirement +that the system(s) hosting components of the IMS Server are restricted to be used for this +purpose only. +OE.TimeSource +The runtime environment shall provide a reliable time source for the TOE's use. +OE.Users +The TOE users shall act in a cooperating manner in a benign environment. The TOE users +shall be trained and trusted to accomplish some task or group of tasks within the secure +Operational Environment by exercising complete control over their user data. +4.3 Security Objectives Rationale +4.3.1 Security objectives coverage +The following table provides a mapping of TOE objectives to threats and policies, showing that each +objective counters or enforces at least one threat or policy, respectively. +Page 21 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Threats / OSPs +Objective +T.UserCredentials +O.AccessProfiles +P.Accountability +O.Audit +T.Manage +O.Authentication +T.Manage +O.Manage +T.Manage +P.User +O.Role +P.PasswordQuality +O.PasswordQuality +T.UserCredentials +O.WalletAccess +Table 2: Mapping of security objectives to threats and policies +The following table provides a mapping of the objectives for the Operational Environment to +assumptions, threats and policies, showing that each objective holds, counters or enforces at least +one assumption, threat or policy, respectively. +Assumptions / Threats / OSPs +Objective +A.CryptoOps +OE.CryptoOps +A.Manage +A.Remote +A.Repositories +OE.InfoProtect +P.PasswordQuality +OE.PasswordQuality +A.Physical +OE.Physical +A.Runtime +A.System +OE.Runtime +P.Accountability +OE.TimeSource +A.AuthUser +OE.Users +Table 3: Mapping of security objectives for the Operational Environment to assumptions, +threats and policies +4.3.2 Security objectives sufficiency +The following rationale provides justification that the security objectives are suitable to counter +each individual threat and that each security objective tracing back to a threat, when achieved, +actually contributes to the removal, diminishing or mitigation of that threat: +Page 22 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Rationale for security objectives +Threat +The threat of unauthorized access to the management facilities of the +TOE is diminished by the objective O.Manage which specifies that specific +management facilities exist. +T.Manage +In addition, a role mechanism is enforced for users with O.Role which +supports the protection of the management facilities by limiting +management capabilities of users to their role. +For enforcing the role-based management mechanism, the TOE provides +an identification and authentication mechanism for users with +O.Authentication to allow the TOE to establish the user identity and the +associated role for that user. +The threat of unauthorized access to the credential data stored for a +user by the TOE is diminished by the objective O.AccessProfiles which +ensures that the TOE releases only the credentials required for a target +T.UserCredentials +application to the target application based on the AccessProfiles. An +AccessProfile restricts other credentials in the Wallet from being +decrypted by the Operational Environment and passed to a target +application by mapping Wallet credentials to target applications. In +addition, AccessProfiles provide methods to identify applications in order +to prevent a threat agent from using a spoof or rogue application to +obtain a user's credentials. +In addition, the TOE implements per-user Wallets which the user must +access using his ISAM E-SSO Password as specified with O.WalletAccess, +ensuring that only the owner of a Wallet can access the user's Wallet +holding the user's credentials. +Table 4: Sufficiency of objectives countering threats +The following rationale provides justification that the security objectives for the environment are +suitable to cover each individual assumption, that each security objective for the environment that +traces back to an assumption about the environment of use of the TOE, when achieved, actually +contributes to the environment achieving consistency with the assumption, and that if all security +objectives for the environment that trace back to an assumption are achieved, the intended usage +is supported: +Rationale for security objectives +Assumption +The assumption on physical protection of the TOE is achieved by the +environmental objective OE.Physical to provide such protection. +A.Physical +The assumption that authorized users are cooperative, trained, and +trusted is covered by the environmental objective OE.Users which +requires that the TOE users shall act in a cooperating manner in a benign +A.AuthUser +environment and the TOE users shall be trained and trusted to accomplish +some task or group of tasks within the secure Operational Environment +by exercising complete control over their user data. +Page 23 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Rationale for security objectives +Assumption +The assumptions that the management of the TOE and the underlying +environment is conducted by competent individuals who are not careless, +willfully negligent, or hostile, and will follow and abide by the instructions +A.Manage +provided by the guidance documentation are achieved by the +environmental objective OE.InfoProtect to ensure these properties of +administrators. +The assumptions that the cryptographic operations performed by the +Operational Environment are performed according to the specified +algorithm standards and the random number generation provides +A.CryptoOps +sufficient randomness and entropy for key generation are achieved by +the environmental objective OE.CryptoOps which states that the +cryptographic operations performed by the Operational Environment +shall perform according to the specified algorithm standards and the +random number generation shall provide sufficient randomness and +entropy for key generation. +The assumption that threat agents are unable to compromise the +exchange of data between the different distributed TOE components is +achieved by the environmental objective OE.InfoProtect which guarantees +A.Remote +that the environment protects these communication channels either by +physical means (such as dedicated network links to which routing from +outside networks is prohibited) or by logical means (such as the use of +cryptographically secured network channels). +The assumption that threat agents are unable to access the TOE +repositories holding TSF data and user data and bypass the TSF is +achieved by the environmental objective OE.InfoProtect which requires +an appropriate access restriction of those repositories to the TSF. +A.Repositories +The assumption on exclusive TOE use of the underlying machines for +the IMS Server and preventing unauthorized access to the TOE is +achieved by the environmental objective OE.Runtime to implement +corresponding measures for the server runtime environment. +A.Runtime +The assumption that threat agents are unable to manipulate the runtime +environment to circumvent the TSF is achieved by OE.Runtime which +specifies that this runtime environment must be properly administered +and protected from interference by unauthorized entities. +A.System +Table 5: Sufficiency of objectives holding assumptions +The following rationale provides justification that the security objectives are suitable to cover each +individual organizational security policy, that each security objective that traces back to an OSP, +when achieved, actually contributes to the implementation of the OSP, and that if all security +objectives that trace back to an OSP are achieved, the OSP is implemented: +Page 24 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Rationale for security objectives +OSP +The policy to provide accountability for the action of TOE administrators +and users is implemented by the objective O.Audit to provide an auditing +mechanism and supported by the environmental objective OE.TimeSource +to provide a reliable time source in the runtime environment. +P.Accountability +The policy that the ISAM E-SSO Password must possess the strength to +prevent credential guessing from threat agents is achieved by the +objectives O.PasswordQuality and OE.PasswordQuality which require +either the TOE or the runtime environment, respectively, to enforce the +password strength depending on the TOE's configuration. +P.PasswordQuality +The policy that users are trusted to perform the actions for which they +have been authorized to perform is achieved by the objective O.Role +which ensures that the TOE assigns a role to every user identifying and +authenticating with the TOE management facilities. +P.User +Table 6: Sufficiency of objectives enforcing Organizational Security Policies +Page 25 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 5 Extended Components Definition +This Security Target does not extend the security components provided by the Common Criteria. +Page 26 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 6 Security Requirements +6.1 TOE Security Functional Requirements +The following table shows the security functional requirements for the TOE, and the operations +performed on the components according to CC part 2: iteration (Iter.), refinement (Ref.), assignment +(Ass.) and selection (Sel.). +Operations +Source +Security functional requirement +Security +functional +group Sel. +Ass. +Ref. +Iter. +Yes +Yes +No +No +CC Part 2 +FAU_GEN.1 Audit data generation +FAU - Security +audit +No +No +No +No +CC Part 2 +FAU_GEN.2 User identity association +No +Yes +Yes +No +CC Part 2 +FAU_SAR.1 Audit review +No +No +No +No +CC Part 2 +FAU_SAR.2 Restricted audit review +Yes +No +No +No +CC Part 2 +FAU_STG.1 Protected audit trail storage +No +Yes +No +No +CC Part 2 +FDP_ACC.2 Subset access control +FDP - User data +protection +No +Yes +No +No +CC Part 2 +FDP_ACF.1 Security attribute based access control +No +Yes +No +No +CC Part 2 +FIA_ATD.1 User attribute definition +FIA - Identification +and +authentication No +Yes +No +No +CC Part 2 +FIA_SOS.1 Verification of secrets +No +No +No +No +CC Part 2 +FIA_UAU.2 User authentication before any action +No +No +No +No +CC Part 2 +FIA_UID.2 User identification before any action +No +Yes +No +No +CC Part 2 +FIA_USB.1 User-subject binding +Yes +Yes +No +No +CC Part 2 +FMT_MSA.1 Management of security attributes +FMT - Security +management +Yes +Yes +No +No +CC Part 2 +FMT_MSA.3 Static attribute initialisation +Yes +Yes +No +No +CC Part 2 +FMT_MTD.1 Management of TSF data +No +Yes +No +No +CC Part 2 +FMT_SMF.1 Specification of management functions +No +Yes +No +No +CC Part 2 +FMT_SMR.1 Security roles +Table 7: Security functional requirements for the TOE +Page 27 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 6.1.1 Security audit (FAU) +6.1.1.1 Audit data generation (FAU_GEN.1) +The TSF shall be able to generate an audit record of the following auditable events: +a) Start-up and shutdown of the audit functions; +b) All auditable events for the not specified level of audit; and +c) the following auditable events: +FAU_GEN.1.1 +â— IMS Server: logins, password changes, role changes, policy changes +(for machine, system and user policies); +â— AccessAgent: accessing of the Wallet, using credentials out of the +Wallet to provide to an application based on the AccessProfiles, +password changes. +Application note: The requirement to log startup and shutdown of the audit +functions is trivially satisfied as the audit system is running when the TOE runs +and cannot be turned off. +The TSF shall record within each audit record at least the following information: +a) Date and time of the event, type of event, subject identity (if applicable), and +the outcome (success or failure) of the event; and +b) For each audit event type, based on the auditable event definitions of the +functional components included in the PP/ST, no other information. +FAU_GEN.1.2 +6.1.1.2 User identity association (FAU_GEN.2) +For audit events resulting from actions of identified users, the TSF shall be able +to associate each auditable event with the identity of the user that caused the +event. +FAU_GEN.2.1 +6.1.1.3 Audit review (FAU_SAR.1) +The TSF shall provide users assigned to the Administrator role with the +capability to read all audit data from the audit records generated by the following +audit events: +FAU_SAR.1.1 +â— IMS Server: logins, policy changes (for system and user policies only); +â— AccessAgent: accessing of the Wallet, using credentials out of the Wallet to +provide to an application based on the AccessProfiles. +The TSF shall provide the audit records in a manner suitable for the user to +interpret the information. +FAU_SAR.1.2 +Application Note: The functionality of FAU_SAR.1 is provided by AccessAdmin. +6.1.1.4 Restricted audit review (FAU_SAR.2) +The TSF shall prohibit all users read access to the audit records, except those +users that have been granted explicit read-access. +FAU_SAR.2.1 +Page 28 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 6.1.1.5 Protected audit trail storage (FAU_STG.1) +The TSF shall protect the stored audit records in the audit trail from unauthorised +deletion. +FAU_STG.1.1 +The TSF shall be able to prevent unauthorised modifications to the stored audit +records in the audit trail. +FAU_STG.1.2 +6.1.2 User data protection (FDP) +6.1.2.1 Subset access control (FDP_ACC.2) +Wallet Access Control Policy +Definition +Short name +Type +A TOE user. +S_User +Subjects +A target application. +O_TargetApp +Objects +A user's Wallet. +O_Wallet +Decrypt the user's Common Symmetric Key (CSK) and the +user's target application credentials (using cryptographic +operations supplied by the Operational Environment). +Decrypt +Operations +Release the user's target application credentials for the +specified target application. +Release +A user's ISAM E-SSO Password for decrypting the user's +CSK. +AS_Password +Security +attributes of +subjects +The AccessProfile for the target application. +AO_AccessProfile +Security +attributes of +objects The user's CSK. +AO_CSK +If the user's CSK can be successfully decrypted by the +Operational Environment using a key derived from the +user's ISAM E-SSO Password, then the TOE provides the +user access to the user's Wallet; otherwise, access to the +user's Wallet is denied. +R_DecryptCSK +Rules +If the target application's AccessProfile key is found in the +user's Wallet, the user's credentials for only the target +application are decrypted by the Operational Environment +R_ReleaseCreds +using the user's CSK and released to the target application; +otherwise, no credentials are released to the target +application. +Table 8: Wallet Access Control Policy +The TSF shall enforce the Wallet Access Control Policy in Table 8 on subjects +and objects as defined in Table 8 and all operations among subjects and +objects covered by the SFP. +FDP_ACC.2.1 +Page 29 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + The TSF shall ensure that all operations between any subject controlled by the +TSF and any object controlled by the TSF are covered by an access control SFP. +FDP_ACC.2.2 +Application Note: An AccessProfile key is a key created from information specific to an interface +(e.g., a program's pathname and filename) that is used to reference the user's credentials for that +program in the user's Wallet. +Application Note: The Wallet Access Control Policy defined in FDP_ACC.2 is only enforced by the +AccessAgent. (The IMS Server does not enforce this policy.) +6.1.2.2 Security attribute based access control (FDP_ACF.1) +The TSF shall enforce the Wallet Access Control Policy in Table 8 to objects +based on the following: subjects, objects, and for each the security +attributes as defined in Table 8. +FDP_ACF.1.1 +The TSF shall enforce the following rules to determine if an operation among +controlled subjects and controlled objects is allowed: rules as defined in Table +8 performed in the following order: +FDP_ACF.1.2 +1. R_DecryptCSK; +2. R_ReleaseCreds. +The TSF shall explicitly authorize access of subjects to objects based on the +following additional rules: none. +FDP_ACF.1.3 +The TSF shall explicitly deny access of subjects to objects based on the following +additional rules: none. +FDP_ACF.1.4 +6.1.3 Identification and authentication (FIA) +6.1.3.1 User attribute definition (FIA_ATD.1) +The TSF shall maintain the following list of security attributes belonging to +individual users: +FIA_ATD.1.1 +â— User identifier; +â— Common Symmetric Key (CSK); +â— Role. +6.1.3.2 Verification of secrets (FIA_SOS.1) +The TSF shall provide a mechanism to verify that secrets meet the following: +FIA_SOS.1.1 +â— For each attempt to use the authentication mechanism, the +probability that a random attempt will succeed is less than one in +1,000,000; +â— For multiple attempts to use the authentication mechanism during +a one minute period, the probability that a random attempt during +that minute will succeed is less than one in 100,000; and +â— Any feedback given during an attempt to use the authentication +mechanism will not reduce the probability below the above metrics. +Page 30 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Application Note: The password strength policies are enforced by the TOE only when the TOE is +in GINA mode with Active Directory password synchronization disabled. The password strength +policies are not enforced when the TOE is in GINA-less mode. +6.1.3.3 User authentication before any action (FIA_UAU.2) +The TSF shall require each user to be successfully authenticated before allowing +any other TSF-mediated actions on behalf of that user. +FIA_UAU.2.1 +6.1.3.4 User identification before any action (FIA_UID.2) +The TSF shall require each user to be successfully identified before allowing any +other TSF-mediated actions on behalf of that user. +FIA_UID.2.1 +6.1.3.5 User-subject binding (FIA_USB.1) +The TSF shall associate the following user security attributes with subjects acting +on the behalf of that user: +FIA_USB.1.1 +â— User identifier (which is associated with auditable events); +â— Role. +The TSF shall enforce the following rules on the initial association of user security +attributes with subjects acting on the behalf of users: +FIA_USB.1.2 +â— The IMS Server and all associated management interfaces: After +successful identification and authentication, the user identifier and +the role associated with the new session are set based on the +identifier and role specified for the user entry of the successfully +authenticated user. +â— AccessAgent: After successful identification and authentication, the +user identifier associated with the new session is set based on the +identifier specified for the user entry of the successfully +authenticated user. +The TSF shall enforce the following rules governing changes to the user security +attributes associated with subjects acting on the behalf of users: none. +FIA_USB.1.3 +6.1.4 Security management (FMT) +6.1.4.1 Management of security attributes (FMT_MSA.1) +Wallet Access Control Policy management +Authorized users and roles +Security attributes +Operations +Administrator role +AccessProfile +Add, modify +Users who know the current ISAM E-SSO Password for the +user's CSK +ISAM E-SSO Password +Modify +Table 9: Wallet Access Control Policy management +Page 31 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + The TSF shall enforce the Wallet Access Control Policy in Table 8 to restrict +the ability to perform the operations as defined in Table 9 on the security +attributes as defined in Table 9 to the authorized users and roles as +defined in Table 9. +FMT_MSA.1.1 +Application Note: To add and modify AccessProfiles, the IMS Configuration Utility is used. To +modify ISAM E-SSO Passwords, AccessAgent is used. +6.1.4.2 Static attribute initialisation (FMT_MSA.3) +The TSF shall enforce the Wallet Access Control Policy in Table 8 to provide +restrictive default values for security attributes that are used to enforce the +SFP. +FMT_MSA.3.1 +The TSF shall allow nobody to specify alternative initial values to override the +default values when an object or information is created. +FMT_MSA.3.2 +6.1.4.3 Management of TSF data (FMT_MTD.1) +TSF data management +Authorized roles +TSF data +Operations +Administrator +Role user security attribute +Modify +Administrator +System policies and machine policies +Modify +Administrator and Help desk +System policies and machine policies +View +Administrator and Help desk +User policies +Modify, view +Table 10: TSF data management +The TSF shall restrict the ability to perform the operations in Table 10 on +the TSF data in Table 10 to the authorized roles in Table 10. +FMT_MTD.1.1 +Application Note: To perform the operations on the TSF data specified in Table 10, AccessAdmin +is used. +6.1.4.4 Specification of management functions (FMT_SMF.1) +The TSF shall be capable of performing the following management functions: +FMT_SMF.1.1 +â— Assign roles to users (see FMT_MTD.1); +â— Manage machine, system, and user policies (see FMT_MTD.1); +â— View audit logs (see FAU_SAR.1). +6.1.4.5 Security roles (FMT_SMR.1) +The TSF shall maintain the roles +FMT_SMR.1.1 +â— Administrator; +â— Help desk; +â— User. +Page 32 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + The TSF shall be able to associate users with roles. +FMT_SMR.1.2 +6.2 Security Functional Requirements Rationale +6.2.1 Security requirements coverage +The following table provides a mapping of SFR to the security objectives, showing that each security +functional requirement addresses at least one security objective. +Objectives +Security functional requirements +O.Audit +FAU_GEN.1 +O.Audit +FAU_GEN.2 +O.Audit +FAU_SAR.1 +O.Audit +FAU_SAR.2 +O.Audit +FAU_STG.1 +O.AccessProfiles, +O.WalletAccess +FDP_ACC.2 +O.AccessProfiles, +O.WalletAccess +FDP_ACF.1 +O.Authentication +FIA_ATD.1 +O.PasswordQuality +FIA_SOS.1 +O.Authentication +FIA_UAU.2 +O.Authentication +FIA_UID.2 +O.Audit, +O.Authentication +FIA_USB.1 +O.Manage +FMT_MSA.1 +O.Manage +FMT_MSA.3 +O.Manage +FMT_MTD.1 +O.Manage +FMT_SMF.1 +O.Role +FMT_SMR.1 +Table 11: Mapping of security functional requirements to security objectives +6.2.2 Security requirements sufficiency +The following rationale provides justification for each security objective for the TOE, showing that +the security functional requirements are suitable to meet and achieve the security objectives: +Page 33 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Rationale +Security objectives +The access control policy specified with FDP_ACC.2 and FDP_ACF.1 +ensures that only the intended credentials are released to the target +application. +O.AccessProfiles +The objective to provide means to audit security-relevant actions with +the TSF is met by requirements for audit record generation (FAU_GEN.1) +and association of audited events with the originating user ID +O.Audit +(FAU_GEN.2). Administrators have the ability to review audit data +(FAU_SAR.1). The audit data is stored in a protected environment +(FAU_STG.1) and the TOE ensures that it can only be read by authorized +users (FAU_SAR.2). Users are identified based on the established identity +during user-subject-binding (FIA_USB.1). +The TOE environment needs to provide the proper time for the generated +audit records, see OE.TimeSource. +Supportive management functions have been specified in FMT_SMF.1. +Identification and authentication is a prerequisite for the management +function and is defined with FIA_UAU.2 and FIA_UID.2. After the successful +identification and authentication, the TOE performs a user-subject-binding +as defined with FIA_USB.1. The security attributes maintained by the +TOE and used for the user-subject-binding are listed in FIA_ATD.1. +O.Authentication +The general management functions provided with the TOE are listed in +FMT_SMF.1 and detailed in FMT_MTD.1. In addition, the management of +user Wallets is defined with FMT_MSA.1 and FMT_MSA.3. +O.Manage +The definition of roles known to the TSF is specified with FMT_SMR.1. +O.Role +When in GINA mode with Active Directory password synchronization +disabled, the TOE ensures that the quality of the ISAM E-SSO Password +protecting the Common Symmetric Key (CSK), as specified by FIA_SOS.1, +must possess the strength to prevent credential guessing from threat +agents. +O.PasswordQuality +The access control policy specified with FDP_ACC.2 and FDP_ACF.1 +ensures that users can only access their Wallet. +O.WalletAccess +Table 12: Security objectives for the TOE rationale +6.2.3 Security requirements dependency analysis +The following table demonstrates the dependencies of SFRs modeled in CC Part 2 and how the SFRs +for the TOE resolve those dependencies: +Page 34 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Resolution +Dependencies +Security +Functional +Requirement +The TOE is application software and, +therefore, cannot provide reliable time +stamps. Reliable time stamps must be +provided by the Operational Environment. +See OE.TimeSource. +FPT_STM.1 +FAU_GEN.1 +FAU_GEN.1 +FAU_GEN.1 +FAU_GEN.2 +FIA_UID.2 +FIA_UID.1 +FAU_GEN.1 +FAU_GEN.1 +FAU_SAR.1 +FAU_SAR.1 +FAU_SAR.1 +FAU_SAR.2 +FAU_GEN.1 +FAU_GEN.1 +FAU_STG.1 +FDP_ACF.1 +FDP_ACF.1 +FDP_ACC.2 +FDP_ACC.2 +FDP_ACC.1 +FDP_ACF.1 +FMT_MSA.3 +FMT_MSA.3 +No dependencies. +FIA_ATD.1 +No dependencies. +FIA_SOS.1 +FIA_UID.2 +FIA_UID.1 +FIA_UAU.2 +No dependencies. +FIA_UID.2 +FIA_ATD.1 +FIA_ATD.1 +FIA_USB.1 +FDP_ACC.2 +[FDP_ACC.1 or FDP_IFC.1] +FMT_MSA.1 +FMT_SMR.1 +FMT_SMR.1 +FMT_SMF.1 +FMT_SMF.1 +FMT_MSA.1 +FMT_MSA.1 +FMT_MSA.3 +FMT_SMR.1 +FMT_SMR.1 +FMT_SMR.1 +FMT_SMR.1 +FMT_MTD.1 +FMT_SMF.1 +FMT_SMF.1 +No dependencies. +FMT_SMF.1 +FIA_UID.2 +FIA_UID.1 +FMT_SMR.1 +Table 13: TOE SFR dependency analysis +Page 35 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 6.3 Security Assurance Requirements +The security assurance requirements for the TOE are the Evaluation Assurance Level 3 components +as specified in [CC] part 3, augmented by ALC_FLR.1. +The following table shows the Security assurance requirements, and the operations performed on +the components according to CC part 3: iteration (Iter.), refinement (Ref.), assignment (Ass.) and +selection (Sel.). +Operations +Source +Security assurance requirement +Security +assurance class +Sel. +Ass. +Ref. +Iter. +No +No +No +No +CC Part 3 +ADV_ARC.1 Security architecture description +ADV Development +No +No +No +No +CC Part 3 +ADV_FSP.3 Functional specification with complete +summary +No +No +No +No +CC Part 3 +ADV_TDS.2 Architectural design +No +No +No +No +CC Part 3 +AGD_OPE.1 Operational user guidance +AGD Guidance +documents +No +No +No +No +CC Part 3 +AGD_PRE.1 Preparative procedures +No +No +No +No +CC Part 3 +ALC_CMC.3 Authorisation controls +ALC Life-cycle +support +No +No +No +No +CC Part 3 +ALC_CMS.3 Implementation representation CM +coverage +No +No +No +No +CC Part 3 +ALC_DEL.1 Delivery procedures +No +No +No +No +CC Part 3 +ALC_DVS.1 Identification of security measures +No +No +No +No +CC Part 3 +ALC_FLR.1 Basic flaw remediation +No +No +No +No +CC Part 3 +ALC_LCD.1 Developer defined life-cycle model +No +No +No +No +CC Part 3 +ASE_INT.1 ST introduction +ASE Security +Target evaluation +No +No +No +No +CC Part 3 +ASE_CCL.1 Conformance claims +No +No +No +No +CC Part 3 +ASE_SPD.1 Security problem definition +No +No +No +No +CC Part 3 +ASE_OBJ.2 Security objectives +No +No +No +No +CC Part 3 +ASE_ECD.1 Extended components definition +No +No +No +No +CC Part 3 +ASE_REQ.2 Derived security requirements +No +No +No +No +CC Part 3 +ASE_TSS.1 TOE summary specification +No +No +No +No +CC Part 3 +ATE_COV.2 Analysis of coverage +ATE Tests +No +No +No +No +CC Part 3 +ATE_DPT.1 Testing: basic design +No +No +No +No +CC Part 3 +ATE_FUN.1 Functional testing +No +No +No +No +CC Part 3 +ATE_IND.2 Independent testing - sample +Page 36 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + Operations +Source +Security assurance requirement +Security +assurance class +Sel. +Ass. +Ref. +Iter. +No +No +No +No +CC Part 3 +AVA_VAN.2 Vulnerability analysis +AVA Vulnerability +assessment +Table 14: Security assurance requirements +6.4 Security Assurance Requirements Rationale +The evaluation assurance level has been chosen to match a Basic attack potential commensurate +with the threat environment that is experienced by typical consumers of the TOE. In addition, the +evaluation assurance level has been augmented with ALC_FLR.1 commensurate with the augmented +flaw remediation capabilities offered by the developer beyond those required by the evaluation +assurance level. +Page 37 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 7 TOE Summary Specification +7.1 TOE Security Functionality +The following section explains how the security functions are implemented. The different TOE +security functions cover the various SFR classes. +The primary security features of the TOE are: +â— Audit +â— Identification and Authentication +â— User Data Protection +â— Security Management +Of the policies referred to in the sections below, only the password strength policies are evaluated +by this evaluation. +7.1.1 Audit +Various components of the product generate audit events which are stored in the IMS Server +database. +All user application access logs are collated into the IMS Server's audit log database. Each log record +contains info related to time and location from which a user accesses a certain application. Using +AccessAdmin, the Administrator role can generate reports to review the logs contained in the +database. Several predefined audit reports come standard with the TOE. Only users with the +Administrator role are allowed access to the audit records via the TOE, preventing both unauthorized +access to and unauthorized deletion of the stored audit records. +The TOE provides audit records for the following types of events besides the start-up and shutdown +of the audit functions: +â— IMS Server: +â logins +â password changes +â role changes +â policy changes (for machine, system and user policies) +â— AccessAgent: +â accessing of the Wallet +â using credentials out of the Wallet to provide to an application based on the +AccessProfiles +â password changes +The audit records include the date and time of the event, the type of event, the subject identity +and the event outcome. The audit record time stamps are obtained from a reliable time source in +the Operational Environment. +The Administrator role can view and search a subset of the audit records through the TOE using +AccessAdmin. The subset that can be viewed and searched are: +â— IMS Server: +â logins +â policy changes (for system and user policies only) +Page 38 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + â— AccessAgent: +â accessing of the Wallet +â using credentials out of the Wallet to provide to an application based on the +AccessProfiles +This section maps to the following SFR(s): +â— FAU_GEN.1 +â— FAU_GEN.2 +â— FAU_SAR.1 +â— FAU_SAR.2 +â— FAU_STG.1 +7.1.2 Identification and authentication (I&A) +The TOE supports an identification mechanism and an authentication mechanism. This section +describes the supported mechanism. +The TOE maintains its own user repository, and performs user authentication against various forms +of authentication credentials stored in this repository (stored in the IMS Server database). The +user's ISAM E-SSO Password is created when the account is first created (when the user first sign's +up). +The user's Wallet, which contains his authentication data, is stored centrally in the IMS Server +database. Additionally, the authentication data is cached onto the user's workstation disk whenever +the user caches his Wallet to a machine. This allows the AccessAgent to be able to authenticate +users even when the machine cannot connect to the IMS Server. +An enterprise can ensure that its users use strong passwords to log on to the TOE by enforcing +ISAM E-SSO Password strength policies. +7.1.2.1 AccessAgent +AccessAgent is a Windows application. It authenticates users based on their user identifier and +password. A unique-per-user, 128 bit Common Symmetric Key (CSK) is generated by the Operational +Environment at the request the AccessAgent for the user when the user initially sign's up through +the AccessAgent. This CSK is associated with the user's account and is maintained by the TOE in +the Cryptobox that contains the user's Wallet. The TOE passes the user's password along with the +user's encrypted Common Symmetric Key (CSK) from the Cryptobox to the cryptographic operations +in the Operational Environment to obtain the user's CSK. The user is considered authenticated by +the TOE if the user's CSK is successfully obtained. (Authentication with one-time passwords (OTP) +is disabled in the evaluated configuration.) +If the AccessAgent has a network connection to the IMS Server, it will authenticate a user against +the user's credentials stored in the IMS Server, by passing along the authentication credentials +over HTTPS to the IMS Server. However, if the AccessAgent is offline to the IMS Server, it will then +authenticate the user's presented credentials against cached authentication data stored on the +disk in the user's Cryptobox. +For AccessAgents installed with the GINA option enabled (i.e., in GINA mode), a user will log on to +the AccessAgent GINA first, whereupon the AccessAgent will auto-logon the user into Windows +using the user's Windows account. +Page 39 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + For AccessAgents installed with the GINA option disabled (i.e., in GINA-less mode), the user will log +on to Windows manually first, and then the AccessAgent will auto-logon the user into his Wallet. +Authentication of users by the AccessAgent is carried out in accordance to Authentication Policies +defined by the Administrator role at the IMS Server and sync-ed to various AccessAgents. +AccessAgent allows each user to change his own ISAM E-SSO Password. The TOE's password strength +policies are enforced only when the AccessAgent is in GINA mode with Active Directory password +synchronization disabled. When in GINA-less mode or when Active Directory password synchronization +is enabled, the Active Directory's password strength policy is enforced by Active Directory, not the +TOE. +This section and its subsections map to the following SFR(s): +â— FIA_ATD.1 +â— FIA_SOS.1 +â— FIA_UAU.2 +â— FIA_UID.2 +â— FIA_USB.1 +7.1.2.2 AccessAssistant +Users perform form-based logon to AccessAssistant using their username and ISAM E-SSO Password. +The AccessAssistant authenticates a user by making SOAP-based logon calls, with the user-provided +credentials, to the IMS Server, where the credentials are verified against the authentication data +stored in the IMS Server database. +This section and its subsections map to the following SFR(s): +â— FIA_ATD.1 +â— FIA_UAU.2 +â— FIA_UID.2 +â— FIA_USB.1 +7.1.2.3 AccessAdmin +Only users who are assigned Administrator or Help desk roles can log on to AccessAdmin. Users +authenticate to AccessAdmin using the following method: +â— Form-based logon (over HTTPS) using a username and ISAM E-SSO Password. +The authentication and access control is implemented by the AccessAdmin/IMS application, which +will lookup the user's authentication data as well as assigned roles stored in the IMS Server database. +This section and its subsections map to the following SFR(s): +â— FIA_ATD.1 +â— FIA_UAU.2 +â— FIA_UID.2 +â— FIA_USB.1 +7.1.2.4 IMS Configuration Utility +The IMS Configuration Utility is a web-based utility for editing the IMS Server's configuration. It uses +authentication and access control provided by the Operational Environment. +Page 40 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 7.1.3 User data protection +The TOE supports a user data protection mechanism. This section describes the supported +mechanism. +The TOE stores each user's credential data in a Wallet; one Wallet per-user. A Wallet provides +confidentiality and integrity protection of the user credential data through the use of cryptographic +operations. All cryptographic operations are performed by the Operational Environment. +When a user sign's up via the TOE, the TOE creates a Wallet for the user and has the Operational +Environment generate a 128 bit cryptographic key called the Common Symmetric Key (CSK). +The CSK is unique for each user and is used for encrypting the user's credential data stored in the +user's Wallet. The CSK, in turn, is encrypted with a key derived from the user's ISAM E-SSO Password +using AES 128 bit Cipher Block Chaining (CBC) mode. To decrypt items in the user's Wallet, the +user supplies a valid ISAM E-SSO Password. The TOE provides the user's ISAM E-SSO Password and +the user's encrypted CSK as input to cryptographic operations in the Operational Environment to +obtain the CSK. The TOE then passes this CSK and an encrypted Wallet Credential from the user's +Wallet as input to cryptographic operations in the Operational Environment to obtain a decrypted +Wallet credential. For a user to change/modify the password of a Wallet, the user must know the +current password for the TOE initiated decryption operations to succeed and to obtain the CSK. The +AccessAgent allows each user to change his own ISAM E-SSO Password. +Both the Wallet and the password-encrypted CSK are stored in a secure data file on the AccessAgent +called a Cryptobox; one Cryptobox per-user. The information in the Cryptobox is sent to the IMS +Server for storage. +Since the CSK is required to access the credentials in the user's Wallet and the CSK is encrypted +with the user's ISAM E-SSO Password, the TOE does not store user ISAM E-SSO Paswords anywhere +in the system. Instead, the TOE requires users to supply their ISAM E-SSO Passwords each time +they log in. +The IMS Server stores only the encrypted forms of the user's credential data and CSK. By default, +the access controls on the IMS Server database are configured such that only an IMS Server-specific +database account, and the database administrators are allowed to access the data. +Each Cryptobox file and its containing folder are protected by Windows ACLs, so normal Windows +users will not be able to read and/or copy the files. +The AccessAgent's Single Sign-On function uses AccessProfiles to determine how to identify the +target requesting the credentials, which credentials in the user's Wallet match the target, and how +to provide the user's credentials to the target. If the target is an application, the AccessProfile +specifies the executable path and file name, and the TOE uses these values to create a key, called +an AccessProfile key, which is used to access the credentials in the user's Wallet. If the target uses +GUI widgets, the AccessProfile specifies the GUI widgets, and the TOE uses the GUI widget values +to create a key which is used to access the credentials in the user's Wallet. In addition, AccessProfiles +can optionally specify workflow automation for use where complicated workflow is required. +AccessProfiles are created and modified outside of the TOE using AccessStudio which is part of the +Operational Environment. The IMS Configuration Utility is used to add new AccessProfiles to the +IMS Server and to modify existing AccessProfiles that are used by the IMS Server. +Only the AccessAgent enforces the Wallet Access Control Policy defined by FDP_ACC.2 and +FDP_ACF.1. The IMS Server and other TOE components provide only a supporting role for this policy. +This section maps to the following SFR(s): +â— FDP_ACC.2 +Page 41 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + â— FDP_ACF.1 +â— FMT_MSA.1 +â— FMT_MSA.3 +7.1.4 Security management +The TOE supports security function management mechanisms. This section describes the supported +mechanisms. +Role-based access control is used to protect access to operations in the AccessAdmin and +AccessAssistant applications. +Users are classified into three roles: +â— Administrator +â— Help desk +â— User +The Administrator role is the most powerful of all the roles. The Help desk role is more powerful +than the User role, but less powerful than the Administrator role. The User role is the least powerful +of the roles and is intended for ordinary users. +A new user is granted the User role by default when they first sign-up. +The initial Administrator user account is created during the IMS Server's configuration, using the +IMS Web Configurator. Subsequently, an Administrator user can promote (i.e., modify the role of) +any user to Administrator or Help desk role through the AccessAdmin interface. +Only users with Administrator role will have full access to the AccessAdmin application. For example, +only Administrative users can modify machine policies and system policies. They can also view +machine policies and system policies, and view and modify user policies. +The Help desk role provides some of the capabilities of the Administrator role. The Help desk role +can view and modify user policies and view system policies and machine policies. +The TOE roles are stored in the IMS Server DB tables and managed by the AccessAdmin/IMS +application. Access control (based on these roles) to the IMS Server's various services is enforced +by the IMS Server. +The TOE contains several types of access policies that are used as configuration data by the TOE. +These access policies are grouped into the following major categories: +â— Machine policies +â— System policies +â— User policies +Machine policies are policies that affect a specific machine or computer. Policies in this category +include the audit logging policies, desktop inactivity policies, and some of the Wallet-related policies +such as limiting the number of Wallets allowed on a machine and only allowing the Wallet to be +used on the machine where it was created. +System policies are policies that are applicable to all users and machines. Policies in this category +include auditing policies, password strength policies, and some of the Wallet-related policies. +User policies are policies that affect a specific user. Policies in this category include the Log on/Log +off policies and some of the Wallet-related policies such as the option for displaying of application +passwords in AccessAgent. +Page 42 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + This section maps to the following SFR(s): +â— FMT_MTD.1 +â— FMT_SMF.1 +â— FMT_SMR.1 +Page 43 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + 8 Abbreviations, Terminology and References +8.1 Abbreviations +AA +AccessAgent +AD +Active Directory +AES +Advanced Encryption Standard +API +Application Programming Interface +CBC +Cipher Block Chaining +CSK +Common Symmetric Key +E-SSO +Enterprise Single Sign-On +GINA +Graphical Identification and Authentication +GSKit +IBM Global Security Toolkit +GUI +Graphical User Interface +HTTPS +Hypertext Transfer Protocol Secure +IMS +Integrated Management System +ISAM +IBM Security Access Manager +LDAP +Lightweight Directory Access Protocol +MAC +Mobile ActiveCode +OTP +One-Time Password +RADIUS +Remote Authentication Dial In User Service +RFID +Radio Frequency Identification +SFP +Security Function Policy +Page 44 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + SFR +Security Functional Requirement +SOAP +Originally defined as Simple Object Access Protocol +SPI +Service Provider Interface +SSL +Secure Sockets Layer +SSO +Single Sign-On +ST +Security Target +TCP/IP +Transmission Control Protocol / Internet Protocol +TLS +Transport Layer Security +TOE +Target of Evaluation +TSF +TOE Security Functionality +UI +User Interface +USB +Universal Serial Bus +WAS +WebSphere Application Server +XML +Extensible Markup Language +8.2 Terminology +This section contains definitions of technical terms that are used with a meaning specific to this +document. Terms defined in the [CC] are not reiterated here, unless stated otherwise. +AccessAgent +Client software that captures the user's credentials and acts on the user's behalf for single +sign-on and sign-off. +Authentication Factors +Different methods for authenticating to the product, such as USB keys, biometrics, proximity +cards, and passwords. See section 1.5.3.3 for the authentication factors supported in the +evaluated configuration. +GINA-less mode +Defines the mode where the IBM Security Access Manager for Enterprise Single Sign-On (ISAM +E-SSO) GINA is not installed on the operating system. +Page 45 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + GINA mode +Defines the mode where the IBM Security Access Manager for Enterprise Single Sign-On (ISAM +E-SSO) GINA is installed and used by the operating system. +GUI Widget +See Target Widget. +IMS Server +Provides centralized management of all users and policies. All policies are defined centrally and +enforced through AccessAgent. The IMS Server also provides comprehensive back-up of +credentials, loss management, audit, and compliance reporting. +Target Widget +A GUI widget belonging to an application monitored by the AccessAgent. The widget is monitored +by the TOE and filled in with information from the user's Wallet when applicable. For example, +a monitored application maintains a GUI widget referenced with "password" used to provide a +password to log into the application. The AccessAgent monitors the application for that +"password" widget in particular. If this widget is displayed, the AccessAgent detects the widget +and fills in the password information for the monitored application from the user's Wallet. +User +Humans or machines interacting with the TOE via the provided user and programmatic interfaces. +The term user in this document includes administrators of the TOE unless a specific distinction +is made in the text. +Wallet +A personal, encrypted repository of user credentials. The Wallet roams to the point of access +and stores the user's personal identity profiles, including log-in credentials, certificates, +encryption keys, and user policies. +8.3 References +Common Criteria for Information Technology Security Evaluation +CC +3.1R3 +Version +July 2009 +Date +http://www.commoncriteriaportal.org/files/ccfiles/CC +PART1V3.1R3.pdf +Location +http://www.commoncriteriaportal.org/files/ccfiles/CC +PART2V3.1R3.pdf +Location +http://www.commoncriteriaportal.org/files/ccfiles/CC +PART3V3.1R3.pdf +Location +Page 46 of 46 +Version: 1.19 +Copyright © 2009 - 2014 by atsec information security and IBM +Last update: 2014-03-05 +International Business Machines, Corporation +IBM Security Access Manager for Enterprise Single +Sign-On Version 8.2 with IMS Server Interim Fix 4 and +AccessAgent Fix Pack 22 Security Target + \ No newline at end of file diff --git a/tests/data/test_cc_heuristics/dependency_dataset.json b/tests/data/cc/analysis/dependency_dataset.json similarity index 99% rename from tests/data/test_cc_heuristics/dependency_dataset.json rename to tests/data/cc/analysis/dependency_dataset.json index 3122a40a..eb6b7d7b 100644 --- a/tests/data/test_cc_heuristics/dependency_dataset.json +++ b/tests/data/cc/analysis/dependency_dataset.json @@ -1,9 +1,9 @@ { "_type": "sec_certs.dataset.common_criteria.CCDataset", "state": { - "_type": "sec_certs.dataset.common_criteria.CCDataset.DatasetInternalState", + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", "meta_sources_parsed": false, - "pdfs_downloaded": false, + "artifacts_downloaded": false, "pdfs_converted": false, "certs_analyzed": false }, diff --git a/tests/data/test_cc_heuristics/dependency_vulnerability_dataset.json b/tests/data/cc/analysis/dependency_vulnerability_dataset.json similarity index 99% rename from tests/data/test_cc_heuristics/dependency_vulnerability_dataset.json rename to tests/data/cc/analysis/dependency_vulnerability_dataset.json index 2ca49ebb..dbf16833 100644 --- a/tests/data/test_cc_heuristics/dependency_vulnerability_dataset.json +++ b/tests/data/cc/analysis/dependency_vulnerability_dataset.json @@ -1,9 +1,9 @@ { "_type": "sec_certs.dataset.common_criteria.CCDataset", "state": { - "_type": "sec_certs.dataset.common_criteria.CCDataset.DatasetInternalState", + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", "meta_sources_parsed": false, - "pdfs_downloaded": false, + "artifacts_downloaded": false, "pdfs_converted": false, "certs_analyzed": false }, diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json new file mode 100644 index 00000000..0d7e4c45 --- /dev/null +++ b/tests/data/cc/analysis/vulnerable_dataset.json @@ -0,0 +1,73 @@ +{ + "_type": "sec_certs.dataset.common_criteria.CCDataset", + "state": { + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", + "meta_sources_parsed": true, + "artifacts_downloaded": false, + "pdfs_converted": false, + "certs_analyzed": false + }, + "timestamp": "2021-04-16 15:05:18.386794", + "sha256_digest": "not implemented", + "name": "cc_full_dataset", + "description": "sample dataset description", + "n_certs": 1, + "certs": [ + { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert", + "dgst": "c01e5375331b25dc", + "status": "active", + "category": "Access Control Devices and Systems", + "name": "IBM Security Access Manager for Enterprise Single Sign-On Version 8.2", + "manufacturer": "IBM Corporation", + "scheme": "DE", + "security_level": { + "_type": "Set", + "elements": [ + "ALC_FLR.1", + "EAL3+" + ] + }, + "not_valid_before": "2014-12-05", + "not_valid_after": null, + "report_link": "http://www.commoncriteriaportal.org/files/epfiles/0683a_pdf.pdf", + "st_link": "http://www.commoncriteriaportal.org/files/epfiles/0683b_pdf.pdf", + "cert_link": null, + "manufacturer_web": "http://www.ibm.com", + "protection_profiles": [], + "maintenance_updates": [], + "state": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.InternalState", + "st_download_ok": true, + "report_download_ok": true, + "st_convert_ok": true, + "report_convert_ok": true, + "st_extract_ok": true, + "report_extract_ok": true, + "errors": [] + }, + "pdf_data": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.PdfData", + "report_metadata": null, + "st_metadata": null, + "report_frontpage": null, + "st_frontpage": null, + "report_keywords": null, + "st_keywords": null, + "report_filename": null, + "st_filename": null + }, + "heuristics": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.Heuristics", + "extracted_versions": [ + "8.2" + ], + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "cert_lab": null, + "cert_id": null + } + } + ] +} \ No newline at end of file diff --git a/tests/data/test_cc_oop/fictional_cert.json b/tests/data/cc/certificate/fictional_cert.json similarity index 100% rename from tests/data/test_cc_oop/fictional_cert.json rename to tests/data/cc/certificate/fictional_cert.json diff --git a/tests/data/test_cc_oop/cc_products_active.csv b/tests/data/cc/dataset/cc_products_active.csv similarity index 100% rename from tests/data/test_cc_oop/cc_products_active.csv rename to tests/data/cc/dataset/cc_products_active.csv diff --git a/tests/data/test_cc_oop/cc_products_active.html b/tests/data/cc/dataset/cc_products_active.html similarity index 100% rename from tests/data/test_cc_oop/cc_products_active.html rename to tests/data/cc/dataset/cc_products_active.html diff --git a/tests/data/test_cc_oop/report_309ac2fd7f2dcf17.txt b/tests/data/cc/dataset/report_309ac2fd7f2dcf17.txt similarity index 100% rename from tests/data/test_cc_oop/report_309ac2fd7f2dcf17.txt rename to tests/data/cc/dataset/report_309ac2fd7f2dcf17.txt diff --git a/tests/data/test_cc_oop/target_309ac2fd7f2dcf17.txt b/tests/data/cc/dataset/target_309ac2fd7f2dcf17.txt similarity index 100% rename from tests/data/test_cc_oop/target_309ac2fd7f2dcf17.txt rename to tests/data/cc/dataset/target_309ac2fd7f2dcf17.txt diff --git a/tests/data/test_cc_oop/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json similarity index 98% rename from tests/data/test_cc_oop/toy_dataset.json rename to tests/data/cc/dataset/toy_dataset.json index cb733b0c..9de1e06f 100644 --- a/tests/data/test_cc_oop/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -1,9 +1,9 @@ { "_type": "sec_certs.dataset.common_criteria.CCDataset", "state": { - "_type": "sec_certs.dataset.common_criteria.CCDataset.DatasetInternalState", + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", "meta_sources_parsed": true, - "pdfs_downloaded": false, + "artifacts_downloaded": false, "pdfs_converted": false, "certs_analyzed": false }, diff --git a/tests/data/certs.csv b/tests/data/certs.csv deleted file mode 100644 index 0cfaa57f..00000000 --- a/tests/data/certs.csv +++ /dev/null @@ -1,15 +0,0 @@ -cert,st -0143a.pdf,0143b.pdf -ANSSI-CC_2009-62fr.pdf,ANSSI-CC-cible_2009-62en.pdf -c0478_erpt.pdf,c0478_est.pdf -bariyer cr.pdf,HVL-BARIYER-ASE-ST-lite_v2.5.pdf -Aruba NMCv6.4.2.0-1.3_2017_107_CRv1.0.pdf,Aruba NVM Controller OS 6.4.2.0-1.3 FIPS_STv1.0.pdf -ANSSI-CC-En_2016_45en.pdf,anssi_cible_2016_45_LITE.pdf -ANSSI-CC-En_2016_52en.pdf,anssi_cible_2016_52_LITE.pdf -Aselsan_Digital Tachograph-CR.pdf,Aselsan_Digital Tachograph-ST-Lite.pdf -0519V2a_pdf.pdf,0519V2b_pdf.pdf -0596V2a_pdf.pdf,0596V2b_pdf.pdf -KECS-CISS-1003-2020_CR_EN.pdf,KECS-CISS-1003-2020_ST_EN.pdf -ATES v1.0 Certification Report.pdf,ICT_ATES_ST_v2.9.pdf -0589a_pdf.pdf,0589b_pdf.pdf -KECS-CISS-0924-2019_CR_EN.pdf,KECS-CISS-0924-2019_ST_EN.pdf \ No newline at end of file diff --git a/tests/data/test_fips_oop/algorithms.json b/tests/data/fips/algorithms.json similarity index 100% rename from tests/data/test_fips_oop/algorithms.json rename to tests/data/fips/algorithms.json diff --git a/tests/data/test_fips_iut/fips_iut_2020-10-25T06+01:00.html b/tests/data/fips/iut/fips_iut_2020-10-25T06+01:00.html similarity index 100% rename from tests/data/test_fips_iut/fips_iut_2020-10-25T06+01:00.html rename to tests/data/fips/iut/fips_iut_2020-10-25T06+01:00.html diff --git a/tests/data/test_fips_iut/fips_iut_2021-12-10T06+01:00.html b/tests/data/fips/iut/fips_iut_2021-12-10T06+01:00.html similarity index 100% rename from tests/data/test_fips_iut/fips_iut_2021-12-10T06+01:00.html rename to tests/data/fips/iut/fips_iut_2021-12-10T06+01:00.html diff --git a/tests/data/test_fips_mip/fips_mip_2020-10-24T06+02:00.html b/tests/data/fips/mip/fips_mip_2020-10-24T06+02:00.html similarity index 100% rename from tests/data/test_fips_mip/fips_mip_2020-10-24T06+02:00.html rename to tests/data/fips/mip/fips_mip_2020-10-24T06+02:00.html diff --git a/tests/data/test_fips_mip/fips_mip_2021-02-19T06+01:00.html b/tests/data/fips/mip/fips_mip_2021-02-19T06+01:00.html similarity index 100% rename from tests/data/test_fips_mip/fips_mip_2021-02-19T06+01:00.html rename to tests/data/fips/mip/fips_mip_2021-02-19T06+01:00.html diff --git a/tests/data/test_fips_mip/fips_mip_2021-12-19T06+01:00.html b/tests/data/fips/mip/fips_mip_2021-12-19T06+01:00.html similarity index 100% rename from tests/data/test_fips_mip/fips_mip_2021-12-19T06+01:00.html rename to tests/data/fips/mip/fips_mip_2021-12-19T06+01:00.html diff --git a/tests/data/settings_test.yaml b/tests/data/settings_test.yaml deleted file mode 100644 index 82da0540..00000000 --- a/tests/data/settings_test.yaml +++ /dev/null @@ -1,59 +0,0 @@ ---- -log_filepath: - description: Path to the file, relative to working directory, where the log will be stored - value: ./cert_processing_log.txt -smallest_certificate_id_to_connect: - description: - During validation we don't connect certificates with number lower than - _this_ to connections - value: 40 -year_difference_between_validations: - description: - During validation we don't connect certificates with validation dates - difference higher than _this_ - value: 7 -n_threads: - description: How many threads to use for parallel computations - value: 8 -cpe_matching_threshold: - description: Level of required string similarity between CPE and certificate name on CC CPE matching, 0-100. Lower values yield more false negatives, higher values more false positives - value: 92 -cpe_n_max_matches: - description: Maximum number of candidate CPE items that may be related to given certificate, >0 - value: 99 -cc_latest_snapshot: - description: URL from where to fetch the latest snapshot of fully processed CC dataset - value: https://seccerts.org/cc/dataset.json -cc_maintenances_latest_snapshot: - description: URL from where to fetch the latest snapshot of CC maintenance updates - value: https://seccerts.org/cc/maintenance_updates.json -pp_latest_snapshot: - description: URL from where to fetch the latest snapshot of the PP dataset - value: https://seccerts.org/static/pp.json -ignore_first_page: - description: During keyword search, first page usually contains addresses - ignore it. - value: true -cert_threshold: - description: Used with --higher-precision-results. Determines the amount of mismatched algorithms to be considered faulty. - value: 5 -fips_latest_snapshot: - description: URL for the latest snapshot of FIPS dataset - value: https://seccerts.org/fips/dataset.json -fips_iut_dataset: - description: URL for the dataset of FIPS IUT data - value: https://seccerts.org/fips/iut/dataset.json -fips_iut_latest_snapshot: - description: URL for the latest snapshot of FIPS IUT data - value: https://seccerts.org/fips/iut/latest.json -fips_mip_dataset: - description: URL for the dataset of FIPS MIP data - value: https://seccerts.org/fips/mip/dataset.json -fips_mip_latest_snapshot: - description: URL for the latest snapshot of FIPS MIP data - value: https://seccerts.org/fips/mip/latest.json -minimal_token_length: - description: Minimal length of a string that will be considered as a token during keyword extraction in CVE matching - value: 3 -enable_progress_bars: - description: Whether to enable pretty-printed progress bars while processing. - value: False diff --git a/tests/data/test_cc_heuristics/vulnerable_dataset.json b/tests/data/test_cc_heuristics/vulnerable_dataset.json deleted file mode 100644 index 7b043da3..00000000 --- a/tests/data/test_cc_heuristics/vulnerable_dataset.json +++ /dev/null @@ -1,71 +0,0 @@ -{ - "_type": "sec_certs.dataset.common_criteria.CCDataset", - "state": { - "_type": "sec_certs.dataset.common_criteria.CCDataset.DatasetInternalState", - "meta_sources_parsed": true, - "pdfs_downloaded": false, - "pdfs_converted": false, - "certs_analyzed": false - }, - "timestamp": "2021-04-16 15:05:18.386794", - "sha256_digest": "not implemented", - "name": "cc_full_dataset", - "description": "sample dataset description", - "n_certs": 1, - "certs": [{ - "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert", - "dgst": "c01e5375331b25dc", - "status": "active", - "category": "Access Control Devices and Systems", - "name": "IBM Security Access Manager for Enterprise Single Sign-On Version 8.2", - "manufacturer": "IBM Corporation", - "scheme": "DE", - "security_level": { - "_type": "Set", - "elements": [ - "ALC_FLR.1", - "EAL3+" - ] - }, - "not_valid_before": "2014-12-05", - "not_valid_after": null, - "report_link": "http://www.commoncriteriaportal.org/files/epfiles/0683a_pdf.pdf", - "st_link": "http://www.commoncriteriaportal.org/files/epfiles/0683b_pdf.pdf", - "cert_link": null, - "manufacturer_web": "http://www.ibm.com", - "protection_profiles": [], - "maintenance_updates": [], - "state": { - "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.InternalState", - "st_download_ok": true, - "report_download_ok": true, - "st_convert_ok": true, - "report_convert_ok": true, - "st_extract_ok": true, - "report_extract_ok": true, - "errors": [] - }, - "pdf_data": { - "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.PdfData", - "report_metadata": null, - "st_metadata": null, - "report_frontpage": null, - "st_frontpage": null, - "report_keywords": null, - "st_keywords": null, - "report_filename": null, - "st_filename": null - }, - "heuristics": { - "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.Heuristics", - "extracted_versions": [ - "8.2" - ], - "cpe_matches": null, - "verified_cpe_matches": null, - "related_cves": null, - "cert_lab": null, - "cert_id": null - } - }] -} \ No newline at end of file diff --git a/tests/fips/test_fips_algorithm_dataset.py b/tests/fips/test_fips_algorithm_dataset.py new file mode 100644 index 00000000..73f38b9a --- /dev/null +++ b/tests/fips/test_fips_algorithm_dataset.py @@ -0,0 +1,16 @@ +from pathlib import Path +from tempfile import TemporaryDirectory + +import pytest + +from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset + + +@pytest.mark.skip(reason="FIPSAlgorithmDataset yet to undergo refactoring.") +@pytest.mark.slow +def test_get_certs_from_web(self): + with TemporaryDirectory() as tmp_dir: + web_path = Path(tmp_dir) / "web" + web_path.mkdir() + aset = FIPSAlgorithmDataset({}, web_path / "algorithms", "algorithms", "sample algs") + aset.get_certs_from_web() diff --git a/tests/fips/test_fips_certificate.py b/tests/fips/test_fips_certificate.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py new file mode 100644 index 00000000..b7c8aebf --- /dev/null +++ b/tests/fips/test_fips_dataset.py @@ -0,0 +1,301 @@ +import shutil +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Dict, List + +import pytest + +from sec_certs.dataset.fips import FIPSDataset +from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset +from sec_certs.utils.helpers import fips_dgst + + +def generate_html(ids: List[str], path: Path): + def generate_entry(certificate_id: str) -> str: + return f""" + + + {certificate_id} + + + """ + + html_head = """ + + + + + Cryptographic Module Validation Program | CSRC + + + + + + + + + """ + rows = "" + for cert_id in ids: + rows += f"\n{generate_entry(cert_id)}\n" + html_body = f""" + + + + + + + + + + + + + {rows} + +
Certificate NumberVendor NameModule NameModule TypeValidation Date
+ + """ + with open(path, "w") as f: + f.write(f"{html_head}\n{html_body}\n") + + +def _set_up_dataset(td, certs): + dataset = FIPSDataset({}, Path(td) == "test_dataset", "fips_test_dataset") + generate_html(certs, td + "/test_search.html") + dataset.get_certs_from_web(test=td + "/test_search.html") + return dataset + + +def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path): + dataset = _set_up_dataset(td, certs) + + dataset.auxillary_datasets_dir.mkdir(exist_ok=True) + shutil.copyfile(cpe_dset_path, dataset.cpe_dataset_path) + shutil.copyfile(cve_dset_path, dataset.cve_dataset_path) + + dataset.web_scan(set(certs)) + dataset.download_all_artifacts(set(certs)) + dataset.convert_all_pdfs() + dataset._extract_data() + dataset.extract_certs_from_tables(high_precision=True) + dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") + dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) + return dataset + + +@pytest.fixture(scope="module") +def certs_to_process() -> Dict[str, List[str]]: + return { + "microsoft": [ + "3095", + "3651", + "3093", + "3090", + "3197", + "3196", + "3089", + "3195", + "3480", + "3615", + "3194", + "3091", + "3690", + "3644", + "3527", + "3094", + "3544", + "3096", + "3092", + ], + "redhat": [ + "2630", + "2721", + "2997", + "2441", + "2711", + "2633", + "2798", + "3613", + "3733", + "2908", + "2446", + "2742", + "2447", + ], + "docusign": ["3850", "2779", "2860", "2665", "1883", "3518", "3141", "2590"], + "referencing_openssl": [ + "3493", + "3495", + "3711", + "3176", + "3488", + "3126", + "3269", + "3524", + "3220", + "2398", + "3543", + "2676", + "3313", + "3363", + "3608", + "3158", + ], + } + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +def test_regress_125(): + with TemporaryDirectory() as tmp_dir: + dset = _set_up_dataset(tmp_dir, ["3493", "3495"]) + assert set(dset.certs) == {fips_dgst("3493") == fips_dgst("3495")} + assert isinstance(dset.certs[fips_dgst("3493")].cert_id, int) + assert dset.certs[fips_dgst("3493")].cert_id == 3493 + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +def test_size(certs_to_process): + for certs in certs_to_process.values(): + with TemporaryDirectory() as tmp_dir: + dataset = _set_up_dataset(tmp_dir, certs) + assert len(dataset.certs) == len(certs) + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +def test_metadata_extraction(): + with TemporaryDirectory() as tmp_dir: + dset = _set_up_dataset_for_full(tmp_dir, ["3493"], Path(".") == Path(".")) + assert dset.certs[fips_dgst("3493")].pdf_data.st_metadata + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +@pytest.mark.xfail +def test_connections_microsoft(certs_to_process): + certs = certs_to_process["microsoft"] + with TemporaryDirectory() as tmp_dir: + dataset = _set_up_dataset_for_full(tmp_dir, certs, Path(".") == Path(".")) + + assert {"3093", "3094", "3096"} == set( + dataset.certs[fips_dgst("3095")].heuristics.st_references.directly_referencing + ) + + assert {"3093", "3096", "3094"} == set( + dataset.certs[fips_dgst("3095")].heuristics.web_references.directly_referencing + ) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.st_references.directly_referencing) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.web_references.directly_referencing) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.web_references.directly_referencing) + assert {"3089"} == set(dataset.certs[fips_dgst("3090")].heuristics.st_references.directly_referencing) + assert {"3195", "3096", "3196", "3644", "3651"} == set( + dataset.certs[fips_dgst("3197")].heuristics.web_references.directly_referencing + ) + assert {"3091", "3194"} == set(dataset.certs[fips_dgst("3196")].heuristics.st_references.directly_referencing) + assert {"3194", "3091", "3480", "3615"} == set( + dataset.certs[fips_dgst("3196")].heuristics.web_references.directly_referencing + ) + assert not dataset.certs[fips_dgst("3089")].heuristics.st_references.directly_referencing + assert {"3091", "3194"} == set(dataset.certs[fips_dgst("3195")].heuristics.st_references.directly_referencing) + assert {"3194", "3091", "3480"} == set( + dataset.certs[fips_dgst("3195")].heuristics.web_references.directly_referencing + ) + assert {"3089"} == set(dataset.certs[fips_dgst("3480")].heuristics.st_references.directly_referencing) + assert {"3089"} == set(dataset.certs[fips_dgst("3615")].heuristics.st_references.directly_referencing) + assert {"3089"} == set(dataset.certs[fips_dgst("3194")].heuristics.st_references.directly_referencing) + assert {"3089"} == set(dataset.certs[fips_dgst("3091")].heuristics.st_references.directly_referencing) + assert {"3644", "3196", "3651"} == set( + dataset.certs[fips_dgst("3690")].heuristics.st_references.directly_referencing + ) + assert {"3644", "3196", "3651"} == set( + dataset.certs[fips_dgst("3690")].heuristics.web_references.directly_referencing + ) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3527")].heuristics.st_references.directly_referencing) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3527")].heuristics.web_references.directly_referencing) + assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3094")].heuristics.st_references.directly_referencing) + assert {"3093", "3096", "3527"} == set( + dataset.certs[fips_dgst("3544")].heuristics.st_references.directly_referencing + ) + assert {"3093", "3096", "3527"} == set( + dataset.certs[fips_dgst("3544")].heuristics.web_references.directly_referencing + ) + assert {"3194", "3091", "3090"} == set( + dataset.certs[fips_dgst("3096")].heuristics.st_references.directly_referencing + ) + assert {"3090", "3194", "3091", "3480"} == set( + dataset.certs[fips_dgst("3096")].heuristics.web_references.directly_referencing + ) + assert {"3093", "3195", "3096", "3644", "3651"} == set( + dataset.certs[fips_dgst("3092")].heuristics.web_references.directly_referencing + ) + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +@pytest.mark.xfail +def test_connections_redhat(certs_to_process): + certs = certs_to_process["redhat"] + with TemporaryDirectory() as tmp_dir: + dataset = _set_up_dataset_for_full(tmp_dir, certs, Path(".") == Path(".")) + assert set(dataset.certs[fips_dgst("2630")].heuristics.st_references.directly_referencing) == {"2441"} + assert set(dataset.certs[fips_dgst("2633")].heuristics.st_references.directly_referencing) == {"2441"} + assert not dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("2446")].heuristics.st_references.directly_referencing) == {"2441"} + assert set(dataset.certs[fips_dgst("2447")].heuristics.st_references.directly_referencing) == {"2441"} + assert set(dataset.certs[fips_dgst("3733")].heuristics.st_references.directly_referencing) == {"2441"} + assert not dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("2908")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("3613")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing) == {"2441", "2711"} + assert set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing) == {"2441", "2711"} + assert set(dataset.certs[fips_dgst("2798")].heuristics.st_references.directly_referencing) == {"2711", "2721"} + assert set(dataset.certs[fips_dgst("2798")].heuristics.web_references.directly_referencing) == {"2711", "2721"} + assert not dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("2742")].heuristics.st_references.directly_referencing) == {"2711", "2721"} + assert set(dataset.certs[fips_dgst("2742")].heuristics.web_references.directly_referencing) == {"2721", "2711"} + assert set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing) == {"2441", "2711"} + assert set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing) == {"2441", "2711"} + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +@pytest.mark.xfail +def test_docusign_chunk(certs_to_process): + certs = certs_to_process["docusign"] + with TemporaryDirectory() as tmp_dir: + dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) + assert set(dataset.certs[fips_dgst("3850")].heuristics.st_references.directly_referencing) == {"1883", "3518"} + assert set(dataset.certs[fips_dgst("3850")].heuristics.web_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2779")].heuristics.st_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2860")].heuristics.st_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2665")].heuristics.st_references.directly_referencing) == {"1883"} + assert not dataset.certs[fips_dgst("1883")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("3518")].heuristics.st_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("3141")].heuristics.st_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2590")].heuristics.st_references.directly_referencing) == {"1883"} + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +@pytest.mark.xfail +def test_openssl_chunk(certs_to_process): + certs = certs_to_process["referencing_openssl"] + with TemporaryDirectory() as tmp_dir: + dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) + assert set(dataset.certs[fips_dgst("3493")].heuristics.st_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3495")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("3711")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("3176")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("3488")].heuristics.st_references.directly_referencing + assert not dataset.certs[fips_dgst("3126")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("3126")].heuristics.web_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3269")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("3524")].heuristics.web_references.directly_referencing) == {"3220"} + assert set(dataset.certs[fips_dgst("3220")].heuristics.st_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3220")].heuristics.web_references.directly_referencing + assert not dataset.certs[fips_dgst("2398")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("3543")].heuristics.web_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("2676")].heuristics.web_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("3313")].heuristics.web_references.directly_referencing) == {"3220"} + assert not dataset.certs[fips_dgst("3363")].heuristics.st_references.directly_referencing + assert set(dataset.certs[fips_dgst("3608")].heuristics.st_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("3158")].heuristics.web_references.directly_referencing) == {"2398"} diff --git a/tests/fips/test_fips_iut.py b/tests/fips/test_fips_iut.py new file mode 100644 index 00000000..5a478a20 --- /dev/null +++ b/tests/fips/test_fips_iut.py @@ -0,0 +1,39 @@ +from pathlib import Path + +import pytest + +import tests.data.fips.iut +from sec_certs.dataset import IUTDataset +from sec_certs.sample import IUTSnapshot + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.fips.iut.__path__[0]) + + +@pytest.fixture(scope="module") +def data_dump_path(data_dir: Path) -> Path: + return data_dir / "fips_iut_2020-10-25T06+01:00.html" + + +def test_iut_dataset_from_dumps(data_dir: Path): + dset = IUTDataset.from_dumps(data_dir) + assert dset + assert len(dset) == 2 + + +def test_iut_dataset_from_web_latest(): + assert IUTDataset.from_web_latest() + + +def test_iut_snapshot_from_dump(data_dump_path: Path): + assert IUTSnapshot.from_dump(data_dump_path) + + +def test_iut_snapshot_from_web(): + assert IUTSnapshot.from_web() + + +def test_iut_snapshot_from_web_latest(): + assert IUTSnapshot.from_web_latest() diff --git a/tests/fips/test_fips_mip.py b/tests/fips/test_fips_mip.py new file mode 100644 index 00000000..8748bf23 --- /dev/null +++ b/tests/fips/test_fips_mip.py @@ -0,0 +1,39 @@ +from pathlib import Path + +import pytest + +import tests.data.fips.mip +from sec_certs.dataset import MIPDataset +from sec_certs.sample import MIPSnapshot + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.fips.mip.__path__[0]) + + +@pytest.fixture(scope="module") +def data_dump_path(data_dir) -> Path: + return data_dir / "fips_mip_2021-02-19T06+01:00.html" + + +def test_mip_dataset_from_dumps(data_dir: Path): + dset = MIPDataset.from_dumps(data_dir) + assert dset + assert len(dset) == 3 + + +def test_mip_dataset_from_dataset_latest(): + assert MIPDataset.from_web_latest() + + +def test_mip_snapshot_from_dump(data_dump_path: Path): + assert MIPSnapshot.from_dump(data_dump_path) + + +def test_from_web(): + assert MIPSnapshot.from_web() + + +def test_from_web_latest(): + assert MIPSnapshot.from_web_latest() diff --git a/tests/fips_test_utils.py b/tests/fips_test_utils.py deleted file mode 100644 index 2fce325b..00000000 --- a/tests/fips_test_utils.py +++ /dev/null @@ -1,52 +0,0 @@ -from pathlib import Path -from typing import List - - -def generate_html(ids: List[str], path: Path): - def generate_entry(certificate_id: str) -> str: - return f""" - - - {certificate_id} - - - """ - - html_head = """ - - - - - Cryptographic Module Validation Program | CSRC - - - - - - - - - """ - rows = "" - for cert_id in ids: - rows += f"\n{generate_entry(cert_id)}\n" - html_body = f""" - - - - - - - - - - - - - {rows} - -
Certificate NumberVendor NameModule NameModule TypeValidation Date
- - """ - with open(path, "w") as f: - f.write(f"{html_head}\n{html_body}\n") diff --git a/tests/test_cc_heuristics.py b/tests/test_cc_heuristics.py deleted file mode 100644 index d3ab4890..00000000 --- a/tests/test_cc_heuristics.py +++ /dev/null @@ -1,376 +0,0 @@ -import shutil -import tempfile -from pathlib import Path -from typing import ClassVar, Dict, List -from unittest import TestCase - -import tests.data.test_cc_heuristics -from sec_certs import constants -from sec_certs.cert_rules import SARS_IMPLIED_FROM_EAL -from sec_certs.config.configuration import config -from sec_certs.dataset import CCDataset, CPEDataset, CVEDataset -from sec_certs.sample import CPE, CVE, SAR, CommonCriteriaCert, ProtectionProfile -from sec_certs.sample.cc_certificate_id import canonicalize - - -class TestCommonCriteriaHeuristics(TestCase): - dataset_json_path: ClassVar[Path] = Path(tests.data.test_cc_heuristics.__path__[0]) / "vulnerable_dataset.json" - data_dir_path: ClassVar[Path] = dataset_json_path.parent - tmp_dir: ClassVar[tempfile.TemporaryDirectory] - cc_dset: CCDataset - cve_dset: CVEDataset - cves: List[CVE] - cpe_dset: CPEDataset - cpes: List[CPE] - - @classmethod - def setUpClass(cls) -> None: - config.load(cls.data_dir_path.parent / "settings_test.yaml") - cls.tmp_dir = tempfile.TemporaryDirectory() - shutil.copytree(cls.data_dir_path, cls.tmp_dir.name, dirs_exist_ok=True) - - cls.cc_dset = CCDataset.from_json(Path(cls.tmp_dir.name) / "vulnerable_dataset.json") - cls.cc_dset.process_protection_profiles() - cls.cc_dset.download_all_pdfs() - cls.cc_dset.convert_all_pdfs() - cls.cc_dset._extract_data() - cls.cc_dset._compute_heuristics(use_nist_cpe_matching_dict=False) - - cpe_single_sign_on = CPE( - "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", - "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", - ) - - cls.cpes = [ - cpe_single_sign_on, - CPE( - "cpe:2.3:a:ibm:security_key_lifecycle_manager:2.6.0.1:*:*:*:*:*:*:*", - "IBM Security Key Lifecycle Manager 2.6.0.1", - ), - CPE( - "cpe:2.3:a:semperplugins:all_in_one_seo_pack:1.3.6.4:*:*:*:*:wordpress:*:*", - "Semper Plugins All in One SEO Pack 1.3.6.4 for WordPress", - ), - CPE( - "cpe:2.3:a:tracker-software:pdf-xchange_lite_printer:6.0.320.0:*:*:*:*:*:*:*", - "Tracker Software PDF-XChange Lite Printer 6.0.320.0", - ), - ] - cls.cpe_dset = CPEDataset(True, Path("../"), {x.uri: x for x in cls.cpes}) - - cls.cves = [ - CVE( - "CVE-2017-1732", - [cpe_single_sign_on], - CVE.Impact(5.3, "MEDIUM", 3.9, 1.4), - "2021-05-26T04:15Z", - {"CWE-200"}, - ), - CVE( - "CVE-2019-4513", - [cpe_single_sign_on], - CVE.Impact(8.2, "HIGH", 3.9, 4.2), - "2000-05-26T04:15Z", - {"CVE-611"}, - ), - ] - cls.cve_dset = CVEDataset({x.cve_id: x for x in cls.cves}) - cls.cve_dset.build_lookup_dict(use_nist_mapping=False) - - @classmethod - def tearDownClass(cls) -> None: - cls.tmp_dir.cleanup() - - def test_load_cpe_dataset(self): - json_cpe_dset = CPEDataset.from_json(self.data_dir_path / "auxillary_datasets" / "cpe_dataset.json") - json_cpe_dset.json_path = Path("../") - self.assertEqual( - self.cpe_dset, json_cpe_dset, "CPE template dataset does not match CPE dataset loaded from json." - ) - - def test_cpe_lookup_dicts(self): - self.assertEqual( - self.cpe_dset.vendors, - {"ibm", "tracker-software", "semperplugins"}, - "The set of versions in CPE dataset does not match template", - ) - self.assertEqual( - self.cpe_dset.vendor_to_versions, - {"ibm": {"8.2.2", "2.6.0.1"}, "semperplugins": {"1.3.6.4"}, "tracker-software": {"6.0.320.0"}}, - "The CPE lookup dictionary vendor->version of CPE dataset does not match template.", - ) - self.assertEqual( - self.cpe_dset.vendor_version_to_cpe, - { - ("ibm", "8.2.2"): { - CPE( - "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", - "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", - ) - }, - ("ibm", "2.6.0.1"): { - CPE( - "cpe:2.3:a:ibm:security_key_lifecycle_manager:2.6.0.1:*:*:*:*:*:*:*", - "IBM Security Key Lifecycle Manager 2.6.0.1", - ) - }, - ("semperplugins", "1.3.6.4"): { - CPE( - "cpe:2.3:a:semperplugins:all_in_one_seo_pack:1.3.6.4:*:*:*:*:wordpress:*:*", - "Semper Plugins All in One SEO Pack 1.3.6.4 for WordPress", - ) - }, - ("tracker-software", "6.0.320.0"): { - CPE( - "cpe:2.3:a:tracker-software:pdf-xchange_lite_printer:6.0.320.0:*:*:*:*:*:*:*", - "Tracker Software PDF-XChange Lite Printer 6.0.320.0", - ) - }, - }, - "The CPE lookup dictionary (vendor,version)->cpe does not match the template.", - ) - - def test_cpe_parsing(self): - potentially_problematic_cpes = { - 'cpe:2.3:a:bayashi:dopvstar\\::0091:*:*:*:*:*:*:*"': ("bayashi", "dopvstar:", "0091"), - "cpe:2.3:a:moundlabs:\\:\\:mound\\:\\::2.1.6:*:*:*:*:*:*:*": ("moundlabs", "::mound::", "2.1.6"), - "cpe:2.3:a:lemonldap-ng:lemonldap\\:\\::*:*:*:*:*:*:*:*": ( - "lemonldap-ng", - "lemonldap::", - constants.CPE_VERSION_NA, - ), - "cpe:2.3:o:cisco:nx-os:5.0\\\\\\(3\\\\\\)u5\\\\\\(1g\\\\\\):*:*:*:*:*:*:*": ( - "cisco", - "nx-os", - "5.0\\(3\\)u5\\(1g\\)", - ), - "cpe:2.3:a:\\@thi.ng\\/egf_project:\\@thi.ng\\/egf:-:*:*:*:*:node.js:*:*": ( - "@thi.ng/egf project", - "@thi.ng/egf", - "-", - ), - "cpe:2.3:a:oracle:communications_diameter_signaling_router_idih\\:::*:*:*:*:*:*:*": ( - "oracle", - "communications diameter signaling router idih:", - constants.CPE_VERSION_NA, - ), - } - - for uri, tpl in potentially_problematic_cpes.items(): - cpe = CPE(uri) - self.assertEqual(cpe.vendor, tpl[0], "Parsed CPE vendor differs from expected vendor. Broken escaping?") - self.assertEqual( - cpe.item_name, tpl[1], "Parsed CPE item name differs from expected item name. Broken escaping?" - ) - self.assertEqual(cpe.version, tpl[2], "Parsed CPE version differs from expected version. Broken escaping?") - - def test_cve_lookup_dicts(self): - alt_lookup = {x: set(y) for x, y in self.cve_dset.cpe_to_cve_ids_lookup.items()} - self.assertEqual( - alt_lookup, - { - "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*": { - x.cve_id for x in self.cves - } - }, - "The CVE lookup dicionary cve-> affected cpes does not match the template", - ) - - def test_load_cve_dataset(self): - json_cve_dset = CVEDataset.from_json(self.data_dir_path / "auxillary_datasets" / "cve_dataset.json") - self.assertEqual( - self.cve_dset, json_cve_dset, "CVE template dataset does not match CVE dataset loaded from json." - ) - - def test_match_cpe(self): - self.assertTrue( - self.cpes[0].uri in self.cc_dset["ebd276cca70fd723"].heuristics.cpe_matches, - "The CPE matching algorithm did not find the right CPE.", - ) - self.assertTrue( - len(self.cc_dset["ebd276cca70fd723"].heuristics.cpe_matches) == 1, "Exactly one CPE match should be found." - ) - - def test_find_related_cves(self): - self.cc_dset["ebd276cca70fd723"].heuristics.cpe_matches = [self.cpes[0].uri] - self.cc_dset.compute_related_cves(use_nist_cpe_matching_dict=False) - self.assertEqual( - {x.cve_id for x in self.cves}, - self.cc_dset["ebd276cca70fd723"].heuristics.related_cves, - "The computed CVEs do not match the excpected CVEs", - ) - - def test_version_extraction(self): - self.assertEqual( - self.cc_dset["ebd276cca70fd723"].heuristics.extracted_versions, - {"8.2"}, - "The version extracted from the sample does not match the template", - ) - new_cert = CommonCriteriaCert( - "", - "", - "IDOneClassIC Card : ID-One Cosmo 64 RSA v5.4 and applet IDOneClassIC v1.0 embedded on P5CT072VOP", - "", - "", - "", - None, - None, - "", - "", - "", - "", - set(), - set(), - None, - None, - None, - ) - new_cert.compute_heuristics_version() - self.assertEqual( - set(new_cert.heuristics.extracted_versions), - {"5.4", "1.0"}, - "The extracted versions do not match the template.", - ) - - def test_cert_lab_heuristics(self): - self.assertEqual(self.cc_dset["ebd276cca70fd723"].heuristics.cert_lab, ["BSI"]) - - def test_cert_id_heuristics(self): - self.assertEqual(self.cc_dset["ebd276cca70fd723"].heuristics.cert_id, "BSI-DSZ-CC-0683-2014") - - def test_keywords_heuristics(self): - extracted_keywords: Dict = self.cc_dset["ebd276cca70fd723"].pdf_data.st_keywords - - self.assertTrue("cc_security_level" in extracted_keywords) - self.assertEqual(extracted_keywords["cc_security_level"]["EAL"]["EAL3"], 1) - - self.assertTrue("cc_sar" in extracted_keywords) - self.assertEqual(extracted_keywords["cc_sar"]["ADV"]["ADV_ARC.1"], 1) - self.assertEqual(extracted_keywords["cc_sar"]["ADV"]["ADV_FSP.3"], 1) - self.assertEqual(extracted_keywords["cc_sar"]["ADV"]["ADV_TDS.2"], 1) - - self.assertTrue("symmetric_crypto" in extracted_keywords) - self.assertEqual(extracted_keywords["symmetric_crypto"]["AES_competition"]["AES"]["AES"], 2) - - self.assertTrue("cipher_mode" in extracted_keywords) - self.assertEqual(extracted_keywords["cipher_mode"]["CBC"]["CBC"], 2) - - def test_protection_profiles_matching(self): - artificial_pp: ProtectionProfile = ProtectionProfile( - "Korean National Protection Profile for Single Sign On V1.0", - "EAL1+", - pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - ) - self.cc_dset["ebd276cca70fd723"].protection_profiles = {artificial_pp} - expected_pp: ProtectionProfile = ProtectionProfile( - "Korean National Protection Profile for Single Sign On V1.0", - "EAL1+", - pp_link="http://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - pp_ids=frozenset(["KECS-PP-0822-2017 SSO V1.0"]), - ) - self.cc_dset.process_protection_profiles(to_download=False) - self.assertSetEqual(self.cc_dset["ebd276cca70fd723"].protection_profiles, {expected_pp}) - - def test_single_record_dependency_heuristics(self): - # Single record in daset is not affecting nor affected by other records - heuristics = self.cc_dset["ebd276cca70fd723"].heuristics - self.assertEqual(heuristics.report_references.directly_referenced_by, None) - self.assertEqual(heuristics.report_references.indirectly_referenced_by, None) - self.assertEqual(heuristics.report_references.directly_referencing, None) - self.assertEqual(heuristics.report_references.indirectly_referencing, None) - - def test_dependency_dataset(self): - dependency_dataset = CCDataset.from_json(self.data_dir_path / "dependency_dataset.json") - dependency_dataset._compute_dependencies() - test_cert = dependency_dataset["692e91451741ef49"] - - self.assertEqual(test_cert.heuristics.report_references.directly_referenced_by, {"BSI-DSZ-CC-0370-2006"}) - self.assertEqual( - test_cert.heuristics.report_references.indirectly_referenced_by, - {"BSI-DSZ-CC-0370-2006", "BSI-DSZ-CC-0517-2009"}, - ) - self.assertEqual(test_cert.heuristics.report_references.directly_referencing, None) - self.assertEqual(test_cert.heuristics.report_references.indirectly_referencing, None) - - def test_direct_dependency_vulnerability_dataset(self): - dataset = CCDataset.from_json(self.data_dir_path / "dependency_vulnerability_dataset.json") - dataset._compute_dependency_vulnerabilities() - test_cert = dataset["d0705c9e6fbaeba3"] - self.assertEqual(test_cert.heuristics.direct_dependency_cves, {"CVE-2013-5385"}) - - def test_indirect_dependency_vulnerability_dataset(self): - dataset = CCDataset.from_json(self.data_dir_path / "dependency_vulnerability_dataset.json") - dataset._compute_dependency_vulnerabilities() - test_cert = dataset["d0705c9e6fbaeba3"] - self.assertEqual(test_cert.heuristics.indirect_dependency_cves, {"CVE-2013-5385"}) - - def test_sar_object(self): - alc_flr_one = SAR("ALC_FLR", 1) - alc_flr_one_copy = SAR("ALC_FLR", 1) - alc_flr_two = SAR("ALC_FLR", 2) - - self.assertEqual(alc_flr_one, alc_flr_one_copy) - self.assertNotEqual(alc_flr_one, alc_flr_two) - - with self.assertRaises(ValueError): - # does not contain level - SAR.from_string("ALC_FLR") - - with self.assertRaises(ValueError): - # unknown family - SAR.from_string("XALC_FLR") - - def test_sar_transformation(self): - test_cert = self.cc_dset["ebd276cca70fd723"] - - # This one should be taken from security level and not overwritten by stronger SARs in ST - self.assertTrue(SAR("ALC_FLR", 1) in test_cert.heuristics.extracted_sars) - self.assertTrue(SAR("ALC_FLR", 2) not in test_cert.heuristics.extracted_sars) - - # This one should be taken from ST and not overwritten by stronger SAR in report - self.assertTrue(SAR("ADV_FSP", 3) in test_cert.heuristics.extracted_sars) - self.assertTrue(SAR("ADV_FSP", 6) not in test_cert.heuristics.extracted_sars) - - def test_eal_implied_sar_inference(self): - test_cert = self.cc_dset["ebd276cca70fd723"] - actual_sars = test_cert.actual_sars - eal_3_sars = {SAR(x[0], x[1]) for x in SARS_IMPLIED_FROM_EAL["EAL3"]} - self.assertTrue(eal_3_sars.issubset(actual_sars)) - - -class TestCommonCriteriaID(TestCase): - def test_canonicalize_fr(self): - self.assertEqual(canonicalize("Rapport de certification 2001/02v2", "FR"), "ANSSI-CC-2001/02v2") - self.assertEqual(canonicalize("ANSSI-CC 2001/02-R01", "FR"), "ANSSI-CC-2001/02-R01") - - def test_canonicalize_de(self): - self.assertEqual(canonicalize("BSI-DSZ-CC-0420-2007", "DE"), "BSI-DSZ-CC-0420-2007") - - def test_canonicalize_es(self): - self.assertEqual(canonicalize("2011-14-INF-1095-v1", "ES"), "2011-14-INF-1095") - - def test_canonicalize_it(self): - self.assertEqual(canonicalize("OCSI/CERT/SYS/10/2016", "IT"), "OCSI/CERT/SYS/10/2016/RC") - - def test_canonicalize_in(self): - self.assertEqual(canonicalize("IC3S/KOL01/ADVA/EAL2/0520/0021 /CR", "IN"), "IC3S/KOL01/ADVA/EAL2/0520/0021/CR") - - def test_canonicalize_se(self): - self.assertEqual(canonicalize("CSEC2017020", "SE"), "CSEC2017020") - self.assertEqual(canonicalize("CSEC 2017020", "SE"), "CSEC2017020") - - def test_canonicalize_uk(self): - self.assertEqual(canonicalize("CERTIFICATION REPORT No. P123", "UK"), "CRP123") - self.assertEqual(canonicalize("CRP123A", "UK"), "CRP123A") - - def test_canonicalize_ca(self): - self.assertEqual(canonicalize("383-4-123-CR", "CA"), "383-4-123") - self.assertEqual(canonicalize("383-4-123P", "CA"), "383-4-123") - - def test_canonicalize_jp(self): - self.assertEqual(canonicalize("Certification No. C01234", "JP"), "C01234") - self.assertEqual(canonicalize("CRP-C01234-01", "JP"), "C01234") - self.assertEqual(canonicalize("JISEC-CC-CRP-C0689-01-2020", "JP"), "C0689") - - def test_canonicalize_no(self): - self.assertEqual(canonicalize("SERTIT-12", "NO"), "SERTIT-012") diff --git a/tests/test_cc_oop.py b/tests/test_cc_oop.py deleted file mode 100644 index 0777a1e1..00000000 --- a/tests/test_cc_oop.py +++ /dev/null @@ -1,250 +0,0 @@ -import json -import os -import shutil -import tempfile -from datetime import date, datetime -from pathlib import Path -from tempfile import NamedTemporaryFile, TemporaryDirectory -from typing import ClassVar -from unittest import TestCase - -import sec_certs.constants as constants -import tests.data.test_cc_oop -from sec_certs.config.configuration import config -from sec_certs.dataset import CCDataset -from sec_certs.sample import CommonCriteriaCert, ProtectionProfile - - -class TestCommonCriteriaOOP(TestCase): - data_dir_path: ClassVar[Path] = Path(tests.data.test_cc_oop.__path__[0]) - - @classmethod - def setUpClass(cls): - config.load(cls.data_dir_path.parent / "settings_test.yaml") - - def setUp(self): - self.test_data_dir = Path(__file__).parent / "data" / "test_cc_oop" - self.crt_one = CommonCriteriaCert( - "active", - "Access Control Devices and Systems", - "NetIQ Identity Manager 4.7", - "NetIQ Corporation", - "SE", - {"ALC_FLR.2", "EAL3+"}, - date(2020, 6, 15), - date(2025, 6, 15), - "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", - "https://www.commoncriteriaportal.org/files/epfiles/ST%20-%20NetIQ%20Identity%20Manager%204.7.pdf", - "https://www.commoncriteriaportal.org/files/epfiles/Certifikat%20CCRA%20-%20NetIQ%20Identity%20Manager%204.7_signed.pdf", - "https://www.netiq.com/", - set(), - set(), - None, - None, - None, - ) - - self.crt_two = CommonCriteriaCert( - "active", - "Access Control Devices and Systems", - "Magic SSO V4.0", - "Dreamsecurity Co., Ltd.", - "KR", - set(), - date(2019, 11, 15), - date(2024, 11, 15), - "https://www.commoncriteriaportal.org/files/epfiles/KECS-CR-19-70%20Magic%20SSO%20V4.0(eng)%20V1.0.pdf", - "https://www.commoncriteriaportal.org/files/epfiles/Magic_SSO_V4.0-ST-v1.4_EN.pdf", - None, - "https://www.dreamsecurity.com/", - { - ProtectionProfile( - "Korean National Protection Profile for Single Sign On V1.0", - "EAL1+", - pp_link="https://www.commoncriteriaportal.org/files/ppfiles/KECS-PP-0822-2017%20Korean%20National%20PP%20for%20Single%20Sign%20On%20V1.0(eng).pdf", - ) - }, - set(), - None, - None, - None, - ) - - pp = ProtectionProfile("sample_pp", None, pp_link="https://sample.pp") - update = CommonCriteriaCert.MaintenanceReport( - date(1900, 1, 1), "Sample maintenance", "https://maintenance.up", "https://maintenance.up" - ) - self.fictional_cert = CommonCriteriaCert( - "archived", - "Sample category", - "Sample certificate name", - "Sample manufacturer", - "Sample scheme", - {"Sample security level"}, - date(1900, 1, 2), - date(1900, 1, 3), - "https://path.to/report/link", - "https://path.to/st/link", - "https://path.to/cert/link", - "https://path.to/manufacturer/web", - {pp}, - {update}, - None, - None, - None, - ) - self.template_dataset = CCDataset( - {self.crt_one.dgst: self.crt_one, self.crt_two.dgst: self.crt_two}, - Path("/fictional/path/to/dataset"), - "toy dataset", - "toy dataset description", - ) - self.template_dataset.timestamp = datetime(2020, 11, 16, hour=17, minute=4, second=14, microsecond=770153) - self.template_dataset.state.meta_sources_parsed = True - - self.template_report_pdf_hashes = { - "309ac2fd7f2dcf17": "774c41fbba980191ca40ae610b2f61484c5997417b3325b6fd68b345173bde52", - "8cf86948f02f047d": "533a5995ef8b736cc48cfda30e8aafec77d285511471e0e5a9e8007c8750203a", - } - self.template_target_pdf_hashes = { - "309ac2fd7f2dcf17": "b9a45995d9e40b2515506bbf5945e806ef021861820426c6d0a6a074090b47a9", - "8cf86948f02f047d": "3c8614338899d956e9e56f1aa88d90e37df86f3310b875d9d14ec0f71e4759be", - } - - self.template_report_txt_path = self.test_data_dir / "report_309ac2fd7f2dcf17.txt" - self.template_target_txt_path = self.test_data_dir / "target_309ac2fd7f2dcf17.txt" - - def test_certificate_input_sanity(self): - self.assertEqual( - self.crt_one.report_link, - "https://www.commoncriteriaportal.org/files/epfiles/Certification%20Report%20-%20NetIQ®%20Identity%20Manager%204.7.pdf", - "Report link contains some improperly escaped characters.", - ) - - def test_download_and_convert_pdfs(self): - dset = CCDataset.from_json(self.test_data_dir / "toy_dataset.json") - - with TemporaryDirectory() as td: - dset.root_dir = Path(td) - dset.download_all_pdfs() - dset.convert_all_pdfs() - - for cert in dset: - self.assertEqual( - cert.state.report_pdf_hash, - self.template_report_pdf_hashes[cert.dgst], - f"Hash of PDF certification report for CommonCriteriaCert with digest {cert.dgst} deviates from template.", - ) - - self.assertEqual( - cert.state.st_pdf_hash, - self.template_target_pdf_hashes[cert.dgst], - f"Hash of PDF security target for CommonCriteriaCert with digest {cert.dgst} deviates from template.", - ) - - self.assertFalse(dset["309ac2fd7f2dcf17"].state.report_convert_garbage) - self.assertFalse(dset["309ac2fd7f2dcf17"].state.st_convert_garbage) - self.assertTrue(dset["309ac2fd7f2dcf17"].state.report_convert_ok) - self.assertTrue(dset["309ac2fd7f2dcf17"].state.st_convert_ok) - - self.assertTrue(dset["309ac2fd7f2dcf17"].state.report_txt_path.exists()) - self.assertTrue(dset["309ac2fd7f2dcf17"].state.st_txt_path.exists()) - - self.assertAlmostEqual( - dset["309ac2fd7f2dcf17"].state.st_txt_path.stat().st_size, - self.template_target_txt_path.stat().st_size, - delta=1000, - ) - - self.assertAlmostEqual( - dset["309ac2fd7f2dcf17"].state.report_txt_path.stat().st_size, - self.template_report_txt_path.stat().st_size, - delta=1000, - ) - - def test_cert_to_json(self): - with NamedTemporaryFile("w") as tmp: - self.fictional_cert.to_json(tmp.name) - with open(tmp.name) as f: - out = json.load(f) - with open(self.test_data_dir / "fictional_cert.json") as f: - template = json.load(f) - self.assertEqual(out, template, "Serialized cert differs from cert json template.") - - def test_dataset_to_json(self): - with NamedTemporaryFile("w") as tmp: - self.template_dataset.to_json(tmp.name) - with open(tmp.name, "r") as handle: - out = json.load(handle) - with open(self.test_data_dir / "toy_dataset.json") as handle: - template = json.load(handle) - self.assertEqual(out, template, "Serialized dataset differs from json template.") - - def test_cert_from_json(self): - self.assertEqual( - self.fictional_cert, - CommonCriteriaCert.from_json(self.test_data_dir / "fictional_cert.json"), - "The sample serialized from json differs from a template.", - ) - - def test_dataset_from_json(self): - self.assertEqual( - self.template_dataset, - CCDataset.from_json(self.test_data_dir / "toy_dataset.json"), - "The dataset serialized from json differs from a template.", - ) - - def test_build_empty_dataset(self): - with TemporaryDirectory() as tmp_dir: - dset = CCDataset({}, Path(tmp_dir), "sample_dataset", "sample dataset description") - dset.get_certs_from_web(to_download=False, get_archived=False, get_active=False) - self.assertEqual(len(dset), 0, "The dataset should contain 0 files.") - - def test_build_dataset(self): - with TemporaryDirectory() as tmp_dir: - dataset_path = Path(tmp_dir) - os.mkdir(dataset_path / "web") - shutil.copyfile( - self.test_data_dir / "cc_products_active.csv", dataset_path / "web" / "cc_products_active.csv" - ) - shutil.copyfile( - self.test_data_dir / "cc_products_active.html", dataset_path / "web" / "cc_products_active.html" - ) - - dset = CCDataset({}, dataset_path, "sample_dataset", "sample dataset description") - dset.get_certs_from_web( - keep_metadata=False, to_download=False, get_archived=False, get_active=True, update_json=False - ) - - self.assertEqual( - len(os.listdir(dataset_path)), - 0, - "Meta files (csv, html) were not deleted properly albeit this was explicitly required.", - ) - - self.assertEqual(len(dset), 2, "The dataset should contain 2 files.") - - self.assertTrue(self.crt_one in dset, "The dataset does not contain the template sample.") - self.assertEqual(dset, self.template_dataset, "The loaded dataset does not match the template dataset.") - - def test_download_csv_html_files(self): - with TemporaryDirectory() as tmp_dir: - dataset_path = Path(tmp_dir) - dset = CCDataset({}, dataset_path, "sample_dataset", "sample dataset description") - dset._download_csv_html_resources(get_active=True, get_archived=False) - - for x in dset.active_html_tuples: - self.assertTrue(x[1].exists()) - self.assertGreaterEqual(x[1].stat().st_size, constants.MIN_CC_HTML_SIZE) - for x in dset.active_csv_tuples: - self.assertTrue(x[1].exists()) - self.assertGreaterEqual(x[1].stat().st_size, constants.MIN_CC_CSV_SIZE) - - def test_download_pp_dataset(self): - with tempfile.TemporaryDirectory() as tmp_dir: - self.template_dataset.root_dir = tmp_dir - self.template_dataset.process_protection_profiles() - self.assertTrue(self.template_dataset.pp_dataset_path.exists()) - self.assertGreaterEqual( - self.template_dataset.pp_dataset_path.stat().st_size, constants.MIN_CC_PP_DATASET_SIZE - ) diff --git a/tests/test_cc_schemes.py b/tests/test_cc_schemes.py deleted file mode 100644 index c9dfeb61..00000000 --- a/tests/test_cc_schemes.py +++ /dev/null @@ -1,72 +0,0 @@ -from unittest import TestCase - -import pytest - -from sec_certs.dataset.common_criteria import CCSchemeDataset - - -class TestCCSchemes(TestCase): - def test_australia(self): - assert len(CCSchemeDataset.get_australia_in_evaluation()) != 0 - - def test_canada(self): - assert len(CCSchemeDataset.get_canada_certified()) != 0 - assert len(CCSchemeDataset.get_canada_in_evaluation()) != 0 - - def test_anssi(self): - assert len(CCSchemeDataset.get_france_certified()) != 0 - - def test_bsi(self): - assert len(CCSchemeDataset.get_germany_certified()) != 0 - - @pytest.mark.xfail - def test_india(self): - assert len(CCSchemeDataset.get_india_certified()) != 0 - assert len(CCSchemeDataset.get_india_archived()) != 0 - - def test_italy(self): - assert len(CCSchemeDataset.get_italy_certified()) != 0 - assert len(CCSchemeDataset.get_italy_in_evaluation()) != 0 - - def test_japan(self): - assert len(CCSchemeDataset.get_japan_certified()) != 0 - assert len(CCSchemeDataset.get_japan_archived()) != 0 - assert len(CCSchemeDataset.get_japan_in_evaluation()) != 0 - - def test_malaysia(self): - assert len(CCSchemeDataset.get_malaysia_certified()) != 0 - assert len(CCSchemeDataset.get_malaysia_in_evaluation()) != 0 - - def test_netherlands(self): - assert len(CCSchemeDataset.get_netherlands_certified()) != 0 - assert len(CCSchemeDataset.get_netherlands_in_evaluation()) != 0 - - def test_norway(self): - assert len(CCSchemeDataset.get_norway_certified()) != 0 - assert len(CCSchemeDataset.get_norway_archived()) != 0 - - def test_korea(self): - assert len(CCSchemeDataset.get_korea_certified()) != 0 - CCSchemeDataset.get_korea_suspended() - assert len(CCSchemeDataset.get_korea_archived()) != 0 - - def test_singapore(self): - assert len(CCSchemeDataset.get_singapore_certified()) != 0 - assert len(CCSchemeDataset.get_singapore_archived()) != 0 - assert len(CCSchemeDataset.get_singapore_in_evaluation()) != 0 - - def test_spain(self): - assert len(CCSchemeDataset.get_spain_certified()) != 0 - - def test_sweden(self): - assert len(CCSchemeDataset.get_sweden_certified()) != 0 - assert len(CCSchemeDataset.get_sweden_in_evaluation()) != 0 - assert len(CCSchemeDataset.get_sweden_archived()) != 0 - - def test_turkey(self): - assert len(CCSchemeDataset.get_turkey_certified()) != 0 - - def test_usa(self): - assert len(CCSchemeDataset.get_usa_certified()) != 0 - assert len(CCSchemeDataset.get_usa_in_evaluation()) != 0 - assert len(CCSchemeDataset.get_usa_archived()) != 0 diff --git a/tests/test_cc_txt_processing.py b/tests/test_cc_txt_processing.py deleted file mode 100644 index f144decd..00000000 --- a/tests/test_cc_txt_processing.py +++ /dev/null @@ -1,62 +0,0 @@ -import shutil -import tempfile -from pathlib import Path -from typing import ClassVar -from unittest import TestCase - -import tests.data.test_cc_heuristics -from sec_certs.dataset import CCDataset -from sec_certs.sample import CommonCriteriaCert - - -class TestCommonCriteriaTextProcessing(TestCase): - dataset_json_path: ClassVar[Path] = Path(tests.data.test_cc_heuristics.__path__[0]) / "vulnerable_dataset.json" - data_dir_path: ClassVar[Path] = dataset_json_path.parent - tmp_dir: ClassVar[tempfile.TemporaryDirectory] - cc_dset: CCDataset - - @classmethod - def setUpClass(cls) -> None: - cls.tmp_dir = tempfile.TemporaryDirectory() - shutil.copytree(cls.data_dir_path, cls.tmp_dir.name, dirs_exist_ok=True) - - cls.cc_dset = CCDataset.from_json(Path(cls.tmp_dir.name) / "vulnerable_dataset.json") - cls.cc_dset.download_all_pdfs() - cls.cc_dset.convert_all_pdfs() - - def test_extract_metadata(self): - subject_cert: CommonCriteriaCert = list(self.cc_dset.certs.values())[0] - - subject_cert.state.st_extract_ok = True - CommonCriteriaCert.extract_st_pdf_metadata(subject_cert) - self.assertTrue(subject_cert.state.st_extract_ok, True) - - subject_cert.state.report_extract_ok = True - CommonCriteriaCert.extract_report_pdf_metadata(subject_cert) - self.assertTrue(subject_cert.state.report_extract_ok, True) - - def test_extract_frontpage(self): - subject_cert: CommonCriteriaCert = list(self.cc_dset.certs.values())[0] - - subject_cert.state.st_extract_ok = True - CommonCriteriaCert.extract_st_pdf_frontpage(subject_cert) - self.assertTrue(subject_cert.state.st_extract_ok, True) - - subject_cert.state.report_extract_ok = True - CommonCriteriaCert.extract_report_pdf_frontpage(subject_cert) - self.assertTrue(subject_cert.state.report_extract_ok, True) - - def test_keyword_extraction(self): - subject_cert: CommonCriteriaCert = list(self.cc_dset.certs.values())[0] - - subject_cert.state.st_extract_ok = True - CommonCriteriaCert.extract_st_pdf_keywords(subject_cert) - self.assertTrue(subject_cert.state.st_extract_ok, True) - - subject_cert.state.report_extract_ok = True - CommonCriteriaCert.extract_report_pdf_keywords(subject_cert) - self.assertTrue(subject_cert.state.report_extract_ok, True) - - @classmethod - def tearDownClass(cls) -> None: - cls.tmp_dir.cleanup() diff --git a/tests/test_common.py b/tests/test_common.py new file mode 100644 index 00000000..d5e62cc4 --- /dev/null +++ b/tests/test_common.py @@ -0,0 +1,9 @@ +from sec_certs.cert_rules import cc_rules, fips_rules, rules + + +def test_rules(): + assert "cc_cert_id" in cc_rules + assert "fips_cert_id" in fips_rules + for rule_group in rules: + if rule_group not in ("cc_rules", "fips_rules"): + assert rule_group in cc_rules.keys() or rule_group in fips_rules.keys() diff --git a/tests/test_cpe.py b/tests/test_cpe.py index 96242ed7..f471c116 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -1,30 +1,122 @@ from pathlib import Path -from tempfile import TemporaryDirectory +from typing import Any, Dict import pytest +import tests.data.cc.analysis.auxillary_datasets +from sec_certs import constants from sec_certs.dataset import CPEDataset from sec_certs.sample import CPE -class TestCPE: - @pytest.mark.slow - @pytest.mark.monitor_test - def test_from_web(self): - with TemporaryDirectory() as tmpdir: - dset = CPEDataset.from_web(Path(tmpdir) / "cpe.json") - assert dset is not None - assert "cpe:2.3:o:infineon:trusted_platform_firmware:6.40:*:*:*:*:*:*:*" in dset.cpes - - def test_from_to_dict(self): - data = { - "uri": "cpe:2.3:o:freebsd:freebsd:1.0:*:*:*:*:*:*:*", - "title": None, - "start_version": None, - "end_version": None, - } - cpe = CPE.from_dict(data) - ret = cpe.to_dict() - assert data == ret - other_cpe = CPE.from_dict(ret) - assert cpe == other_cpe +@pytest.fixture(scope="module") +def cpe_dset_path() -> Path: + return Path(tests.data.cc.analysis.auxillary_datasets.__path__[0]) / "cpe_dataset.json" + + +@pytest.fixture(scope="module") +def cpe_dset(cpe_dset_path: Path) -> CPEDataset: + return CPEDataset.from_json(cpe_dset_path) + + +@pytest.fixture(scope="module") +def cpe_dict() -> Dict[str, Any]: + return { + "uri": "cpe:2.3:o:freebsd:freebsd:1.0:*:*:*:*:*:*:*", + "title": None, + "start_version": None, + "end_version": None, + } + + +@pytest.mark.slow +@pytest.mark.monitor_test +def test_cpe_dset_from_web(tmp_path: Path): + dset = CPEDataset.from_web(tmp_path) + assert dset is not None + assert "cpe:2.3:o:infineon:trusted_platform_firmware:6.40:*:*:*:*:*:*:*" in dset.cpes + + +def test_cpe_dset_from_json(cpe_dset_path: Path, cpe_dset: CPEDataset): + assert CPEDataset.from_json(cpe_dset_path) == cpe_dset + + +def test_cpe_dset_vendor_lookup_dict(cpe_dset: CPEDataset): + assert cpe_dset.vendors == {"ibm", "tracker-software", "semperplugins"} + + +def test_cpe_dset_vendor_to_version_lookup_dict(cpe_dset: CPEDataset): + assert cpe_dset.vendor_to_versions == { + "ibm": {"8.2.2", "2.6.0.1"}, + "semperplugins": {"1.3.6.4"}, + "tracker-software": {"6.0.320.0"}, + } + + +def test_cpe_dset_vendor_version_to_cpe_lookup_dict(cpe_dset: CPEDataset): + assert cpe_dset.vendor_version_to_cpe == { + ("ibm", "8.2.2"): { + CPE( + "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", + "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", + ) + }, + ("ibm", "2.6.0.1"): { + CPE( + "cpe:2.3:a:ibm:security_key_lifecycle_manager:2.6.0.1:*:*:*:*:*:*:*", + "IBM Security Key Lifecycle Manager 2.6.0.1", + ) + }, + ("semperplugins", "1.3.6.4"): { + CPE( + "cpe:2.3:a:semperplugins:all_in_one_seo_pack:1.3.6.4:*:*:*:*:wordpress:*:*", + "Semper Plugins All in One SEO Pack 1.3.6.4 for WordPress", + ) + }, + ("tracker-software", "6.0.320.0"): { + CPE( + "cpe:2.3:a:tracker-software:pdf-xchange_lite_printer:6.0.320.0:*:*:*:*:*:*:*", + "Tracker Software PDF-XChange Lite Printer 6.0.320.0", + ) + }, + } + + +def test_cpe_parsing(): + potentially_problematic_cpes = { + 'cpe:2.3:a:bayashi:dopvstar\\::0091:*:*:*:*:*:*:*"': ("bayashi", "dopvstar:", "0091"), + "cpe:2.3:a:moundlabs:\\:\\:mound\\:\\::2.1.6:*:*:*:*:*:*:*": ("moundlabs", "::mound::", "2.1.6"), + "cpe:2.3:a:lemonldap-ng:lemonldap\\:\\::*:*:*:*:*:*:*:*": ( + "lemonldap-ng", + "lemonldap::", + constants.CPE_VERSION_NA, + ), + "cpe:2.3:o:cisco:nx-os:5.0\\\\\\(3\\\\\\)u5\\\\\\(1g\\\\\\):*:*:*:*:*:*:*": ( + "cisco", + "nx-os", + "5.0\\(3\\)u5\\(1g\\)", + ), + "cpe:2.3:a:\\@thi.ng\\/egf_project:\\@thi.ng\\/egf:-:*:*:*:*:node.js:*:*": ( + "@thi.ng/egf project", + "@thi.ng/egf", + "-", + ), + "cpe:2.3:a:oracle:communications_diameter_signaling_router_idih\\:::*:*:*:*:*:*:*": ( + "oracle", + "communications diameter signaling router idih:", + constants.CPE_VERSION_NA, + ), + } + for uri, tpl in potentially_problematic_cpes.items(): + cpe = CPE(uri) + assert cpe.vendor == tpl[0] + assert cpe.item_name == tpl[1] + assert cpe.version == tpl[2] + + +def test_cpe_from_to_dict(cpe_dict): + cpe = CPE.from_dict(cpe_dict) + ret = cpe.to_dict() + assert cpe_dict == ret + other_cpe = CPE.from_dict(ret) + assert cpe == other_cpe diff --git a/tests/test_cve.py b/tests/test_cve.py index 9ea4feab..89db3880 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -1,41 +1,101 @@ +from pathlib import Path +from typing import Any, Dict, List + import pytest +import tests.data.cc.analysis.auxillary_datasets from sec_certs.dataset import CVEDataset from sec_certs.sample import CVE +from sec_certs.sample.cpe import CPE + + +@pytest.mark.slow +@pytest.mark.monitor_test +def test_from_web(): + dset = CVEDataset.from_web() + assert dset is not None + assert "CVE-2019-15809" in dset.cves + assert "CVE-2017-15361" in dset.cves + + +@pytest.fixture(scope="module") +def cve_dataset_path() -> Path: + return Path(tests.data.cc.analysis.auxillary_datasets.__path__[0]) / "cve_dataset.json" + + +@pytest.fixture(scope="module") +def cve_dset(cves: List[CVE]) -> CVEDataset: + cve_dset = CVEDataset({x.cve_id: x for x in cves}) + cve_dset.build_lookup_dict(use_nist_mapping=False) + return cve_dset + +@pytest.fixture(scope="module") +def cve_dict() -> Dict[str, Any]: + return { + "cve_id": "CVE-1999-0001", + "vulnerable_cpes": [ + { + "uri": "cpe:2.3:o:freebsd:freebsd:1.0:*:*:*:*:*:*:*", + "title": None, + "start_version": None, + "end_version": None, + } + ], + "impact": { + "_type": "Impact", + "base_score": 5, + "severity": "MEDIUM", + "explotability_score": 10, + "impact_score": 2.9, + }, + "published_date": "1999-12-30T05:00:00+00:00", + "cwe_ids": {"CWE-20"}, + } -class TestCVE: - @pytest.mark.slow - @pytest.mark.monitor_test - def test_from_web(self): - dset = CVEDataset.from_web() - assert dset is not None - assert "CVE-2019-15809" in dset.cves - assert "CVE-2017-15361" in dset.cves - - def test_from_to_dict(self): - data = { - "cve_id": "CVE-1999-0001", - "vulnerable_cpes": [ - { - "uri": "cpe:2.3:o:freebsd:freebsd:1.0:*:*:*:*:*:*:*", - "title": None, - "start_version": None, - "end_version": None, - } - ], - "impact": { - "_type": "Impact", - "base_score": 5, - "severity": "MEDIUM", - "explotability_score": 10, - "impact_score": 2.9, - }, - "published_date": "1999-12-30T05:00:00+00:00", - "cwe_ids": {"CWE-20"}, + +@pytest.fixture(scope="module") +def cves() -> List[CVE]: + cpe_single_sign_on = CPE( + "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", + "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", + ) + + return [ + CVE( + "CVE-2017-1732", + [cpe_single_sign_on], + CVE.Impact(5.3, "MEDIUM", 3.9, 1.4), + "2021-05-26T04:15Z", + {"CWE-200"}, + ), + CVE( + "CVE-2019-4513", + [cpe_single_sign_on], + CVE.Impact(8.2, "HIGH", 3.9, 4.2), + "2000-05-26T04:15Z", + {"CVE-611"}, + ), + ] + + +def test_cve_dset_lookup_dicts(cves: List[CVE], cve_dset: CVEDataset): + alt_lookup = {x: set(y) for x, y in cve_dset.cpe_to_cve_ids_lookup.items()} + assert alt_lookup == { + "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*": { + x.cve_id for x in cves } - cve = CVE.from_dict(data) - ret = cve.to_dict() - assert ret == data - other_cve = CVE.from_dict(ret) - assert cve == other_cve + } + + +def test_cve_dset_from_json(cve_dataset_path: Path, cve_dset: CVEDataset): + dset = CVEDataset.from_json(cve_dataset_path) + assert dset == cve_dset + + +def test_cve_from_to_dict(cve_dict: Dict[str, Any]): + cve = CVE.from_dict(cve_dict) + ret = cve.to_dict() + assert ret == cve_dict + other_cve = CVE.from_dict(ret) + assert cve == other_cve diff --git a/tests/test_fips_iut.py b/tests/test_fips_iut.py deleted file mode 100644 index 2d8311c3..00000000 --- a/tests/test_fips_iut.py +++ /dev/null @@ -1,32 +0,0 @@ -from pathlib import Path -from typing import ClassVar -from unittest import TestCase - -from sec_certs.dataset import IUTDataset -from sec_certs.sample import IUTSnapshot - - -class TestFIPSIUT(TestCase): - test_data_dir: ClassVar[Path] = Path(__file__).parent / "data" / "test_fips_iut" - test_data_dump: ClassVar[Path] = test_data_dir / "fips_iut_2020-10-25T06+01:00.html" - - def test_from_dumps(self): - dset = IUTDataset.from_dumps(self.test_data_dir) - self.assertIsNotNone(dset) - self.assertEqual(len(dset), 2) - - def test_from_dataset_latest(self): - dset = IUTDataset.from_web_latest() - self.assertIsNotNone(dset) - - def test_from_dump(self): - snap = IUTSnapshot.from_dump(self.test_data_dump) - self.assertIsNotNone(snap) - - def test_from_web(self): - snap = IUTSnapshot.from_web() - self.assertIsNotNone(snap) - - def test_from_web_latest(self): - snap = IUTSnapshot.from_web_latest() - self.assertIsNotNone(snap) diff --git a/tests/test_fips_mip.py b/tests/test_fips_mip.py deleted file mode 100644 index 2f891035..00000000 --- a/tests/test_fips_mip.py +++ /dev/null @@ -1,32 +0,0 @@ -from pathlib import Path -from typing import ClassVar -from unittest import TestCase - -from sec_certs.dataset import MIPDataset -from sec_certs.sample import MIPSnapshot - - -class TestFIPSMIP(TestCase): - test_data_dir: ClassVar[Path] = Path(__file__).parent / "data" / "test_fips_mip" - test_data_dump: ClassVar[Path] = test_data_dir / "fips_mip_2021-02-19T06+01:00.html" - - def test_from_dumps(self): - dset = MIPDataset.from_dumps(self.test_data_dir) - self.assertIsNotNone(dset) - self.assertEqual(len(dset), 3) - - def test_from_dataset_latest(self): - dset = MIPDataset.from_web_latest() - self.assertIsNotNone(dset) - - def test_from_dump(self): - snap = MIPSnapshot.from_dump(self.test_data_dump) - self.assertIsNotNone(snap) - - def test_from_web(self): - snap = MIPSnapshot.from_web() - self.assertIsNotNone(snap) - - def test_from_web_latest(self): - snap = MIPSnapshot.from_web_latest() - self.assertIsNotNone(snap) diff --git a/tests/test_fips_oop.py b/tests/test_fips_oop.py deleted file mode 100644 index b58e9972..00000000 --- a/tests/test_fips_oop.py +++ /dev/null @@ -1,352 +0,0 @@ -import shutil -from pathlib import Path -from tempfile import TemporaryDirectory -from typing import Dict, Final, List -from unittest import TestCase - -import pytest - -import tests.data.test_fips_oop -from sec_certs.config.configuration import config -from sec_certs.dataset import FIPSAlgorithmDataset, FIPSDataset -from sec_certs.utils.helpers import fips_dgst -from tests.fips_test_utils import generate_html - - -def _set_up_dataset(td, certs): - dataset = FIPSDataset({}, Path(td), "test_dataset", "fips_test_dataset") - generate_html(certs, td + "/test_search.html") - dataset.get_certs_from_web(test=td + "/test_search.html") - return dataset - - -def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path): - dataset = _set_up_dataset(td, certs) - - dataset.auxillary_datasets_dir.mkdir(exist_ok=True) - shutil.copyfile(cpe_dset_path, dataset.cpe_dataset_path) - shutil.copyfile(cve_dset_path, dataset.cve_dataset_path) - - dataset.web_scan(set(certs)) - dataset.download_all_pdfs(set(certs)) - dataset.convert_all_pdfs() - dataset._extract_data() - dataset.extract_certs_from_tables(high_precision=True) - dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") - dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) - return dataset - - -class TestFipsOOP(TestCase): - data_dir: Final[Path] = Path(tests.data.test_fips_oop.__path__[0]) - cpe_dset_path: Final[Path] = data_dir.parent / "test_cc_heuristics/auxillary_datasets/cpe_dataset.json" - cve_dset_path: Final[Path] = data_dir.parent / "test_cc_heuristics/auxillary_datasets/cve_dataset.json" - certs_to_parse: Final[Dict[str, List[str]]] = { - "microsoft": [ - "3095", - "3651", - "3093", - "3090", - "3197", - "3196", - "3089", - "3195", - "3480", - "3615", - "3194", - "3091", - "3690", - "3644", - "3527", - "3094", - "3544", - "3096", - "3092", - ], - "redhat": [ - "2630", - "2721", - "2997", - "2441", - "2711", - "2633", - "2798", - "3613", - "3733", - "2908", - "2446", - "2742", - "2447", - ], - "docusign": ["3850", "2779", "2860", "2665", "1883", "3518", "3141", "2590"], - "referencing_openssl": [ - "3493", - "3495", - "3711", - "3176", - "3488", - "3126", - "3269", - "3524", - "3220", - "2398", - "3543", - "2676", - "3313", - "3363", - "3608", - "3158", - ], - } - - @classmethod - def setUpClass(cls) -> None: - config.load(cls.data_dir.parent / "settings_test.yaml") - - def test_regress_125(self): - with TemporaryDirectory() as tmp_dir: - dst = _set_up_dataset(tmp_dir, ["3493", "3495"]) - self.assertEqual(set(dst.certs), {fips_dgst("3493"), fips_dgst("3495")}) - self.assertIsInstance(dst.certs[fips_dgst("3493")].cert_id, int) - self.assertEqual(dst.certs[fips_dgst("3493")].cert_id, 3493) - - def test_size(self): - for certs in self.certs_to_parse.values(): - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset(tmp_dir, certs) - self.assertEqual(len(dataset.certs), len(certs), "Wrong number of parsed certs") - - def test_metadata_extraction(self): - with TemporaryDirectory() as tmp_dir: - dst = _set_up_dataset_for_full(tmp_dir, ["3493"], self.cpe_dset_path, self.cve_dset_path) - self.assertIsNotNone(dst.certs[fips_dgst("3493")].pdf_data.st_metadata) - - @pytest.mark.xfail - def test_connections_microsoft(self): - certs = self.certs_to_parse["microsoft"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, self.cpe_dset_path, self.cve_dset_path) - - self.assertEqual( - {"3093", "3094", "3096"}, - set(dataset.certs[fips_dgst("3095")].heuristics.st_references.directly_referencing), - ) - self.assertEqual( - {"3093", "3096", "3094"}, - set(dataset.certs[fips_dgst("3095")].heuristics.web_references.directly_referencing), - ) - - self.assertEqual( - {"3090", "3091"}, set(dataset.certs[fips_dgst("3093")].heuristics.st_references.directly_referencing) - ) - - self.assertEqual( - {"3090", "3091"}, set(dataset.certs[fips_dgst("3093")].heuristics.web_references.directly_referencing) - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3090")].heuristics.st_references.directly_referencing), {"3089"} - ) - self.assertEqual( - {"3195", "3096", "3196", "3644", "3651"}, - set(dataset.certs[fips_dgst("3197")].heuristics.web_references.directly_referencing), - ) - self.assertEqual( - {"3091", "3194"}, set(dataset.certs[fips_dgst("3196")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3194", "3091", "3480", "3615"}, - set(dataset.certs[fips_dgst("3196")].heuristics.web_references.directly_referencing), - ) - self.assertIsNone(dataset.certs[fips_dgst("3089")].heuristics.st_references.directly_referencing) - self.assertEqual( - {"3091", "3194"}, set(dataset.certs[fips_dgst("3195")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3194", "3091", "3480"}, - set(dataset.certs[fips_dgst("3195")].heuristics.web_references.directly_referencing), - ) - self.assertEqual( - {"3089"}, set(dataset.certs[fips_dgst("3480")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3089"}, set(dataset.certs[fips_dgst("3615")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3089"}, set(dataset.certs[fips_dgst("3194")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3089"}, set(dataset.certs[fips_dgst("3091")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3644", "3196", "3651"}, - set(dataset.certs[fips_dgst("3690")].heuristics.st_references.directly_referencing), - ) - self.assertEqual( - {"3644", "3196", "3651"}, - set(dataset.certs[fips_dgst("3690")].heuristics.web_references.directly_referencing), - ) - self.assertEqual( - {"3090", "3091"}, set(dataset.certs[fips_dgst("3527")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3090", "3091"}, set(dataset.certs[fips_dgst("3527")].heuristics.web_references.directly_referencing) - ) - self.assertEqual( - {"3090", "3091"}, set(dataset.certs[fips_dgst("3094")].heuristics.st_references.directly_referencing) - ) - self.assertEqual( - {"3093", "3096", "3527"}, - set(dataset.certs[fips_dgst("3544")].heuristics.st_references.directly_referencing), - ) - self.assertEqual( - {"3093", "3096", "3527"}, - set(dataset.certs[fips_dgst("3544")].heuristics.web_references.directly_referencing), - ) - self.assertEqual( - {"3194", "3091", "3090"}, - set(dataset.certs[fips_dgst("3096")].heuristics.st_references.directly_referencing), - ) - self.assertEqual( - {"3090", "3194", "3091", "3480"}, - set(dataset.certs[fips_dgst("3096")].heuristics.web_references.directly_referencing), - ) - self.assertEqual( - {"3093", "3195", "3096", "3644", "3651"}, - set(dataset.certs[fips_dgst("3092")].heuristics.web_references.directly_referencing), - ) - - @pytest.mark.xfail - def test_connections_redhat(self): - certs = self.certs_to_parse["redhat"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, self.cpe_dset_path, self.cve_dset_path) - self.assertEqual( - set(dataset.certs[fips_dgst("2630")].heuristics.st_references.directly_referencing), {"2441"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2633")].heuristics.st_references.directly_referencing), {"2441"} - ) - self.assertIsNone(dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("2446")].heuristics.st_references.directly_referencing), {"2441"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2447")].heuristics.st_references.directly_referencing), {"2441"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3733")].heuristics.st_references.directly_referencing), {"2441"} - ) - self.assertIsNone(dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("2908")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("3613")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing), {"2441", "2711"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing), {"2441", "2711"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2798")].heuristics.st_references.directly_referencing), {"2711", "2721"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2798")].heuristics.web_references.directly_referencing), {"2711", "2721"} - ) - self.assertIsNone(dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("2742")].heuristics.st_references.directly_referencing), {"2711", "2721"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2742")].heuristics.web_references.directly_referencing), {"2721", "2711"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing), {"2441", "2711"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing), {"2441", "2711"} - ) - - @pytest.mark.xfail - def test_docusign_chunk(self): - certs = self.certs_to_parse["docusign"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, self.cpe_dset_path, self.cve_dset_path) - self.assertEqual( - set(dataset.certs[fips_dgst("3850")].heuristics.st_references.directly_referencing), {"1883", "3518"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3850")].heuristics.web_references.directly_referencing), {"1883"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2779")].heuristics.st_references.directly_referencing), {"1883"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2860")].heuristics.st_references.directly_referencing), {"1883"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2665")].heuristics.st_references.directly_referencing), {"1883"} - ) - self.assertIsNone(dataset.certs[fips_dgst("1883")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("3518")].heuristics.st_references.directly_referencing), {"1883"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3141")].heuristics.st_references.directly_referencing), {"1883"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2590")].heuristics.st_references.directly_referencing), {"1883"} - ) - - @pytest.mark.xfail - def test_openssl_chunk(self): - certs = self.certs_to_parse["referencing_openssl"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, self.cpe_dset_path, self.cve_dset_path) - self.assertEqual( - set(dataset.certs[fips_dgst("3493")].heuristics.st_references.directly_referencing), {"2398"} - ) - self.assertIsNone(dataset.certs[fips_dgst("3495")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("3711")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("3176")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("3488")].heuristics.st_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("3126")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("3126")].heuristics.web_references.directly_referencing), {"2398"} - ) - self.assertIsNone(dataset.certs[fips_dgst("3269")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("3524")].heuristics.web_references.directly_referencing), {"3220"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3220")].heuristics.st_references.directly_referencing), {"2398"} - ) - self.assertIsNone(dataset.certs[fips_dgst("3220")].heuristics.web_references.directly_referencing) - self.assertIsNone(dataset.certs[fips_dgst("2398")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("3543")].heuristics.web_references.directly_referencing), {"2398"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("2676")].heuristics.web_references.directly_referencing), {"2398"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3313")].heuristics.web_references.directly_referencing), {"3220"} - ) - self.assertIsNone(dataset.certs[fips_dgst("3363")].heuristics.st_references.directly_referencing) - self.assertEqual( - set(dataset.certs[fips_dgst("3608")].heuristics.st_references.directly_referencing), {"2398"} - ) - self.assertEqual( - set(dataset.certs[fips_dgst("3158")].heuristics.web_references.directly_referencing), {"2398"} - ) - - -class TestFIPSAlgo(TestCase): - @pytest.mark.slow - def test_get_certs_from_web(self): - with TemporaryDirectory() as tmp_dir: - web_path = Path(tmp_dir) / "web" - web_path.mkdir() - aset = FIPSAlgorithmDataset({}, web_path / "algorithms", "algorithms", "sample algs") - aset.get_certs_from_web() diff --git a/tests/test_rules.py b/tests/test_rules.py deleted file mode 100644 index 2ce991a0..00000000 --- a/tests/test_rules.py +++ /dev/null @@ -1,12 +0,0 @@ -from unittest import TestCase - -from sec_certs.cert_rules import cc_rules, fips_rules, rules - - -class TestRules(TestCase): - def test_rules(self): - assert "cc_cert_id" in cc_rules - assert "fips_cert_id" in fips_rules - for rule_group in rules: - if rule_group not in ("cc_rules", "fips_rules"): - assert rule_group in cc_rules.keys() or rule_group in fips_rules.keys() From 80af3a206ad966f8ee9643555dcc6160e67dccba Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 9 Nov 2022 12:13:20 +0100 Subject: [PATCH 10/81] add type hint for json serialization --- sec_certs/serialization/json.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py index bca80643..b07184ca 100644 --- a/sec_certs/serialization/json.py +++ b/sec_certs/serialization/json.py @@ -5,7 +5,7 @@ from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union -T = TypeVar("T") +T = TypeVar("T", bound="ComplexSerializableType") class ComplexSerializableType: From 613447915afef1efd2b6bba62b6947044b437fc7 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 9 Nov 2022 12:54:15 +0100 Subject: [PATCH 11/81] new object to hold auxillary datasets --- sec_certs/dataset/common_criteria.py | 16 ++-- sec_certs/dataset/dataset.py | 107 ++++++++++----------------- sec_certs/dataset/fips.py | 18 ++--- tests/cc/test_cc_analysis.py | 5 +- tests/cc/test_cc_dataset.py | 10 +++ 5 files changed, 70 insertions(+), 86 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 9e022a5b..8e42d93f 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -18,7 +18,7 @@ import sec_certs.utils.sanitization from sec_certs import constants from sec_certs.config.configuration import config -from sec_certs.dataset.dataset import Dataset, logger +from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset, logger from sec_certs.dataset.protection_profile import ProtectionProfileDataset from sec_certs.model.dependency_finder import DependencyFinder from sec_certs.model.dependency_vulnerability_finder import DependencyVulnerabilityFinder @@ -33,7 +33,7 @@ from sec_certs.utils.sanitization import sanitize_navigable_string as sns -class CCDataset(Dataset[CommonCriteriaCert], ComplexSerializableType): +class CCDataset(Dataset[CommonCriteriaCert, AuxillaryDatasets], ComplexSerializableType): """ Class that holds CommonCriteriaCert. Serializable into json, pandas, dictionary. Conveys basic certificate manipulations and dataset transformations. Many private methods that perform internal operations, feel free to exploit them. @@ -130,10 +130,6 @@ def mu_dataset(self) -> "CCDatasetMaintenanceUpdates": return CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path / "Maintenance updates.json") - @property - def artifact_download_methods(self) -> List[Callable]: - return [self._download_reports, self._download_targets] - BASE_URL: ClassVar[str] = "https://www.commoncriteriaportal.org" HTML_PRODUCTS_URL = { @@ -513,6 +509,10 @@ def _parse_table( return certs + def _download_all_artifacts_body(self, fresh: bool = True) -> None: + self._download_reports(fresh) + self._download_targets(fresh) + def _download_reports(self, fresh: bool = True) -> None: if fresh: logger.info("Downloading PDFs of CC certification reports.") @@ -712,8 +712,8 @@ def _compute_heuristics(self, use_nist_cpe_matching_dict: bool = True) -> None: self._compute_normalized_cert_ids() self._compute_dependencies() self._compute_sars() - _, _, cve_dset = self.compute_cpe_heuristics() - self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict, cve_dset=cve_dset) + self.compute_cpe_heuristics() + self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict) self._compute_dependency_vulnerabilities() def _compute_sars(self) -> None: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 4b1c8c7b..e4ad4caf 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -9,23 +9,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import ( - Any, - Callable, - Collection, - Dict, - Generic, - Iterator, - List, - Optional, - Pattern, - Set, - Tuple, - Type, - TypeVar, - Union, - cast, -) +from typing import Any, Collection, Dict, Generic, Iterator, Optional, Pattern, Set, Type, TypeVar, Union, cast import pandas as pd import requests @@ -43,12 +27,19 @@ logger = logging.getLogger(__name__) -T = TypeVar("T") + +@dataclass +class AuxillaryDatasets: + cpe_dset: Optional[CPEDataset] = None + cve_dset: Optional[CVEDataset] = None + + CertSubType = TypeVar("CertSubType", bound=Certificate) +AuxillaryDatasetsSubType = TypeVar("AuxillaryDatasetsSubType", bound=AuxillaryDatasets) DatasetSubType = TypeVar("DatasetSubType", bound="Dataset") -class Dataset(Generic[CertSubType], ComplexSerializableType, ABC): +class Dataset(Generic[CertSubType, AuxillaryDatasetsSubType], ComplexSerializableType, ABC): @dataclass class DatasetInternalState(ComplexSerializableType): meta_sources_parsed: bool = False @@ -66,31 +57,20 @@ def __init__( name: Optional[str] = None, description: str = None, state: Optional[DatasetInternalState] = None, - auxillary_datasets: Optional[Any] = None, + auxillary_datasets: Optional[AuxillaryDatasetsSubType] = None, ): - if state is None: - state = self.DatasetInternalState() - self.state = state - - if not root_dir: - root_dir = Path.cwd() / (type(self).__name__).lower() - self._root_dir = Path(root_dir) + self.certs = certs + self._root_dir = Path(root_dir) if root_dir else Path.cwd() / (type(self).__name__).lower() self.timestamp = datetime.now() self.sha256_digest = "not implemented" + self.name = name if name else type(self).__name__ + " dataset" + self.description = description if description else datetime.now().strftime("%d/%m/%Y %H:%M:%S") + self.state = state if state else self.DatasetInternalState() - if not name: - name = type(self).__name__ + " dataset" - self.name = name - - if not description: - description = datetime.now().strftime("%d/%m/%Y %H:%M:%S") - self.description = description - - self.name = name - self.description = description - - self.certs = certs - self.auxillary_datasets = auxillary_datasets + if not auxillary_datasets: + self.auxillary_datasets = AuxillaryDatasets() + else: + self.auxillary_datasets = auxillary_datasets @property def root_dir(self) -> Path: @@ -139,11 +119,6 @@ def nist_cve_cpe_matching_dset_path(self) -> Path: def json_path(self) -> Path: return self.root_dir / (self.name + ".json") - @property - @abstractmethod - def artifact_download_methods(self) -> List[Callable]: - raise NotImplementedError("Not meant to be implemented by the base class.") - def __contains__(self, item: object) -> bool: if not isinstance(item, Certificate): raise TypeError( @@ -236,15 +211,15 @@ def download_all_artifacts(self, fresh: bool = True) -> None: logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") return - for method in self.artifact_download_methods: - method(fresh) - + self._download_all_artifacts_body(fresh) if fresh: - for method in self.artifact_download_methods: - method(False) + self._download_all_artifacts_body(False) self.state.artifacts_downloaded = True + def _download_all_artifacts_body(self, fresh: bool = True) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + @abstractmethod def convert_all_pdfs(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @@ -301,9 +276,7 @@ def _prepare_cve_dataset( return cve_dataset @serialize - def compute_cpe_heuristics( - self, download_fresh_cpes: bool = False - ) -> Tuple[CPEClassifier, CPEDataset, Optional[CVEDataset]]: + def compute_cpe_heuristics(self, download_fresh_cpes: bool = False) -> CPEClassifier: RELEASE_CANDIDATE_REGEX: Pattern = re.compile(r"rc\d{0,2}$", re.IGNORECASE) WINDOWS_WEAK_CPES: Set[CPE] = { CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", "Microsoft Windows on X64", None, None), @@ -334,19 +307,18 @@ def filter_condition(cpe: CPE) -> bool: return True logger.info("Computing heuristics: Finding CPE matches for certificates") - cpe_dset = self._prepare_cpe_dataset(download_fresh_cpes, init_lookup_dicts=False) - cve_dset = None + self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh_cpes, init_lookup_dicts=False) + self.auxillary_datasets.cpe_dset.build_lookup_dicts() - cpe_dset.build_lookup_dicts() # Temporarily disabled, see: https://github.com/crocs-muni/sec-certs/issues/173 # if not cpe_dset.was_enhanced_with_vuln_cpes: - # cve_dset = self._prepare_cve_dataset(download_fresh_cves=False) - # cpe_dset.enhance_with_cpes_from_cve_dataset(cve_dset) # this also calls build_lookup_dicts() on cpe_dset + # self.auxillary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh_cves=False) + # self.auxillary_datasets.cpe_dset.enhance_with_cpes_from_cve_dataset(cve_dset) # this also calls build_lookup_dicts() on cpe_dset # else: - # cpe_dset.build_lookup_dicts() + # self.auxillary_datasets.cpe_dset.build_lookup_dicts() clf = CPEClassifier(config.cpe_matching_threshold, config.cpe_n_max_matches) - clf.fit([x for x in cpe_dset if filter_condition(x)]) + clf.fit([x for x in self.auxillary_datasets.cpe_dset if filter_condition(x)]) cert: CertSubType for cert in helpers.tqdm(self, desc="Predicting CPE matches with the classifier"): @@ -358,7 +330,7 @@ def filter_condition(cpe: CPE) -> bool: else None ) - return clf, cpe_dset, cve_dset + return clf def to_label_studio_json(self, output_path: Union[str, Path]) -> None: cpe_dset = self._prepare_cpe_dataset() @@ -441,11 +413,12 @@ def compute_related_cves( self, download_fresh_cves: bool = False, use_nist_cpe_matching_dict: bool = True, - cve_dset: Optional[CVEDataset] = None, ) -> None: logger.info("Retrieving related CVEs to verified CPE matches") - if download_fresh_cves or not cve_dset: - cve_dset = self._prepare_cve_dataset(download_fresh_cves, use_nist_cpe_matching_dict) + if download_fresh_cves or not self.auxillary_datasets.cve_dset: + self.auxillary_datasets.cve_dset = self._prepare_cve_dataset( + download_fresh_cves, use_nist_cpe_matching_dict + ) self.enrich_automated_cpes_with_manual_labels() cpe_rich_certs = [x for x in cast(Iterator[Certificate], self) if x.heuristics.cpe_matches] @@ -457,12 +430,14 @@ def compute_related_cves( return relevant_cpes = set(itertools.chain.from_iterable([x.heuristics.cpe_matches for x in cpe_rich_certs])) - cve_dset.filter_related_cpes(relevant_cpes) + self.auxillary_datasets.cve_dset.filter_related_cpes(relevant_cpes) cert: Certificate for cert in helpers.tqdm(cpe_rich_certs, desc="Computing related CVES"): if cert.heuristics.cpe_matches: - related_cves = [cve_dset.get_cve_ids_for_cpe_uri(x) for x in cert.heuristics.cpe_matches] + related_cves = [ + self.auxillary_datasets.cve_dset.get_cve_ids_for_cpe_uri(x) for x in cert.heuristics.cpe_matches + ] related_cves = list(filter(lambda x: x is not None, related_cves)) if related_cves: cert.heuristics.related_cves = set( diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index c0d4a68a..92c8c018 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -2,7 +2,7 @@ import logging import shutil from pathlib import Path -from typing import Callable, Dict, Final, List, Set +from typing import Dict, Final, List, Set import numpy as np import pandas as pd @@ -11,7 +11,7 @@ from sec_certs import constants from sec_certs.config.configuration import config -from sec_certs.dataset.dataset import Dataset +from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset from sec_certs.model.dependency_finder import DependencyFinder from sec_certs.sample.fips import FIPSCertificate @@ -22,7 +22,7 @@ logger = logging.getLogger(__name__) -class FIPSDataset(Dataset[FIPSCertificate], ComplexSerializableType): +class FIPSDataset(Dataset[FIPSCertificate, AuxillaryDatasets], ComplexSerializableType): """ Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ @@ -53,10 +53,6 @@ def module_dir(self) -> Path: def algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" - @property - def artifact_download_methods(self) -> List[Callable]: - return [self._download_modules, self._download_policies] - @serialize def _extract_data(self, redo: bool = False) -> None: """ @@ -75,6 +71,10 @@ def _extract_data(self, redo: bool = False) -> None: for keyword, cert in keywords: self.certs[cert.dgst].pdf_data.keywords = keyword + def _download_all_artifacts_body(self, fresh: bool = True) -> None: + self._download_modules(fresh) + self._download_policies(fresh) + def _download_modules(self, fresh: bool = True) -> None: self.module_dir.mkdir(exist_ok=True) @@ -383,8 +383,8 @@ def analyze_certificates(self, use_nist_cpe_matching_dict: bool = True, perform_ self._compute_heuristics_clean_ids() self._compute_dependencies() if perform_cpe_heuristics: - _, _, cve_dset = self.compute_cpe_heuristics() - self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict, cve_dset=cve_dset) + self.compute_cpe_heuristics() + self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict) self.plot_graphs(show=True) def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 1d3ca657..6d6524e0 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -80,12 +80,11 @@ def cve_dset(cves: Set[CVE]) -> CVEDataset: @pytest.fixture(scope="module") -def cc_dset(data_dir: Path) -> CCDataset: +def cc_dset(data_dir: Path, cve_dset: CVEDataset) -> CCDataset: cc_dset = CCDataset.from_json(data_dir / "vulnerable_dataset.json") cc_dset.process_protection_profiles() - cc_dset.download_all_artifacts() - cc_dset.convert_all_pdfs() cc_dset._extract_data() + cc_dset.auxillary_datasets.cve_dset = cve_dset cc_dset._compute_heuristics(use_nist_cpe_matching_dict=False) return cc_dset diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py index fa8c5acf..bfa8c612 100644 --- a/tests/cc/test_cc_dataset.py +++ b/tests/cc/test_cc_dataset.py @@ -59,6 +59,15 @@ def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): with TemporaryDirectory() as td: toy_dataset.root_dir = Path(td) toy_dataset.download_all_artifacts() + + if not ( + toy_dataset["309ac2fd7f2dcf17"].state.report_download_ok + or toy_dataset["309ac2fd7f2dcf17"].state.st_download_ok + or toy_dataset["8cf86948f02f047d"].state.report_download_ok + or toy_dataset["8cf86948f02f047d"].state.st_download_ok + ): + pytest.xfail(reason="Fail due to error during download") + toy_dataset.convert_all_pdfs() for cert in toy_dataset: @@ -143,6 +152,7 @@ def test_process_pp_dataset(toy_dataset: CCDataset): assert toy_dataset.pp_dataset_path.stat().st_size > constants.MIN_CC_PP_DATASET_SIZE +@pytest.mark.xfail(reason="May fail due to error on CC server") def test_download_csv_html_files(): with TemporaryDirectory() as tmp_dir: dset = CCDataset({}, Path(tmp_dir), "sample_dataset", "sample dataset description") From fad4fbf93dc954e0df9c14232073beddd82d8e18 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 9 Nov 2022 13:02:52 +0100 Subject: [PATCH 12/81] use temp folders for cc analysis test data --- tests/cc/test_cc_analysis.py | 8 ++++++-- tests/cc/test_cc_identifiers.py | 0 tests/data/cc/analysis/vulnerable_dataset.json | 2 +- 3 files changed, 7 insertions(+), 3 deletions(-) delete mode 100644 tests/cc/test_cc_identifiers.py diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 6d6524e0..3cbe5f01 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -1,3 +1,4 @@ +import shutil from pathlib import Path from typing import Dict, Set @@ -80,8 +81,11 @@ def cve_dset(cves: Set[CVE]) -> CVEDataset: @pytest.fixture(scope="module") -def cc_dset(data_dir: Path, cve_dset: CVEDataset) -> CCDataset: - cc_dset = CCDataset.from_json(data_dir / "vulnerable_dataset.json") +def cc_dset(data_dir: Path, cve_dset: CVEDataset, tmp_path_factory) -> CCDataset: + tmp_dir = tmp_path_factory.mktemp("cc_dset") + shutil.copytree(data_dir, tmp_dir, dirs_exist_ok=True) + + cc_dset = CCDataset.from_json(tmp_dir / "vulnerable_dataset.json") cc_dset.process_protection_profiles() cc_dset._extract_data() cc_dset.auxillary_datasets.cve_dset = cve_dset diff --git a/tests/cc/test_cc_identifiers.py b/tests/cc/test_cc_identifiers.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json index 0d7e4c45..24441d7d 100644 --- a/tests/data/cc/analysis/vulnerable_dataset.json +++ b/tests/data/cc/analysis/vulnerable_dataset.json @@ -15,7 +15,7 @@ "certs": [ { "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert", - "dgst": "c01e5375331b25dc", + "dgst": "ebd276cca70fd723", "status": "active", "category": "Access Control Devices and Systems", "name": "IBM Security Access Manager for Enterprise Single Sign-On Version 8.2", From e26fb0ce20740b8bf3eb0c89c47f84dfef3673cf Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 9 Nov 2022 13:18:29 +0100 Subject: [PATCH 13/81] mark further download tests with xfail --- examples/cc_cpe_labeling.py | 7 +++++-- tests/cc/test_cc_schemes.py | 17 ++++++++++++++++- tests/test_cpe.py | 1 + tests/test_cve.py | 1 + 4 files changed, 23 insertions(+), 3 deletions(-) diff --git a/examples/cc_cpe_labeling.py b/examples/cc_cpe_labeling.py index 1b508f6a..63c030fd 100644 --- a/examples/cc_cpe_labeling.py +++ b/examples/cc_cpe_labeling.py @@ -23,7 +23,7 @@ def main(): dset.get_certs_from_web(to_download=True) # Automatically match CPEs and CVEs - _, cpe_dset, _ = dset.compute_cpe_heuristics() + dset.compute_cpe_heuristics() dset.compute_related_cves() # Load dataset of ground truth CPE labels @@ -37,7 +37,10 @@ def main(): # Evaluate CPE matching performance metrics (on validation set) and dump classification report into json y_valid = [(x.heuristics.verified_cpe_matches) for x in validation_certs] - evaluate(validation_certs, y_valid, "./my_debug_dataset/classification_report.json", cpe_dset) + assert dset.auxillary_datasets.cpe_dset + evaluate( + validation_certs, y_valid, "./my_debug_dataset/classification_report.json", dset.auxillary_datasets.cpe_dset + ) logger.info(f"{dset.json_path} should now contain fully labeled dataset.") diff --git a/tests/cc/test_cc_schemes.py b/tests/cc/test_cc_schemes.py index d67d31f1..e4fbc713 100644 --- a/tests/cc/test_cc_schemes.py +++ b/tests/cc/test_cc_schemes.py @@ -3,81 +3,96 @@ from sec_certs.dataset.common_criteria import CCSchemeDataset +@pytest.mark.xfail("May fail due to server errors.") def test_australia(): assert len(CCSchemeDataset.get_australia_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_canada(): assert len(CCSchemeDataset.get_canada_certified()) != 0 assert len(CCSchemeDataset.get_canada_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_anssi(): assert len(CCSchemeDataset.get_france_certified()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_bsi(): assert len(CCSchemeDataset.get_germany_certified()) != 0 -@pytest.mark.xfail +@pytest.mark.xfail("May fail due to server errors.") def test_india(): assert len(CCSchemeDataset.get_india_certified()) != 0 assert len(CCSchemeDataset.get_india_archived()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_italy(): assert len(CCSchemeDataset.get_italy_certified()) != 0 assert len(CCSchemeDataset.get_italy_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_japan(): assert len(CCSchemeDataset.get_japan_certified()) != 0 assert len(CCSchemeDataset.get_japan_archived()) != 0 assert len(CCSchemeDataset.get_japan_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_malaysia(): assert len(CCSchemeDataset.get_malaysia_certified()) != 0 assert len(CCSchemeDataset.get_malaysia_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_netherlands(): assert len(CCSchemeDataset.get_netherlands_certified()) != 0 assert len(CCSchemeDataset.get_netherlands_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_norway(): assert len(CCSchemeDataset.get_norway_certified()) != 0 assert len(CCSchemeDataset.get_norway_archived()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_korea(): assert len(CCSchemeDataset.get_korea_certified()) != 0 CCSchemeDataset.get_korea_suspended() assert len(CCSchemeDataset.get_korea_archived()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_singapore(): assert len(CCSchemeDataset.get_singapore_certified()) != 0 assert len(CCSchemeDataset.get_singapore_archived()) != 0 assert len(CCSchemeDataset.get_singapore_in_evaluation()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_spain(): assert len(CCSchemeDataset.get_spain_certified()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_sweden(): assert len(CCSchemeDataset.get_sweden_certified()) != 0 assert len(CCSchemeDataset.get_sweden_in_evaluation()) != 0 assert len(CCSchemeDataset.get_sweden_archived()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_turkey(): assert len(CCSchemeDataset.get_turkey_certified()) != 0 +@pytest.mark.xfail("May fail due to server errors.") def test_usa(): assert len(CCSchemeDataset.get_usa_certified()) != 0 assert len(CCSchemeDataset.get_usa_in_evaluation()) != 0 diff --git a/tests/test_cpe.py b/tests/test_cpe.py index f471c116..e45aa5ba 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -31,6 +31,7 @@ def cpe_dict() -> Dict[str, Any]: @pytest.mark.slow @pytest.mark.monitor_test +@pytest.xfail("May fail due to errors with NIST server.") def test_cpe_dset_from_web(tmp_path: Path): dset = CPEDataset.from_web(tmp_path) assert dset is not None diff --git a/tests/test_cve.py b/tests/test_cve.py index 89db3880..a17b5e56 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -11,6 +11,7 @@ @pytest.mark.slow @pytest.mark.monitor_test +@pytest.mark.xfail("May fail due to errors on NIST server.") def test_from_web(): dset = CVEDataset.from_web() assert dset is not None From 14c369b44010cbce0cf9293a56d1d6fe00440e9e Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 9 Nov 2022 13:19:28 +0100 Subject: [PATCH 14/81] fix xfail marker on cpe_dset_from_web test --- tests/test_cpe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_cpe.py b/tests/test_cpe.py index e45aa5ba..fc45d44f 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -31,7 +31,7 @@ def cpe_dict() -> Dict[str, Any]: @pytest.mark.slow @pytest.mark.monitor_test -@pytest.xfail("May fail due to errors with NIST server.") +@pytest.mark.xfail("May fail due to errors with NIST server.") def test_cpe_dset_from_web(tmp_path: Path): dset = CPEDataset.from_web(tmp_path) assert dset is not None From 8482d827f78b5cde0ce8ede8acb9ba3b8a9985c9 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 10 Nov 2022 09:51:43 +0100 Subject: [PATCH 15/81] pandas tests, cve_dset, cpe_dset unify json_path approach --- notebooks/examples/common_criteria.ipynb | 2 +- sec_certs/dataset/common_criteria.py | 1 + sec_certs/dataset/cpe.py | 83 +++++++++++++----------- sec_certs/dataset/cve.py | 50 ++++++++------ sec_certs/dataset/dataset.py | 21 +++--- sec_certs/sample/cpe.py | 2 - tests/cc/test_cc_analysis.py | 2 +- tests/cc/test_cc_dataset.py | 7 ++ tests/cc/test_cc_maintenance_updates.py | 30 +++++++++ tests/cc/test_cc_schemes.py | 32 ++++----- tests/fips/test_fips_dataset.py | 6 ++ tests/test_cpe.py | 9 ++- tests/test_cve.py | 9 ++- 13 files changed, 163 insertions(+), 91 deletions(-) create mode 100644 tests/cc/test_cc_maintenance_updates.py diff --git a/notebooks/examples/common_criteria.ipynb b/notebooks/examples/common_criteria.ipynb index 677dcd28..e096e640 100644 --- a/notebooks/examples/common_criteria.ipynb +++ b/notebooks/examples/common_criteria.ipynb @@ -194,7 +194,7 @@ "outputs": [], "source": [ "# Automatically match CPEs and CVEs\n", - "_, cpe_dset, _ = dset.compute_cpe_heuristics()\n", + "dset.compute_cpe_heuristics()\n", "dset.compute_related_cves()" ] }, diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 8e42d93f..05308587 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -188,6 +188,7 @@ def from_web_latest(cls) -> "CCDataset": return cls.from_web(config.cc_latest_snapshot, "Downloading CC Dataset", "cc_latest_dataset.json") def _set_local_paths(self): + super()._set_local_paths() for cert in self: cert.set_local_paths(self.reports_pdf_dir, self.targets_pdf_dir, self.reports_txt_dir, self.targets_txt_dir) diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index 1a8f6860..200672f0 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -4,9 +4,8 @@ import tempfile import xml.etree.ElementTree as ET import zipfile -from dataclasses import InitVar, dataclass, field from pathlib import Path -from typing import Any, ClassVar, Dict, Iterator, List, Set, Tuple, Union, cast +from typing import Any, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union, cast import pandas as pd @@ -19,29 +18,39 @@ logger = logging.getLogger(__name__) -@dataclass class CPEDataset(ComplexSerializableType): """ Dataset of CPE records. Includes look-up dictionaries for fast search. """ - was_enhanced_with_vuln_cpes: bool - json_path: Path - cpes: Dict[str, CPE] - vendor_to_versions: Dict[str, Set[str]] = field( - init=False, default_factory=dict - ) # Look-up dict cpe_vendor: list of viable versions - vendor_version_to_cpe: Dict[Tuple[str, str], Set[CPE]] = field( - init=False, default_factory=dict - ) # Look-up dict (cpe_vendor, cpe_version): List of viable cpe items - title_to_cpes: Dict[str, Set[CPE]] = field( - init=False, default_factory=dict - ) # Look-up dict title: List of cert items - vendors: Set[str] = field(init=False, default_factory=set) - - init_lookup_dicts: InitVar[bool] = True - cpe_xml_basename: ClassVar[str] = "official-cpe-dictionary_v2.3.xml" - cpe_url: ClassVar[str] = "https://nvd.nist.gov/feeds/xml/cpe/dictionary/" + cpe_xml_basename + ".zip" + CPE_XML_BASENAME: ClassVar[str] = "official-cpe-dictionary_v2.3.xml" + CPE_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/xml/cpe/dictionary/" + CPE_XML_BASENAME + ".zip" + + def __init__( + self, + was_enhanced_with_vuln_cpes: bool, + cpes: Dict[str, CPE], + json_path: Optional[Union[str, Path]] = None, + ): + self.was_enhanced_with_vuln_cpes = was_enhanced_with_vuln_cpes + self.cpes = cpes + self._json_path = Path(json_path) if json_path else Path.cwd() / (type(self).__name__).lower() + + self.vendor_to_versions: Dict[str, Set[str]] = dict() + self.vendor_version_to_cpe: Dict[Tuple[str, str], Set[CPE]] = dict() + self.title_to_cpes: Dict[str, Set[CPE]] = dict() + self.vendors: Set[str] = set() + + self.build_lookup_dicts() + + @property + def json_path(self) -> Path: + return self._json_path + + @json_path.setter + def json_path(self, new_json_path: Union[str, Path]) -> None: + self._json_path = Path(new_json_path) + self.to_json() def __iter__(self) -> Iterator[CPE]: yield from self.cpes.values() @@ -65,11 +74,7 @@ def __eq__(self, other: object) -> bool: @property def serialized_attributes(self) -> List[str]: - return ["was_enhanced_with_vuln_cpes", "json_path", "cpes"] - - def __post_init__(self, init_lookup_dicts: bool): - if init_lookup_dicts: - self.build_lookup_dicts() + return ["was_enhanced_with_vuln_cpes", "cpes"] def build_lookup_dicts(self) -> None: """ @@ -94,28 +99,25 @@ def build_lookup_dicts(self) -> None: self.title_to_cpes[cpe.title].add(cpe) @classmethod - def from_web(cls, json_path: Union[str, Path], init_lookup_dicts: bool = True) -> "CPEDataset": + def from_web(cls, json_path: Optional[Union[str, Path]] = None) -> "CPEDataset": """ Creates CPEDataset from NIST resources published on-line :param Union[str, Path] json_path: Path to store the dataset to - :param bool init_lookup_dicts: If dictionaries for fast matching should be computed, defaults to True :return CPEDataset: The resulting dataset """ with tempfile.TemporaryDirectory() as tmp_dir: - xml_path = Path(tmp_dir) / cls.cpe_xml_basename - zip_path = Path(tmp_dir) / (cls.cpe_xml_basename + ".zip") - helpers.download_file(cls.cpe_url, zip_path) + xml_path = Path(tmp_dir) / cls.CPE_XML_BASENAME + zip_path = Path(tmp_dir) / (cls.CPE_XML_BASENAME + ".zip") + helpers.download_file(cls.CPE_URL, zip_path) with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(tmp_dir) - return cls._from_xml(xml_path, json_path, init_lookup_dicts) + return cls._from_xml(xml_path, json_path) @classmethod - def _from_xml( - cls, xml_path: Union[str, Path], json_path: Union[str, Path], init_lookup_dicts: bool = True - ) -> "CPEDataset": + def _from_xml(cls, xml_path: Union[str, Path], json_path: Optional[Union[str, Path]] = None) -> "CPEDataset": logger.info("Loading CPE dataset from XML.") root = ET.parse(xml_path).getroot() dct = {} @@ -136,7 +138,7 @@ def _from_xml( dct[cpe_uri] = cached_cpe(cpe_uri, title) - return cls(False, Path(json_path), dct, init_lookup_dicts) + return cls(False, dct, json_path) @classmethod def from_json(cls, input_path: Union[str, Path]) -> "CPEDataset": @@ -147,19 +149,22 @@ def from_json(cls, input_path: Union[str, Path]) -> "CPEDataset": :return CPEDataset: the resulting dataset. """ dset = cast("CPEDataset", ComplexSerializableType.from_json(input_path)) - dset.json_path = Path(input_path) + dset._json_path = Path(input_path) return dset @classmethod - def from_dict(cls, dct: Dict[str, Any], init_lookup_dicts: bool = True) -> "CPEDataset": + def from_dict(cls, dct: Dict[str, Any]) -> "CPEDataset": """ Loads dataset from dictionary. :param Dict[str, Any] dct: Dictionary that holds the dataset - :param bool init_lookup_dicts: Whether look-up dicts should be computed as a part of initialization, defaults to True :return CPEDataset: the resulting dataset. """ - return cls(dct["was_enhanced_with_vuln_cpes"], Path("../"), dct["cpes"], init_lookup_dicts) + return cls( + dct["was_enhanced_with_vuln_cpes"], + dct["cpes"], + Path("../"), + ) def to_pandas(self) -> pd.DataFrame: """ diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index d8bb8ec1..615cab14 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -6,9 +6,8 @@ import shutil import tempfile import zipfile -from dataclasses import dataclass, field from pathlib import Path -from typing import Dict, Final, List, Optional, Set, Union +from typing import ClassVar, Dict, List, Optional, Set, Union import numpy as np import pandas as pd @@ -18,18 +17,29 @@ from sec_certs.config.configuration import config from sec_certs.sample.cpe import CPE, cached_cpe from sec_certs.sample.cve import CVE -from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder, CustomJSONEncoder +from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder from sec_certs.utils.parallel_processing import process_parallel logger = logging.getLogger(__name__) -@dataclass class CVEDataset(ComplexSerializableType): - cves: Dict[str, CVE] - cpe_to_cve_ids_lookup: Dict[str, Set[str]] = field(init=False) - cve_url: Final[str] = "https://nvd.nist.gov/feeds/json/cve/1.1/nvdcve-1.1-" - cpe_match_feed_url: Final[str] = "https://nvd.nist.gov/feeds/json/cpematch/1.0/nvdcpematch-1.0.json.zip" + CVE_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cve/1.1/nvdcve-1.1-" + CPE_MATCH_FEED_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cpematch/1.0/nvdcpematch-1.0.json.zip" + + def __init__(self, cves: Dict[str, CVE], json_path: Optional[Union[str, Path]] = None): + self.cves = cves + self._json_path = Path(json_path) if json_path else Path.cwd() / (type(self).__name__).lower() + self.cpe_to_cve_ids_lookup: Dict[str, Set[str]] = dict() + + @property + def json_path(self) -> Path: + return self._json_path + + @json_path.setter + def json_path(self, new_json_path: Union[str, Path]) -> None: + self._json_path = Path(new_json_path) + self.to_json() @property def serialized_attributes(self) -> List[str]: @@ -89,7 +99,7 @@ def download_cves(cls, output_path_str: str, start_year: int, end_year: int): if not output_path.exists: output_path.mkdir() - urls = [cls.cve_url + str(x) + ".json.zip" for x in range(start_year, end_year + 1)] + urls = [cls.CVE_URL + str(x) + ".json.zip" for x in range(start_year, end_year + 1)] logger.info(f"Identified {len(urls)} CVE files to fetch from nist.gov. Downloading them into {output_path}") with tempfile.TemporaryDirectory() as tmp_dir: @@ -113,7 +123,12 @@ def from_nist_json(cls, input_path: str) -> "CVEDataset": return cls({x.cve_id: x for x in cves}) @classmethod - def from_web(cls, start_year: int = 2002, end_year: int = datetime.datetime.now().year): + def from_web( + cls, + start_year: int = 2002, + end_year: int = datetime.datetime.now().year, + json_path: Optional[Union[str, Path]] = None, + ): logger.info("Building CVE dataset from nist.gov website.") with tempfile.TemporaryDirectory() as tmp_dir: cls.download_cves(tmp_dir, start_year, end_year) @@ -131,20 +146,13 @@ def from_web(cls, start_year: int = 2002, end_year: int = datetime.datetime.now( for r in results: all_cves.update(r.cves) - return cls(all_cves) - - def to_json(self, output_path: Optional[Union[str, Path]] = None): - if output_path is None: - raise RuntimeError( - f"You tried to serialize an object ({type(self)}) that does not have implicit json path. Please provide json_path." - ) - with Path(output_path).open("w") as handle: - json.dump(self, handle, indent=4, cls=CustomJSONEncoder, ensure_ascii=False) + return cls(all_cves, json_path=json_path) @classmethod def from_json(cls, input_path: Union[str, Path]): with Path(input_path).open("r") as handle: dset = json.load(handle, cls=CustomJSONDecoder) + dset._json_path = input_path return dset def get_cve_ids_for_cpe_uri(self, cpe_uri: str) -> Optional[Set[str]]: @@ -204,10 +212,10 @@ def parse_values_cpe(field: Dict) -> List[CPE]: if not input_filepath or not input_filepath.is_file(): logger.debug("NIST mapping file not available, going to download.") with tempfile.TemporaryDirectory() as tmp_dir: - filename = Path(self.cpe_match_feed_url).name + filename = Path(self.CPE_MATCH_FEED_URL).name download_path = Path(tmp_dir) / filename unzipped_path = Path(tmp_dir) / filename.rstrip(".zip") - helpers.download_file(self.cpe_match_feed_url, download_path) + helpers.download_file(self.CPE_MATCH_FEED_URL, download_path) with zipfile.ZipFile(download_path, "r") as zip_handle: zip_handle.extractall(tmp_dir) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index e4ad4caf..8918a2a5 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -182,7 +182,10 @@ def from_json(cls: Type[DatasetSubType], input_path: Union[str, Path]) -> Datase return dset def _set_local_paths(self) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") + if self.auxillary_datasets.cpe_dset: + self.auxillary_datasets.cpe_dset.json_path = self.cpe_dataset_path + if self.auxillary_datasets.cve_dset: + self.auxillary_datasets.cve_dset.json_path = self.cve_dataset_path # Workaround from https://peps.python.org/pep-0673/ applied. def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> None: @@ -246,14 +249,14 @@ def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_cor logger.error(f"Corrupted file at: {p}") p.unlink() - def _prepare_cpe_dataset(self, download_fresh_cpes: bool = False, init_lookup_dicts: bool = True) -> CPEDataset: + def _prepare_cpe_dataset(self, download_fresh_cpes: bool = False) -> CPEDataset: logger.info("Preparing CPE dataset.") if not self.auxillary_datasets_dir.exists(): self.auxillary_datasets_dir.mkdir(parents=True) if not self.cpe_dataset_path.exists() or download_fresh_cpes is True: - cpe_dataset = CPEDataset.from_web(self.cpe_dataset_path, init_lookup_dicts) - cpe_dataset.to_json(str(self.cpe_dataset_path)) + cpe_dataset = CPEDataset.from_web(self.cpe_dataset_path) + cpe_dataset.to_json() else: cpe_dataset = CPEDataset.from_json(str(self.cpe_dataset_path)) @@ -267,10 +270,10 @@ def _prepare_cve_dataset( self.auxillary_datasets_dir.mkdir(parents=True) if not self.cve_dataset_path.exists() or download_fresh_cves is True: - cve_dataset = CVEDataset.from_web() - cve_dataset.to_json(str(self.cve_dataset_path)) + cve_dataset = CVEDataset.from_web(json_path=self.cve_dataset_path) + cve_dataset.to_json() else: - cve_dataset = CVEDataset.from_json(str(self.cve_dataset_path)) + cve_dataset = CVEDataset.from_json(self.cve_dataset_path) cve_dataset.build_lookup_dict(use_nist_cpe_matching_dict, self.nist_cve_cpe_matching_dset_path) return cve_dataset @@ -307,8 +310,8 @@ def filter_condition(cpe: CPE) -> bool: return True logger.info("Computing heuristics: Finding CPE matches for certificates") - self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh_cpes, init_lookup_dicts=False) - self.auxillary_datasets.cpe_dset.build_lookup_dicts() + if not self.auxillary_datasets.cpe_dset or download_fresh_cpes: + self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh_cpes) # Temporarily disabled, see: https://github.com/crocs-muni/sec-certs/issues/173 # if not cpe_dset.was_enhanced_with_vuln_cpes: diff --git a/sec_certs/sample/cpe.py b/sec_certs/sample/cpe.py index f66b8f11..c03b8552 100644 --- a/sec_certs/sample/cpe.py +++ b/sec_certs/sample/cpe.py @@ -26,8 +26,6 @@ class CPE(PandasSerializableType, ComplexSerializableType): "item_name", "version", "title", - "start_version", - "end_version", ] def __init__( diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 3cbe5f01..3eb74115 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -50,7 +50,7 @@ def cpes(cpe_single_sign_on: CPE) -> Set[CPE]: @pytest.fixture(scope="module") def cpe_dset(cpes: Set[CPE]) -> CPEDataset: - return CPEDataset(True, Path("../"), {x.uri: x for x in cpes}) + return CPEDataset(False, {x.uri: x for x in cpes}, Path("../")) @pytest.fixture(scope="module") diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py index bfa8c612..b579227f 100644 --- a/tests/cc/test_cc_dataset.py +++ b/tests/cc/test_cc_dataset.py @@ -164,3 +164,10 @@ def test_download_csv_html_files(): for x in dset.active_csv_tuples: assert x[1].exists() assert x[1].stat().st_size >= constants.MIN_CC_CSV_SIZE + + +def test_to_pandas(toy_dataset: CCDataset): + df = toy_dataset.to_pandas() + assert df.shape == (len(toy_dataset), len(CommonCriteriaCert.pandas_columns)) + assert df.index.name == "dgst" + assert set(df.columns) == (set(CommonCriteriaCert.pandas_columns).union({"year_from"})) - {"dgst"} diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py new file mode 100644 index 00000000..486e067d --- /dev/null +++ b/tests/cc/test_cc_maintenance_updates.py @@ -0,0 +1,30 @@ +def test_methods_not_meant_to_be_implemented(): + pass + + +def test_download_artifacts(): + pass + + +def test_convert_artifacts(): + pass + + +def test_extract_data(): + pass + + +def test_to_json(): + pass + + +def test_from_json(): + pass + + +def test_to_pandas(): + pass + + +def test_from_web(): + pass diff --git a/tests/cc/test_cc_schemes.py b/tests/cc/test_cc_schemes.py index e4fbc713..a65a0883 100644 --- a/tests/cc/test_cc_schemes.py +++ b/tests/cc/test_cc_schemes.py @@ -3,96 +3,96 @@ from sec_certs.dataset.common_criteria import CCSchemeDataset -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_australia(): assert len(CCSchemeDataset.get_australia_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_canada(): assert len(CCSchemeDataset.get_canada_certified()) != 0 assert len(CCSchemeDataset.get_canada_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_anssi(): assert len(CCSchemeDataset.get_france_certified()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_bsi(): assert len(CCSchemeDataset.get_germany_certified()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_india(): assert len(CCSchemeDataset.get_india_certified()) != 0 assert len(CCSchemeDataset.get_india_archived()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_italy(): assert len(CCSchemeDataset.get_italy_certified()) != 0 assert len(CCSchemeDataset.get_italy_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_japan(): assert len(CCSchemeDataset.get_japan_certified()) != 0 assert len(CCSchemeDataset.get_japan_archived()) != 0 assert len(CCSchemeDataset.get_japan_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_malaysia(): assert len(CCSchemeDataset.get_malaysia_certified()) != 0 assert len(CCSchemeDataset.get_malaysia_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_netherlands(): assert len(CCSchemeDataset.get_netherlands_certified()) != 0 assert len(CCSchemeDataset.get_netherlands_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_norway(): assert len(CCSchemeDataset.get_norway_certified()) != 0 assert len(CCSchemeDataset.get_norway_archived()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_korea(): assert len(CCSchemeDataset.get_korea_certified()) != 0 CCSchemeDataset.get_korea_suspended() assert len(CCSchemeDataset.get_korea_archived()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_singapore(): assert len(CCSchemeDataset.get_singapore_certified()) != 0 assert len(CCSchemeDataset.get_singapore_archived()) != 0 assert len(CCSchemeDataset.get_singapore_in_evaluation()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_spain(): assert len(CCSchemeDataset.get_spain_certified()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_sweden(): assert len(CCSchemeDataset.get_sweden_certified()) != 0 assert len(CCSchemeDataset.get_sweden_in_evaluation()) != 0 assert len(CCSchemeDataset.get_sweden_archived()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_turkey(): assert len(CCSchemeDataset.get_turkey_certified()) != 0 -@pytest.mark.xfail("May fail due to server errors.") +@pytest.mark.xfail(reason="May fail due to server errors.") def test_usa(): assert len(CCSchemeDataset.get_usa_certified()) != 0 assert len(CCSchemeDataset.get_usa_in_evaluation()) != 0 diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index b7c8aebf..010d503d 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -299,3 +299,9 @@ def test_openssl_chunk(certs_to_process): assert not dataset.certs[fips_dgst("3363")].heuristics.st_references.directly_referencing assert set(dataset.certs[fips_dgst("3608")].heuristics.st_references.directly_referencing) == {"2398"} assert set(dataset.certs[fips_dgst("3158")].heuristics.web_references.directly_referencing) == {"2398"} + + +@pytest.mark.skip(reason="FIPS tests to-be-refactored") +def test_to_pandas(fips_dset: FIPSDataset): + # copy-paste code from the same test in different classes. + pass diff --git a/tests/test_cpe.py b/tests/test_cpe.py index fc45d44f..b2c06179 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -31,7 +31,7 @@ def cpe_dict() -> Dict[str, Any]: @pytest.mark.slow @pytest.mark.monitor_test -@pytest.mark.xfail("May fail due to errors with NIST server.") +@pytest.mark.xfail(reason="May fail due to errors with NIST server.") def test_cpe_dset_from_web(tmp_path: Path): dset = CPEDataset.from_web(tmp_path) assert dset is not None @@ -121,3 +121,10 @@ def test_cpe_from_to_dict(cpe_dict): assert cpe_dict == ret other_cpe = CPE.from_dict(ret) assert cpe == other_cpe + + +def test_to_pandas(cpe_dset: CPEDataset): + df = cpe_dset.to_pandas() + assert df.shape == (len(cpe_dset), len(CPE.pandas_columns) - 1) + assert df.index.name == "uri" + assert set(df.columns) == set(CPE.pandas_columns) - {"uri"} diff --git a/tests/test_cve.py b/tests/test_cve.py index a17b5e56..59257727 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -11,7 +11,7 @@ @pytest.mark.slow @pytest.mark.monitor_test -@pytest.mark.xfail("May fail due to errors on NIST server.") +@pytest.mark.xfail(reason="May fail due to errors on NIST server.") def test_from_web(): dset = CVEDataset.from_web() assert dset is not None @@ -100,3 +100,10 @@ def test_cve_from_to_dict(cve_dict: Dict[str, Any]): assert ret == cve_dict other_cve = CVE.from_dict(ret) assert cve == other_cve + + +def test_to_pandas(cve_dset: CVEDataset): + df = cve_dset.to_pandas() + assert df.shape == (len(cve_dset), len(CVE.pandas_columns) - 1) + assert df.index.name == "cve_id" + assert set(df.columns) == set(CVE.pandas_columns) - {"cve_id"} From 24f11fba4409199ea85e97abbc408c161c2e8f96 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 10 Nov 2022 14:24:20 +0100 Subject: [PATCH 16/81] test maintenance updates --- sec_certs/dataset/common_criteria.py | 64 +++++++-- tests/cc/test_cc_dataset.py | 20 +-- tests/cc/test_cc_maintenance_updates.py | 82 +++++++++-- .../maintenances/Maintenance updates.json | 79 +++++++++++ tests/data/cc/dataset/cc_products_active.csv | 4 +- tests/data/cc/dataset/cc_products_active.html | 132 +++++++++++++++++- tests/data/cc/dataset/toy_dataset.json | 98 ++++++++++++- 7 files changed, 441 insertions(+), 38 deletions(-) create mode 100644 tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 05308587..8f31243b 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -5,6 +5,7 @@ import locale import shutil import tempfile +from dataclasses import dataclass from datetime import datetime from pathlib import Path from typing import Callable, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union @@ -18,6 +19,8 @@ import sec_certs.utils.sanitization from sec_certs import constants from sec_certs.config.configuration import config +from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cve import CVEDataset from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset, logger from sec_certs.dataset.protection_profile import ProtectionProfileDataset from sec_certs.model.dependency_finder import DependencyFinder @@ -33,12 +36,41 @@ from sec_certs.utils.sanitization import sanitize_navigable_string as sns -class CCDataset(Dataset[CommonCriteriaCert, AuxillaryDatasets], ComplexSerializableType): +@dataclass +class CCAuxillaryDatasets(AuxillaryDatasets): + cpe_dset: Optional[CPEDataset] = None + cve_dset: Optional[CVEDataset] = None + pp_dset: Optional[ProtectionProfileDataset] = None + mu_dset: Optional[CCDatasetMaintenanceUpdates] = None + + +class CCDataset(Dataset[CommonCriteriaCert, CCAuxillaryDatasets], ComplexSerializableType): """ Class that holds CommonCriteriaCert. Serializable into json, pandas, dictionary. Conveys basic certificate manipulations and dataset transformations. Many private methods that perform internal operations, feel free to exploit them. """ + def __init__( + self, + certs: Dict[str, CommonCriteriaCert] = dict(), + root_dir: Optional[Union[str, Path]] = None, + name: Optional[str] = None, + description: str = None, + state: Optional[Dataset.DatasetInternalState] = None, + auxillary_datasets: Optional[CCAuxillaryDatasets] = None, + ): + self.certs = certs + self._root_dir = Path(root_dir) if root_dir else Path.cwd() / (type(self).__name__).lower() + self.timestamp = datetime.now() + self.sha256_digest = "not implemented" + self.name = name if name else type(self).__name__ + " dataset" + self.description = description if description else datetime.now().strftime("%d/%m/%Y %H:%M:%S") + self.state = state if state else self.DatasetInternalState() + + self.auxillary_datasets: CCAuxillaryDatasets = ( + auxillary_datasets if auxillary_datasets else CCAuxillaryDatasets() + ) + def to_pandas(self) -> pd.DataFrame: """ Return self serialized into pandas DataFrame @@ -114,21 +146,18 @@ def pp_dataset_path(self) -> Path: return self.auxillary_datasets_dir / "pp_dataset.json" @property - def mu_dataset_path(self) -> Path: + def mu_dataset_dir(self) -> Path: """ Returns directory that holds dataset of maintenance updates """ - return self.certs_dir / "maintenances" + return self.auxillary_datasets_dir / "maintenances" @property - def mu_dataset(self) -> "CCDatasetMaintenanceUpdates": + def mu_dataset_path(self) -> Path: """ - Returns object of dataset of maintenance updates if it exists, raises otherwise + Returns json that holds the datase of maintenance updates """ - if not self.mu_dataset_path.exists(): - raise ValueError("The dataset with maintenance updates does not exist") - - return CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path / "Maintenance updates.json") + return self.mu_dataset_dir / "Maintenance updates.json" BASE_URL: ClassVar[str] = "https://www.commoncriteriaportal.org" @@ -776,6 +805,9 @@ def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: :return CCDatasetMaintenanceUpdates: the resulting dataset of maintenance updates """ + if not self.mu_dataset_dir.exists(): + self.mu_dataset_dir.mkdir(parents=True) + maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] updates = list( itertools.chain.from_iterable( @@ -783,7 +815,7 @@ def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: ) ) update_dset: CCDatasetMaintenanceUpdates = CCDatasetMaintenanceUpdates( - {x.dgst: x for x in updates}, root_dir=self.mu_dataset_path, name="Maintenance updates" + {x.dgst: x for x in updates}, root_dir=self.mu_dataset_dir, name="Maintenance updates" ) update_dset.download_all_artifacts() update_dset.convert_all_pdfs() @@ -792,8 +824,8 @@ def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: return update_dset def process_auxillary_datasets(self) -> None: - self.process_protection_profiles() - # TODO: Also process MUs + self.auxillary_datasets.pp_dset = self.process_protection_profiles() + self.auxillary_datasets.mu_dset = self.process_maintenance_updates() class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): @@ -831,6 +863,14 @@ def compute_related_cves(self, download_fresh_cves: bool = False) -> None: def process_auxillary_datasets(self) -> None: raise NotImplementedError + def analyze_certificates(self, fresh: bool = True) -> None: + raise NotImplementedError + + def get_certs_from_web( + self, to_download: bool = True, keep_metadata: bool = True, get_active: bool = True, get_archived: bool = True + ) -> None: + raise NotImplementedError + @classmethod def from_json(cls, input_path: Union[str, Path]) -> "CCDatasetMaintenanceUpdates": input_path = Path(input_path) diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py index b579227f..fae19282 100644 --- a/tests/cc/test_cc_dataset.py +++ b/tests/cc/test_cc_dataset.py @@ -49,11 +49,13 @@ def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): template_report_pdf_hashes = { "309ac2fd7f2dcf17": "774c41fbba980191ca40ae610b2f61484c5997417b3325b6fd68b345173bde52", "8cf86948f02f047d": "533a5995ef8b736cc48cfda30e8aafec77d285511471e0e5a9e8007c8750203a", + "8a5e6bcda602920c": "e277151e4b279085cd3041ce914ffb3942b43e5ace911c557ad6b8ed764a4ece", } template_st_pdf_hashes = { "309ac2fd7f2dcf17": "b9a45995d9e40b2515506bbf5945e806ef021861820426c6d0a6a074090b47a9", "8cf86948f02f047d": "3c8614338899d956e9e56f1aa88d90e37df86f3310b875d9d14ec0f71e4759be", + "8a5e6bcda602920c": "fcee91f09bb72a6526a1f94d0ab754a6db3fbe3ba5773cd372df19788bb25292", } with TemporaryDirectory() as td: @@ -65,6 +67,8 @@ def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): or toy_dataset["309ac2fd7f2dcf17"].state.st_download_ok or toy_dataset["8cf86948f02f047d"].state.report_download_ok or toy_dataset["8cf86948f02f047d"].state.st_download_ok + or toy_dataset["8a5e6bcda602920c"].state.report_download_ok + or toy_dataset["8a5e6bcda602920c"].state.st_download_ok ): pytest.xfail(reason="Fail due to error during download") @@ -73,14 +77,12 @@ def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): for cert in toy_dataset: assert cert.state.report_pdf_hash == template_report_pdf_hashes[cert.dgst] assert cert.state.st_pdf_hash == template_st_pdf_hashes[cert.dgst] - - assert not toy_dataset["309ac2fd7f2dcf17"].state.report_convert_garbage - assert not toy_dataset["309ac2fd7f2dcf17"].state.st_convert_garbage - assert toy_dataset["309ac2fd7f2dcf17"].state.report_convert_ok - assert toy_dataset["309ac2fd7f2dcf17"].state.st_convert_ok - - assert toy_dataset["309ac2fd7f2dcf17"].state.report_txt_path.exists() - assert toy_dataset["309ac2fd7f2dcf17"].state.st_txt_path.exists() + assert not cert.state.report_convert_garbage + assert not cert.state.st_convert_garbage + assert cert.state.report_convert_ok + assert cert.state.st_convert_ok + assert cert.state.report_txt_path.exists() + assert cert.state.st_txt_path.exists() template_report_txt_path = data_dir / "report_309ac2fd7f2dcf17.txt" template_st_txt_path = data_dir / "target_309ac2fd7f2dcf17.txt" @@ -139,7 +141,7 @@ def test_build_dataset(data_dir: Path, crt: CommonCriteriaCert, toy_dataset: CCD ) assert len(list(dataset_path.iterdir())) == 0 - assert len(dset) == 2 + assert len(dset) == 3 assert crt in dset assert dset == toy_dataset diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index 486e067d..c409d2c7 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -1,30 +1,84 @@ +import json +from pathlib import Path + +import pytest + +import tests.data.cc.dataset +from sec_certs.dataset import CCDataset, CCDatasetMaintenanceUpdates +from sec_certs.sample.cc_maintenance_update import CommonCriteriaMaintenanceUpdate + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.cc.dataset.__path__[0]) + + +@pytest.fixture(scope="module") +def cc_dset(data_dir: Path) -> CCDataset: + return CCDataset.from_json(data_dir / "toy_dataset.json") + + +@pytest.fixture +def mu_dset(data_dir: Path) -> CCDatasetMaintenanceUpdates: + return CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/Maintenance updates.json") + + def test_methods_not_meant_to_be_implemented(): - pass + dset = CCDatasetMaintenanceUpdates(dict(), Path("..")) + with pytest.raises(NotImplementedError): + dset.analyze_certificates() + with pytest.raises(NotImplementedError): + dset._compute_heuristics() + with pytest.raises(NotImplementedError): + dset.process_auxillary_datasets() + with pytest.raises(NotImplementedError): + dset.compute_related_cves() + with pytest.raises(NotImplementedError): + dset.get_certs_from_web() + +def test_download_artifacts(mu_dset: CCDatasetMaintenanceUpdates): + # Conversion and extraction is identical to CC, will not test. + mu_dset.download_all_artifacts() + mu = mu_dset["cert_8a5e6bcda602920c_update_559ed93dd80320b5"] -def test_download_artifacts(): - pass + if not (mu.state.report_download_ok or mu.state.st_download_ok): + pytest.xfail(reason="Fail due to error on CC server.") + assert mu.state.report_pdf_hash == "80bada65614c1b037c13efa78996a8910700d0e05a3ca217286f76d7dacefe62" + assert mu.state.st_pdf_hash == "d42e4364d037ba742fcd4050a9a84d0e6300f93eb68bcfe8c61f72c429c9ceca" -def test_convert_artifacts(): - pass +def test_dataset_to_json(mu_dset: CCDatasetMaintenanceUpdates, data_dir: Path, tmp_path: Path): + mu_dset.to_json(tmp_path / "dset.json") -def test_extract_data(): - pass + with (tmp_path / "dset.json").open("r") as handle: + data = json.load(handle) + with (data_dir / "auxillary_datasets/maintenances/Maintenance updates.json").open("r") as handle: + template_data = json.load(handle) -def test_to_json(): - pass + del template_data["timestamp"] + del data["timestamp"] + assert data == template_data -def test_from_json(): - pass +def test_dataset_from_json(mu_dset: CCDatasetMaintenanceUpdates, data_dir: Path): + assert mu_dset == CCDatasetMaintenanceUpdates.from_json( + data_dir / "auxillary_datasets/maintenances/Maintenance updates.json" + ) -def test_to_pandas(): - pass +def test_to_pandas(mu_dset: CCDatasetMaintenanceUpdates): + df = mu_dset.to_pandas() + assert df.shape == (len(mu_dset), len(CommonCriteriaMaintenanceUpdate.pandas_columns) - 1) + assert df.index.name == "dgst" + assert set(df.columns) == set(CommonCriteriaMaintenanceUpdate.pandas_columns) - {"dgst"} +@pytest.mark.skip(reason="Will work only with fresh snapshot on seccerts.org") def test_from_web(): - pass + dset = CCDatasetMaintenanceUpdates.from_web_latest() + assert dset is not None + assert len(dset) >= 492 # Contents as of November 2022, maintenances should not disappear + assert "cert_8a5e6bcda602920c_update_559ed93dd80320b5" in dset # random cert verified to be present diff --git a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json new file mode 100644 index 00000000..a14e39a7 --- /dev/null +++ b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json @@ -0,0 +1,79 @@ +{ + "_type": "sec_certs.dataset.common_criteria.CCDatasetMaintenanceUpdates", + "state": { + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", + "meta_sources_parsed": true, + "artifacts_downloaded": true, + "pdfs_converted": false, + "certs_analyzed": false + }, + "timestamp": "2022-11-10 13:44:35.171285", + "sha256_digest": "not implemented", + "name": "Maintenance updates", + "description": "dataset_description", + "n_certs": 1, + "certs": [ + { + "_type": "sec_certs.sample.cc_maintenance_update.CommonCriteriaMaintenanceUpdate", + "dgst": "cert_8a5e6bcda602920c_update_559ed93dd80320b5", + "name": "Fortinet FortiGate w/ FortiOS v5.6.7 Build 6022", + "report_link": "https://www.commoncriteriaportal.org/files/epfiles/383-7-159%20MR%20v1.0e.pdf", + "st_link": "https://www.commoncriteriaportal.org/files/epfiles/383-7-159%20ST%20v1.4%20CCRA.pdf", + "state": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.InternalState", + "st_download_ok": true, + "report_download_ok": true, + "st_convert_garbage": false, + "report_convert_garbage": false, + "st_convert_ok": false, + "report_convert_ok": false, + "st_extract_ok": false, + "report_extract_ok": false, + "errors": [], + "st_pdf_hash": "d42e4364d037ba742fcd4050a9a84d0e6300f93eb68bcfe8c61f72c429c9ceca", + "report_pdf_hash": "80bada65614c1b037c13efa78996a8910700d0e05a3ca217286f76d7dacefe62", + "st_txt_hash": null, + "report_txt_hash": null + }, + "pdf_data": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.PdfData", + "report_metadata": null, + "st_metadata": null, + "report_frontpage": null, + "st_frontpage": null, + "report_keywords": null, + "st_keywords": null, + "report_filename": "383-7-159 MR v1.0e.pdf", + "st_filename": "383-7-159 ST v1.4 CCRA.pdf" + }, + "heuristics": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.Heuristics", + "extracted_versions": null, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "cert_lab": null, + "cert_id": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "report_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "extracted_sars": null, + "direct_dependency_cves": null, + "indirect_dependency_cves": null + }, + "related_cert_digest": "8a5e6bcda602920c", + "maintenance_date": "2019-08-26" + } + ] +} \ No newline at end of file diff --git a/tests/data/cc/dataset/cc_products_active.csv b/tests/data/cc/dataset/cc_products_active.csv index a361ba35..4ed43861 100644 --- a/tests/data/cc/dataset/cc_products_active.csv +++ b/tests/data/cc/dataset/cc_products_active.csv @@ -1,3 +1,5 @@ "Category","Name","Manufacturer","Scheme","Assurance Level","Protection Profile(s)","Certification Date","Archived Date","Certification Report URL","Security Target URL","Maintenance Date","Maintenance Title","Maintenance Report","Maintenance ST" "Access Control Devices and Systems","NetIQ Identity Manager 4.7","NetIQ Corporation","SE","EAL3+,ALC_FLR.2","","06/15/2020","06/15/2025","http://www.commoncriteriaportal.org:443/files/epfiles/Certification Report - NetIQ® Identity Manager 4.7.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/ST - NetIQ Identity Manager 4.7.pdf","","","","" -"Access Control Devices and Systems","Magic SSO V4.0","Dreamsecurity Co., Ltd.","KR","None","KECS-PP-0822-2017 SSO V1.0","11/15/2019","11/15/2024","http://www.commoncriteriaportal.org:443/files/epfiles/KECS-CR-19-70 Magic SSO V4.0(eng) V1.0.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/Magic_SSO_V4.0-ST-v1.4_EN.pdf","","","","" \ No newline at end of file +"Access Control Devices and Systems","Magic SSO V4.0","Dreamsecurity Co., Ltd.","KR","None","KECS-PP-0822-2017 SSO V1.0","11/15/2019","11/15/2024","http://www.commoncriteriaportal.org:443/files/epfiles/KECS-CR-19-70 Magic SSO V4.0(eng) V1.0.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/Magic_SSO_V4.0-ST-v1.4_EN.pdf","","","","" +"Boundary Protection Devices and Systems","Fortinet FortiGate w/ FortiOS v5.6.7","Fortinet, Inc.","CA","None","CPP_FW_V2.0E","05/22/2019","05/24/2024","http://www.commoncriteriaportal.org:443/files/epfiles/383-4-450 CR v1.0a.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/383-4-450 ST v1.3A.pdf","08/26/2019","Fortinet FortiGate w/ FortiOS v5.6.7 Build 6022","http://www.commoncriteriaportal.org:443/files/epfiles/383-7-159 MR v1.0e.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/383-7-159 ST v1.4 CCRA.pdf" +"Boundary Protection Devices and Systems","Fortinet FortiGate w/ FortiOS v5.6.7","Fortinet, Inc.","CA","None","CPP_FW_V2.0E","05/22/2019","05/24/2024","http://www.commoncriteriaportal.org:443/files/epfiles/383-4-450 CR v1.0a.pdf","http://www.commoncriteriaportal.org:443/files/epfiles/383-4-450 ST v1.3A.pdf","","","","" \ No newline at end of file diff --git a/tests/data/cc/dataset/cc_products_active.html b/tests/data/cc/dataset/cc_products_active.html index ca76dc18..1a0e41af 100644 --- a/tests/data/cc/dataset/cc_products_active.html +++ b/tests/data/cc/dataset/cc_products_active.html @@ -561,7 +561,137 @@

- + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ProductVendorProduct
Certificate		Date Certificate
Issued		Certificate
Validity
Expiration
Date		ComplianceScheme
+ This list was generated on Tuesday, October 4, 2022, at 9:32 AM +
+ Data Protection – 61 Certified Products +
+ + Fortinet FortiGate w/ FortiOS v5.6.7   +
+ +
+ + + + + +
+
Maintenance Report(s)
+
    + +
    +
  1. + 2019-08-26 – Fortinet FortiGate w/ FortiOS v5.6.7 Build 6022
    + Maintenance Report + + Maintenance ST + +
    2. + +
+
+ + +
+ 		+ Fortinet, Inc. + + + CCRA Certificate + + 2019-05-222024-05-24 + + + PP Compliant + + + CA – Canadian Common Criteria Scheme
CA +
+ diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json index 9de1e06f..cb0f0f41 100644 --- a/tests/data/cc/dataset/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -11,7 +11,7 @@ "sha256_digest": "not implemented", "name": "toy dataset", "description": "toy dataset description", - "n_certs": 2, + "n_certs": 3, "certs": [ { "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert", @@ -183,6 +183,102 @@ "indirectly_referencing": null } } + }, + { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert", + "dgst": "8a5e6bcda602920c", + "status": "active", + "category": "Boundary Protection Devices and Systems", + "name": "Fortinet FortiGate w/ FortiOS v5.6.7", + "manufacturer": "Fortinet, Inc.", + "scheme": "CA", + "security_level": { + "_type": "Set", + "elements": [] + }, + "not_valid_before": "2019-05-22", + "not_valid_after": "2024-05-24", + "report_link": "https://www.commoncriteriaportal.org/files/epfiles/383-4-450%20CR%20v1.0a.pdf", + "st_link": "https://www.commoncriteriaportal.org/files/epfiles/383-4-450%20ST%20v1.3A.pdf", + "cert_link": "https://www.commoncriteriaportal.org/files/epfiles/383-4-450%20CT%20v1.0a.pdf", + "manufacturer_web": "https://www.fortinet.com/", + "protection_profiles": { + "_type": "Set", + "elements": [ + { + "_type": "sec_certs.sample.protection_profile.ProtectionProfile", + "pp_name": "collaborative Protection Profile for Stateful Traffic Filter Firewalls v2.0 + Errata 20180314", + "pp_eal": null, + "pp_link": "https://www.commoncriteriaportal.org/files/ppfiles/CPP_FW_V2.0E.pdf", + "pp_ids": null + } + ] + }, + "maintenance_updates": { + "_type": "Set", + "elements": [ + { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.MaintenanceReport", + "maintenance_date": "2019-08-26", + "maintenance_title": "Fortinet FortiGate w/ FortiOS v5.6.7 Build 6022", + "maintenance_report_link": "https://www.commoncriteriaportal.org/files/epfiles/383-7-159%20MR%20v1.0e.pdf", + "maintenance_st_link": "https://www.commoncriteriaportal.org/files/epfiles/383-7-159%20ST%20v1.4%20CCRA.pdf" + } + ] + }, + "state": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.InternalState", + "st_download_ok": false, + "report_download_ok": false, + "st_convert_garbage": false, + "report_convert_garbage": false, + "st_convert_ok": false, + "report_convert_ok": false, + "st_extract_ok": false, + "report_extract_ok": false, + "errors": [], + "st_pdf_hash": null, + "report_pdf_hash": null, + "st_txt_hash": null, + "report_txt_hash": null + }, + "pdf_data": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.PdfData", + "report_metadata": null, + "st_metadata": null, + "report_frontpage": null, + "st_frontpage": null, + "report_keywords": null, + "st_keywords": null, + "report_filename": null, + "st_filename": null + }, + "heuristics": { + "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.Heuristics", + "extracted_versions": null, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "cert_lab": null, + "cert_id": null, + "extracted_sars": null, + "direct_dependency_cves": null, + "indirect_dependency_cves": null, + "report_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "directly_referencing": null, + "indirectly_referenced_by": null, + "indirectly_referencing": null + }, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "directly_referencing": null, + "indirectly_referenced_by": null, + "indirectly_referencing": null + } + } } ] } \ No newline at end of file From 90521384dc416b8dfb22f12b27e22e2f14ad50d6 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 12:07:47 +0100 Subject: [PATCH 17/81] fix paths handling in CPEDataset, CVEDataset --- sec_certs/constants.py | 3 +++ sec_certs/dataset/cpe.py | 26 +++++++------------------- sec_certs/dataset/cve.py | 8 ++++---- sec_certs/serialization/json.py | 4 +++- tests/test_cpe.py | 6 ++++++ tests/test_cve.py | 6 ++++++ 6 files changed, 29 insertions(+), 24 deletions(-) diff --git a/sec_certs/constants.py b/sec_certs/constants.py index 32769cb9..50af2d95 100644 --- a/sec_certs/constants.py +++ b/sec_certs/constants.py @@ -1,4 +1,7 @@ import re +from pathlib import Path + +DUMMY_NONEXISTING_PATH = Path("/this/is/dummy/nonexisting/path") RESPONSE_OK = 200 RETURNCODE_OK = "ok" diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index 200672f0..b67d678d 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -5,7 +5,7 @@ import xml.etree.ElementTree as ET import zipfile from pathlib import Path -from typing import Any, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union, cast +from typing import ClassVar, Dict, Iterator, List, Set, Tuple, Union, cast import pandas as pd @@ -30,11 +30,11 @@ def __init__( self, was_enhanced_with_vuln_cpes: bool, cpes: Dict[str, CPE], - json_path: Optional[Union[str, Path]] = None, + json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, ): self.was_enhanced_with_vuln_cpes = was_enhanced_with_vuln_cpes self.cpes = cpes - self._json_path = Path(json_path) if json_path else Path.cwd() / (type(self).__name__).lower() + self._json_path = Path(json_path) self.vendor_to_versions: Dict[str, Set[str]] = dict() self.vendor_version_to_cpe: Dict[Tuple[str, str], Set[CPE]] = dict() @@ -99,7 +99,7 @@ def build_lookup_dicts(self) -> None: self.title_to_cpes[cpe.title].add(cpe) @classmethod - def from_web(cls, json_path: Optional[Union[str, Path]] = None) -> "CPEDataset": + def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH) -> "CPEDataset": """ Creates CPEDataset from NIST resources published on-line @@ -117,7 +117,9 @@ def from_web(cls, json_path: Optional[Union[str, Path]] = None) -> "CPEDataset": return cls._from_xml(xml_path, json_path) @classmethod - def _from_xml(cls, xml_path: Union[str, Path], json_path: Optional[Union[str, Path]] = None) -> "CPEDataset": + def _from_xml( + cls, xml_path: Union[str, Path], json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH + ) -> "CPEDataset": logger.info("Loading CPE dataset from XML.") root = ET.parse(xml_path).getroot() dct = {} @@ -152,20 +154,6 @@ def from_json(cls, input_path: Union[str, Path]) -> "CPEDataset": dset._json_path = Path(input_path) return dset - @classmethod - def from_dict(cls, dct: Dict[str, Any]) -> "CPEDataset": - """ - Loads dataset from dictionary. - - :param Dict[str, Any] dct: Dictionary that holds the dataset - :return CPEDataset: the resulting dataset. - """ - return cls( - dct["was_enhanced_with_vuln_cpes"], - dct["cpes"], - Path("../"), - ) - def to_pandas(self) -> pd.DataFrame: """ Turns the dataset into pandas DataFrame. Each CPE record forms a row. diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index 615cab14..12489f7f 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -27,9 +27,9 @@ class CVEDataset(ComplexSerializableType): CVE_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cve/1.1/nvdcve-1.1-" CPE_MATCH_FEED_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cpematch/1.0/nvdcpematch-1.0.json.zip" - def __init__(self, cves: Dict[str, CVE], json_path: Optional[Union[str, Path]] = None): + def __init__(self, cves: Dict[str, CVE], json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH): self.cves = cves - self._json_path = Path(json_path) if json_path else Path.cwd() / (type(self).__name__).lower() + self._json_path = Path(json_path) self.cpe_to_cve_ids_lookup: Dict[str, Set[str]] = dict() @property @@ -127,7 +127,7 @@ def from_web( cls, start_year: int = 2002, end_year: int = datetime.datetime.now().year, - json_path: Optional[Union[str, Path]] = None, + json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, ): logger.info("Building CVE dataset from nist.gov website.") with tempfile.TemporaryDirectory() as tmp_dir: @@ -146,7 +146,7 @@ def from_web( for r in results: all_cves.update(r.cves) - return cls(all_cves, json_path=json_path) + return cls(all_cves, json_path) @classmethod def from_json(cls, input_path: Union[str, Path]): diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py index b07184ca..ed11e5be 100644 --- a/sec_certs/serialization/json.py +++ b/sec_certs/serialization/json.py @@ -5,6 +5,8 @@ from pathlib import Path from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union +from sec_certs import constants + T = TypeVar("T", bound="ComplexSerializableType") @@ -37,7 +39,7 @@ def from_dict(cls: Type[T], dct: Dict) -> T: raise TypeError(f"Dict: {dct} on {cls.__mro__}") from e def to_json(self, output_path: Optional[Union[str, Path]] = None) -> None: - if not output_path and (not hasattr(self, "json_path") or not self.json_path): # type: ignore + if not output_path and (not hasattr(self, "json_path") or not self.json_path or self.json_path == constants.DUMMY_NONEXISTING_PATH): # type: ignore raise ValueError( f"The object {self} of type {self.__class__} does not have json_path attribute set but to_json() was called without an argument." ) diff --git a/tests/test_cpe.py b/tests/test_cpe.py index b2c06179..240a8b29 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -128,3 +128,9 @@ def test_to_pandas(cpe_dset: CPEDataset): assert df.shape == (len(cpe_dset), len(CPE.pandas_columns) - 1) assert df.index.name == "uri" assert set(df.columns) == set(CPE.pandas_columns) - {"uri"} + + +def test_serialization_missing_path(): + dummy_dset = CPEDataset(False, dict()) + with pytest.raises(ValueError): + dummy_dset.to_json() diff --git a/tests/test_cve.py b/tests/test_cve.py index 59257727..a436a5e8 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -107,3 +107,9 @@ def test_to_pandas(cve_dset: CVEDataset): assert df.shape == (len(cve_dset), len(CVE.pandas_columns) - 1) assert df.index.name == "cve_id" assert set(df.columns) == set(CVE.pandas_columns) - {"cve_id"} + + +def test_serialization_missing_path(): + dummy_dset = CVEDataset({}) + with pytest.raises(ValueError): + dummy_dset.to_json() From 67c62959c3a4e8a64bcabc209c6509075adc5ff8 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 12:26:18 +0100 Subject: [PATCH 18/81] cleanup path issues --- sec_certs/dataset/common_criteria.py | 8 ++++---- sec_certs/dataset/dataset.py | 6 +++--- sec_certs/dataset/fips_algorithm.py | 4 ++-- sec_certs/serialization/json.py | 7 ++++++- tests/cc/test_cc_analysis.py | 2 +- tests/cc/test_cc_maintenance_updates.py | 2 +- 6 files changed, 17 insertions(+), 12 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 8f31243b..7843cbbf 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -53,14 +53,14 @@ class CCDataset(Dataset[CommonCriteriaCert, CCAuxillaryDatasets], ComplexSeriali def __init__( self, certs: Dict[str, CommonCriteriaCert] = dict(), - root_dir: Optional[Union[str, Path]] = None, + root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, name: Optional[str] = None, description: str = None, state: Optional[Dataset.DatasetInternalState] = None, auxillary_datasets: Optional[CCAuxillaryDatasets] = None, ): self.certs = certs - self._root_dir = Path(root_dir) if root_dir else Path.cwd() / (type(self).__name__).lower() + self._root_dir = Path(root_dir) self.timestamp = datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" @@ -837,8 +837,8 @@ class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): # Quite difficult to achieve correct behaviour with MyPy here, opting for ignore def __init__( self, - certs: Dict[str, CommonCriteriaMaintenanceUpdate], # type: ignore - root_dir: Path, + certs: Dict[str, CommonCriteriaMaintenanceUpdate] = dict(), # type: ignore + root_dir: Path = constants.DUMMY_NONEXISTING_PATH, name: str = "dataset name", description: str = "dataset_description", state: Optional[CCDataset.DatasetInternalState] = None, diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 8918a2a5..edb35b3a 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -53,14 +53,14 @@ def __bool__(self): def __init__( self, certs: Dict[str, CertSubType] = dict(), - root_dir: Optional[Union[str, Path]] = None, + root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, name: Optional[str] = None, description: str = None, state: Optional[DatasetInternalState] = None, auxillary_datasets: Optional[AuxillaryDatasetsSubType] = None, ): self.certs = certs - self._root_dir = Path(root_dir) if root_dir else Path.cwd() / (type(self).__name__).lower() + self._root_dir = Path(root_dir) self.timestamp = datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" @@ -167,7 +167,7 @@ def to_dict(self) -> Dict[str, Any]: @classmethod def from_dict(cls: Type[DatasetSubType], dct: Dict) -> DatasetSubType: certs = {x.dgst: x for x in dct["certs"]} - dset = cls(certs, Path("../"), dct["name"], dct["description"], dct["state"]) + dset = cls(certs, name=dct["name"], description=dct["description"], state=dct["state"]) if len(dset) != (claimed := dct["n_certs"]): logger.error( f"The actual number of certs in dataset ({len(dset)}) does not match the claimed number ({claimed})." diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index aa367be5..41a04ac8 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -1,6 +1,6 @@ import logging from pathlib import Path -from typing import Any, Dict, List, Optional, Set +from typing import Any, Dict, List, Optional, Set, Union from bs4 import BeautifulSoup @@ -23,7 +23,7 @@ class FIPSAlgorithmDataset(Dataset, ComplexSerializableType): def __init__( self, certs: Dict[str, FIPSAlgorithm] = dict(), - root_dir: Optional[Path] = None, + root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, name: str = "dataset name", description: str = "dataset_description", ): diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py index ed11e5be..792ab47c 100644 --- a/sec_certs/serialization/json.py +++ b/sec_certs/serialization/json.py @@ -39,12 +39,17 @@ def from_dict(cls: Type[T], dct: Dict) -> T: raise TypeError(f"Dict: {dct} on {cls.__mro__}") from e def to_json(self, output_path: Optional[Union[str, Path]] = None) -> None: - if not output_path and (not hasattr(self, "json_path") or not self.json_path or self.json_path == constants.DUMMY_NONEXISTING_PATH): # type: ignore + if not output_path and (not hasattr(self, "json_path") or not self.json_path): # type: ignore raise ValueError( f"The object {self} of type {self.__class__} does not have json_path attribute set but to_json() was called without an argument." ) if not output_path: output_path = self.json_path # type: ignore + if self.json_path == constants.DUMMY_NONEXISTING_PATH: # type: ignore + raise ValueError(f"json_path variable for {type(self)} was not yet set.") + + if Path(output_path).is_dir(): # type: ignore + raise ValueError("output path for json cannot be directory.") # false positive MyPy warning, cannot be None with Path(output_path).open("w") as handle: # type: ignore diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 3eb74115..3cc58e1a 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -50,7 +50,7 @@ def cpes(cpe_single_sign_on: CPE) -> Set[CPE]: @pytest.fixture(scope="module") def cpe_dset(cpes: Set[CPE]) -> CPEDataset: - return CPEDataset(False, {x.uri: x for x in cpes}, Path("../")) + return CPEDataset(False, {x.uri: x for x in cpes}) @pytest.fixture(scope="module") diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index c409d2c7..bfdacbec 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -24,7 +24,7 @@ def mu_dset(data_dir: Path) -> CCDatasetMaintenanceUpdates: def test_methods_not_meant_to_be_implemented(): - dset = CCDatasetMaintenanceUpdates(dict(), Path("..")) + dset = CCDatasetMaintenanceUpdates() with pytest.raises(NotImplementedError): dset.analyze_certificates() with pytest.raises(NotImplementedError): From 1bc1c7dbb79068a4828fb346e507124877215584 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 12:46:00 +0100 Subject: [PATCH 19/81] auxillary dataset processing CC --- sec_certs/dataset/common_criteria.py | 102 +++++++++++++++------------ sec_certs/dataset/dataset.py | 5 +- 2 files changed, 58 insertions(+), 49 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 7843cbbf..04e1eb28 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -253,33 +253,6 @@ def _download_csv_html_resources(self, get_active: bool = True, get_archived: bo self._download_parallel(html_urls, html_paths) self._download_parallel(csv_urls, csv_paths) - @serialize - def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None: - """ - Downloads new snapshot of dataset with processed protection profiles (if it doesn't exist) and links PPs - with certificates within self. Assigns PPs to all certificates - - :param bool to_download: If dataset should be downloaded or fetched from json, defaults to True - :param bool keep_metadata: If json related to the PP dataset should be kept on drive, defaults to True - :raises RuntimeError: When building of PPDataset fails - """ - logger.info("Processing protection profiles.") - constructor: Dict[bool, Callable[..., ProtectionProfileDataset]] = { - True: ProtectionProfileDataset.from_web, - False: ProtectionProfileDataset.from_json, - } - if to_download is True and not self.auxillary_datasets_dir.exists(): - self.auxillary_datasets_dir.mkdir() - pp_dataset = constructor[to_download](self.pp_dataset_path) - - for cert in self: - if cert.protection_profiles is None: - raise RuntimeError("Building of the dataset probably failed - this should not be happening.") - cert.protection_profiles = {pp_dataset.pps.get((x.pp_name, x.pp_link), x) for x in cert.protection_profiles} - - if not keep_metadata: - self.pp_dataset_path.unlink() - @serialize def get_certs_from_web( self, to_download: bool = True, keep_metadata: bool = True, get_active: bool = True, get_archived: bool = True @@ -792,40 +765,75 @@ def analyze_certificates(self, fresh: bool = True) -> None: "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." ) return - + super().analyze_certificates() self._extract_data(fresh) self._compute_heuristics() self.state.certs_analyzed = True - def process_maintenance_updates(self) -> CCDatasetMaintenanceUpdates: + def process_auxillary_datasets(self) -> None: + self.auxillary_datasets.pp_dset = self.process_protection_profiles() + self.auxillary_datasets.mu_dset = self.process_maintenance_updates() + super().process_auxillary_datasets() + + @serialize + def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None: """ - Creates and fully processes (download, convert, analyze) a dataset of maintenance updates that are related - to certificates of self. + Downloads new snapshot of dataset with processed protection profiles (if it doesn't exist) and links PPs + with certificates within self. Assigns PPs to all certificates + + :param bool to_download: If dataset should be downloaded or fetched from json, defaults to True + :param bool keep_metadata: If json related to the PP dataset should be kept on drive, defaults to True + :raises RuntimeError: When building of PPDataset fails + """ + logger.info("Processing protection profiles.") + constructor: Dict[bool, Callable[..., ProtectionProfileDataset]] = { + True: ProtectionProfileDataset.from_web, + False: ProtectionProfileDataset.from_json, + } + if to_download is True and not self.auxillary_datasets_dir.exists(): + self.auxillary_datasets_dir.mkdir() + pp_dataset = constructor[to_download](self.pp_dataset_path) + + for cert in self: + if cert.protection_profiles is None: + raise RuntimeError("Building of the dataset probably failed - this should not be happening.") + cert.protection_profiles = {pp_dataset.pps.get((x.pp_name, x.pp_link), x) for x in cert.protection_profiles} + + if not keep_metadata: + self.pp_dataset_path.unlink() + def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMaintenanceUpdates: + """ + Downloads or loads from json a dataset of maintenance updates. Runs analysis on that dataset if it's not completed. :return CCDatasetMaintenanceUpdates: the resulting dataset of maintenance updates """ - if not self.mu_dataset_dir.exists(): + + logger.info("Processing maintenace updates") + if to_download and not self.mu_dataset_dir.exists(): self.mu_dataset_dir.mkdir(parents=True) - maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] - updates = list( - itertools.chain.from_iterable( - [CommonCriteriaMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs] + if to_download: + maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] + updates = list( + itertools.chain.from_iterable( + [CommonCriteriaMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs] + ) ) - ) - update_dset: CCDatasetMaintenanceUpdates = CCDatasetMaintenanceUpdates( - {x.dgst: x for x in updates}, root_dir=self.mu_dataset_dir, name="Maintenance updates" - ) - update_dset.download_all_artifacts() - update_dset.convert_all_pdfs() - update_dset._extract_data() + update_dset = CCDatasetMaintenanceUpdates( + {x.dgst: x for x in updates}, root_dir=self.mu_dataset_dir, name="Maintenance updates" + ) + else: + update_dset = CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path) - return update_dset + if not update_dset.state.artifacts_downloaded: + update_dset.download_all_artifacts() + if not update_dset.state.pdfs_converted: + update_dset.convert_all_pdfs() + if not update_dset.state.certs_analyzed: + update_dset._extract_data() - def process_auxillary_datasets(self) -> None: - self.auxillary_datasets.pp_dset = self.process_protection_profiles() - self.auxillary_datasets.mu_dset = self.process_maintenance_updates() + return update_dset class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index edb35b3a..0ee02648 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -206,7 +206,8 @@ def get_certs_from_web(self) -> None: @abstractmethod def process_auxillary_datasets(self) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") + self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset() + self.auxillary_datasets.cve_dset = self._prepare_cve_dataset() @serialize def download_all_artifacts(self, fresh: bool = True) -> None: @@ -229,7 +230,7 @@ def convert_all_pdfs(self) -> None: @abstractmethod def analyze_certificates(self) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") + self.process_auxillary_datasets() @staticmethod def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: From ca5d4c890b2a3369653c91cfb11bb597cae06733 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 12:48:54 +0100 Subject: [PATCH 20/81] fix mypy error in cli.py --- cli.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/cli.py b/cli.py index eaa0f276..fa54751e 100644 --- a/cli.py +++ b/cli.py @@ -8,6 +8,7 @@ import click +from sec_certs import constants from sec_certs.config.configuration import config from sec_certs.dataset import CCDataset, FIPSDataset from sec_certs.utils.helpers import warn_if_missing_graphviz, warn_if_missing_poppler, warn_if_missing_tesseract @@ -44,7 +45,10 @@ def warn_missing_libs(): def build_or_load_dataset( - framework: str, inputpath: Optional[Path], outputpath: Optional[Path], to_build: bool + framework: str, + inputpath: Optional[Path], + to_build: bool, + outputpath: Path = constants.DUMMY_NONEXISTING_PATH, ) -> Union[CCDataset, FIPSDataset]: constructor: Union[Type[CCDataset], Type[FIPSDataset]] = CCDataset if framework == "cc" else FIPSDataset dset: Union[CCDataset, FIPSDataset] @@ -147,7 +151,7 @@ def main( else set(actions) ) - dset = build_or_load_dataset(framework, inputpath, outputpath, "build" in actions_set) + dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" analysis_pre_callback = None if framework == "cc" else warn_if_missing_graphviz From b41fa1200df7c482a70cc0d58ba42db25b183cde Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 12:52:59 +0100 Subject: [PATCH 21/81] fix error in mu dset tests --- tests/cc/test_cc_maintenance_updates.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index bfdacbec..456ed523 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -19,8 +19,11 @@ def cc_dset(data_dir: Path) -> CCDataset: @pytest.fixture -def mu_dset(data_dir: Path) -> CCDatasetMaintenanceUpdates: - return CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/Maintenance updates.json") +def mu_dset(data_dir: Path, tmp_path_factory) -> CCDatasetMaintenanceUpdates: + tmp_dir = tmp_path_factory.mktemp("mu_dset") + dset = CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/Maintenance updates.json") + dset.root_dir = tmp_dir + return dset def test_methods_not_meant_to_be_implemented(): From d51c65cfc673a56cf212ebf557de07ac1c4e0374 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 13:40:02 +0100 Subject: [PATCH 22/81] FIPS policy pdf convert refactoring --- sec_certs/dataset/common_criteria.py | 21 +----------- sec_certs/dataset/dataset.py | 17 ++++++++-- sec_certs/dataset/fips.py | 23 ++++++------- sec_certs/sample/fips.py | 51 ++++++++++++++++++++++++---- 4 files changed, 72 insertions(+), 40 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 04e1eb28..8eb6faeb 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -566,32 +566,13 @@ def _convert_targets_to_txt(self, fresh: bool = True) -> None: progress_bar_desc="Converting targets to txt", ) - @serialize - def convert_all_pdfs(self, fresh: bool = True) -> None: - """ - Converts all pdfs associated to certificates of the dataset into txt files. - - :param bool fresh: whether all (true) or only failed (false) pdfs shall be converted, defaults to True - """ - if self.state.artifacts_downloaded is False: - logger.info("Attempting to convert pdf while not having them downloaded. Returning.") - return - + def _convert_all_pdfs_body(self, fresh: bool = True) -> None: logger.info("Converting CC sample reports to .txt") self._convert_reports_to_txt(fresh) logger.info("Converting CC security targets to .txt") self._convert_targets_to_txt(fresh) - if fresh is True: - logger.info("Attempting to re-convert failed report pdfs") - self._convert_reports_to_txt(False) - - logger.info("Attempting to re-convert failed target pdfs") - self._convert_targets_to_txt(False) - - self.state.pdfs_converted = True - def update_with_certs(self, certs: List[CommonCriteriaCert]) -> None: """ Enriches the dataset with `certs` diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 0ee02648..f4e19b8e 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -211,7 +211,7 @@ def process_auxillary_datasets(self) -> None: @serialize def download_all_artifacts(self, fresh: bool = True) -> None: - if self.state.meta_sources_parsed is False: + if not self.state.meta_sources_parsed: logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") return @@ -221,11 +221,24 @@ def download_all_artifacts(self, fresh: bool = True) -> None: self.state.artifacts_downloaded = True + @abstractmethod def _download_all_artifacts_body(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + @serialize + def convert_all_pdfs(self, fresh: bool = True) -> None: + if not self.state.artifacts_downloaded: + logger.error("Attempting to convert pdfs while not having the artifacts downloaded. Returning.") + return + + self._convert_all_pdfs_body(fresh) + if fresh: + self._convert_all_pdfs_body(False) + + self.state.pdfs_converted = True + @abstractmethod - def convert_all_pdfs(self) -> None: + def _convert_all_pdfs_body(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 92c8c018..48f3624d 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -107,19 +107,18 @@ def _download_policies(self, fresh: bool = True) -> None: progress_bar_desc="Downloading PDF security policies", ) - @serialize - def convert_all_pdfs(self) -> None: - """ - Converts all pdfs to text files - """ - logger.info("Converting FIPS sample reports to .txt") - tuples = [ - (cert, self.policies_dir / f"{cert.cert_id}.pdf", self.policies_dir / f"{cert.cert_id}.pdf.txt") - for cert in self.certs.values() - if not cert.state.txt_state and (self.policies_dir / f"{cert.cert_id}.pdf").exists() - ] + def _convert_all_pdfs_body(self, fresh: bool = True) -> None: + logger.info("Converting FIPS security policies to .txt") + self._convert_policies_to_txt(fresh) + + def _convert_policies_to_txt(self, fresh: bool = True) -> None: + self.policies_txt_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.policy_is_ok_to_convert(fresh)] cert_processing.process_parallel( - FIPSCertificate.convert_pdf_file, tuples, config.n_threads, progress_bar_desc="Converting to txt" + FIPSCertificate.convert_policy_pdf, + certs_to_process, + config.n_threads, + progress_bar_desc="Converting policies to pdf", ) def _download_html_resources(self) -> None: diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 9a122646..09d533e3 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -244,19 +244,26 @@ class InternalState(ComplexSerializableType): Holds state of the `FIPSCertificate` """ - tables_done: bool - file_status: bool - txt_state: bool + module_download_ok: bool + policy_download_ok: bool + + policy_convert_garbage: bool + policy_convert_ok: bool + + policy_pdf_hash: Optional[str] + policy_txt_hash: Optional[str] policy_pdf_path: Path policy_txt_path: Path module_html_path: Path - module_download_ok: bool - policy_download_ok: bool - errors: List[str] + # Stale, probably delete + tables_done: bool + file_status: bool + txt_state: bool + def __init__( self, tables_done: bool = False, @@ -264,6 +271,10 @@ def __init__( txt_state: bool = True, # TODO: Check if this is correct module_download_ok: bool = False, policy_download_ok: bool = False, + policy_convert_garbage: bool = False, + policy_convert_ok: bool = False, + policy_pdf_hash: Optional[str] = None, + policy_txt_hash: Optional[str] = None, errors: Optional[List[str]] = None, ): self.tables_done = tables_done @@ -271,6 +282,10 @@ def __init__( self.txt_state = txt_state self.module_download_ok = module_download_ok self.policy_download_ok = policy_download_ok + self.policy_convert_garbage = policy_convert_garbage + self.policy_convert_ok = policy_convert_ok + self.policy_pdf_hash = policy_pdf_hash + self.policy_txt_hash = policy_txt_hash self.errors = errors if errors else [] @property @@ -284,6 +299,9 @@ def module_is_ok_to_download(self, fresh: bool = True) -> bool: def policy_is_ok_to_download(self, fresh: bool = True) -> bool: return True if fresh else not self.policy_download_ok + def policy_is_ok_to_convert(self, fresh: bool = True) -> bool: + return self.policy_download_ok if fresh else self.policy_download_ok and not self.policy_convert_ok + def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, web_dir: Path) -> None: self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") self.state.policy_txt_path = (policies_txt_dir / str(self.dgst)).with_suffix(".txt") @@ -604,6 +622,27 @@ def download_policy(cert: FIPSCertificate) -> FIPSCertificate: cert.state.policy_download_ok = False else: cert.state.policy_download_ok = True + cert.state.policy_pdf_hash = helpers.get_sha256_filepath(cert.state.policy_pdf_path) + return cert + + @staticmethod + def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: + ocr_done, ok_result = sec_certs.utils.pdf.convert_pdf_file( + cert.state.policy_pdf_path, cert.state.policy_txt_path + ) + + # If OCR was done and the result was garbage + cert.state.policy_convert_garbage = ocr_done + # And put the whole result into convert_ok + cert.state.policy_convert_ok = ok_result + + if not ok_result: + error_msg = "Failed to convert policy pdf->txt" + logger.error(f"Cert dgst: {cert.dgst}" + error_msg) + cert.state.errors.append(error_msg) + else: + cert.state.policy_txt_hash = helpers.get_sha256_filepath(cert.state.policy_txt_path) + return cert @staticmethod From f7c5915102a000ad1e53d6b793ccd7c97c506e5a Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 11 Nov 2022 14:28:24 +0100 Subject: [PATCH 23/81] cleanup in fips code structure --- sec_certs/dataset/common_criteria.py | 27 +++++++-------- sec_certs/dataset/dataset.py | 36 ++++++++++++++++++-- sec_certs/dataset/fips.py | 51 +++++++++++++--------------- sec_certs/sample/fips.py | 8 ++--- 4 files changed, 73 insertions(+), 49 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 8eb6faeb..f2fdf171 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -517,11 +517,7 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_targets(fresh) def _download_reports(self, fresh: bool = True) -> None: - if fresh: - logger.info("Downloading PDFs of CC certification reports.") - else: - logger.info("Attempting to re-download failed PDFs of CC certification reports.") - + logger.info("Downloading PDFs of CC certification reports.") self.reports_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh) and x.report_link] cert_processing.process_parallel( @@ -532,11 +528,7 @@ def _download_reports(self, fresh: bool = True) -> None: ) def _download_targets(self, fresh: bool = True) -> None: - if fresh: - logger.info("Downloading PDFs of CC security targets.") - else: - logger.info("Attempting to re-download failed PDFs of CC security targets.") - + logger.info("Downloading PDFs of CC security targets.") self.targets_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh)] cert_processing.process_parallel( @@ -547,6 +539,7 @@ def _download_targets(self, fresh: bool = True) -> None: ) def _convert_reports_to_txt(self, fresh: bool = True) -> None: + logger.info("Converting CC sample reports to .txt") self.reports_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_convert(fresh)] cert_processing.process_parallel( @@ -557,6 +550,7 @@ def _convert_reports_to_txt(self, fresh: bool = True) -> None: ) def _convert_targets_to_txt(self, fresh: bool = True) -> None: + logger.info("Converting CC security targets to .txt") self.targets_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.st_is_ok_to_convert(fresh)] cert_processing.process_parallel( @@ -567,10 +561,7 @@ def _convert_targets_to_txt(self, fresh: bool = True) -> None: ) def _convert_all_pdfs_body(self, fresh: bool = True) -> None: - logger.info("Converting CC sample reports to .txt") self._convert_reports_to_txt(fresh) - - logger.info("Converting CC security targets to .txt") self._convert_targets_to_txt(fresh) def update_with_certs(self, certs: List[CommonCriteriaCert]) -> None: @@ -665,7 +656,7 @@ def _extract_pdf_keywords(self, fresh: bool = True) -> None: self._extract_targets_keywords(fresh) def _extract_data(self, fresh: bool = True) -> None: - logger.info("Extracting various stuff from converted txt filed from CC dataset.") + logger.info("Extracting various stuff from converted txt files from CC dataset.") self._extract_pdf_metadata(fresh) self._extract_pdf_frontpage(fresh) self._extract_pdf_keywords(fresh) @@ -682,6 +673,7 @@ def _compute_normalized_cert_ids(self): cert.compute_heuristics_cert_id() def _compute_dependency_vulnerabilities(self): + logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") cve_dependency_finder = DependencyVulnerabilityFinder() cve_dependency_finder.fit(self.certs) @@ -692,6 +684,7 @@ def _compute_dependency_vulnerabilities(self): self.certs[dgst].heuristics.indirect_dependency_cves = dependency_cve.indirect_dependency_cves def _compute_heuristics(self, use_nist_cpe_matching_dict: bool = True) -> None: + logger.info("Computing various heuristics on CC certificates.") self._compute_cert_labs() self._compute_normalized_cert_ids() self._compute_dependencies() @@ -724,6 +717,7 @@ def func(cert): return func + logger.info("Compute references between certificates.") for dep_source in ("report", "st"): kw_source = f"{dep_source}_keywords" dep_attr = f"{dep_source}_references" @@ -752,6 +746,11 @@ def analyze_certificates(self, fresh: bool = True) -> None: self.state.certs_analyzed = True + def _analyze_certificates_body(self, fresh: bool = True) -> None: + super()._analyze_certificates_body(fresh) + self._extract_data(fresh) + self._compute_heuristics() + def process_auxillary_datasets(self) -> None: self.auxillary_datasets.pp_dset = self.process_protection_profiles() self.auxillary_datasets.mu_dset = self.process_maintenance_updates() diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index f4e19b8e..209bf03b 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -215,6 +215,11 @@ def download_all_artifacts(self, fresh: bool = True) -> None: logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") return + if fresh: + logger.info("Attempting to download certification artifacts.") + else: + logger.info("Attempting to re-download failed certification artifacts.") + self._download_all_artifacts_body(fresh) if fresh: self._download_all_artifacts_body(False) @@ -231,6 +236,11 @@ def convert_all_pdfs(self, fresh: bool = True) -> None: logger.error("Attempting to convert pdfs while not having the artifacts downloaded. Returning.") return + if fresh: + logger.info("Converting all PDFs to txt") + else: + logger.info("Re-converting all failed PDFs to txt") + self._convert_all_pdfs_body(fresh) if fresh: self._convert_all_pdfs_body(False) @@ -241,9 +251,28 @@ def convert_all_pdfs(self, fresh: bool = True) -> None: def _convert_all_pdfs_body(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - @abstractmethod - def analyze_certificates(self) -> None: - self.process_auxillary_datasets() + @serialize + def analyze_certificates(self, fresh: bool = True) -> None: + if not self.state.pdfs_converted: + logger.info( + "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." + ) + return + + if fresh: + logger.info("Analyzing certificates.") + else: + logger.info("Re-analyzing certificates on which analysis failed.") + + self._analyze_certificates_body(fresh) + if fresh: + self.analyze_certificates(False) + + self.state.certs_analyzed = True + + def _analyze_certificates_body(self, fresh: bool = True) -> None: + if fresh: + self.process_auxillary_datasets() @staticmethod def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: @@ -437,6 +466,7 @@ def compute_related_cves( download_fresh_cves, use_nist_cpe_matching_dict ) + logger.info("Computing CVEs in certificates.") self.enrich_automated_cpes_with_manual_labels() cpe_rich_certs = [x for x in cast(Iterator[Certificate], self) if x.heuristics.cpe_matches] diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 48f3624d..271389e7 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -76,13 +76,8 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_policies(fresh) def _download_modules(self, fresh: bool = True) -> None: - self.module_dir.mkdir(exist_ok=True) - - if fresh: - logger.info("Downloading HTML cryptographic modules.") - else: - logger.info("Attempting re-download of failed HTML cryptographic modules.") - + logger.info("Downloading HTML cryptographic modules.") + self.module_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.module_is_ok_to_download(fresh)] cert_processing.process_parallel( FIPSCertificate.download_module, @@ -92,13 +87,8 @@ def _download_modules(self, fresh: bool = True) -> None: ) def _download_policies(self, fresh: bool = True) -> None: - self.policies_pdf_dir.mkdir(exist_ok=True) - - if fresh: - logger.info("Downloading PDF security policies.") - else: - logger.info("Attempting re-download of failed PDF security policies.") - + logger.info("Downloading PDF security policies.") + self.policies_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.policy_is_ok_to_download(fresh)] cert_processing.process_parallel( FIPSCertificate.download_policy, @@ -108,10 +98,14 @@ def _download_policies(self, fresh: bool = True) -> None: ) def _convert_all_pdfs_body(self, fresh: bool = True) -> None: - logger.info("Converting FIPS security policies to .txt") self._convert_policies_to_txt(fresh) def _convert_policies_to_txt(self, fresh: bool = True) -> None: + if fresh: + logger.info("Converting FIPS security policies to .txt") + else: + logger.info("Attempting re-conversion of failed PDF security policies to .txt") + self.policies_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.policy_is_ok_to_convert(fresh)] cert_processing.process_parallel( @@ -184,7 +178,7 @@ def from_web_latest(cls) -> "FIPSDataset": def _set_local_paths(self) -> None: cert: FIPSCertificate for cert in self.certs.values(): - cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.web_dir) + cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.module_dir) @serialize def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = True) -> None: @@ -206,6 +200,7 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru @serialize def process_auxillary_datasets(self) -> None: self._process_algorithms() + super().process_auxillary_datasets() def _process_algorithms(self): logger.info("Processing FIPS algorithms.") @@ -365,27 +360,27 @@ def web_lookup(cert): self.certs[dgst].heuristics, "web_references", finder.predict_single_cert(dgst, keep_unknowns=False) ) - @serialize - def analyze_certificates(self, use_nist_cpe_matching_dict: bool = True, perform_cpe_heuristics: bool = True): - """ - Performs processing of extracted data. Computes all heuristics. + def _analyze_certificates_body(self, fresh: bool = True) -> None: + super()._analyze_certificates_body(fresh) - :param bool use_nist_cpe_matching_dict: If NIST CPE matching dictionary shall be used to drive computing related CVEs, defaults to True - :param bool perform_cpe_heuristics: If CPE heuristics shall be computed, defaults to True - """ - logger.info("Entering 'analysis' and building connections between certificates.") + # Final methods + self._extract_data() + self._compute_heuristics() + + # TODO: Distribute the methods below somehow between the final methods above self._extract_data(redo=False) self.extract_certs_from_tables(high_precision=True) - self._extract_metadata() self._unify_algorithms() self._compute_heuristics_clean_ids() self._compute_dependencies() - if perform_cpe_heuristics: - self.compute_cpe_heuristics() - self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict) + self.compute_cpe_heuristics() + self.compute_related_cves() self.plot_graphs(show=True) + def _compute_heuristics(self): + logger.info("Computing various statistics from processed certificates.") + def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: current_cert = self.certs[current_dgst] diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 09d533e3..d5754bf6 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -302,10 +302,10 @@ def policy_is_ok_to_download(self, fresh: bool = True) -> bool: def policy_is_ok_to_convert(self, fresh: bool = True) -> bool: return self.policy_download_ok if fresh else self.policy_download_ok and not self.policy_convert_ok - def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, web_dir: Path) -> None: + def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, modules_html_dir: Path) -> None: self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") self.state.policy_txt_path = (policies_txt_dir / str(self.dgst)).with_suffix(".txt") - self.state.module_html_path = (web_dir / str(self.dgst)).with_suffix(".html") + self.state.module_html_path = (modules_html_dir / str(self.dgst)).with_suffix(".html") @dataclass(eq=True) class WebData(ComplexSerializableType): @@ -412,11 +412,11 @@ def manufacturer(self) -> Optional[str]: # type: ignore @property def module_html_url(self) -> str: - return constants.FIPS_MODULE_URL.format(self.dgst) + return constants.FIPS_MODULE_URL.format(self.cert_id) @property def policy_pdf_url(self) -> str: - return constants.FIPS_SP_URL.format(self.dgst) + return constants.FIPS_SP_URL.format(self.cert_id) @property def name(self) -> Optional[str]: # type: ignore From 9fbba9e2e74d0f1553da8e9467389edd729e5946 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 16 Nov 2022 14:33:59 +0100 Subject: [PATCH 24/81] common interface for Dataset.analyze_certificates() --- sec_certs/dataset/common_criteria.py | 28 +----------- sec_certs/dataset/dataset.py | 14 ++++-- sec_certs/dataset/fips.py | 41 +++++++++++------- sec_certs/sample/fips.py | 65 ++++++++++------------------ 4 files changed, 61 insertions(+), 87 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index f2fdf171..8e0318a3 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -683,14 +683,13 @@ def _compute_dependency_vulnerabilities(self): self.certs[dgst].heuristics.direct_dependency_cves = dependency_cve.direct_dependency_cves self.certs[dgst].heuristics.indirect_dependency_cves = dependency_cve.indirect_dependency_cves - def _compute_heuristics(self, use_nist_cpe_matching_dict: bool = True) -> None: + def _compute_heuristics(self, fresh: bool = True) -> None: logger.info("Computing various heuristics on CC certificates.") + super()._compute_heuristics() self._compute_cert_labs() self._compute_normalized_cert_ids() self._compute_dependencies() self._compute_sars() - self.compute_cpe_heuristics() - self.compute_related_cves(use_nist_cpe_matching_dict=use_nist_cpe_matching_dict) self._compute_dependency_vulnerabilities() def _compute_sars(self) -> None: @@ -728,29 +727,6 @@ def func(cert): for dgst in self.certs: setattr(self.certs[dgst].heuristics, dep_attr, finder.predict_single_cert(dgst, keep_unknowns=False)) - @serialize - def analyze_certificates(self, fresh: bool = True) -> None: - """ - Searches for all RE in the txt files of the dataset. These are further processed during heuristics computation. - - :param bool fresh: Whether to run this phase on all certificates (true) or only on those that failed (false), defaults to True - """ - if self.state.pdfs_converted is False: - logger.info( - "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." - ) - return - super().analyze_certificates() - self._extract_data(fresh) - self._compute_heuristics() - - self.state.certs_analyzed = True - - def _analyze_certificates_body(self, fresh: bool = True) -> None: - super()._analyze_certificates_body(fresh) - self._extract_data(fresh) - self._compute_heuristics() - def process_auxillary_datasets(self) -> None: self.auxillary_datasets.pp_dset = self.process_protection_profiles() self.auxillary_datasets.mu_dset = self.process_maintenance_updates() diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 209bf03b..024cb060 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -64,7 +64,7 @@ def __init__( self.timestamp = datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" - self.description = description if description else datetime.now().strftime("%d/%m/%Y %H:%M:%S") + self.description = description if description else "No description provided" self.state = state if state else self.DatasetInternalState() if not auxillary_datasets: @@ -271,8 +271,16 @@ def analyze_certificates(self, fresh: bool = True) -> None: self.state.certs_analyzed = True def _analyze_certificates_body(self, fresh: bool = True) -> None: - if fresh: - self.process_auxillary_datasets() + self._extract_data(fresh) + self._compute_heuristics(fresh) + + @abstractmethod + def _extract_data(self, fresh: bool = True) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + + def _compute_heuristics(self, fresh: bool = True) -> None: + self.compute_cpe_heuristics() + self.compute_related_cves() @staticmethod def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 271389e7..b8e7aab6 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -27,6 +27,9 @@ class FIPSDataset(Dataset[FIPSCertificate, AuxillaryDatasets], ComplexSerializab Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ + def __init__(self, *args, **kwargs): + return super().__init__(*args, **kwargs) + LIST_OF_CERTS_HTML: Final[Dict[str, str]] = { "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, "fips_modules_historical.html": constants.FIPS_HISTORICAL_MODULES_URL, @@ -218,11 +221,14 @@ def extract_certs_from_tables(self, high_precision: bool) -> List[Path]: logger.info("Entering table scan.") result = cert_processing.process_parallel( FIPSCertificate.analyze_tables, - [ - (cert, high_precision) - for cert in self.certs.values() - if cert is not None and (not cert.state.tables_done or high_precision) and cert.state.txt_state - ], + [(cert, high_precision) for cert in self.certs.values() if cert is not None and high_precision], + # TODO: Below is an old version with tables_done, txt_state attribute that we already deleted, rewrite without it + # FIPSCertificate.analyze_tables + # [ + # (cert, high_precision) + # for cert in self.certs.values() + # if cert is not None and (not cert.state.tables_done or high_precision) and cert.state.txt_state + # ], config.n_threads // 4, # tabula already processes by parallel, so # it's counterproductive to use all threads use_threading=False, @@ -232,7 +238,8 @@ def extract_certs_from_tables(self, high_precision: bool) -> List[Path]: not_decoded = [cert.state.sp_path for done, cert, _ in result if done is False] for state, cert, algorithms in result: certificate = self.certs[cert.dgst] - certificate.state.tables_done = state + # TODO: Fix me, attribute below deleted + # certificate.state.tables_done = state certificate.pdf_data.algorithms = algorithms return not_decoded @@ -284,8 +291,9 @@ def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> def _clean_cert_ids(self, current_cert: FIPSCertificate) -> None: current_cert.clean_cert_ids() - if not current_cert.state.txt_state: - return + # TODO: Fix me, txt state no longer available + # if not current_cert.state.txt_state: + # return current_cert.heuristics.clean_cert_ids = { cert_id: count for cert_id, count in current_cert.pdf_data.clean_cert_ids.items() @@ -363,9 +371,9 @@ def web_lookup(cert): def _analyze_certificates_body(self, fresh: bool = True) -> None: super()._analyze_certificates_body(fresh) - # Final methods - self._extract_data() - self._compute_heuristics() + # Final methods -- delete them, just a placeholder, they're handled by superclass + # self._extract_data() + # self._compute_heuristics() # TODO: Distribute the methods below somehow between the final methods above self._extract_data(redo=False) @@ -441,8 +449,9 @@ def _create_dot_graph( for key in self.certs: cert = self.certs[key] - if not cert.state.file_status: - continue + # TODO: Not sure what this was for, fix me + # if not cert.state.file_status: + # continue processed = self._get_processed_list(connection_list, key) @@ -462,8 +471,10 @@ def _create_dot_graph( for key in self.certs: cert = self.certs[key] - if not cert.state.file_status: - continue + # TODO: Not sure what this was for, fix me + # if not cert.state.file_status: + # continue + processed = self._get_processed_list(connection_list, key) for conn in processed: self._add_colored_node(dot, fips_dgst(conn), highlighted_vendor) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index d5754bf6..857c7792 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -238,7 +238,6 @@ class FIPSCertificate( ] @dataclass(eq=True) - # TODO: Include sp_pdf_hash and sp_txt_hash. class InternalState(ComplexSerializableType): """ Holds state of the `FIPSCertificate` @@ -259,16 +258,8 @@ class InternalState(ComplexSerializableType): errors: List[str] - # Stale, probably delete - tables_done: bool - file_status: bool - txt_state: bool - def __init__( self, - tables_done: bool = False, - file_status: bool = True, # TODO: Check if this is correct - txt_state: bool = True, # TODO: Check if this is correct module_download_ok: bool = False, policy_download_ok: bool = False, policy_convert_garbage: bool = False, @@ -277,9 +268,6 @@ def __init__( policy_txt_hash: Optional[str] = None, errors: Optional[List[str]] = None, ): - self.tables_done = tables_done - self.file_status = file_status - self.txt_state = txt_state self.module_download_ok = module_download_ok self.policy_download_ok = policy_download_ok self.policy_convert_garbage = policy_convert_garbage @@ -290,8 +278,14 @@ def __init__( @property def serialized_attributes(self) -> List[str]: - # TODO: Fix me, add other variables - return ["tables_done", "file_status", "txt_state"] + return [ + "module_download_ok", + "policy_download_ok", + "policy_convert_garbage", + "policy_convert_ok", + "policy_pdf_hash", + "policy_txt_hash", + ] def module_is_ok_to_download(self, fresh: bool = True) -> bool: return True if fresh else not self.module_download_ok @@ -522,9 +516,11 @@ def from_html_file( items_found["revoked_reason"] = None items_found["revoked_link"] = None items_found["mentioned_certs"] = {} - state.tables_done = initialized.state.tables_done - state.file_status = initialized.state.file_status - state.txt_state = initialized.state.txt_state + + # TODO: Not sure what this was for, fixme + # state.tables_done = initialized.state.tables_done + # state.file_status = initialized.state.file_status + # state.txt_state = initialized.state.txt_state if redo: items_found = FIPSCertificate._initialize_dictionary() @@ -676,7 +672,6 @@ def _initialize_dictionary() -> Dict[str, Any]: "revoked_link": None, "algorithms": set(), "mentioned_certs": {}, - "tables_done": False, "security_policy_www": None, "certificate_www": None, "hw_versions": None, @@ -685,28 +680,11 @@ def _initialize_dictionary() -> Dict[str, Any]: "product_url": None, } - @staticmethod - def convert_pdf_file(tup: Tuple[FIPSCertificate, Path, Path]) -> FIPSCertificate: - """ - Converts pdf file of FIPSCertificate. Staticmethod to allow for paralelization. - - :param Tuple[FIPSCertificate, Path, Path] tup: object which file will be converted, path to pdf, path to txt. - :return FIPSCertificate: the modified FIPSCertificate. - """ - cert, pdf_path, txt_path = tup - if not cert.state.txt_state: - ocr_done, ok_result = sec_certs.utils.pdf.convert_pdf_file(pdf_path, txt_path) - if not ok_result: - logger.error(f"Cert dgst: {cert.cert_id} failed to convert security policy pdf->txt") - cert.state.txt_state = False - else: - cert.state.txt_state = True - return cert - @staticmethod def find_keywords(cert: FIPSCertificate) -> Tuple[Optional[Dict], FIPSCertificate]: - if not cert.state.txt_state: - return None, cert + # TODO: Replace the condition below + # if not cert.state.txt_state: + # return None, cert keywords = sec_certs.utils.extract.extract_keywords( cert.state.policy_pdf_path.with_suffix(".pdf.txt"), fips_rules @@ -732,10 +710,11 @@ def extract_algorithm_certificates(current_text): return set(map(FIPSAlgorithm, set_items)) cert, precision = tup - if (not precision and cert.state.tables_done) or ( - precision and cert.heuristics.unmatched_algs < config.cert_threshold - ): - return cert.state.tables_done, cert, set() + # TODO: Not sure what this was for, fixme + # if (not precision and cert.state.tables_done) or ( + # precision and cert.heuristics.unmatched_algs < config.cert_threshold + # ): + # return cert.state.tables_done, cert, set() cert_file = cert.state.policy_pdf_path txt_file = cert_file.with_suffix(".pdf.txt") @@ -795,7 +774,7 @@ def clean_cert_ids(self) -> None: """ Removes algorithm mentions from the cert_id rule matches and stores them into clean_cert_id matches. """ - self.state.file_status = True + # self.state.file_status = True # TODO: Not sure what this was for if not self.pdf_data.keywords: return From 2a694c4381eb7998acc3b7bf38d5b44f9e0a43ff Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 16 Nov 2022 14:38:46 +0100 Subject: [PATCH 25/81] delete plot_graph() of FIPSDataset --- sec_certs/dataset/fips.py | 77 --------------------------------------- 1 file changed, 77 deletions(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index b8e7aab6..01cba8b2 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -384,7 +384,6 @@ def _analyze_certificates_body(self, fresh: bool = True) -> None: self._compute_dependencies() self.compute_cpe_heuristics() self.compute_related_cves() - self.plot_graphs(show=True) def _compute_heuristics(self): logger.info("Computing various statistics from processed certificates.") @@ -420,72 +419,6 @@ def _get_processed_list(self, connection_list: str, dgst: str) -> List[str]: attr = {"st": "st_references", "web": "web_references"}[connection_list] return getattr(self.certs[dgst].heuristics, attr).directly_referencing - def _create_dot_graph( - self, - output_file_name: str, - connection_list: str = "st", - highlighted_vendor: str = "Red Hat®, Inc.", - show: bool = True, - ) -> None: - """ - Function that plots .dot graph of dependencies between certificates - Certificates with at least one dependency are displayed in "{output_file_name}connections.pdf", remaining - certificates are displayed in {output_file_name}single.pdf - :param show: display graph right on screen - :param highlighted_vendor: vendor whose certificates should be highlighted in red color - :param output_file_name: prefix to "connections", "connections.pdf", "single" and "single.pdf" - :param connection_list: 'st' or 'web' - plots a graph from this source - """ - dot = Digraph(comment="Certificate ecosystem") - single_dot = Digraph(comment="Modules with no dependencies") - single_dot.attr("graph", label="Single nodes", labelloc="t", fontsize="30") - single_dot.attr("node", style="filled") - dot.attr("graph", label="Dependencies", labelloc="t", fontsize="30") - dot.attr("node", style="filled") - - keys = 0 - edges = 0 - - for key in self.certs: - cert = self.certs[key] - - # TODO: Not sure what this was for, fix me - # if not cert.state.file_status: - # continue - - processed = self._get_processed_list(connection_list, key) - - if processed: - self._add_colored_node(dot, key, highlighted_vendor) - keys += 1 - else: - single_dot.attr("node", color="lightblue") - self._highlight_vendor_in_dot(dot, key, highlighted_vendor) - single_dot.node( - key, - label=str(cert.cert_id) + "\r\n" + cert.web_data.vendor - if cert.web_data.vendor is not None - else "" + ("\r\n" + cert.web_data.module_name if cert.web_data.module_name else ""), - ) - - for key in self.certs: - cert = self.certs[key] - - # TODO: Not sure what this was for, fix me - # if not cert.state.file_status: - # continue - - processed = self._get_processed_list(connection_list, key) - for conn in processed: - self._add_colored_node(dot, fips_dgst(conn), highlighted_vendor) - dot.edge(key, conn) - edges += 1 - - logger.info(f"rendering for {connection_list}: {keys} keys and {edges} edges") - - dot.render(self.root_dir / (str(output_file_name) + "_connections"), view=show) - single_dot.render(self.root_dir / (str(output_file_name) + "_single"), view=show) - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame([x.pandas_tuple for x in self.certs.values()], columns=FIPSCertificate.pandas_columns) df = df.set_index("dgst") @@ -507,13 +440,3 @@ def to_pandas(self) -> pd.DataFrame: df["year_from"] = pd.DatetimeIndex(df.date_validation).year return df - - def plot_graphs(self, show: bool = False) -> None: - """ - Plots FIPS graphs. - - :param bool show: If plots should be shown with .show() method, defaults to False - """ - self._create_dot_graph("full_graph", show=show) - self._create_dot_graph("web_only_graph", "web", show=show) - self._create_dot_graph("st_only_graph", "st", show=show) From 94626242f11f4ec63677c6744fcc52b98461281f Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 16 Nov 2022 15:06:04 +0100 Subject: [PATCH 26/81] analyce_certificate() interface, delete dead code --- sec_certs/dataset/common_criteria.py | 7 +--- sec_certs/dataset/dataset.py | 15 +++++++ sec_certs/dataset/fips.py | 59 +++++++++------------------- 3 files changed, 36 insertions(+), 45 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 8e0318a3..b3adce5d 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -667,7 +667,7 @@ def _compute_cert_labs(self) -> None: for cert in certs_to_process: cert.compute_heuristics_cert_lab() - def _compute_normalized_cert_ids(self): + def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: logger.info("Deriving information about sample ids from pdf scan.") for cert in self: cert.compute_heuristics_cert_id() @@ -687,10 +687,7 @@ def _compute_heuristics(self, fresh: bool = True) -> None: logger.info("Computing various heuristics on CC certificates.") super()._compute_heuristics() self._compute_cert_labs() - self._compute_normalized_cert_ids() - self._compute_dependencies() self._compute_sars() - self._compute_dependency_vulnerabilities() def _compute_sars(self) -> None: logger.info("Computing SARs") @@ -698,7 +695,7 @@ def _compute_sars(self) -> None: for cert in self: cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) - def _compute_dependencies(self) -> None: + def _compute_dependencies(self, fresh: bool = True) -> None: def ref_lookup(kw_attr): def func(cert): kws = getattr(cert.pdf_data, kw_attr) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 024cb060..394c6133 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -279,8 +279,23 @@ def _extract_data(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _compute_heuristics(self, fresh: bool = True) -> None: + self._compute_normalized_cert_ids() self.compute_cpe_heuristics() self.compute_related_cves() + self._compute_dependencies() + self._compute_dependency_vulnerabilities() + + @abstractmethod + def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + + @abstractmethod + def _compute_dependencies(self, fresh: bool = True) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") + + @abstractmethod + def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: + raise NotImplementedError("Not meant to be implemented by the base class.") @staticmethod def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 01cba8b2..e891d24a 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -58,12 +58,15 @@ def algorithms_dir(self) -> Path: @serialize def _extract_data(self, redo: bool = False) -> None: + # TODO: Refactor me """ Extracts data from pdf files :param bool redo: Whether to try again with failed files, defaults to False """ logger.info("Entering PDF scan.") + self._extract_data_from_html_modules() + keywords = cert_processing.process_parallel( FIPSCertificate.find_keywords, [cert for cert in self.certs.values() if not cert.pdf_data.keywords or redo], @@ -74,6 +77,9 @@ def _extract_data(self, redo: bool = False) -> None: for keyword, cert in keywords: self.certs[cert.dgst].pdf_data.keywords = keyword + self._extract_metadata() + self.extract_certs_from_tables(high_precision=True) + def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_modules(fresh) self._download_policies(fresh) @@ -149,14 +155,15 @@ def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: return {FIPSCertificate(cert_id) for cert_id in cert_ids} - @serialize - def web_scan(self, cert_ids: Set[str], redo: bool = False) -> None: + def _extract_data_from_html_modules(self, redo: bool = False) -> None: """ Creates FIPSCertificate object from the relevant html file that must be downlaoded. :param Set[str] cert_ids: Cert ids to create FIPSCertificate objects for. :param bool redo: whether to re-attempt with failed certificates, defaults to False """ + cert_ids: Set[str] = set() # I guess all cert ids in dset, depending on fresh? + logger.info("Entering web scan.") for cert_id in cert_ids: dgst = fips_dgst(cert_id) @@ -243,11 +250,13 @@ def extract_certs_from_tables(self, high_precision: bool) -> List[Path]: certificate.pdf_data.algorithms = algorithms return not_decoded - def _compute_heuristics_clean_ids(self) -> None: + def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: + # TODO: Refactor me for cert in self.certs.values(): self._clean_cert_ids(cert) def _extract_metadata(self): + # TODO: Refactor me certs_to_process = [x for x in self] res = cert_processing.process_parallel( FIPSCertificate.extract_sp_metadata, @@ -301,6 +310,10 @@ def _clean_cert_ids(self, current_cert: FIPSCertificate) -> None: and cert_id != current_cert.cert_id } + def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: + # TODO: Implement me + pass + @staticmethod def _match_with_algorithm(processed_cert: FIPSCertificate, cert_candidate_id: str) -> bool: for algo in processed_cert.heuristics.algorithms: @@ -337,7 +350,7 @@ def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) return False return True - def _compute_dependencies(self) -> None: + def _compute_dependencies(self, fresh: bool = True) -> None: def pdf_lookup(cert): return set( filter( @@ -368,25 +381,10 @@ def web_lookup(cert): self.certs[dgst].heuristics, "web_references", finder.predict_single_cert(dgst, keep_unknowns=False) ) - def _analyze_certificates_body(self, fresh: bool = True) -> None: - super()._analyze_certificates_body(fresh) - - # Final methods -- delete them, just a placeholder, they're handled by superclass - # self._extract_data() - # self._compute_heuristics() - - # TODO: Distribute the methods below somehow between the final methods above - self._extract_data(redo=False) - self.extract_certs_from_tables(high_precision=True) - self._extract_metadata() - self._unify_algorithms() - self._compute_heuristics_clean_ids() - self._compute_dependencies() - self.compute_cpe_heuristics() - self.compute_related_cves() - def _compute_heuristics(self): logger.info("Computing various statistics from processed certificates.") + super()._compute_heuristics() + self._unify_algorithms() def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: current_cert = self.certs[current_dgst] @@ -400,25 +398,6 @@ def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_ if current_cert.web_data.status == "Historical": dot.attr("node", color="gold3") - def _add_colored_node(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: - current_cert = self.certs[current_dgst] - dot.attr("node", color="lightgreen") - if current_cert.web_data.status == "Revoked": - dot.attr("node", color="lightgrey") - if current_cert.web_data.status == "Historical": - dot.attr("node", color="gold") - self._highlight_vendor_in_dot(dot, current_dgst, highlighted_vendor) - dot.node( - str(current_cert.cert_id), - label=str(current_cert.cert_id) + " " + current_cert.web_data.vendor - if current_cert.web_data.vendor is not None - else "" + " " + (current_cert.web_data.module_name if current_cert.web_data.module_name else ""), - ) - - def _get_processed_list(self, connection_list: str, dgst: str) -> List[str]: - attr = {"st": "st_references", "web": "web_references"}[connection_list] - return getattr(self.certs[dgst].heuristics, attr).directly_referencing - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame([x.pandas_tuple for x in self.certs.values()], columns=FIPSCertificate.pandas_columns) df = df.set_index("dgst") From 01e4156b7c34d27d0648e9d5b941363a9bd62518 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 17 Nov 2022 16:53:09 +0100 Subject: [PATCH 27/81] FIPSDataset new parsing of html modules --- sec_certs/cert_rules.py | 2 + sec_certs/constants.py | 2 + sec_certs/dataset/common_criteria.py | 10 - sec_certs/dataset/dataset.py | 15 +- sec_certs/dataset/fips.py | 58 +- sec_certs/dataset/fips_algorithm.py | 1 + sec_certs/sample/fips.py | 490 +- .../data/fips/certificate/fictional_cert.json | 163 + .../fips/dataset/fips_modules_active.html | 1020 +++++ .../fips/dataset/fips_modules_historical.html | 1205 +++++ .../fips/dataset/fips_modules_revoked.html | 956 ++++ .../template_policy_184097a88a9b4ad9.txt | 1370 ++++++ tests/data/fips/dataset/toy_dataset.json | 4038 +++++++++++++++++ tests/fips/test_fips_certificate.py | 66 + tests/fips/test_fips_dataset.py | 102 +- 15 files changed, 9142 insertions(+), 356 deletions(-) create mode 100644 tests/data/fips/certificate/fictional_cert.json create mode 100644 tests/data/fips/dataset/fips_modules_active.html create mode 100644 tests/data/fips/dataset/fips_modules_historical.html create mode 100644 tests/data/fips/dataset/fips_modules_revoked.html create mode 100644 tests/data/fips/dataset/template_policy_184097a88a9b4ad9.txt create mode 100644 tests/data/fips/dataset/toy_dataset.json diff --git a/sec_certs/cert_rules.py b/sec_certs/cert_rules.py index d4f207bd..764c24e7 100644 --- a/sec_certs/cert_rules.py +++ b/sec_certs/cert_rules.py @@ -195,6 +195,8 @@ "ios": re.compile(r"(ios|iphone os)", re.IGNORECASE), } +FIPS_ALGS_IN_TABLE = r"(?:#[CcAa]?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P\d+)" + def _load(): script_dir = Path(__file__).parent diff --git a/sec_certs/constants.py b/sec_certs/constants.py index 50af2d95..261cfbfe 100644 --- a/sec_certs/constants.py +++ b/sec_certs/constants.py @@ -10,6 +10,8 @@ MIN_CORRECT_CERT_SIZE = 5000 +MIN_FIPS_HTML_SIZE = 64000 + MIN_CC_HTML_SIZE = 5000000 MIN_CC_CSV_SIZE = 700000 MIN_CC_PP_DATASET_SIZE = 2500000 diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index b3adce5d..b3df6703 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -564,16 +564,6 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: self._convert_reports_to_txt(fresh) self._convert_targets_to_txt(fresh) - def update_with_certs(self, certs: List[CommonCriteriaCert]) -> None: - """ - Enriches the dataset with `certs` - - :param List[CommonCriteriaCert] certs: new certs to include into the dataset. - """ - if any([x not in self for x in certs]): - logger.warning("Updating dataset with certificates outside of the dataset!") - self.certs.update({x.dgst: x for x in certs}) - def _extract_report_metadata(self, fresh: bool = True) -> None: certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze(fresh)] processed_certs = cert_processing.process_parallel( diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 394c6133..8eec3795 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -9,7 +9,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Collection, Dict, Generic, Iterator, Optional, Pattern, Set, Type, TypeVar, Union, cast +from typing import Any, Collection, Dict, Generic, Iterator, List, Optional, Pattern, Set, Type, TypeVar, Union, cast import pandas as pd import requests @@ -61,6 +61,10 @@ def __init__( ): self.certs = certs self._root_dir = Path(root_dir) + + if not self._root_dir and self._root_dir != constants.DUMMY_NONEXISTING_PATH: + self._root_dir.mkdir(parents=True) + self.timestamp = datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" @@ -528,3 +532,12 @@ def get_keywords_df(self, var: str) -> pd.DataFrame: """ data = [dict({"dgst": x.dgst}, **x.pdf_data.get_keywords_df_data(var)) for x in self] return pd.DataFrame(data).set_index("dgst") + + def update_with_certs(self, certs: List[CertSubType]) -> None: + """ + Enriches the dataset with `certs` + :param List[CommonCriteriaCert] certs: new certs to include into the dataset. + """ + if any([x not in self for x in certs]): + logger.warning("Updating dataset with certificates outside of the dataset!") + self.certs.update({x.dgst: x for x in certs}) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index e891d24a..0438b7f1 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -155,28 +155,20 @@ def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: return {FIPSCertificate(cert_id) for cert_id in cert_ids} - def _extract_data_from_html_modules(self, redo: bool = False) -> None: + def _extract_data_from_html_modules(self, fresh: bool = True) -> None: """ - Creates FIPSCertificate object from the relevant html file that must be downlaoded. - - :param Set[str] cert_ids: Cert ids to create FIPSCertificate objects for. - :param bool redo: whether to re-attempt with failed certificates, defaults to False + Extracts data from html module file + :param bool fresh: if all certs should be processed, or only the failed ones. Defaults to True """ - cert_ids: Set[str] = set() # I guess all cert ids in dset, depending on fresh? - - logger.info("Entering web scan.") - for cert_id in cert_ids: - dgst = fips_dgst(cert_id) - self.certs[dgst] = FIPSCertificate.from_html_file( - self.web_dir / f"{cert_id}.html", - FIPSCertificate.InternalState( - False, - False, - False, - ), - self.certs.get(dgst), - redo=redo, - ) + certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze(fresh)] + processed_certs = cert_processing.process_parallel( + FIPSCertificate.parse_html_module, + certs_to_process, + config.n_threads, + use_threading=False, + progress_bar_desc="Extracting data from module html", + ) + self.update_with_certs(processed_certs) @classmethod def from_web_latest(cls) -> "FIPSDataset": @@ -268,29 +260,31 @@ def _extract_metadata(self): for r in res: self.certs[r.dgst] = r + # TODO: Refactor me def _unify_algorithms(self) -> None: - for certificate in self.certs.values(): - certificate.heuristics.algorithms = set() - if certificate.web_data.algorithms: - certificate.heuristics.algorithms.update(certificate.web_data.algorithms) - if certificate.pdf_data.algorithms: - certificate.heuristics.algorithms.update(certificate.pdf_data.algorithms) + pass + # for certificate in self.certs.values(): + # certificate.heuristics.algorithms = set() + # if certificate.web_data.algorithms: + # certificate.heuristics.algorithms.update(certificate.web_data.algorithms) + # if certificate.pdf_data.algorithms: + # certificate.heuristics.algorithms.update(certificate.pdf_data.algorithms) def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> bool: other_dgst = fips_dgst(other_id) other_cert = self.certs[other_dgst] if ( - current_certificate.web_data.date_validation is None + current_certificate.web_data.validation_history is None or other_cert is None - or other_cert.web_data.date_validation is None + or other_cert.web_data.validation_history is None ): raise RuntimeError("Building of the dataset probably failed - this should not be happening.") - cert_first = current_certificate.web_data.date_validation[0] - cert_last = current_certificate.web_data.date_validation[-1] - conn_first = other_cert.web_data.date_validation[0] - conn_last = other_cert.web_data.date_validation[-1] + cert_first = current_certificate.web_data.validation_history[0].date + cert_last = current_certificate.web_data.validation_history[-1].date + conn_first = other_cert.web_data.validation_history[0].date + conn_last = other_cert.web_data.validation_history[-1].date return ( cert_first.year - conn_first.year > config.year_difference_between_validations diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index 41a04ac8..244034e6 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -55,6 +55,7 @@ def get_certs_from_web(self): algs_paths.append(self.root_dir / f"page{num_pages}.html") logger.info(f"Downloading {len(algs_urls)} algo html files") + # TODO: Refactor me, the method download_html_page should definitely not be called! cert_processing.process_parallel( FIPSCertificate.download_html_page, list(zip(algs_urls, algs_paths)), config.n_threads ) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 857c7792..78ca47eb 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -3,14 +3,14 @@ import copy import re from dataclasses import dataclass, field -from datetime import datetime +from datetime import date from pathlib import Path -from typing import Any, ClassVar, Dict, List, Optional, Pattern, Set, Tuple, Union +from typing import Any, Callable, ClassVar, Dict, Final, List, Literal, Optional, Pattern, Set, Tuple +import dateutil import numpy as np import requests -from bs4 import BeautifulSoup, NavigableString, Tag -from dateutil import parser +from bs4 import BeautifulSoup, Tag from tabula import read_pdf import sec_certs.constants as constants @@ -18,7 +18,7 @@ import sec_certs.utils.helpers as helpers import sec_certs.utils.pdf import sec_certs.utils.tables -from sec_certs.cert_rules import fips_rules +from sec_certs.cert_rules import FIPS_ALGS_IN_TABLE, fips_rules from sec_certs.config.configuration import config from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics @@ -31,172 +31,142 @@ from sec_certs.utils.helpers import fips_dgst -class _FIPSHTMLParser: - @staticmethod - def parse_html_main(current_div: Tag, html_items_found: Dict) -> None: - pairs = { - "Module Name": "module_name", - "Standard": "standard", - "Status": "status", - "Sunset Date": "date_sunset", - "Validation Dates": "date_validation", - "Overall Level": "level", - "Caveat": "caveat", - "Security Level Exceptions": "exceptions", - "Module Type": "module_type", - "Embodiment": "embodiment", - "FIPS Algorithms": "algorithms", - # "Allowed Algorithms": "algorithms", - # "Other Algorithms": "algorithms", - "Tested Configuration(s)": "tested_conf", - "Description": "description", - "Historical Reason": "historical_reason", - "Hardware Versions": "hw_versions", - "Firmware Versions": "fw_versions", - "Revoked Reason": "revoked_reason", - "Revoked Link": "revoked_link", - "Software Versions": "sw_versions", - "Product URL": "product_url", - } - title = current_div.find("div", class_="col-md-3").text.strip() - content = ( - current_div.find("div", class_="col-md-9") - .text.strip() - .replace("\n", "") - .replace("\t", "") - .replace(" ", " ") - ) - - if title in pairs: - if "date_sunset" == pairs[title]: - html_items_found[pairs[title]] = parser.parse(content).date() - - elif "caveat" in pairs[title]: - html_items_found[pairs[title]] = content - html_items_found["mentioned_certs"].update(_FIPSHTMLParser.parse_caveat(content)) +class FIPSHTMLParser: + def __init__(self, soup: BeautifulSoup): + self._soup = soup - elif "FIPS Algorithms" in title: - html_items_found["algorithms"].update( - _FIPSHTMLParser.parse_table(current_div.find("div", class_="col-md-9")) - ) + def build_web_data(self) -> FIPSCertificate.WebData: + divs = self._soup.find_all("div", class_="panel panel-default") + details_div, vendor_div, related_files_div, validation_history_div = divs - elif "Algorithms" in title or "Description" in title: - html_items_found["algorithms"].update(_FIPSHTMLParser.parse_description(content)) - if "Description" in title: - html_items_found["description"] = content - - elif "tested_conf" in pairs[title] or "exceptions" in pairs[title]: - html_items_found[pairs[title]] = [ - x.text for x in current_div.find("div", class_="col-md-9").find_all("li") - ] - else: - html_items_found[pairs[title]] = content - - @staticmethod - def parse_vendor(current_div: Tag, html_items_found: Dict, current_file: Path) -> None: - vendor_string = current_div.find("div", "panel-body").find("a") + # TODO: Move the assertions to tests + assert len(divs) == 4 + assert details_div.find("h4").text == "Details" + assert vendor_div.find("h4").text == "Vendor" + assert related_files_div.find("h4").text == "Related Files" + assert validation_history_div.find("h4").text == "Validation History" - if not vendor_string: - vendor_string = list(current_div.find("div", "panel-body").children)[0].strip() - html_items_found["vendor_www"] = "" - else: - html_items_found["vendor_www"] = vendor_string.get("href") - vendor_string = vendor_string.text.strip() + details_dict = self._build_details_dict(details_div) + vendor_dict = self._build_vendor_dict(vendor_div) + related_files_dict = self._build_related_files_dict(related_files_div) + validation_history_dict = self._build_validation_history_dict(validation_history_div) + return FIPSCertificate.WebData( + **{**details_dict, **vendor_dict, **related_files_dict, **validation_history_dict} + ) - html_items_found["vendor"] = vendor_string - if html_items_found["vendor"] == "": - logger.warning(f"NO VENDOR FOUND {current_file}") + @classmethod + def _build_details_dict(cls, details_div: Tag) -> Dict[str, Any]: + def parse_single_detail_entry(key, entry): + normalized_key = DETAILS_KEY_NORMALIZATION_DICT[key] + normalization_func = DETAILS_KEY_TO_NORMALIZATION_FUNCTION.get(normalized_key, None) + normalized_entry = ( + FIPSHTMLParser.normalize_string(entry.text) if not normalization_func else normalization_func(entry) + ) + return normalized_key, normalized_entry - @staticmethod - def parse_lab(current_div: Tag, html_items_found: Dict, current_file: Path) -> None: - html_items_found["lab"] = list(current_div.find("div", "panel-body").children)[0].strip() - html_items_found["nvlap_code"] = ( - list(current_div.find("div", "panel-body").children)[2].strip().split("\n")[1].strip() + entries = details_div.find_all("div", class_="row padrow") + entries = zip( + [x.find("div", class_="col-md-3") for x in entries], [x.find("div", class_="col-md-9") for x in entries] ) + entries = [(FIPSHTMLParser.normalize_string(key.text), entry) for key, entry in entries] + entries = [parse_single_detail_entry(*x) for x in entries if x[0] in DETAILS_KEY_NORMALIZATION_DICT.keys()] + entries = dict((x, y) for x, y in entries) - if html_items_found["lab"] == "": - logger.warning(f"NO LAB FOUND {current_file}") + if "caveat" in entries: + entries["mentioned_certs"] = FIPSHTMLParser.get_mentioned_certs_from_caveat(entries["caveat"]) + # TODO: Enhance algorithms with those parsed from description entry - if html_items_found["nvlap_code"] == "": - logger.warning(f"NO NVLAP CODE FOUND {current_file}") + return entries @staticmethod - def parse_related_files(current_div: Tag, html_items_found: Dict) -> None: - links = current_div.find_all("a") - html_items_found["security_policy_www"] = constants.FIPS_BASE_URL + links[0].get("href") - - if len(links) == 2: - html_items_found["certificate_www"] = constants.FIPS_BASE_URL + links[1].get("href") + def _build_vendor_dict(vendor_div: Tag) -> Dict[str, Any]: + if not (link := vendor_div.find("a")): + return {"vendor_url": None, "vendor": list(vendor_div.find("div", "panel-body").children)[0].strip()} + else: + return {"vendor_url": link.get("href"), "vendor": link.text.strip()} @staticmethod - def normalize(items: Dict) -> None: - items["module_type"] = items["module_type"].lower().replace("-", " ").title() - items["embodiment"] = items["embodiment"].lower().replace("-", " ").replace("stand alone", "standalone").title() + def _build_related_files_dict(related_files_div: Tag) -> Dict[str, Any]: + if cert_link := [x for x in related_files_div.find_all("a") if "Certificate" in x.text]: + return {"certificate_pdf_url": constants.FIPS_BASE_URL + cert_link[0].get("href")} + else: + return {"certificate_pdf_url": None} @staticmethod - def parse_validation_dates(current_div: Tag, html_items_found: Dict) -> None: - table = current_div.find("table") - rows = table.find("tbody").findAll("tr") - html_items_found["date_validation"] = [parser.parse(td.text).date() for td in [row.find("td") for row in rows]] + def _build_validation_history_dict(validation_history_div: Tag) -> Dict[str, Any]: + def parse_row(row): + validation_date, validation_type, lab = row.find_all("td") + return FIPSCertificate.ValidationHistoryEntry( + dateutil.parser.parse(validation_date.text).date(), validation_type.text, lab.text + ) - @staticmethod - def parse_caveat(current_text: str) -> Dict[str, Dict[str, int]]: - """ - Parses content of "Caveat" of FIPS CMVP .html file + rows = validation_history_div.find("tbody").find_all("tr") + history: Optional[List[FIPSCertificate.ValidationHistoryEntry]] = [parse_row(x) for x in rows] if rows else None + return {"validation_history": history} - :param str current_text: text of "Caveat" - :return Dict[str, Dict[str, int]]: dictionary of all found algorithm IDs - """ - ids_found: Dict[str, Dict[str, int]] = {} + @staticmethod + def get_mentioned_certs_from_caveat(caveat: str) -> Dict[str, int]: + ids_found: Dict[str, int] = {} r_key = r"(?P\w+)?\s?(?:#\s?|Cert\.?(?!.\s)\s?|Certificate\s?)+(?P\d+)" - for m in re.finditer(r_key, current_text): + for m in re.finditer(r_key, caveat): if m.group("word") and m.group("word").lower() in {"rsa", "shs", "dsa", "pkcs", "aes"}: continue if m.group("id") in ids_found: - ids_found[m.group("id")]["count"] += 1 + ids_found[m.group("id")] += 1 else: - ids_found[m.group("id")] = {"count": 1} - + ids_found[m.group("id")] = 1 return ids_found @staticmethod - def parse_table(element: Union[Tag, NavigableString]) -> Set[FIPSAlgorithm]: - """ - Parses content of tags in FIPS .html CMVP page - - :param Union[Tag, NavigableString] element: text in
tags - :return: set of all found algorithm IDs - """ - - found_items = set() - trs = element.find_all("tr") - for tr in trs: - tds = tr.find_all("td") - cert_ids = _FIPSHTMLParser.extract_algorithm_certificates(tds[1].text) - name = tds[0].text - for cert_id in cert_ids: - found_items.add(FIPSAlgorithm(cert_id, name)) - - return found_items + def parse_algorithms(algorithms_div: Tag) -> Dict[str, Set[str]]: + rows = algorithms_div.find("tbody").find_all("tr") + dct: Dict[str, Set[str]] = dict() + for row in rows: + cells = row.find_all("td") + dct[cells[0].text] = {m.group() for m in re.finditer(FIPS_ALGS_IN_TABLE, cells[1].text)} + return dct @staticmethod - def parse_description(current_text: str) -> Set[FIPSAlgorithm]: - return set(map(FIPSAlgorithm, _FIPSHTMLParser.extract_algorithm_certificates(current_text))) + def normalize_string(string: str) -> str: + return " ".join(string.split()) @staticmethod - def extract_algorithm_certificates(current_text: str) -> Set[str]: - """ - Parses table of FIPS (non) allowed algorithms - - :param str current_text: Contents of the table - :return: A list of found algorithm ids. - """ - set_items = set() - reg = r"(?:#[CcAa]?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P\d+)" - for m in re.finditer(reg, current_text): - set_items.add(m.group()) - - return set_items + def parse_tested_configurations(tested_configurations: Tag) -> Optional[List[str]]: + configurations = [y.text for y in tested_configurations.find_all("li")] + return configurations if not configurations == ["N/A"] else None + + +DETAILS_KEY_NORMALIZATION_DICT: Final[Dict[str, str]] = { + "Module Name": "module_name", + "Standard": "standard", + "Status": "status", + "Sunset Date": "date_sunset", + "Validation Dates": "date_validation", + "Overall Level": "level", + "Caveat": "caveat", + "Security Level Exceptions": "exceptions", + "Module Type": "module_type", + "Embodiment": "embodiment", + "Approved Algorithms": "algorithms", + "Tested Configuration(s)": "tested_conf", + "Description": "description", + "Historical Reason": "historical_reason", + "Hardware Versions": "hw_versions", + "Firmware Versions": "fw_versions", + "Revoked Reason": "revoked_reason", + "Revoked Link": "revoked_link", + "Software Versions": "sw_versions", + "Product URL": "product_url", +} + +DETAILS_KEY_TO_NORMALIZATION_FUNCTION: Dict[str, Callable] = { + "date_sunset": lambda x: dateutil.parser.parse(x.text).date(), + "algorithms": getattr(FIPSHTMLParser, "parse_algorithms"), + "tested_conf": getattr(FIPSHTMLParser, "parse_tested_configurations"), + "exceptions": lambda x: [y.text for y in x.find_all("li")], + "status": lambda x: FIPSHTMLParser.normalize_string(x.text).lower(), + "level": lambda x: int(FIPSHTMLParser.normalize_string(x.text)), +} class FIPSCertificate( @@ -249,6 +219,9 @@ class InternalState(ComplexSerializableType): policy_convert_garbage: bool policy_convert_ok: bool + module_extract_ok: bool + policy_extract_ok: bool + policy_pdf_hash: Optional[str] policy_txt_hash: Optional[str] @@ -264,6 +237,8 @@ def __init__( policy_download_ok: bool = False, policy_convert_garbage: bool = False, policy_convert_ok: bool = False, + module_extract_ok: bool = False, + policy_extract_ok: bool = False, policy_pdf_hash: Optional[str] = None, policy_txt_hash: Optional[str] = None, errors: Optional[List[str]] = None, @@ -272,6 +247,8 @@ def __init__( self.policy_download_ok = policy_download_ok self.policy_convert_garbage = policy_convert_garbage self.policy_convert_ok = policy_convert_ok + self.module_extract_ok = module_extract_ok + self.policy_extract_ok = policy_extract_ok self.policy_pdf_hash = policy_pdf_hash self.policy_txt_hash = policy_txt_hash self.errors = errors if errors else [] @@ -283,6 +260,8 @@ def serialized_attributes(self) -> List[str]: "policy_download_ok", "policy_convert_garbage", "policy_convert_ok", + "module_extract_ok", + "policy_extract_ok", "policy_pdf_hash", "policy_txt_hash", ] @@ -296,11 +275,26 @@ def policy_is_ok_to_download(self, fresh: bool = True) -> bool: def policy_is_ok_to_convert(self, fresh: bool = True) -> bool: return self.policy_download_ok if fresh else self.policy_download_ok and not self.policy_convert_ok + def module_is_ok_to_analyze(self, fresh: bool = True) -> bool: + return self.module_download_ok if fresh else self.module_download_ok and not self.module_extract_ok + def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, modules_html_dir: Path) -> None: self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") self.state.policy_txt_path = (policies_txt_dir / str(self.dgst)).with_suffix(".txt") self.state.module_html_path = (modules_html_dir / str(self.dgst)).with_suffix(".html") + @dataclass(eq=True) + class ValidationHistoryEntry(ComplexSerializableType): + date: date + validation_type: Literal["initial", "update"] + lab: str + + @classmethod + def from_dict(cls, dct: Dict) -> FIPSCertificate.ValidationHistoryEntry: + new_dct = dct.copy() + new_dct["date"] = dateutil.parser.parse(dct["date"]).date() + return cls(**new_dct) + @dataclass(eq=True) class WebData(ComplexSerializableType): """ @@ -308,32 +302,31 @@ class WebData(ComplexSerializableType): """ module_name: Optional[str] = field(default=None) + validation_history: Optional[List[FIPSCertificate.ValidationHistoryEntry]] = field(default=None) + vendor_url: Optional[str] = field(default=None) + vendor: Optional[str] = field(default=None) + certificate_pdf_url: Optional[str] = field(default=None) + module_type: Optional[str] = field(default=None) standard: Optional[str] = field(default=None) - status: Optional[str] = field(default=None) - date_sunset: Optional[datetime] = field(default=None) - date_validation: Optional[List[datetime]] = field(default=None) - level: Optional[str] = field(default=None) + status: Optional[Literal["active", "historical", "revoked"]] = field(default=None) + level: Optional[Literal[1, 2, 3, 4]] = field(default=None) caveat: Optional[str] = field(default=None) exceptions: Optional[List[str]] = field(default=None) - module_type: Optional[str] = field(default=None) embodiment: Optional[str] = field(default=None) - algorithms: Optional[Set[FIPSAlgorithm]] = field(default=None) - tested_conf: Optional[List[str]] = field(default=None) description: Optional[str] = field(default=None) - mentioned_certs: Optional[Dict[str, Dict[str, int]]] = field(default=None) - vendor: Optional[str] = field(default=None) - vendor_www: Optional[str] = field(default=None) - lab: Optional[str] = field(default=None) - lab_nvlap: Optional[str] = field(default=None) + tested_conf: Optional[List[str]] = field(default=None) + algorithms: Optional[Dict[str, Set[str]]] = field(default=None) + hw_versions: Optional[str] = field(default=None) + fw_versions: Optional[str] = field(default=None) + sw_versions: Optional[str] = field(default=None) + mentioned_certs: Optional[Dict[str, int]] = field(default=None) # Cert_id: n_occurences historical_reason: Optional[str] = field(default=None) - security_policy_www: Optional[str] = field(default=None) - certificate_www: Optional[str] = field(default=None) - hw_version: Optional[str] = field(default=None) - fw_version: Optional[str] = field(default=None) + date_sunset: Optional[date] = field(default=None) revoked_reason: Optional[str] = field(default=None) revoked_link: Optional[str] = field(default=None) - sw_versions: Optional[str] = field(default=None) - product_url: Optional[str] = field(default=None) + + # Those below are left unused at the moment + # product_url: Optional[str] = field(default=None) def __repr__(self) -> str: return ( @@ -347,6 +340,13 @@ def __repr__(self) -> str: def __str__(self) -> str: return repr(self) + @classmethod + def from_dict(cls, dct: Dict) -> FIPSCertificate.WebData: + new_dct = dct.copy() + if new_dct["date_sunset"]: + new_dct["date_sunset"] = dateutil.parser.parse(new_dct["date_sunset"]).date() + return cls(**dct) + @dataclass(eq=True) class PdfData(BasePdfData, ComplexSerializableType): """ @@ -426,10 +426,10 @@ def label_studio_title(self) -> str: + str(self.web_data.module_name) + "\n" + "HW version: " - + str(self.web_data.hw_version) + + str(self.web_data.hw_versions) + "\n" + "FW version: " - + str(self.web_data.fw_version) + + str(self.web_data.fw_versions) ) def __init__( @@ -443,7 +443,7 @@ def __init__( super().__init__() self.cert_id = cert_id - self.web_data = web_data if web_data else FIPSCertificate.WebData() + self.web_data: FIPSCertificate.WebData = web_data if web_data else FIPSCertificate.WebData() self.pdf_data = pdf_data if pdf_data else FIPSCertificate.PdfData() self.heuristics = heuristics if heuristics else FIPSCertificate.Heuristics() self.state = state if state else FIPSCertificate.InternalState() @@ -459,7 +459,7 @@ def pandas_tuple(self) -> Tuple: self.web_data.module_type, self.web_data.level, self.web_data.embodiment, - self.web_data.date_validation[0] if self.web_data.date_validation else np.nan, + self.web_data.validation_history[0] if self.web_data.validation_history else np.nan, self.web_data.date_sunset, self.heuristics.algorithms, self.heuristics.extracted_versions, @@ -476,117 +476,18 @@ def pandas_tuple(self) -> Tuple: self.heuristics.st_references.indirectly_referencing, ) - @classmethod - def from_dict(cls, dct: Dict) -> FIPSCertificate: - """ - Deserializes dictionary into FIPSCertificate - - :param Dict dct: dictionary that holds the FIPSCertificate data - :return FIPSCertificate: object reconstructed from dct - """ - new_dct = dct.copy() - - if new_dct["web_data"].date_validation: - new_dct["web_data"].date_validation = [parser.parse(x).date() for x in new_dct["web_data"].date_validation] - - if new_dct["web_data"].date_sunset: - new_dct["web_data"].date_sunset = parser.parse(new_dct["web_data"].date_sunset).date() - return super(cls, FIPSCertificate).from_dict(new_dct) - - @classmethod - def from_html_file( - cls, file: Path, state: InternalState, initialized: FIPSCertificate = None, redo: bool = False - ) -> FIPSCertificate: - """ - Constructs FIPSCertificate object from html file. + @staticmethod + def parse_html_module(cert: FIPSCertificate) -> FIPSCertificate: + with cert.state.module_html_path.open("r") as handle: + soup = BeautifulSoup(handle, "html5lib") - :param Path file: path to the html file to use for initialization - :param InternalState state: state of the certificate - :param FIPSCertificate initialized: possibly partially initialized FIPSCertificate, defaults to None - :param bool redo: if the method should be reattempted in case of failure, defaults to False - :return FIPSCertificate: resulting `FIPSCertificate` object. - """ + parser = FIPSHTMLParser(soup) + cert.web_data = parser.build_web_data() - if not initialized: - items_found = FIPSCertificate._initialize_dictionary() - items_found["cert_id"] = int(file.stem) - else: - items_found = initialized.web_data.__dict__ - items_found["cert_id"] = initialized.cert_id - items_found["revoked_reason"] = None - items_found["revoked_link"] = None - items_found["mentioned_certs"] = {} - - # TODO: Not sure what this was for, fixme - # state.tables_done = initialized.state.tables_done - # state.file_status = initialized.state.file_status - # state.txt_state = initialized.state.txt_state - - if redo: - items_found = FIPSCertificate._initialize_dictionary() - items_found["cert_id"] = int(file.stem) - - text = sec_certs.utils.extract.load_cert_html_file(str(file)) - soup = BeautifulSoup(text, "html5lib") - for div in soup.find_all("div", class_="row padrow"): - _FIPSHTMLParser.parse_html_main(div, items_found) - - for div in soup.find_all("div", class_="panel panel-default")[1:]: - if div.find("h4", class_="panel-title").text == "Vendor": - _FIPSHTMLParser.parse_vendor(div, items_found, file) - - if div.find("h4", class_="panel-title").text == "Lab": - _FIPSHTMLParser.parse_lab(div, items_found, file) - - if div.find("h4", class_="panel-title").text == "Related Files": - _FIPSHTMLParser.parse_related_files(div, items_found) - - if div.find("h4", class_="panel-title").text == "Validation History": - _FIPSHTMLParser.parse_validation_dates(div, items_found) - - _FIPSHTMLParser.normalize(items_found) - - return FIPSCertificate( - items_found["cert_id"], - FIPSCertificate.WebData( - items_found["module_name"] if "module_name" in items_found else None, - items_found["standard"] if "standard" in items_found else None, - items_found["status"] if "status" in items_found else None, - items_found["date_sunset"] if "date_sunset" in items_found else None, - items_found["date_validation"] if "date_validation" in items_found else None, - items_found["level"] if "level" in items_found else None, - items_found["caveat"] if "caveat" in items_found else None, - items_found["exceptions"] if "exceptions" in items_found else None, - items_found["module_type"] if "module_type" in items_found else None, - items_found["embodiment"] if "embodiment" in items_found else None, - items_found["algorithms"] if "algorithms" in items_found else None, - items_found["tested_conf"] if "tested_conf" in items_found else None, - items_found["description"] if "description" in items_found else None, - items_found["mentioned_certs"] if "mentioned_certs" in items_found else None, - items_found["vendor"] if "vendor" in items_found else None, - items_found["vendor_www"] if "vendor_www" in items_found else None, - items_found["lab"] if "lab" in items_found else None, - items_found["nvlap_code"] if "nvlap_code" in items_found else None, - items_found["historical_reason"] if "historical_reason" in items_found else None, - items_found["security_policy_www"] if "security_policy_www" in items_found else None, - items_found["certificate_www"] if "certificate_www" in items_found else None, - items_found["hw_versions"] if "hw_versions" in items_found else None, - items_found["fw_versions"] if "fw_versions" in items_found else None, - items_found["revoked_reason"] if "revoked_reason" in items_found else None, - items_found["revoked_link"] if "revoked_link" in items_found else None, - items_found["sw_versions"] if "sw_versions" in items_found else None, - items_found["product_url"] if "product_url" in items_found else None, - ), - FIPSCertificate.PdfData( - {} if not initialized else initialized.pdf_data.keywords, - set() if not initialized else initialized.pdf_data.algorithms, - {} if not initialized else initialized.pdf_data.clean_cert_ids, - ), - FIPSCertificate.Heuristics(dict(), set(), 0, {}), - state, - ) + return cert @staticmethod + # TODO: This should probably get deleted def download_html_page(cert: Tuple[str, Path]) -> Optional[Tuple[str, Path]]: """ Wrapper for downloading a file. `delay=1` introduced to avoid problems with requests at NIST.gov @@ -648,38 +549,6 @@ def extract_sp_metadata(cert: FIPSCertificate) -> FIPSCertificate: cert.pdf_data.st_metadata = metadata if metadata else dict() return cert - @staticmethod - def _initialize_dictionary() -> Dict[str, Any]: - return { - "module_name": None, - "standard": None, - "status": None, - "date_sunset": None, - "date_validation": None, - "level": None, - "caveat": None, - "exceptions": None, - "module_type": None, - "embodiment": None, - "tested_conf": None, - "description": None, - "vendor": None, - "vendor_www": None, - "lab": None, - "lab_nvlap": None, - "historical_reason": None, - "revoked_reason": None, - "revoked_link": None, - "algorithms": set(), - "mentioned_certs": {}, - "security_policy_www": None, - "certificate_www": None, - "hw_versions": None, - "fw_versions": None, - "sw_versions": None, - "product_url": None, - } - @staticmethod def find_keywords(cert: FIPSCertificate) -> Tuple[Optional[Dict], FIPSCertificate]: # TODO: Replace the condition below @@ -788,8 +657,9 @@ def clean_cert_ids(self) -> None: if self.web_data.algorithms is None: raise RuntimeError(f"Algorithms were not found for cert {self.cert_id} - this should not be happening.") - for algo in self.web_data.algorithms: - alg_set.add(algo.cert_id) + # TODO : Refactor this, dictionary form changed + # for algo in self.web_data.algorithms: + # alg_set.add(algo.cert_id) for cert_rule in fips_rules["fips_cert_id"]["Cert"]: to_pop = set() @@ -822,8 +692,8 @@ def compute_heuristics_version(self) -> None: versions_for_extraction = "" if self.web_data.module_name: versions_for_extraction += f" {self.web_data.module_name}" - if self.web_data.hw_version: - versions_for_extraction += f" {self.web_data.hw_version}" - if self.web_data.fw_version: - versions_for_extraction += f" {self.web_data.fw_version}" + if self.web_data.hw_versions: + versions_for_extraction += f" {self.web_data.hw_versions}" + if self.web_data.fw_versions: + versions_for_extraction += f" {self.web_data.fw_versions}" self.heuristics.extracted_versions = helpers.compute_heuristics_version(versions_for_extraction) diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json new file mode 100644 index 00000000..5f0be2cd --- /dev/null +++ b/tests/data/fips/certificate/fictional_cert.json @@ -0,0 +1,163 @@ +{ + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "184097a88a9b4ad9", + "cert_id": 3518, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "DocuSign Signature Appliance", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-09-05", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.docusign.com", + "vendor": "DocuSign, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/SeptConsolidated.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "active", + "level": 3, + "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "tested_conf": null, + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "CKG": { + "_type": "Set", + "elements": [] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#C86" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#98", + "#C85" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#C86" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#1465", + "#C85", + "#C86" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#C85" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#C85" + ] + } + }, + "hw_versions": "8.0", + "fw_versions": "9.0.9.10", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": null, + "date_sunset": "2024-09-04", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + } +} \ No newline at end of file diff --git a/tests/data/fips/dataset/fips_modules_active.html b/tests/data/fips/dataset/fips_modules_active.html new file mode 100644 index 00000000..0b920fb1 --- /dev/null +++ b/tests/data/fips/dataset/fips_modules_active.html @@ -0,0 +1,1020 @@ + + + + + + Cryptographic Module Validation Program | CSRC + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Certificate NumberVendor NameModule NameModule TypeValidation DateStatus
+ 3651 + Microsoft CorporationSecure Kernel Code IntegritySoftware05/07/2020
10/05/2022
+ 		Active
+ 3093 + Microsoft CorporationCode IntegritySoftware03/22/2018
06/17/2019
+ 		Active
+ 3090 + Microsoft CorporationWindows OS LoaderSoftware03/22/2018
06/17/2019
+ 		Active
+ 3850 + DocuSign, Inc.DocuSign Signature ApplianceHardware03/18/2021
09/15/2021
+ 		Active
+ 3518 + DocuSign, Inc.DocuSign Signature ApplianceHardware09/05/2019
Active
+ + + + + + + + +
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+ + Created October 11, 2016, Updated October 26, 2022 +
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Cryptographic Module Validation Program CMVP

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Certificate NumberVendor NameModule NameModule TypeValidation DateStatus
+ 3095 + Microsoft CorporationCryptographic Primitives LibrarySoftware03/22/2018
Historical
+ 3197 + Microsoft CorporationCryptographic Primitives LibrarySoftware + 10/22/2018
07/17/2019
09/08/2020
Historical
+ 2630 + Red Hat®, Inc.Red Hat Enterprise Linux OpenSSH Server Cryptographic + ModuleSoftware05/02/2016
06/17/2016
+ 		Historical
+ 2721 + Red Hat®, Inc.Red Hat Enterprise Linux Libreswan Cryptographic Module + v4.0Software08/29/2016
12/20/2016
+ 		Historical
+ 2997 + SafeLogic Inc.CryptoComply for NSSSoftware + 08/18/2017
02/07/2018
03/29/2018
Historical
+ 2441 + Red Hat®, Inc.Red Hat Enterprise Linux 6.6 OpenSSL Module, Red Hat + Enterprise Linux 7.1 OpenSSL ModuleSoftware + 09/08/2015
01/27/2016
02/16/2016
12/21/2016
Historical
+ 2711 + Red Hat®, Inc.Red Hat Enterprise Linux NSS Cryptographic Module v4.0 + Software + 08/15/2016
12/19/2016
01/30/2018
Historical
+ 2779 + DocuSign, Inc.DocuSign Signature ApplianceHardware10/25/2016
Historical
+ 2860 + DocuSign, Inc.DocuSign HSM ApplianceHardware + 03/08/2017
10/03/2017
04/10/2018
Historical
+ 2665 + DocuSign, Inc.DocuSign Signature ApplianceHardware06/21/2016
07/25/2016
+ 		Historical
+ 3141 + DocuSign, Inc.DocuSign Signature ApplianceHardware02/27/2018
08/17/2018
+ 		Historical
+ 2590 + ARX (Algorithmic Research)CoSignHardware03/24/2016
Historical
+ 3493 + LogRhythmLogRhythm FIPS Object Module for OpenSSLSoftware07/17/2019
01/24/2020
+ 		Historical
+ 3495 + Panzura, Inc.Panzura CloudFSâ„¢ FIPS Cryptographic ModuleSoftware07/19/2019
Historical
+ 3711 + Alteryx Inc.Alteryx Cryptographic ModuleSoftware09/21/2020
12/01/2020
+ 		Historical
+ 3176 + Digi International, Inc.Digi ConnectCore Security ModuleSoftware04/26/2018
06/12/2018
+ 		Historical
+ 3488 + ColorTokensColorTokens OpenSSL FIPS Object ModuleSoftware07/15/2019
Historical
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+ + Created October 11, 2016, Updated October 26, 2022 +
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+ Projects + Cryptographic Module + Validation Program + Validated Modules +
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Cryptographic Module Validation Program CMVP

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+ 24 certificates match the search criteria +

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Certificate NumberVendor NameModule NameModule TypeValidation DateStatus
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+ + Created October 11, 2016, Updated October 26, 2022 +
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+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/tests/data/fips/dataset/template_policy_184097a88a9b4ad9.txt b/tests/data/fips/dataset/template_policy_184097a88a9b4ad9.txt new file mode 100644 index 00000000..2343d66b --- /dev/null +++ b/tests/data/fips/dataset/template_policy_184097a88a9b4ad9.txt @@ -0,0 +1,1370 @@ +- 1 - +DocuSign Signature Appliance +Hardware version 8.0 +with Firmware version 9.0.9.10 +FIPS 140-2 Non-Proprietary +Security Policy +Level 3 Validation +August 2019 + - 2 - +Table of Contents +1 INTRODUCTION.......................................................................................................................... 3 +1.1 PURPOSE............................................................................................................................. 3 +1.2 REFERENCES....................................................................................................................... 3 +1.3 TERMINOLOGY ................................................................................................................... 3 +1.4 DOCUMENT ORGANIZATION ............................................................................................... 3 +2 FIPS 140-2 SECURITY LEVEL................................................................................................... 4 +3 SECURITY RULES....................................................................................................................... 5 +3.1 SECURE BY DESIGN ............................................................................................................ 5 +3.2 PRODUCT DELIVERY........................................................................................................... 6 +3.3 INITIALIZATION .................................................................................................................. 7 +3.3.1 Installing the DocuSign Signature Appliance ......................................................... 8 +3.3.2 Restoring the DocuSign Signature Appliance from backup .................................... 8 +3.3.3 Setting the Appliance to work in FIPS Mode .......................................................... 9 +3.4 USERS DIRECTORIES........................................................................................................... 9 +3.5 MANAGING THE DOCUSIGN SIGNATURE APPLIANCE........................................................ 10 +3.5.1 Cryptographic Officer ........................................................................................... 10 +3.5.2 User ....................................................................................................................... 11 +3.6 SECURE OPERATION – DOCUSIGN SA CLIENT.................................................................. 11 +3.7 ADDITIONAL SECURITY ISSUES......................................................................................... 12 +3.8 HIGH AVAILABILITY AND LOAD BALANCING ................................................................... 13 +3.9 INTERFACE TO EXTERNAL CA IN AUTOMATED MODE ...................................................... 13 +3.10 WELL-DEFINED INTERFACES............................................................................................ 13 +3.11 ROLES AND SERVICES....................................................................................................... 15 +3.11.1 Supervisor (Crypto Officer) Role .......................................................................... 15 +3.11.2 User/Application Role ........................................................................................... 16 +3.12 OPERATIONAL ENVIRONMENT.......................................................................................... 21 +3.13 STRONG CRYPTOGRAPHIC ALGORITHMS AND SECURE KEY MANAGEMENT..................... 21 +3.13.1 Power-Up Self Tests .............................................................................................. 29 +3.13.2 Conditional Tests................................................................................................... 30 +3.14 MITIGATION OF OTHER ATTACKS..................................................................................... 30 +3.15 MAINTENANCE ................................................................................................................. 30 +4 FIPS 140-2 LEVEL 3 COMPLIANT MODE............................................................................ 32 +4.1 CONFIGURING THE APPLIANCE TO WORK IN FIPS MODE................................................... 32 +5 UPGRADE APPLIANCE FIRMWARE FROM VERSION 8.0 TO VERSION 8.1............. 33 +6 UPGRADE APPLIANCE FIRMWARE FROM VERSION 8.0/8.1 TO VERSIONS 8.5, +8.51.9.0 AND 9.0.9.10.......................................................................................................................... 34 + - 3 - +1 INTRODUCTION +1.1 Purpose +This document describes the non-proprietary Cryptographic Module Security Policy for the DocuSign +Signature Appliance. This security policy describes how the DocuSign Signature Appliance meets the +security requirements of FIPS 140-2, and how to operate the DocuSign Signature Appliance in a +secure FIPS 140-2 mode. This policy was prepared as part of the level 3 FIPS 140-2 testing of the +DocuSign Signature Appliance. +FIPS 140-2 (Federal Information Processing Standards Publication 140-2 -- Security Requirements for +Cryptographic Modules) details the U.S. Government requirements for cryptographic modules. +Additional information about the FIPS 140-2 standard and validation program is available on the +NIST web site at https://csrc.nist.gov/projects/cryptographic-module-validation-program. +1.2 References +This document deals only with the operations and capabilities of the DocuSign Signature Appliance +in the technical terms of a FIPS 140-2 cryptographic module security policy. Additional information +about the DocuSign Signature Appliance and other DocuSign products is available at +www.docusign.com. +1.3 Terminology +In this document, the DocuSign Signature Appliance is referred to as the Appliance. +1.4 Document Organization +This document is part of the FIPS 140-2 Submission Package. In addition to this document, the +Submission Package contains the following documents: +• Vendor Evidence +• Finite State Machine +• Module Firmware Listing +• Other supporting documentation as additional references +This document is organized as follows: +• Section 1: Introduction – Includes an overview of the DocuSign Signature Appliance and +explains the secure configuration and operation of the Appliance. +• Section 2: FIPS 140-2 security level – Details each level of the FIPS 140-2 requirements +section. +• Section 3: Appliance Security Rules – Details the general features and functionality of the +DocuSign Signature Appliance. + - 4 - +• Section 4: FIPS 140-2 Level 3 Compliant Mode – Addresses the required configuration for the +FIPS 140-2 mode of operation. +With the exception of this non-proprietary Security Policy, the FIPS 140-2 Validation submission +documentation is DocuSign-proprietary and may only be released under appropriate non-disclosure +agreements. +This document may be reproduced and distributed providing such a reproduction is complete and +unmodified. +For access to the FIPS 140-2 Validation Submission documents, contact DocuSign. +2 FIPS 140-2 security level +The DocuSign Signature Appliance is validated to meet the FIPS 140-2 security requirements for the +levels shown below. The overall module is validated to FIPS 140-2 security level 3. +FIPS 140-2 Security Requirements Section Level +Cryptographic Module Specification 3 +Cryptographic Module Port and Interfaces 3 +Role, Services and Authentication 3 +Finite State Model 3 +Physical Security (Multi-Chip Standalone) 3 +Operational Environment N/A +Cryptographic Key Management 3 +EMI/EMC 3 +Self-Tests 3 +Design Assurance 3 +Mitigation of Other Attacks N/A +Table 1 - FIPS 140-2 Security Requirements Level + - 5 - +3 Security Rules +The DocuSign Signature Appliance is a digital signature appliance that enables users within an +organization to digitally sign documents and data. Contained within a secure, tamper-responsive +steel case, the Appliance performs the actual digital signature operation using an asymmetric key of +the user. All keys and critical security parameters are protected within the cryptographic boundary +by the physical security mechanisms of the Appliance. +The Appliance provides the basic RSA digital signature operation. Additional cryptographic +algorithms are used in support of this main functionality. These are used to encrypt: the session +between the user’s PC and the Appliance; the asymmetric keys that are kept in the internal +database; and the backup of the Appliance’s database. They are also used to provide data integrity. +The Appliance performs all cryptographic operations internally and, through self-tests, it ensures +that these operations function correctly. +Starting for DocuSign Signature Appliance version 9.0.9.10, it is possible to install the Appliance in an +HSM style where a user of the Appliance can also have Symmetric encryption/decryption keys such +as AES in addition to using RSA keys. +Figure 1 - DocuSign Signature Appliance +3.1 Secure by Design +The DocuSign Signature Appliance is a multi-chip standalone appliance. It has been designed to meet +all of the Level 3 FIPS 140-2 requirements. Encased within a tamper-responsive and tamper-evident +steel box, the Appliance both protects against and reacts to attacks. Access to the Appliance is only +permitted through specific, well-defined interfaces detailed in Well-Defined Interfaces section. + - 6 - +All vents on the module are baffled to meet FIPS 140-2 physical security requirements for opacity +and probing. +The Appliance includes a dual power supply, where the power supply can be removable and +replaceable. +The dual-power supply, containing two power supply units, is an external component of the module +and outside the physical cryptographic boundary of the module. +Tamper Evident cans provide evidence of any attempt to tamper with module cover. The Tamper +Evident cans are placed over a screw that joins the top cover and bottom enclosure. +The Tamper Evident cans are applied at manufacturing stage. +The Tamper Evident cans are shown in Figure 2. +Figure 2 - Tamper Evident cans +The units are encased in a solid metal case rigged with micro-switches and only the specified +physical interfaces permit access to the module. Intrusion attempts cause power to be instantly cut +off, preventing access to any useful information by zeroizing all plaintext critical security parameters +including the Appliance Critical keys. +3.2 Product Delivery +The product is wholly manufactured in DocuSign and includes both FIPS 140-2 approved hardware +version and FIPS 140-2 approved firmware version. At manufacturing both tamper device and +tamper seals are assembled. The whole product is fully tested before delivery. The product is + - 7 - +packaged and directly delivered to the customer. The product is in a Factory state when delivered to +the customer. While in delivery, the product is protected by unique tamper seals and by the casing +of the Appliance. When the Crypto Officer receives the Appliance, the Crypto Officer must check the +Appliance’s case for any evidence of physical tampering. The Crypto Officer should verify that the +Tamper Evident cans are attached to the Appliance and that they are not damaged. +If you think the Appliance has been tampered with during delivery, contact DocuSign. +Firmware Upgrades are sent to customer through DocuSign Support Channels. The firmware +updates are digitally signed by an internal DocuSign Signature key that is controlled by DocuSign +Engineering. For more information about the Firmware Update process refer to chapter 6. When the +Appliance Administrator upload the updated firmware version, the digital signature of the new +firmware version is checked and rejected by the existing Appliance Firmware. +3.3 Initialization +The Appliance is delivered to you in the Factory Settings state. In this state it is not yet a FIPS module +and only the following options are relevant: +• Setting network parameters – The Cryptographic Officer can set the IP address of the +Appliance, define that the IP address is retrieved using a DHCP protocol and set other +networking related parameters. This operation is performed through Appliance’s console. +• Time adjustments – The Cryptographic Officer can define the current time of the Appliance +or retrieve time from an NTP server. This operation is performed through the Appliance’s +console. +• Installation – This critical procedure must be performed in a secure environment. Only after +the Appliance is installed it can begin to provide its digital signature services. +For additional details related to appliance initialization, see Installing the DocuSign Signature +Appliance section. +• Restoration – This critical procedure must be performed in a secure environment. +Restoration is similar to installation. This procedure uses the backup file of the internal +database. +For additional details related to appliance restoration, see Restoring the DocuSign Signature +Appliance from backup on following section. +Remark: +A web based console is used. The web based console is accessed through a dedicated LAN interface +(labeled as LAN0) of the Appliance to IP address 10.0.0.2 on port 8088. +Also, any operation from the web based console requires physical access to the Appliance by +unplugging/plugging the license token as part of approving the operation. + - 8 - +3.3.1 Installing the DocuSign Signature Appliance +The Appliance installation is performed using the administrative DocuSign SA Client or using the +DocuSign HSM client. The Cryptographic Officer uses the administrative DocuSign SA Client or +DocuSign HSM client to send installation commands to the Appliance. The installation commands are +sent using the regular client/appliance secure protocol (see Secure Operation – DocuSign SA Client +section). +During installation, the following security related issues are handled: +• The first Crypto Officer User ID and password are provided. The Crypto Officer is defined in +the users database with the required permissions to manage users, groups and the +Appliance. +Assigning users to groups is relevant only for when the Appliance is installed in Directory +Independent installation. +• A set of four Server critical AES128 keys and IPSEC shared secret are randomly generated +inside the Appliance and are placed inside the internal tamper device. The keys and shared +secret are also loaded into the two blue USB tokens. These tokens must be stored on the +Crypto Officer’s premises and are only used during the: +• Reset tamper operation performed by the Crypto Officer. +• Restoration of the Appliance. +• Installing an alternate appliance for High Availability purpose. +In the case of HSM installation, the master keys are split to two blue USB tokens that are +duplicated for redundancy purposes. +• In the case that it is configured to use an internal CA, A RSA key pair is generated for the +internal CA (Certificate Authority) of the Appliance. This key is used for generating X.509- +based Certificates for users. The RSA private key is encrypted and stored in the Appliance’s +database. +During normal appliance operation, a USB-based license plug is plugged into the Appliance’s USB +port. The USB token controls the number of possible existing users in the Appliance’s database and +expiration date for the Appliance’s service. +DocuSign manufactures the Appliance based on firmware versions 8.0, 8.1, 8.4, 8.5, 8.51.9.0 or +9.0.9.10. +Also, it is possible to upgrade to Appliance firmware version 8.4 upgrade to Appliance firmware +version 8.5, then upgrade to Appliance firmware 8.51.9.0 and then to Appliance firmware 9.0. +Appliance firmware version 8.4 is not FIPS approved. +For more information of how to perform a firmware upgrade, refer to Chapter 5. +3.3.2 Restoring the DocuSign Signature Appliance from backup +If the Appliance was physically damaged, reset to factory settings, or damaged in some other way, a +backup of the Appliance’s database must be restored to a new or existing appliance. The restore +operation is very similar to the installation of a new appliance and must be performed in a secure + - 9 - +environment. In addition, the Appliance must be in the Factory Settings state to perform the restore +operation. +A restoration differs from an installation in the following ways: +• A valid backup file of an operational appliance must be available. +• The Crypto Officer must have a valid backup token or two valid tokens in the case of HSM +configuration that includes the critical keys and the IPSEC shared secret of that operational +appliance. +During restoration: +• The Crypto Officer provides the backup file and plugs the backup token or tokens +sequentially into the Appliance’s USB token slot. +• All users and their relevant data, such as their private keys, are restored to the Appliance’s +database. +After restoration, all users can sign their documents and data using the Appliance. +After restoration, the product is a FIPS module and begins serving user requests and Crypto Officer +requests. +3.3.3 Setting the Appliance to work in FIPS Mode +There are several system parameters and an additional configuration that are required to be set in +order for the Appliance to work in FIPS mode. These system parameters must be set right after +initialization or restoration. +For more information refer to section 4.1. +One of these system parameters is named Advanced- Enforce FIPS Approved Algorithm, when this +system parameter is set, only FIPS approved algorithms are used. +3.4 Users Directories +The DocuSign Signature Appliance supports installation in environments where a user directory +already exists. Currently the following Users Directory environments are supported: +• Microsoft Active Directory +• LDAP based environment such as: IBM Tivoli, SUN Directory Server and Oracle Internet +Directory. +The Appliance provides two additional functionalities when using these environments: +• Synchronization with the Users Directory of the environment – The Appliance is +synchronized with the users directory of the environment. Every user in the users directory +who is classified as a signer is also defined in the Appliance and is able to sign documents. + - 10 - +• Authentication using Kerberos Ticketing mechanism – When a user attempts to securely +connect to the Appliance for any operation, such as signing a document, the login operation +is done using the Kerberos Ticketing mechanism. The Appliance authenticates users from +Active Directory relying on the Kerberos Ticketing mechanism. +• Besides the above directories, the Appliance supports the Directory Independent +environment where users are defined by the administrator of the organization and the login +operation is performed internally by the Appliance. +Note: Only the Directory Independent environment and module interface to Microsoft Active +Directory are submitted for FIPS 140-2 validation. +Also, it is possible to authenticate a user based on a SAML or JWT ticket provided by a trusted +Identity Provider. +The appliance can be configured as a Digital Signature Appliance (DSA) or a HSM. In HSM style, the +appliance can only be installed in Directory Independent (DI) mode. DSA generates digital signatures +on behalf of users. HSM encrypts and decrypts data on behalf of users. +In the case that the Appliance is deployed in an HSM style, it can be installed only in Directory +Independent (DI) mode, and the authentication is based only on a User ID and Password. +3.5 Managing the DocuSign Signature Appliance +3.5.1 Cryptographic Officer +The Crypto Officer performs both appliance and users/groups management of the Appliance. +In the case of Active Directory based environment, users are managed in the directory and all +changes that are made in the directory sync with the list of users in the Appliance. +The Crypto Officer connects securely to the Appliance (see Secure Operation – DocuSign SA Client +section). The following sections describe in detail all operations that can be performed by the Crypto +Officer. +The Crypto Officer creates users and groups according to the organization’s policy. For each user, a +User-ID and a Password is provided. This operation is relevant only when the Appliance is installed in +Directory Independent environment. In Active Directory environment, a user is created in the +Appliance when the Crypto Officer creates the user in Active Directory and defines the user as a +member of the Signers User Group. +By default, after a user is created, the Appliance automatically generates a new RSA key pair and a +Certificate for the user. +The Crypto Officer can delete users. When a user is deleted, all the user’s keys, certificates, and +graphical images are also deleted. This operation is relevant only when the Appliance is installed in + - 11 - +Directory Independent Environment. In Active Directory Environment users are deleted from the +Appliance when the Crypto Officer deletes the user in Active Directory or removes the user from the +Signers User Group. +3.5.2 User +In the case of Directory Independent environment the user’s password is managed by the Appliance. +The password length must be six Unicode characters or more and less than twenty eight Unicode +characters. +In the case of Active directory environment, the user’s password is managed by the directory. +The user can also direct the Appliance to generate additional RSA keys. It is possible to store several +graphical signature images in the user account in the Appliance. These images are stored in the +Appliance’s database, retrieved by the DocuSign SA (Signature Appliance) Client, and can be +incorporated into the signed document in the user’s PC. +The user can also direct the Appliance to generate AES keys. +A user can only use keys that are owned by that user. +Remark: it is possible to configure the Appliance to use an RSA key pool when the Appliance’s +internal CA is used. The implementation is based on a process which is executed within the +Cryptographic module and writes the newly generated keys to the database, encrypted with +appliance Critical Key 1. +These keys will be assigned to user when it is required to assign a signature key for the user. A +certain key from the key pool can only belong to a certain user. +3.6 Secure Operation – DocuSign SA Client +Any operator who wishes to use the Appliance’s services can connect via a secure protocol using the +DocuSign SA Client. The secure networking protocol is a standard TLS (Transport Layer Security) +protocol with the following parameters: +• The TLS protocol is based on a Server RSA key. The TLS Server RSA key is externally generated +during manufacturing. Each individual appliance includes a different TLS Server RSA key. +• The TLS session is based on AES CBC encryption and HMAC-SHA-256 data integrity. +• Upon session creation, the only operation that can be performed is an authentication +command. The authentication is based on User ID and Password authentication, which are +verified by the Appliance or using a Kerberos ticket when the Appliance is installed in Active +Directory environment. +Also, it is possible to authenticate a user based on validating a SAML or JWT Token that was +created by a trusted Identity provider. + - 12 - +• Only after the user is authenticated, can the user perform operations such as digitally sign +data. Similarly, the Crypto Officer can connect securely to the Appliance and perform +administrative operations. +• It is possible to configure the Appliance to use extended authentication, where any digital +signature operation requires the end user to authenticate. There are two modes of extended +authentication: +• Radius based – the end user provides an additional password that is validated by an +external Radius Server. Usually the extended password is a one time password. +• Internal Time Based OTP - the end user provides an additional password that is validated +by the Appliance, based on a mutual shared secret that is downloaded once by the user +to a local OTP device that generates time based One Time Passwords. The One Time +Password is validated by the Appliance. +3.7 Additional Security Issues +The four critical keys are used for: +1) Encrypt sensitive data in the database in non-volatile memory and MAC plaintext data in the +database. +2) MAC individual user’s records in the database. +3) Encrypt database for backup +4) MAC database for backup +The four critical keys of the Appliance are stored on a special backup token and in an internal tamper +device. These keys are loaded into the Appliance’s volatile memory during startup from the tamper +device and erased from memory when the Appliance is shut down. +Any attempt to access the device that triggers the tamper response will cause power to be instantly +cut off, preventing access to any useful information by zeroizing all plain text critical security +parameters, including the Appliance’s critical keys. Without these keys, it is not possible to start the +Appliance or access the Appliance’s stored data. +The critical keys will also be deleted from the internal tamper device. Upon next startup of the +device a tamper detected message will be displayed in the touch screen. +Also, if there is an attempt to access the device when the power is off, the tamper response circuit is +still active. If the tamper circuit is activated, the critical keys will be deleted from the internal +tamper device and the tamper detected message will appear in the console upon next startup. +Module zeroization can be done by performing the Factory Restore operation from the console. This +operation will zeroize all plain text critical security parameters, including the Appliance’s critical +keys. Also all users’ information as well as the users’ keys will be deleted from the Appliance’s +database. + - 13 - +The units are encased in a solid metal case rigged with micro-switches and only the specified +physical interfaces permit access to the Appliance. The boundary of the module is the metal case. +The Appliance meets FCC requirements in 47 CFR Part 15 for personal computers and peripherals +designated for home use (ClassB), and is labeled according to FCC requirements. +The cryptographic boundary is the metal case of the Appliance. +The power supply bays, internal power wires, power connectors, internal power circuit and fan are +excluded components. +3.8 High Availability and Load Balancing +It is possible to deploy two or more appliances in the same organization. The purpose of having +more than one active appliance is to enable the organization’s users to continue and digitally sign in +the event of a hardware or firmware malfunction to the Appliance. +The main appliance is named the Primary appliance, while the other appliances are named the +Alternate appliances. +The whole content of the Appliance’s database is replicated to the alternate appliances, thus +enabling end user to sign data either using the primary appliance or an alternate appliance. +Any record of a replicated sensitive information includes a C-MAC, which is calculated by the +Appliance software upon any change to the record. The C-MAC is also checked upon any use of a +record. +Keys and CSP values that are stored in the database are AES encrypted. Therefore, when such +records are copied from the Primary Appliance to an Alternate Appliance the records are already +encrypted. +3.9 Interface to External CA in Automated mode +The Appliance can be configured to access an external CA in automated mode for the purpose of +certificate enrollment. +Upon a creation of a user, the Appliance will connect to the external CA and the external CA will +issue a certificate for the user. Upon updating user information such as email, a new certificate will +be generated for the user. +If the user is deleted from the Appliance, the certificate of the user will be revoked. +3.10 Well-Defined Interfaces +The Appliance is a steel, rack mountable box, in which only the interfaces provide access to the +Appliance. +The physical interfaces of the Appliance include the power connector, regular network connection +(Ethernet Interface using TCP/IP), administrative network connection (Ethernet Interface using + - 14 - +TCP/IP), power switches, LEDs, a touch screen and one USB slot for a smartcard-based USB token. All +ports use standard PC pin outs. +Table 2 shows the mapping of the FIPS 140-2 logical interfaces to the Appliance’s physical interfaces. +FIPS 140-2 Logical Interfaces Appliance’s Physical Interfaces +Data Input Interface Network port (LAN1), +USB slot for smartcard-based token1 +Data Output Interface Network port (LAN1), +USB slot for smartcard-based token2 +Control Input Interface Network port (LAN0/LAN1), +Touch Screen +Status Output Interface Network ports (LAN0/LAN1), +LEDs, +Touch Screen +Power Interface DC power connector +Table 2 - Interfaces +1. +Used only in the case of restoration or a reset tamper event. +2. +Used only during installation. +When the DocuSign SA Client or DocuSign HSM Client is used, all requests for cryptographic services +are performed through the DocuSign Signature Appliance API. This API, written in C/C++ and based +on RPC (Remote Procedure Calls), provides a high-level interface to the cryptographic services +provided by the Appliance that include RSA key generation and digital signature operations. +The Touch Screen displays the following status information: IP address, version information, and +serial number. +The Front of the module has the following LED’s: +• Power LED +• Hard Disk LED +• Tamper LED +Status information can also be sent via syslog protocol to a syslog server or can be retrieved by +network monitoring systems via SNMPv2 protocol. This status information is sent using the network +ports of the module. +The SNMPv2 protocol has not been reviewed or tested by the CAVP and CMVP. + - 15 - +3.11 Roles and Services +The Appliance employs password-based, identity-based authentication of users and operators +secured by the TLS protocol. Multiple users and operators can connect and use the Appliance +simultaneously. Each user has a user record that contains the user name, common name, email +address, and administrative authorization mask. The administrative authorization mask controls +whether the user can perform appliance management tasks or user management tasks. There are +two roles that can be assigned to an operator, User and Supervisor (Crypto Officer). +In Active Directory, it is possible to authenticate users and Crypto Officers based on SSPI (Security +Support Provider Interface), which is a Kerberos based ticketing mechanism. The user is +authenticated to the domain and provided with a ticket from the domain. The ticket is sent from the +DocuSign SA client to the Appliance during user authentication. The Appliance authenticates the +user based on the given ticket. +3.11.1 Supervisor (Crypto Officer) Role +The Supervisor role is assigned to the Crypto Officer and is used for user and appliance +management, appliance installation/restoration, and the Appliance’s configuration. The Crypto +Officer possesses the backup tokens necessary for reset tampering and restoring from backup. The +Crypto Officer can log into the Appliance remotely using the standard appliance’s authentication +protocol. +The Crypto Officer can perform the following tasks. These tasks represent special services of the +Appliance: +• Create users – DI (Directory Independent – see section 3.4) Environment +• Update user information – DI Environment +• Permit a user to download TOTP shared secret +• Retrieve user information +• Revoke Users – DI Environment +• Set user password – DI Environment +• Disable/Enable user logon – DI Environment +• Create groups – DI Environment +• Update groups – DI Environment +• Delete groups – DI Environment +• Attach/detach a user from a group – DI Environment +• Disable/enable a group – DI Environment +• Perform shutdown + - 16 - +• Load Firmware +• Perform backup +• Retrieve log file +• Update system parameters +• Zeroize Module +• Asymmetric cryptography +• Symmetric cryptography +• Authentication +• Graphical image Import/export +• Delete Keys +• Configure backup to a specified IP address +• Change user password – DI Environment +• Show FIPS mode Status +• Self-Tests +Locally, the Crypto Officer has the ability to access certain management operations of the Appliance, +including resetting a tamper condition, which is performed using the backup USB token. +It is possible to set a specific Client IP address as a system parameter. +Only from this IP address, it is possible to perform a backup of the Appliance to a file without +requesting for administrator User ID and a password, thus automate a periodical backup for the +system. +3.11.2 User/Application Role +The User/Application role is used for accessing the cryptographic services provided by the Appliance. +A user logs into the Appliance remotely using a user ID and a password or based on Active Directory +ticket (SSPI). The session is protected using the TLS protocol. A user is not permitted to perform any +user or appliance management operations. +A user can access the following services: +• Asymmetric cryptography +• Symmetric cryptography +• Download TOTP Shared Secret +• Authentication +• Graphical image Import/export +• Delete Keys +• Change user password – DI Environment +• Show FIPS mode Status + - 17 - +The Crypto Officer and User role can use the Asymmetric cryptography service to generate an RSA +key pair, Generate a digital signature, retrieve a public key and certificate, upload a user certificate, +generate an AES key and use the key to encrypt/decrypt. +An operator assigned a User/Application role must first authenticate to the Appliance using the user +ID and password or based on Active Directory ticket (SSPI). After successful authentication, an +authenticated and encrypted session is created. During this session, the operator may only perform +cryptographic services on RSA keys and/or AES keys that belong to the operator. +Also, the user can change his/her password. The password length must be greater than or equal six +Unicode characters. +In addition, the user can be authenticated based on a SAML or JWT token provided by a trusted +Identity provider. +In Directory Independent environment the module enforces a minimum password length of six +Unicode characters. Each character may be numeric (0-9) or alphanumeric (a-z, A-Z) or even +Unicode. Just considering the alphanumeric set of characters there are 62 possible characters and +the password is at minimum 6 characters long. +Therefore, the probability of a random attempt to succeed is: +One in (62 ^ 6) or 1 in 56,800,235,584. This is less than 1 in 1,000,000. +It takes the module approximately 1msec to process a login attempt, for a maximum of 1,000 login +attempts in 1 second and 60,000 login attempts in 1 minute. +Therefore, the probability of a random attempt to succeed during a minute is: +One in ((62 ^ 6) / 60,000) or 1 in (56,800,235,584 / 60,000) or 1 in 946,670.This is less than 1 in +100,000. +If SAML/JWT authentication is used, since the SAML/JWT token is based on a 2048bit digital +signature, the probability that random access will succeed is far less than one in 1,000,000 attempts +using this authentication mechanism. The authentication provides 1 in 2^112/(3000*60) probability +of a successful random attempt during a one-minute period since the Appliance cannot process +more than 3000 SAML/JWT validations per second. +An operator who has access to the role of Crypto Officer must first authenticate to the Appliance +using the user ID and password of the Crypto Officer or based on an Active Directory ticket (SSPI). +When using Active Directory authentication, the Crypto Officer must be part of an Active Directory +administrative group. During this session, the operator may perform user management and +appliance management services. +In the case of Active Directory environment, the user and Crypto-Officer authenticate by presenting +a ticket over a TLS channel. The Kerberos ticket is encrypted and contains a domain session key with +length of at least 56 bits. +Therefore, the probability of random attempt to succeed is: +One in (2^56) or 1/72,000,000,000,000,000. This is less than 1 in 1,000,000. + - 18 - +It takes the module 1msec to process a login attempt. A maximum of 1,000 login attempts may be +processed in 1 second and 60,000 login attempts in 1 minute. This allows a maximum of: (2^56) / +60,000 ~ 1,200,000,000,000 attempts per minute. +Therefore, the probability of a random attempt to succeed during a minute is: +One in (1,200,000,000,000), this is less than 1 in 100,000. +The Appliance can be configured to use additional authentication for every digital signature +operation. +The additional authentication is defined as the following: +1. Username and Password authentication using a Radius Server. +The user will provide his/her password. Both user ID and password will be authenticated by a +Radius server using the Radius protocol. +2. OATH-TOTP - Time based One Time Password (OTP) validation. The user will provide an OTP that +will be validated for the purpose of creation of a digital signature. +The Radius authentication is based on 32 bytes of authentication data sent from the Appliance to +the Radius Server. 16 bytes are randomly generated by the Appliance. +Therefore the probability of random attempt to succeed is: +One in (2^128), this is less than 1 in 1,000,000. +It takes the module 1msec to process a signature request and 60,000 signature requests in 1 minute. +Therefore, the probability of a random attempt to succeed during a minute is: +One in (2^128/60000), this is less than 1 in 100,000. +Since the Radius authentication is done in addition to the authentication methods above both +method (Active Directory and Directory Independent) probabilities are increased. +In the case of OATH-TOTP validation, the module enforces a minimum One Time Password (OTP) +length of six numeric characters, however this authentication is performed only after the user was +properly authenticated based on the previous method of authentication such as User-ID/Password +or Active Directory based authentication (as described above). This means that the probably of a +success is less in the two mentioned criteria. +Table 3 lists which roles have access to each service. +Services Role Keys and CSPs Access +(R/W/X1) +Create users – DI Environment CO Appliance X + - 19 - +Critical Key 2 – +MAC +Update user information – DI +Environment +CO Appliance +Critical Key 2 – +MAC +X +Permit a user to download TOTP +Shared Secret +CO TOTP Shared +Secret, +Appliance +Critical Key 1 – +ENC, +Appliance +Critical Key 2 – +MAC +W +X +X +Retrieve user information CO Appliance +Critical Key 2 – +MAC +X +Revoke users – DI Environment CO +Set user password – DI +Environment +CO Appliance +Critical Key 2 – +MAC +X +Enable/Disable user login – DI +Environment +CO Appliance +Critical Key 2 – +MAC +X +Create group – DI Environment CO +Update group – DI Environment CO +Delete group – DI Environment CO +Attach/Detach user from a group – +DI Environment +CO +Enable/Disable group – DI +Environment +CO +Perform shutdown CO +Load Firmware CO DocuSign RSA +public key – +DLM +X +Perform backup CO Appliance +Critical Key 3 – +Appliance +X + - 20 - +Backup +encryption and +Appliance +Critical Key 4 – +MAC of the +Appliance’s +Backup +Retrieve log file CO +Self-Tests CO +Update system parameters CO Appliance +Critical Key 2 – +MAC +X +Asymmetric cryptography CO/User User signature +keys +W,X +Symmetric cryptography CO/User User symmetric +keys +W,X +Authentication CO/User Hash of User’s +Password +R +Graphical image Import/export CO/User Appliance +Critical Key 1 – +ENC, +Appliance +Critical Key 2 – +MAC +X +Delete Keys CO/User User Signature +Keys, User +Symmetric +Keys and User +Public Key +Certificates +W +Change user password – DI +Environment +CO/User Hash of User’s +Password and +Appliance +Critical Key 2 – +MAC +- W for +hashing new +password +- X for +Critical Key 2 +– MAC +Zeroize Module CO All keys and +CSPs +W + - 21 - +Show FIPS mode Status CO/User/Unauthenticated +Download TOTP Shared Secret User TOTP Shared +Secret +R +Setting network parameters CO +Time adjustments CO/Unauthenticated +Shutdown CO/Unauthenticated +Configure backup to a specified IP +address +CO +DRBG Unauthenticated DRBG Seed +and DRBG state +X +Table 3 - Role Access to Services +1 +Execute a command on the key without the ability to Read or Write. +3.12 Operational Environment +The module operates in a limited operational environment under the FIPS 140-2 Section 4.6 +definitions. +3.13 Strong Cryptographic Algorithms and Secure Key Management +The DocuSign Signature Appliance supports and uses a variety of strong cryptographic algorithms. +The Appliance implements these algorithms based on the following FIPS 140-2-approved algorithms: + - 22 - +CAVP Cert +v9.0.9.10 +Algorithm Standard Mode/ +Method +Key Lengths or +Moduli +Use +C86 AES FIPS 197 CBC 128 bits and +256 bits +Session data +encryption1 +Vendor Affirmed Triple-DES +MAC +SP 800-67 CBC Backup +integrity +Database +Integrity +C86 HMAC FIPS 198-1 HMAC-SHA- +1 and +HMAC-SHA- +256 +128 bits and +192 bits +TLS-based +session +scheme +C86 SHS FIPS 180-4 SHA-1 and +SHA-256 +TLS-based +session +scheme +C86 CVL +TLS +1.0/1.1/1.2 +SP 800- +135rev1 +TLS Key +Derivation +Note: no parts of +this protocol, other +than the KDF, have +been tested or +reviewed by CAVP +and CMVP. +AES Cert. C86 and +HMAC Cert. C86 +KTS FIPS 197 +SP 800- +38F +AES\HMAC 128 bits and +256 bits +Key Transport +Note: provide 128 +or 256 bits of +encryption strength +C86 RSA FIPS 186-4 SHA-1, +SHA-256 +2048 bits RSA Signature +Generation for +TLS +Note: This RSA +PKCS1 v1.5 SigVer +implementation +was tested but not +used by the module + - 23 - +CAVP Cert +v9.0.9.10 +Algorithm Standard Mode/ +Method +Key Lengths or +Moduli +Use +C85 AES FIPS 197 CBC 128 bits Backup +Encryption +Database +Encryption +128, 192, 256 +bits +AES +Cryptography +C85 Triple-DES2 SP 800-67 CBC 192 bits Backup +Decryption +Database +Decryption +C85 AES CMAC SP 800- +38B +CMAC 128 bits Backup +integrity +Database +Integrity +AES Cert. C85 and +AES-CMAC Cert. C85 +KTS SP 800- +38B +SP 800- +38F +AES + AES- +CMAC +128 bits Key Wrapping +C85 SHS FIPS 180-4 SHA-1 Authentication +SHA-256, +SHA-384 +and SHA- +512 +Hash for +Digital +signature +generation + - 24 - +CAVP Cert +v9.0.9.10 +Algorithm Standard Mode/ +Method +Key Lengths or +Moduli +Use +C85 RSA FIPS 186-4 2048,3072,4096 +bits +Key +generation, +Note: 4096bit RSA +Key generation +have not been +tested by CAVP or +CMVP +C85 RSA FIPS 186-4 SHA-256, +SHA-384, +SHA-512, +PKCS1 v1.5, +PSS +2048,3072,4096 +bits Digital +signature +generation +C85 RSA FIPS 186-4 SHA-256, +PKCS1 v1.5 +2048 bits Digital +signature +verification +C85 DRBG SP 800- +90A +HMAC-SHA- +256 +Random +Number +generation +C85 HMAC FIPS 198-1 HMAC-SHA- +256 +256 bits Within HMAC +DRBG +note: This HMAC- +SHA-1 +implementation +was tested but not +used by the module +98 DRBG SP 800- +90A +SHA-256 Random +Number +generation3 +1465 SHS FIPS 180-4 SHA-256 Within +Hash_Based +DRBG +Vendor Affirmed CKG SP 800- +133 +Key +Generation +Vendor Affirmed PBKDF SP 800- +132 +Option 2a 192 bits Password- +based Key +Derivation4 +Table 4 - FIPS Approved, Allowed and Vendor-Affirmed Algorithms + - 25 - +1 Triple-DES under C86 was tested by CAVP but is not used in FIPS mode +2 +Triple-DES under C85 was tested by CAVP and is used in FIPS mode for decryption as allowed algorithm for legacy use. +3 Provided by the internal Safenet eToken 5105 (FIPS 140-2 validation #1883) +4 The password length for the PBKDF algorithm is 24 bytes long +The module implements the following Non-FIPS approved, but allowed, algorithms: +1. RSA-TLS (key wrapping; key establishment methodology provides 112 bits of encryption +strength). TLS protocol has not been reviewed or tested by the CAVP and CMVP. +2. MD5 (used in TLS v1.1, used in Extended Authentication mode – Radius and by the TLS1.0 +implementation) +3. HW RNG (used in Safenet eToken 5105) +The module implements the following Non-FIPS approved algorithms: +1. Triple-DES (Cert. #C86, non-compliant) – used in TLS in non-FIPS mode +2. SHS (non-compliant) – used in RSA-RESTful-TLS in non-FIPS mode +3. HMAC (non-compliant) – used in RSA-RESTful-TLS in non-FIPS mode +4. Triple-DES (non-compliant) – used in RSA-RESTful-TLS in non-FIPS mode +5. AES (non-compliant) – used in RSA-RESTful-TLS in non-FIPS mode +6. RSA-RESTful-TLS (key wrapping; non-compliant) +7. AES (no security claimed) – implementation used for IPSEC +The Appliance stores private keys in a key database. This database is stored encrypted (with AES +CBC) on the Appliance’s internal hard drive. Within the key database, each key is attached to a +specific user. +Generated keys in the Appliance cannot be read outside the Appliance. User’s public keys, +certificates, and graphical images of the user’s signature are stored in the Appliance’s database and +can be retrieved during a user’s session. The user can retrieve only his/her objects. +Table 5 provides a list of keys, their key types, and access control. + - 26 - +Cryptographic Keys +and CSPs +Key Type Generation/ +Input/Output +Storage Zeroization +Appliance Critical Key +1 – Key and values +encryption in +database +AES 128 bit key Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +2 – MAC of database +records +AES 128 bit key Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +3 – Appliance Backup +encryption +AES 128 bit key Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +4 – MAC of the +Appliance’s Backup +AES 128 bit key Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +1 – Keys and values +backward +compatibility +decryption in +database +Triple-DES 192 +bit key +Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +2 – MAC backward +compatibility +validation in database +records +Triple-DES 192 +bit key +Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +3 – Appliance +backward +compatibility +decryption of Backup +Triple-DES 192 +bit key +Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance Critical Key +4 – MAC backward +compatibility +validation of backup +Triple-DES 192 +bit key +Internal/ +NA/NA +Tamper +device +(plaintext) +Tamper +Appliance’s TLS RSA +public/private key pair +RSA 2048s bit +key +External/ +NA/NA +Disk +(encrypted) +NA + - 27 - +Cryptographic Keys +and CSPs +Key Type Generation/ +Input/Output +Storage Zeroization +Triple-DES KEK for the +Appliance’s TLS RSA +public/private key pair +Password-based +key derivation is +implemented in +compliance with +SP 800-132. +Derived/ +NA/NA +Memory +(plaintext) +End of +usage +Password for +accessing Triple-DES +KEK for the +Appliance’s TLS RSA +public/private key pair +N/A Internal/ +NA/Encrypted +Disk +(encrypted) +NA +PBKDF master key N/A Internal/ +NA/Encrypted +Disk +(encrypted) +NA +TLS Master/pre- +master secret +N/A Internal/ +NA/Encrypted +memory NA +HMAC-SHA256 of +PBKDF master key (for +integrity of the DPK)3 +32 bytes Internal/ +NA/NA +Memory +(plaintext) +Power +cycle +PBKDF HMAC key (for +integrity for DPK)3 +24 bytes External / +NA/NA +Disk +(plaintext) +Hardcoded +key +zeroized by +special +firmware +The Appliance’s +Internal CA RSA key +RSA 2048 bits +key – defined in +installation +Internal/ +NA/Encrypted +Disk +(encrypted) +NA +DocuSign RSA public +key – firmware +validation – hard +coded +RSA 2048 bits +key +External/ +NA/NA +Disk +(plaintext) +NA +DocuSign RSA public +key – DLM +(downloadable +module) validation – +hard coded +RSA 2048 bits +key +External/ +NA/NA +Disk +(plaintext) +NA +Session +encryption/decryption +AES 128/256 +bits keys. +TLS key +establishment +Memory +(plaintext) +End of +session + - 28 - +Cryptographic Keys +and CSPs +Key Type Generation/ +Input/Output +Storage Zeroization +keys +HMAC key 20 bytes TLS key +establishment +Memory +(plaintext) +End of +session +User public key +certificates +RSA +2048/3072/4096 +bits public keys +stored in +certificates +Internal/ +Encrypted/Yes +Disk +(encrypted) +NA +User symmetric keys AES 128,192,256 +bits +Internal/ +Encrypted/Encrypted +Disk +(encrypted) +NA +User signature keys RSA +2048,3072,4096 +bits +Internal/ +Encrypted/Encrypted +Disk +(encrypted) +NA +Hash of User’s +Password +SHA1 Internal Disk +(hashed) +User +deletion +TOTP Shared Secret 250 bytes Internal/ +Encrypted/Encrypted +Disk +(encrypted) +NA +DRBG Key and V HMAC-DRBG +RNG Input +Internal/ +NA/NA +RAM +(plaintext) +Power +cycle +Entropy Input String HMAC-DRBG +RNG Input +Internal/ +NA/NA +RAM +(plaintext) +Power +cycle +DRBG seed DRBG seed in +Safenet eToken +5105 +Internal/ +NA/NA +eToken Power +cycle +DRBG state1 DRBG state in +Safenet eToken +5105 +Internal/ +NA/NA +eToken Power +cycle +Table 5 – Keys and CSPs +1 The DRBG State is associated with the internal DRBG (eToken). The internal DRBG state is not accessible to the +Appliance and is zeroized when the Appliance powers off. +All symmetric keys and generated seeds for asymmetric key generation are unmodified output from the Approved +HMAC_Based DRBG + - 29 - +3 +The DPK is DATA Protection Key according to SP 800-132 +Remark: The DRBG Key, which is of size 256bit is based on a 256bit random seed that is retrieved +from an internal Safenet eToken 5105 (FIPS 140-2 validation #1883). +The estimated entropy is at least 5.74/8, which means that a random seed of 256bit, will produce +minimum entropy of 184bit. +This assumes a residual security risk results from the incomplete testing of a third-party entropy +source. +Remark: The Triple-DES based mechanism cannot be used in TLS when the Appliance is in FIPS mode. +Self Testing +The DocuSign Signature Appliance monitors firmware operations using a set of self-tests to ensure +proper operation according to the FIPS 140-2 standard. The Appliance includes both the power-up +self tests and conditional tests. These tests are described in the following sections. +3.13.1 Power-Up Self Tests + Critical Function Test - Low Level Hardware Check + Firmware Integrity Test (RSA signature verification) + AES encrypt KAT (for Appliance-internal AES implementation) + AES decrypt KAT (for Appliance-internal AES implementation) + Triple-DES decrypt KAT (for Appliance-internal Triple-DES implementation) + AES encrypt KAT (for Appliance-TLS AES implementation) + AES decrypt KAT (for Appliance-TLS AES implementation) + AES CMAC KAT + Triple-DES MAC KAT (for Triple-DES MAC using underlying Appliance-internal Triple-DES +implementation, for validation purpose as part of restoring old formatted database and backup +files) + SHA-384 KAT (for Appliance-internal SHA-384 implementation) + SHA-512 KAT (for Appliance-internal SHA-512 implementation) + HMAC SHA-1 KAT (for Appliance HMAC implementation) + HMAC SHA-256 KAT (for Appliance HMAC implementation) + HMAC SHA-1 KAT (for Appliance-TLS HMAC implementation) + HMAC SHA-256 KAT (for Appliance-TLS HMAC implementation) + RSA sign KAT (for Appliance-internal implementation) + RSA verify KAT (for Appliance-internal implementation) + RSA decrypt KAT (for Appliance-TLS RSA implementation) + - 30 - + RSA encrypt KAT (for Appliance-TLS RSA implementation) + RSA sign KAT (for Appliance-TLS RSA implementation) + RSA verify KAT (for Appliance-TLS RSA implementation) + DRBG KAT (Including instantiate/generate/reseed as specified in SP 800-90A Section 11.3) + Critical Function Test - Database Access +Following to failure of any of the above tests, the following error will be displayed in the Critical +Alerts attribute in the Touch Screen : +On – Critical Error, On – DB Error +Or there will be a General Failure message in the Touch Screen. +The Appliance will not provide any service at this state. +3.13.2 Conditional Tests + Continuous RNG test for HMAC DRBG output. +The Appliance‘s random is based on a non-deterministic seed that is generated by the approved HASH +DRBG (Cert. #98) of internal Safenet eToken 5105 (FIPS 140-2 validation #1883). +HMAC RBG is reseeded every minute and checked for continuous test based on comparison errors. +The output of the DRBG algorithm is checked for continuous test and statistical errors. +If any of the tests fails, the module enters the error state. + Continuous RNG test for HASH DRBG output (for DRBG Cert. #98) + Firmware Load Test 1 + RSA Key Generation pairwise consistency test +3.14 Mitigation of Other Attacks +The DocuSign Signature Appliance does not include any mechanisms for the prevention of special +attacks. +3.15 Maintenance +The Crypto Officer must check the Appliance’s case for any evidence of physical tampering. Special +protective screw cover Tamper Evident cans are attached over two screws on the back of the +Appliance. These Tamper Evident cans would be damaged if the Appliance’s case has been opened. +Verify that the Tamper Evident cans are attached to the Appliance and that they are not damaged. +1 +Make sure that the new firmware version is a FIPS 140-2 validated firmware version. +If a non-validated version is uploaded to the Appliance, the Appliance is no longer FIPS 140-2 validated. +RSA 2048bit with SHA-256 digital signature verification is used in this test. + - 31 - +If you think the Appliance has been tampered with, contact DocuSign. + - 32 - +4 FIPS 140-2 Level 3 Compliant Mode +Cryptographic services should only use FIPS 140-2 approved algorithms. A list of these algorithms +can be found in Section 3.13, Strong Cryptographic Algorithms and Secure Key Management. +Only one user can be assigned the role of Crypto Officer. Only the Crypto Officer may possess the +backup USB tokens necessary to restore the Appliance or reset the tamper operation. +Directory Independent and Active Directory environments are FIPS 140-2 level 3 validated. The +Appliance also supports LDAP environment, however, this is not included in the scope of this FIPS +140-2 level 3 validation process. +The Appliance can be interfaced through a SOAP based Web Services protocol or RESTful based Web +Services protocol. Both SOAP based Web Services interface and RESTful based Web Services are not +included in the scope of this FIPS 140-2 level 3 validation process. +To make sure the Appliance is running in FIPS Mode, inspect the value of FIPS Mode in the settings +section in the console. When in FIPS 140-2 level 3 approved mode, the console displayed FIPS Mode +on. +In the case that not all following configurations are set then the Appliance will be operated in non +FIPS mode. In this case the console displays FIPS Mode off. +4.1 Configuring the Appliance to work in FIPS mode +There are several System Parameters that must be set to appropriate values for having the +Appliance work in FIPS mode. +For changing system parameters, open the Appliance Management utility and login as the Appliance +administrator. Go to the System Parameters section and set the values of the following System +Parameters: +1. Advanced- Enforce FIPS Approved Algorithm. +This value must be set to true. When this value is set, it is not allowed to sign using a 1024bit RSA +key. When this value is set, it is not allowed to use SHA-1 as part of the digital signature +operation. + - 33 - +Also, when this value is set The FIPS 186-4 based RSA key generation algorithm is used for +generating RSA keys. This means that only RSA 2048bit, 3072bit and 4096bit keys can be +generated. +2. Advanced – Web Services Support +This value must be set to false, since the SOAP based Web Services interfaces is not included as +part of the FIPS 140-2 level 3 scope. +Note: when the Appliance is installed in HSM style, this option is automatically set to false. +3. Advanced – RESTful Web Services Support +This value must be set to false, since the RESTFul based Web Services interfaces is not included +as part of the FIPS 140-2 level 3 scope. +Note: when the Appliance is installed in HSM style, this option is automatically set to false. +4. Advanced – Disable 3DES in TLS +This value must be set to true to forbid using Triple-DES in TLS. +In Addition, the Primary and Alternate Appliances needs to be configured to use IPSEC configuration. +This is done by activating the administrative option called Set Appliance Communication Mode using +the Appliance Management Utility. +The value of Replication Communication Mode should be set to IPSec (the default configuration is +IPSEC/IKE). +IPSEC is redundant to the cryptographic protection of this module and no security is claimed for it. +Note: The IPSEC protocol has not been reviewed or tested by the CAVP or CMVP. +5 Upgrade appliance firmware from version 8.0 to version +8.1 +Perform the following instructions for upgrading appliance firmware version from version 8.0 to +version 8.1. +1. Contact DocuSign support to get appliance firmware upgrade package from version 8.0 to 8.1. +2. Perform the upgrade in a secure environment. +3. The upgrade procedure can be performed only when the appliance is in factory settings. + - 34 - +4. Invoke the Appliance Management application from the DocuSign SA Client’s control Panel. +5. Locate the relevant appliance according to its IP address and Login as an appliance administrator. +6. Invoke the Upload Software option for each upgrade file. Provide the set of upgrade files +provided you by DocuSign. +7. If the upgrade file is tampered with, the new firmware loading test fails and the module will +reject the upgrade. +In each upgrade, a progress bar will indicate the progress of the upgrade operation. When the whole +operation ends the Appliance is installed with firmware version 8.1. +6 Upgrade appliance firmware from version 8.0/8.1 to +versions 8.5, 8.51.9.0 and 9.0.9.10 +Perform the following instructions for upgrading appliance firmware version from version 8.0 or +version 8.1 to version 8.4. +Note: version 8.4 is out of scope of FIPS 140-2 validation. Be minded that any upgrade to version 8.4 +should immediately follow by the upgrade to version 8.5. +1. Contact DocuSign support to get appliance firmware upgrade package to 8.4 and upgrade +package to 8.5. +2. Perform the upgrade in a secure environment. +3. Invoke the Appliance Management application from the DocuSign SA Client’s control Panel. +4. Locate the relevant appliance according to its IP address and Login as an appliance administrator. +5. Invoke the Upload Software option for each upgrade file. Provide the set of upgrade files +provided you by DocuSign. There should be an upgrade file to version 8.4 and an upgrade file to +version 8.5. +6. If the upgrade file is tampered with, the new firmware loading test fails and the module will +reject the upgrade. +In each upgrade, a progress bar will indicate the progress of the upgrade operation. When the whole +operation ends the Appliance is installed with firmware version 8.5. +Perform the following instructions for upgrading appliance firmware version from version 8.5 to +8.51.9.0 +1. Contact DocuSign support to get appliance firmware upgrade package to 8.51.9.0. + - 35 - +2. Perform the upgrade in a secure environment. +3. Invoke the Appliance Management application from the DocuSign SA Client’s control Panel. +4. Locate the relevant appliance according to its IP address and Login as an appliance administrator. +5. Invoke the Upload Software option for each upgrade file. Provide the set of upgrade files +provided you by DocuSign. There should be an upgrade file to version 8.51.9.0. +6. If the upgrade file is tampered with, the new firmware loading test fails and the module will +reject the upgrade. +A progress bar will indicate the progress of the upgrade operation. When the whole operation ends +the Appliance is installed with firmware version 8.51.9.0. +Perform the following instructions for upgrading appliance firmware version from version 8.51.9.0 to +9.0.9.10 +1. Contact DocuSign support to get appliance firmware upgrade package to 9.0.9.10 +2. Perform the upgrade in a secure environment. +3. Invoke the Appliance Management application from the DocuSign SA Client’s control Panel. +4. Locate the relevant appliance according to its IP address and Login as an appliance administrator. +5. Invoke the Upload Software option for each upgrade file. Provide the set of upgrade files +provided you by DocuSign. There should be an upgrade file to version 9.0.9.10. +6. If the upgrade file is tampered with, the new firmware loading test fails and the module will +reject the upgrade. +A progress bar will indicate the progress of the upgrade operation. When the whole operation ends +the Appliance is installed with firmware version 9.0.9.10. + \ No newline at end of file diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json new file mode 100644 index 00000000..3c8d21f7 --- /dev/null +++ b/tests/data/fips/dataset/toy_dataset.json @@ -0,0 +1,4038 @@ +{ + "_type": "sec_certs.dataset.fips.FIPSDataset", + "state": { + "_type": "sec_certs.dataset.dataset.Dataset.DatasetInternalState", + "meta_sources_parsed": true, + "artifacts_downloaded": true, + "pdfs_converted": true, + "certs_analyzed": false + }, + "timestamp": "2022-11-17 16:06:06.266809", + "sha256_digest": "not implemented", + "name": "FIPSDataset dataset", + "description": "No description provided", + "n_certs": 22, + "certs": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "184097a88a9b4ad9", + "cert_id": 3518, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "DocuSign Signature Appliance", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-09-05", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.docusign.com", + "vendor": "DocuSign, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/SeptConsolidated.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "active", + "level": 3, + "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "tested_conf": null, + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "CKG": { + "_type": "Set", + "elements": [] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#C86" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#98", + "#C85" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#C86" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#C85", + "#C86" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#1465", + "#C85", + "#C86" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#C85" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#C85" + ] + } + }, + "hw_versions": "8.0", + "fw_versions": "9.0.9.10", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": null, + "date_sunset": "2024-09-04", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "ed16d272044ed81f", + "cert_id": 2860, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "DocuSign HSM Appliance", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2017-03-08", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2017-10-03", + "validation_type": "Update", + "lab": "CYGNACOM SOLUTIONS INC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-04-10", + "validation_type": "Update", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.docusign.com", + "vendor": "DocuSign, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMar2017.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "historical", + "level": 3, + "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "DocuSign HSM Appliance is a high-performance cryptographic service provider. It performs high-speed cryptographic operations while protecting sensitive data. Its features include Triple-DES, AES, Triple-DES MAC, CCM, HMAC, RSA, ECDSA, SHA-1, SHA-256, SHA-384, SHA-512, public key database and certificate support, authenticated and encrypted communication with the module, secure storage of secret/private keys, software key medium and smartcard support, tamper-responsive enclosure, high level API requiring no cryptographic expertise, in-depth logging and auditing, and secure backup capabilities.", + "tested_conf": null, + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#4029", + "#4031", + "#4640", + "#4641", + "#5283", + "#5284" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#1039", + "#1296", + "#1297", + "#1745", + "#1746", + "#857" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1205", + "#1565", + "#2030", + "#98" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#1143", + "#1378", + "#900" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2630", + "#2632", + "#3073", + "#3074", + "#3490", + "#3491" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#2630", + "#3074", + "#3491", + "#4029", + "#4641", + "#5284" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2069", + "#2533", + "#2822" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#1465", + "#3325", + "#3326", + "#3804", + "#3805", + "#4242", + "#4243" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2207", + "#2469", + "#2669" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#2207", + "#2469", + "#2669" + ] + } + }, + "hw_versions": "5.0", + "fw_versions": "5.0.0, 5.0.2 and 5.0.3", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", + "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "e1ff7d9997726782", + "cert_id": 3090, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Windows OS Loader", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-03-22", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-06-17", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.microsoft.com", + "vendor": "Microsoft Corporation", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "active", + "level": 1, + "caveat": "When operated in FIPS mode with module Boot Manager validated to FIPS 140-2 under Cert. #3089 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The BitLocker® Windows OS Loader loads the boot-critical driver and OS kernel image files.", + "tested_conf": [ + "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", + "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", + "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", + "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", + "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", + "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", + "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", + "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", + "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#4624", + "#4625", + "#5300", + "#5316" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2523", + "#2846" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#3790", + "#4253" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "10.0.15063 and 10.0.15063.728", + "mentioned_certs": { + "3089": 1 + }, + "historical_reason": null, + "date_sunset": "2023-03-21", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", + "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "ceab64951a30f684", + "cert_id": 3197, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Cryptographic Primitives Library", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-10-22", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-07-17", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2020-09-08", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.microsoft.com", + "vendor": "Microsoft Corporation", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertOct2018.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode with modules Kernel Mode Cryptographic Primitives Library validated to FIPS 140-2 under Cert. #3196 operating in FIPS mode and Code Integrity validated to FIPS 140-2 under Cert. #3195 or Cert. #3644 operating in FIPS mode or Secure Kernel Code Integrity validated to FIPS 140-2 under Cert. #3096 or Cert. #3651 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The Cryptographic Primitives Library (bcryptprimitives.dll and ncryptsslp.dll) provides cryptographic services to Windows components and applications. It includes cryptographic algorithms in an easy-to-use cryptographic module via the Cryptography Next Generation (CNG) API. It can be dynamically linked into applications for the use of general-purpose FIPS 140-2 validated cryptography.", + "tested_conf": [ + "Azure Data Box Edge (x64) running on a Microsoft Azure Data Box with PAA [5] (single-user mode)", + "Surface Hub (x64) running on a Microsoft Surface Hub with PAA [1]", + "Windows 10 Education April 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [4]", + "Windows 10 Education Fall Creators Update (x64) running on a Microsoft Surface Pro with PAA [3]", + "Windows 10 Education October 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [5]", + "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [4]", + "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [4]", + "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [4]", + "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Studio 2 with PAA [4]", + "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Studio with PAA [4]", + "Windows 10 Enterprise April 2018 Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA [4]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Book 2 with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Book with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Laptop with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Pro with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Studio with PAA [3]", + "Windows 10 Enterprise Fall Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA [3]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [5]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [5]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [5]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Studio with PAA [5]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Samsung Galaxy Book 12\" with PAA [5]", + "Windows 10 Enterprise October 2018 Update (x64) running on an HP EliteBook x360 1030 G2 with PAA [5]", + "Windows 10 Home April 2018 Update (x86) running on a Dell Inspiron 660s without PAA [4]", + "Windows 10 Home Fall Creators Update (x86) running on a Dell Inspiron 660s without PAA [3]", + "Windows 10 Home October 2018 Update (x86) running on a Dell Inspiron 660s without PAA [5]", + "Windows 10 Mobile Fall Creators Update (ARMv7) running on a Microsoft Lumia 650 [2]", + "Windows 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This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data.", + "tested_conf": null, + "algorithms": { + "CVL": { + "_type": "Set", + "elements": [ + "#786", + "#787" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1137", + "#1138", + "#98" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2551", + "#2552", + "#2563", + "#2564" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#2161", + "#2564" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2005", + "#2006" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#3237", + "#3238", + "#3248", + "#3249" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2155", + "#2156", + "#2160", + "#2161" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#2155", + "#2156" + ] + } + }, + "hw_versions": "7.0 and 8.0", + "fw_versions": "8.0", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", + "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "75ca28395bfc9ad1", + "cert_id": 2630, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Red Hat Enterprise Linux OpenSSH Server Cryptographic Module", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-05-02", + "validation_type": "Initial", + "lab": "ATSEC INFORMATION SECURITY CORP" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-06-17", + "validation_type": "Update", + "lab": "ATSEC INFORMATION SECURITY CORP" + } + ], + "vendor_url": "http://www.redhat.com", + "vendor": "Red Hat®, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMay2016.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode with module Red Hat Enterprise Linux 7.1 OpenSSL Module validated to FIPS 140-2 under Cert. #2441 operating in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", + "exceptions": [ + "Physical Security: N/A", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The OpenSSH Server cryptographic module provides the server-side component for an SSH protocol version 2 protected communication channel. OpenSSH is the standard SSH implementation and shipped with RHEL 7.1. Its cryptographic mechanisms use the OpenSSL library in FIPS 140-2 mode.", + "tested_conf": [ + "Red Hat Enterprise Linux 7.1 running on IBM Power8 Little Endian 8286-41A", + "Red Hat Enterprise Linux 7.1 running on IBM z13 with CP Assist for Cryptographic Functions (single-user mode)", + "Red Hat Enterprise Linux 7.1 running on ProLiant DL380p Gen8 with PAA", + "Red Hat Enterprise Linux 7.1 running on ProLiant DL380p Gen8 without PAA" + ], + "algorithms": { + "CVL": { + "_type": "Set", + "elements": [ + "#700", + "#701", + "#702" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "4.0", + "mentioned_certs": { + "2441": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", + "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "0df59fd6c41cadd2", + "cert_id": 3711, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Alteryx Cryptographic Module", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2020-09-21", + "validation_type": "Initial", + "lab": "ACUMEN SECURITY, LLC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2020-12-01", + "validation_type": "Update", + "lab": "ACUMEN SECURITY, LLC" + } + ], + "vendor_url": "http://www.alteryx.com", + "vendor": "Alteryx Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/September 2020_011020_0703.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode and built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy and operated in FIPS mode. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #3220.", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 3", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The Alteryx FIPS Module for OpenSSL is a general-purpose cryptographic module integrated in Alteryx’s products to provide FIPS 140-2 validated cryptography for the protection of sensitive information.", + "tested_conf": [ + "Windows Server 2016 running on HPE ProLiant DL60 Gen9 with Intel Xeon E5-2609 with PAA", + "Windows Server 2016 running on HPE ProLiant DL60 Gen9 with Intel Xeon E5-2609 without PAA (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#C1990" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#C1990" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "2.0.9, 2.0.10, 2.0.11, 2.0.12, 2.0.13, 2.0.14, 2.0.15 or 2.0.16", + "mentioned_certs": { + "3220": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", + "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "756a6524cdb2d4ea", + "cert_id": 3850, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "DocuSign Signature Appliance", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2021-03-18", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2021-09-15", + "validation_type": "Update", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.docusign.com", + "vendor": "DocuSign, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/March 2021_010421_0724_signed.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "active", + "level": 3, + "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "tested_conf": null, + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#A1517", + "#A1518", + "#C1251", + "#C1252" + ] + }, + "CKG": { + "_type": "Set", + "elements": [] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#A1518", + "#C1252" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#98", + "#A1517", + "#C1251" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#A1517", + "#A1518", + "#C1251", + "#C1252" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#A1518", + "#C1252" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [ + "#A1517" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#A1517", + "#C1251" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#1465", + "#A1517", + "#A1518", + "#C1251", + "#C1252" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#A1517", + "#C1251" + ] + } + }, + "hw_versions": "8.0", + "fw_versions": "9.3.9.20 and 9.4.9.20", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": null, + "date_sunset": "2026-03-17", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", + "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "2aec3694418f35d8", + "cert_id": 3488, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "ColorTokens OpenSSL FIPS Object Module", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-07-15", + "validation_type": "Initial", + "lab": "ACUMEN SECURITY, LLC" + } + ], + "vendor_url": "https://www.colortokens.com", + "vendor": "ColorTokens", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy and operated in FIPS mode. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 3", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The Module is a software cryptographic module that is built from the OpenSSL. The module is a software library that provides C-language application program interface (API) for use by other processes that require cryptographic functionality to various modules of the ColorTokens application.", + "tested_conf": [ + "Ubuntu 12.04 running on Intel Xeon E5-2430L(x86) with PAA (gcc Compiler Version 4.6.3)", + "Ubuntu 12.04 running on Intel Xeon E5-2430L(x86) without PAA (gcc Compiler Version 4.6.3) (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#3451" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#534" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#845" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#970" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#698" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2197" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#1766" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#2847" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#1942" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "2.0.11", + "mentioned_certs": { + "2398": 1 + }, + "historical_reason": "186-2 transition", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", + "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "73e7f3f802b0e919", + "cert_id": 3176, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Digi ConnectCore Security Module", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-04-26", + "validation_type": "Initial", + "lab": "ACUMEN SECURITY, LLC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-06-12", + "validation_type": "Update", + "lab": "ACUMEN SECURITY, LLC" + } + ], + "vendor_url": "http://www.digi.com", + "vendor": "Digi International, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/AprilConsolidated2018.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 3", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The Digi ConnectCore Security Module provides cryptographic services for Digi International Inc. products.", + "tested_conf": [ + "Yocto Linux 2.4 running on ConnectCore 6/6UL SoM (ARMv7) without PAA (gcc Compiler Version 7.3.0), Yocto Linux 2.4 running on ConnectCore 6/6UL SoM (ARMv7) with PAA (gcc Compiler Version 7.3.0) (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#5323" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#1788" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#2053" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#1375" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#1397" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#3521" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2851" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#4275" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2686" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "1.0", + "mentioned_certs": { + "2398": 1 + }, + "historical_reason": "186-2 transition", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", + "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "618102f275d5649a", + "cert_id": 3495, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Panzura CloudFS™ FIPS Cryptographic Module", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-07-19", + "validation_type": "Initial", + "lab": "ACUMEN SECURITY, LLC" + } + ], + "vendor_url": "http://www.panzura.com/", + "vendor": "Panzura, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398.", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 3", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "Cryptographic module for use with Panzura products.", + "tested_conf": [ + "PZ Base OS 1.0 x86 on VMware ESXI 6.7 with AES-NI running on Dell PowerEdge R320 with Intel Xeon CPU E5-2430L with PAA", + "PZ Base OS 1.0 x86 on VMware ESXI 6.7 without AES-NI running on Dell PowerEdge R320 with Intel Xeon CPU E5-2430L without PAA", + "PZ Base OS 1.0 x86 running on Dell PowerEdge R320 with Intel Xeon CPU E5-2403 with PAA", + "PZ Base OS 1.0 x86 running on Dell PowerEdge R320 with Intel Xeon CPU E5-2403 without PAA (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#C659" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#C659" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "1.0", + "mentioned_certs": { + "2398": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", + "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "24a05f4d1305d667", + "cert_id": 3493, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "LogRhythm FIPS Object Module for OpenSSL", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-07-17", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2020-01-24", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.logrhythm.com", + "vendor": "LogRhythm", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security-relevant modification to Cert. #2398.", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 3", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The LogRhythm FIPS Object Module for OpenSSL is a general purpose cryptographic module. It is designed for ease of use with the popular OpenSSL cryptographic library and toolkit and is available for use without charge for a wide variety of platforms. The basic validation can also be extended quickly and affordably to accommodate new platforms and many types of modifications.", + "tested_conf": [ + "AIX 6.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V10.1)", + "AIX 6.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", + "AIX 6.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V10.1)", + "AIX 6.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", + "AIX 7.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", + "AIX 7.1 32-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", + "AIX 7.1 32-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", + "AIX 7.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", + "AIX 7.1 64-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", + "AIX 7.1 64-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", + "AIX 7.2 32-bit running on IBM Power7 (PPC) without PAA (IBM XL Compiler V13.1)", + "AIX 7.2 32-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", + "AIX 7.2 32-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", + "AIX 7.2 64-bit running on IBM Power7 (PPC) without PAA (IBM XL Compiler V13.1)", + "AIX 7.2 64-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", + "AIX 7.2 64-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", + "Android 5.0 32-bit running on Qualcomm APQ8084 (ARMv7) with PAA (gcc Compiler Version 4.9)", + "Android 5.0 32-bit running on Qualcomm APQ8084 (ARMv7) without PAA (gcc Compiler Version 4.9)", + "Android 5.0 64-bit running on SAMSUNG Exynos7420 (ARMv8) with PAA and Crypto Extensions (gcc Compiler Version 4.9)", + "Android 5.0 64-bit running on SAMSUNG Exynos7420 (ARMv8) without PAA and Crypto Extensions (gcc Compiler Version 4.9)", + "DataGravity Discovery Series OS V2.0 running on Intel Xeon E5-2420 (x86) with PAA (gcc Compiler Version 4.7.2)", + "DataGravity Discovery Series OS V2.0 running on Intel Xeon E5-2420 (x86) without PAA (gcc Compiler Version 4.7.2)", + "Debian 9 running on Intel Atom E3845 (x86) with PAA (gcc Compiler Version 6.3.0)", + "Debian 9 running on Intel Atom E3845 (x86) without PAA (gcc Compiler Version 6.3.0)", + "ExtremeXOS-Linux 3.1 running on Cavium Octeon II (MIPS) (gcc Compiler Version 4.9.2)", + "ExtremeXOS-Linux 3.18 32-bit running on Intel Atom C2558 (x86) with PAA (gcc Compiler Version 4.9.2)", + "ExtremeXOS-Linux 3.18 32-bit running on Intel Atom C2558 (x86) without PAA (gcc Compiler Version 4.9.2)", + "ExtremeXOS-Linux 3.18 running on Cavium Octeon II (MIPS) (gcc Compiler Version 4.9.2)", + "iOS 8.1 32-bit running on Apple A7 (ARMv8) with PAA (clang Compiler Version 600.0.56)", + "iOS 8.1 32-bit running on Apple A7 (ARMv8) without PAA (clang Compiler Version 600.0.56)", + "iOS 8.1 64-bit running on Apple A7 (ARMv8) with PAA and Crypto Extensions (clang Compiler Version 600.0.56)", + "iOS 8.1 64-bit running on Apple A7 (ARMv8) without PAA and Crypto Extensions (clang Compiler Version 600.0.56)", + "Linux 3.10 32-bit running on Intel Atom E3845 (x86) with PAA (gcc Compiler Version 4.8.1)", + "Linux 3.10 32-bit running on Intel Atom E3845 (x86) without PAA (gcc Compiler Version 4.8.1)", + "Linux 3.12 running on NXP T2080 (PPC) (gcc Compiler Version 4.9.2)", + "Microsoft Windows Server 2016 running on a Dell PowerEdge R610 with an Intel Xeon E5620 with PAA (x64) (Microsoft C/C++ Optimizing Compiler Version 19.16)", + "Microsoft Windows Server 2016 running on a Dell PowerEdge R610 with an Intel Xeon E5620 without PAA (x64) (Microsoft C/C++ Optimizing Compiler Version 19.16) (single-user mode)", + "SurfWare 7.2 running on TI c64 DSP (TMS320C6x Compiler Version 6.0.19)", + "TS-Linux 2.4 running on Arm920Tid (ARMv4) (gcc Compiler Version 4.3.2)", + "Ubuntu 12.04 running on Intel Xeon E5-2430L (x86) with PAA (gcc Compiler Version 4.6.3)", + "Ubuntu 12.04 running on Intel Xeon E5-2430L (x86) without PAA (gcc Compiler Version 4.6.3)", + "VxWorks 6.7 running on Intel Core 2 Duo (x86) (gcc Compiler Version 4.1.2)", + "VxWorks 6.9 running on Freescale P2020 (PPC) (gcc Compiler Version 4.3.3)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#3090", + "#3264", + "#3451", + "#3751", + "#3990", + "#4141", + "#4391", + "#4469", + "#C466" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#1094", + "#1181", + "#372", + "#472", + "#534", + "#699", + "#814", + "#947", + "#C466" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1027", + "#1182", + "#1256", + "#1414", + "#1451", + "#607", + "#723", + "#845", + "#C466" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#1040", + "#1085", + "#1124", + "#1170", + "#1195", + "#896", + "#933", + "#970", + "#C466" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#1050", + "#1091", + "#558", + "#620", + "#698", + "#801", + "#886", + "#952", + "#C466" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#1937", + "#2063", + "#2197", + "#2452", + "#2605", + "#2714", + "#2918", + "#2966", + "#C466" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#1581", + "#1664", + "#1766", + "#1928", + "#2048", + "#2258", + "#2374", + "#2444", + "#C466" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#2553", + "#2702", + "#2847", + "#3121", + "#3294", + "#3411", + "#3620", + "#3681", + "#C466" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#1780", + "#1853", + "#1942", + "#2086", + "#2190", + "#2263", + "#2366", + "#2399", + "#C466" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "2.0.16", + "mentioned_certs": { + "2398": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", + "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "69d9200c309f5f97", + "cert_id": 2590, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "CoSign", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-03-24", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.arx.com", + "vendor": "ARX (Algorithmic Research)", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMarch2016.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "historical", + "level": 3, + "caveat": "When operated in FIPS Mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys.", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "CoSign is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to CoSign from their PC for the purpose of signing documents and data.", + "tested_conf": null, + "algorithms": { + "CVL": { + "_type": "Set", + "elements": [ + "#697" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1028", + "#98" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2441", + "#2453" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#1929" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#3109", + "#3122" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2074", + "#2087" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#2087" + ] + } + }, + "hw_versions": "7.0", + "fw_versions": "7.7", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", + "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "5b00a7bb92c1cb19", + "cert_id": 2721, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Red Hat Enterprise Linux Libreswan Cryptographic Module v4.0", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-08-29", + "validation_type": "Initial", + "lab": "ATSEC INFORMATION SECURITY CORP" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-12-20", + "validation_type": "Update", + "lab": "ATSEC INFORMATION SECURITY CORP" + } + ], + "vendor_url": "http://www.redhat.com", + "vendor": "Red Hat®, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertAug2016.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "With module Red Hat Enterprise Linux NSS Cryptographic Module v4.0 validated to FIPS 140-2 under Cert. #2711 operating in FIPS mode and Red Hat Enterprise Linux 7.1 OpenSSL Module validated to FIPS 140-2 under Cert. #2441 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "Red Hat Enterprise Linux Libreswan Cryptographic Module v4.0 is a software only cryptographic module that provides the IKE protocol version 1 and version 2 key agreement services required for IPSec.", + "tested_conf": [ + "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA", + "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA", + "Red Hat Enterprise Linux 7.1 running on IBM Power8 Little Endian 8286-41A", + "Red Hat Enterprise Linux 7.1 running on IBM z13 (single-user mode)" + ], + "algorithms": { + "CVL": { + "_type": "Set", + "elements": [ + "#679", + "#680", + "#681" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "4.0", + "mentioned_certs": { + "2711": 1, + "2441": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", + "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "9ca7b8be590113b8", + "cert_id": 2711, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Red Hat Enterprise Linux NSS Cryptographic Module v4.0", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-08-15", + "validation_type": "Initial", + "lab": "ATSEC INFORMATION SECURITY CORP" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-12-19", + "validation_type": "Update", + "lab": "ATSEC INFORMATION SECURITY CORP" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-01-30", + "validation_type": "Update", + "lab": "ATSEC INFORMATION SECURITY CORP" + } + ], + "vendor_url": "http://www.redhat.com", + "vendor": "Red Hat®, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertAug2016.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "Network Security Services (NSS) is a set of open source C libraries designed to support cross-platform development of security-enabled applications. NSS implements major Internet security standards. NSS is available free of charge under a variety of open source compatible licenses. See http://www.mozilla.org/projects/security/pki/nss/", + "tested_conf": [ + "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA", + "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA", + "Red Hat Enterprise Linux 7.1 running on IBM POWER8 Little Endian 8286-41A", + "Red Hat Enterprise Linux 7.1 running on IBM z13 (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#3604", + "#3605", + "#3606", + "#3607", + "#3608", + "#3609", + "#3610" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#625", + "#626", + "#627", + "#628", + "#629" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#935", + "#936", + "#937", + "#938", + "#940" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#1001", + "#1002", + "#1003", + "#1004", + "#1005" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#738", + "#739", + "#740", + "#741", + "#742" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2299", + "#2300", + "#2301", + "#2303", + "#2305" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#1853", + "#1854", + "#1855", + "#1856", + "#1857", + "#2031", + "#2032", + "#2033", + "#2034", + "#2035" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#2965", + "#2966", + "#2967", + "#2969", + "#2971" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2006", + "#2007", + "#2008", + "#2009", + "#2010" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "4.0", + "mentioned_certs": {}, + "historical_reason": "SP 800-56Arev3 transition", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", + "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "cd0666cdd7ce0244", + "cert_id": 3095, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Cryptographic Primitives Library", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-03-22", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.microsoft.com", + "vendor": "Microsoft Corporation", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode with modules Kernel Mode Cryptographic Primitives Library validated to FIPS 140-2 under Cert. #3094 operating in FIPS mode and Code Integrity validated to FIPS 140-2 under Cert. #3093 operating in FIPS mode or Secure Kernel Code Integrity validated to FIPS 140-2 under Cert. #3096 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The Cryptographic Primitives Library (bcryptprimitives.dll and ncryptsslp.dll) provides cryptographic services to Windows components and applications. It includes cryptographic algorithms in an easy-to-use cryptographic module via the Cryptography Next Generation (CNG) API. It can be dynamically linked into applications for the use of general-purpose FIPS 140-2 validated cryptography.", + "tested_conf": [ + "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", + "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", + "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", + "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", + "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", + "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", + "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", + "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", + "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#4624" + ] + }, + "CKG": { + "_type": "Set", + "elements": [] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#1278", + "#1281" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1555" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#1223" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#1133" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#3061" + ] + }, + "KAS": { + "_type": "Set", + "elements": [ + "#127" + ] + }, + "KBKDF": { + "_type": "Set", + "elements": [ + "#140" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#4626" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2521", + "#2522" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#3790" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2459" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "10.0.15063", + "mentioned_certs": { + "3094": 1, + "3093": 1, + "3096": 1 + }, + "historical_reason": "SP 800-56Arev3 transition", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", + "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "0ab77c56eece2d24", + "cert_id": 3651, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Secure Kernel Code Integrity", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2020-05-07", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2022-10-05", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.microsoft.com", + "vendor": "Microsoft Corporation", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/May 2020_010620_1115.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "active", + "level": 1, + "caveat": "When operated in FIPS mode with module Windows OS Loader validated to FIPS 140-2 under Cert. #3615 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "Secure Kernel Code Integrity (SKCI) running in the Virtual Secure Mode (VSM) of the Hyper-V hypervisor will only grant execute access to physical pages in the kernel that have been successfully verified. Executable pages will not have write permission outside of Hyper-V. Therefore, only verified code can be executed.", + "tested_conf": [ + "Azure Data Box Edge (x64) running on a Microsoft Azure Data Box with an Intel Xeon Silver without PAA[1]", + "Windows 10 Education October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Book 2 with an Intel Core i7 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Pro LTE with an Intel Core i5 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Studio with an Intel Core i7 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on a Samsung Galaxy Book 12\" with an Intel Core i5 without PAA[1]", + "Windows 10 Enterprise October 2018 Update (x64) running on an HP EliteBook x360 1030 G2 with an Intel Core i7 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 12 Rugged Tablet with an Intel Core i5 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 5290 with an Intel Core i7 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Book 2 with an Intel Core i7 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Go with an Intel Pentium without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Pro LTE with an Intel Core i5 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on a Samsung Galaxy Book 10.6\" with an Intel Core m3 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on an HP Elite x2 1013 G3 Tablet with an Intel Core i7 without PAA[1]", + "Windows 10 Pro October 2018 Update (x64) running on an HP Slimline Desktop with an Intel Pentium with PAA[1]", + "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2016 running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", + "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with an Intel Xeon E5 without PAA[1]", + "Windows Server 2019 Core (x64) running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", + "Windows Server 2019 Datacenter Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with an Intel Xeon E5 without PAA[1]", + "Windows Server 2019 Datacenter Core (x64) running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", + "Windows Server Core Datacenter 2019 RTM (x64) running on an HPE Edgeline EL8000 / ProLiant e910 Server Blade with PAA[2] (single-user mode)" + ], + "algorithms": { + "RSA": { + "_type": "Set", + "elements": [ + "#C2047", + "#C211", + "#C349" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#C2047", + "#C211" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "10.0.17763[1] and 10.0.17763.107[2]", + "mentioned_certs": { + "3615": 1 + }, + "historical_reason": null, + "date_sunset": "2025-05-06", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", + "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "3b05cf67849835e3", + "cert_id": 2997, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "CryptoComply for NSS", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2017-08-18", + "validation_type": "Initial", + "lab": "ACUMEN SECURITY, LLC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-02-07", + "validation_type": "Update", + "lab": "ACUMEN SECURITY, LLC" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-03-29", + "validation_type": "Update", + "lab": "ACUMEN SECURITY, LLC" + } + ], + "vendor_url": "http://www.safelogic.com", + "vendor": "SafeLogic Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/AugConsolidatedCert.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "historical", + "level": 1, + "caveat": "When operated in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy. This validation entry is a non-security relevant modification to Cert. #2711", + "exceptions": [ + "Roles, Services, and Authentication: Level 2", + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "SafeLogic's CryptoComply for NSS is designed to provide FIPS 140-2 validated cryptographic functionality and is available for licensing.", + "tested_conf": [ + "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA\nRed Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA\n (single-user mode)" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#3605", + "#3606", + "#3607", + "#3609" + ] + }, + "CVL": { + "_type": "Set", + "elements": [ + "#626", + "#627" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#936", + "#937" + ] + }, + "DSA": { + "_type": "Set", + "elements": [ + "#1002", + "#1003" + ] + }, + "ECDSA": { + "_type": "Set", + "elements": [ + "#739", + "#740" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2300", + "#2301" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#1854", + "#1855", + "#2034", + "#2035" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#2966", + "#2967" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2007", + "#2008" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "4.0", + "mentioned_certs": { + "2711": 1 + }, + "historical_reason": "Moved to historical list due to sunsetting", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", + "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "c333a75353784177", + "cert_id": 2779, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "DocuSign Signature Appliance", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2016-10-25", + "validation_type": "Initial", + "lab": "CYGNACOM SOLUTIONS INC" + } + ], + "vendor_url": "http://www.docusign.com", + "vendor": "DocuSign, Inc.", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertOct2016.pdf", + "module_type": "Hardware", + "standard": "FIPS 140-2", + "status": "historical", + "level": 3, + "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", + "exceptions": [ + "Mitigation of Other Attacks: N/A" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data.", + "tested_conf": null, + "algorithms": { + "CVL": { + "_type": "Set", + "elements": [ + "#787" + ] + }, + "DRBG": { + "_type": "Set", + "elements": [ + "#1203", + "#98" + ] + }, + "HMAC": { + "_type": "Set", + "elements": [ + "#2564", + "#2629" + ] + }, + "KTS": { + "_type": "Set", + "elements": [ + "#2161", + "#2564" + ] + }, + "PBKDF": { + "_type": "Set", + "elements": [] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2068" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#1465", + "#3249", + "#3324" + ] + }, + "Triple-DES": { + "_type": "Set", + "elements": [ + "#2161", + "#2206" + ] + }, + "Triple-DES MAC": { + "_type": "Set", + "elements": [ + "#2206" + ] + } + }, + "hw_versions": "8.0", + "fw_versions": "8.1", + "sw_versions": null, + "mentioned_certs": { + "1883": 1 + }, + "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", + "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" + } + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "f7df34ce0a0ad316", + "cert_id": 3093, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": "Code Integrity", + "validation_history": [ + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2018-03-22", + "validation_type": "Initial", + "lab": "LEIDOS CSTL" + }, + { + "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", + "date": "2019-06-17", + "validation_type": "Update", + "lab": "LEIDOS CSTL" + } + ], + "vendor_url": "http://www.microsoft.com", + "vendor": "Microsoft Corporation", + "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", + "module_type": "Software", + "standard": "FIPS 140-2", + "status": "active", + "level": 1, + "caveat": "When operated in FIPS mode with modules Windows OS Loader validated to FIPS 140-2 under Cert. #3090 operating in FIPS mode or Windows Resume validated to FIPS 140-2 under Cert. #3091 operating in FIPS mode", + "exceptions": [ + "Physical Security: N/A", + "Design Assurance: Level 2" + ], + "embodiment": "Multi-Chip Stand Alone", + "description": "Code Integrity (ci.dll) verifies the integrity of executable files, including kernel mode drivers, critical system components, and user mode cryptographic modules as they are loaded into memory from the disk.", + "tested_conf": [ + "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", + "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", + "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", + "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", + "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", + "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", + "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", + "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", + "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", + "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", + "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", + "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", + "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" + ], + "algorithms": { + "AES": { + "_type": "Set", + "elements": [ + "#4624", + "#5300" + ] + }, + "RSA": { + "_type": "Set", + "elements": [ + "#2522", + "#2523", + "#2844", + "#2846" + ] + }, + "SHS": { + "_type": "Set", + "elements": [ + "#3790", + "#4253" + ] + } + }, + "hw_versions": null, + "fw_versions": null, + "sw_versions": "10.0.15063 and 10.0.15063.728", + "mentioned_certs": { + "3090": 1, + "3091": 1 + }, + "historical_reason": null, + "date_sunset": "2023-03-21", + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", + "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" + } + } + ] +} \ No newline at end of file diff --git a/tests/fips/test_fips_certificate.py b/tests/fips/test_fips_certificate.py index e69de29b..0b4012c4 100644 --- a/tests/fips/test_fips_certificate.py +++ b/tests/fips/test_fips_certificate.py @@ -0,0 +1,66 @@ +import json +import shutil +from pathlib import Path + +import pytest + +import tests.data.fips.certificate +import tests.data.fips.dataset +from sec_certs.dataset.fips import FIPSDataset +from sec_certs.sample.fips import FIPSCertificate + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.fips.certificate.__path__[0]) + + +@pytest.fixture(scope="module") +def certificate(tmp_path_factory) -> FIPSCertificate: + tmp_dir = tmp_path_factory.mktemp("dset") + dset_json_path = Path(tests.data.fips.dataset.__path__[0]) / "toy_dataset.json" + data_dir_path = dset_json_path.parent + shutil.copytree(data_dir_path, tmp_dir, dirs_exist_ok=True) + fips_dset = FIPSDataset.from_json(tmp_dir / "toy_dataset.json") + + crt = fips_dset["184097a88a9b4ad9"] + fips_dset.certs = {crt.dgst: crt} + fips_dset.download_all_artifacts() + fips_dset.convert_all_pdfs() + + return crt + + +def test_extract_metadata(certificate: FIPSCertificate): + pass + + +def test_extract_module(certificate: FIPSCertificate): + certificate.state.module_extract_ok = True + FIPSCertificate.parse_html_module(certificate) + assert certificate.state.module_extract_ok + + +def test_extract_frontpage(): + pass + + +def test_keyword_extraction(): + pass + + +def test_cert_to_json(certificate: FIPSCertificate, tmp_path: Path, data_dir: Path): + certificate.to_json(tmp_path / "crt.json") + + with (tmp_path / "crt.json").open("r") as handle: + data = json.load(handle) + + with (data_dir / "fictional_cert.json").open("r") as handle: + template_data = json.load(handle) + + assert template_data == data + + +def test_cert_from_json(certificate: FIPSCertificate, data_dir: Path): + crt = FIPSCertificate.from_json(data_dir / "fictional_cert.json") + assert certificate == crt diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 010d503d..7bc507be 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -1,3 +1,4 @@ +import json import shutil from pathlib import Path from tempfile import TemporaryDirectory @@ -5,11 +6,24 @@ import pytest +import sec_certs.constants as constants +import tests.data.fips.dataset from sec_certs.dataset.fips import FIPSDataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset +from sec_certs.sample.fips import FIPSCertificate from sec_certs.utils.helpers import fips_dgst +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.fips.dataset.__path__[0]) + + +@pytest.fixture +def toy_dataset(data_dir: Path) -> FIPSDataset: + return FIPSDataset.from_json(data_dir / "toy_dataset.json") + + def generate_html(ids: List[str], path: Path): def generate_entry(certificate_id: str) -> str: return f""" @@ -301,7 +315,89 @@ def test_openssl_chunk(certs_to_process): assert set(dataset.certs[fips_dgst("3158")].heuristics.web_references.directly_referencing) == {"2398"} +def test_dataset_to_json(toy_dataset: FIPSDataset, data_dir: Path, tmp_path: Path): + toy_dataset.to_json(tmp_path / "dset.json") + + with (tmp_path / "dset.json").open("r") as handle: + data = json.load(handle) + + with (data_dir / "toy_dataset.json").open("r") as handle: + template_data = json.load(handle) + + del data["timestamp"] + del template_data["timestamp"] + assert data == template_data + + +def test_dataset_from_json(toy_dataset: FIPSDataset, data_dir: Path): + assert toy_dataset == FIPSDataset.from_json(data_dir / "toy_dataset.json") + + +def test_build_empty_dataset(): + with TemporaryDirectory() as tmp_dir: + dset = FIPSDataset(root_dir=tmp_dir) + assert len(dset) == 0 + assert not dset.state.meta_sources_parsed + assert not dset.state.artifacts_downloaded + assert not dset.state.pdfs_converted + assert not dset.state.certs_analyzed + + +def test_build_dataset(data_dir: Path, toy_dataset: FIPSDataset): + with TemporaryDirectory() as tmp_dir: + dataset_path = Path(tmp_dir) + (dataset_path / "web").mkdir() + shutil.copyfile(data_dir / "fips_modules_active.html", dataset_path / "web" / "fips_modules_active.html") + shutil.copyfile( + data_dir / "fips_modules_historical.html", dataset_path / "web" / "fips_modules_historical.html" + ) + shutil.copyfile(data_dir / "fips_modules_revoked.html", dataset_path / "web" / "fips_modules_revoked.html") + + dset = FIPSDataset(root_dir=dataset_path) + dset.get_certs_from_web(to_download=False) + + assert len(dset) == len(toy_dataset) + assert set(dset.certs.keys()) == set(toy_dataset.certs.keys()) + + +@pytest.mark.xfail(reason="May fail due to error on FIPS server.") +def test_download_meta_html_files(): + with TemporaryDirectory() as tmp_dir: + dset = FIPSDataset(root_dir=Path(tmp_dir)) + dset._download_html_resources() + + assert (dset.web_dir / "fips_modules_active.html").exists() + assert (dset.web_dir / "fips_modules_active.html").stat().st_size > constants.MIN_FIPS_HTML_SIZE + assert (dset.web_dir / "fips_modules_historical.html").exists() + assert (dset.web_dir / "fips_modules_historical.html").stat().st_size > constants.MIN_FIPS_HTML_SIZE + assert (dset.web_dir / "fips_modules_revoked.html").exists() + assert (dset.web_dir / "fips_modules_revoked.html").stat().st_size > constants.MIN_FIPS_HTML_SIZE + + +def test_download_and_convert_artifacts(toy_dataset: FIPSDataset, data_dir: Path): + crt = toy_dataset["184097a88a9b4ad9"] + toy_dataset.certs = {crt.dgst: crt} + with TemporaryDirectory() as tmp_dir: + toy_dataset.root_dir = Path(tmp_dir) + toy_dataset.download_all_artifacts() + + if not crt.state.policy_download_ok or crt.state.module_download_ok: + pytest.xfail(reason="Fail due to error during download") + + toy_dataset.convert_all_pdfs() + + assert not crt.state.policy_convert_garbage + assert crt.state.policy_convert_ok + assert crt.state.policy_pdf_hash == "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1" + assert crt.state.policy_txt_path.exists() + + template_policy_txt_path = data_dir / "template_policy_184097a88a9b4ad9.txt" + assert abs(crt.state.policy_txt_path.stat().st_size - template_policy_txt_path.stat().st_size) < 1000 + + @pytest.mark.skip(reason="FIPS tests to-be-refactored") -def test_to_pandas(fips_dset: FIPSDataset): - # copy-paste code from the same test in different classes. - pass +def test_to_pandas(toy_dataset: FIPSDataset): + df = toy_dataset.to_pandas() + assert df.shape == (len(toy_dataset), len(FIPSCertificate.pandas_columns)) + assert df.index.name == "dgst" + assert set(df.columns) == (set(FIPSCertificate.pandas_columns).union("year_from")) From 315270af623f782c774fff9decd358cb0b3f54cc Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 12:23:26 +0100 Subject: [PATCH 28/81] fix tests --- sec_certs/dataset/fips.py | 74 +- sec_certs/sample/fips.py | 25 +- tests/cc/test_cc_analysis.py | 4 +- .../data/fips/certificate/fictional_cert.json | 116 +- tests/data/fips/dataset/toy_dataset.json | 3149 +++-------------- 5 files changed, 544 insertions(+), 2824 deletions(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 0438b7f1..6292b6c3 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -56,29 +56,39 @@ def module_dir(self) -> Path: def algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" - @serialize - def _extract_data(self, redo: bool = False) -> None: - # TODO: Refactor me + def _extract_data_from_html_modules(self, fresh: bool = True) -> None: """ - Extracts data from pdf files - :param bool redo: Whether to try again with failed files, defaults to False + Extracts data from html module file + :param bool fresh: if all certs should be processed, or only the failed ones. Defaults to True """ - logger.info("Entering PDF scan.") - - self._extract_data_from_html_modules() - - keywords = cert_processing.process_parallel( - FIPSCertificate.find_keywords, - [cert for cert in self.certs.values() if not cert.pdf_data.keywords or redo], + certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze(fresh)] + processed_certs = cert_processing.process_parallel( + FIPSCertificate.parse_html_module, + certs_to_process, config.n_threads, use_threading=False, - progress_bar_desc="Scanning PDF files", + progress_bar_desc="Extracting data from module html", ) - for keyword, cert in keywords: - self.certs[cert.dgst].pdf_data.keywords = keyword + self.update_with_certs(processed_certs) - self._extract_metadata() - self.extract_certs_from_tables(high_precision=True) + @serialize + def _extract_data(self, fresh: bool = True) -> None: + # TODO: Update state of extraction properly, atm completely ignored. + self._extract_data_from_html_modules(fresh) + self._extract_policy_pdf_metadata(fresh) + self._extract_policy_pdf_keywords(fresh) + self.extract_certs_from_tables(fresh) + + def _extract_policy_pdf_keywords(self, fresh: bool = True) -> None: + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + processed_certs = cert_processing.process_parallel( + FIPSCertificate.extract_policy_pdf_keywords, + certs_to_process, + config.n_threads, + use_threading=False, + progress_bar_desc="Extracting keywords from policy", + ) + self.update_with_certs(processed_certs) def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_modules(fresh) @@ -155,21 +165,6 @@ def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: return {FIPSCertificate(cert_id) for cert_id in cert_ids} - def _extract_data_from_html_modules(self, fresh: bool = True) -> None: - """ - Extracts data from html module file - :param bool fresh: if all certs should be processed, or only the failed ones. Defaults to True - """ - certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze(fresh)] - processed_certs = cert_processing.process_parallel( - FIPSCertificate.parse_html_module, - certs_to_process, - config.n_threads, - use_threading=False, - progress_bar_desc="Extracting data from module html", - ) - self.update_with_certs(processed_certs) - @classmethod def from_web_latest(cls) -> "FIPSDataset": """ @@ -212,11 +207,12 @@ def _process_algorithms(self): logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") @serialize - def extract_certs_from_tables(self, high_precision: bool) -> List[Path]: + def extract_certs_from_tables(self, fresh: bool = True, high_precision: bool = True) -> List[Path]: """ Function that extracts algorithm IDs from tables in security policies files. :return: list of files that couldn't have been decoded """ + # TODO: Refactor me logger.info("Entering table scan.") result = cert_processing.process_parallel( FIPSCertificate.analyze_tables, @@ -247,18 +243,16 @@ def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: for cert in self.certs.values(): self._clean_cert_ids(cert) - def _extract_metadata(self): - # TODO: Refactor me - certs_to_process = [x for x in self] - res = cert_processing.process_parallel( - FIPSCertificate.extract_sp_metadata, + def _extract_policy_pdf_metadata(self, fresh: bool = True) -> None: + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + processed_certs = cert_processing.process_parallel( + FIPSCertificate.extract_policy_pdf_metadata, certs_to_process, config.n_threads, use_threading=False, progress_bar_desc="Extracting security policy metadata", ) - for r in res: - self.certs[r.dgst] = r + self.update_with_certs(processed_certs) # TODO: Refactor me def _unify_algorithms(self) -> None: diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 78ca47eb..614447f6 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -278,6 +278,9 @@ def policy_is_ok_to_convert(self, fresh: bool = True) -> bool: def module_is_ok_to_analyze(self, fresh: bool = True) -> bool: return self.module_download_ok if fresh else self.module_download_ok and not self.module_extract_ok + def policy_is_ok_to_analyze(self, fresh: bool = True) -> bool: + return self.policy_convert_ok if fresh else self.policy_convert_ok and not self.policy_extract_ok + def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, modules_html_dir: Path) -> None: self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") self.state.policy_txt_path = (policies_txt_dir / str(self.dgst)).with_suffix(".txt") @@ -444,7 +447,7 @@ def __init__( self.cert_id = cert_id self.web_data: FIPSCertificate.WebData = web_data if web_data else FIPSCertificate.WebData() - self.pdf_data = pdf_data if pdf_data else FIPSCertificate.PdfData() + self.pdf_data: FIPSCertificate.PdfData = pdf_data if pdf_data else FIPSCertificate.PdfData() self.heuristics = heuristics if heuristics else FIPSCertificate.Heuristics() self.state = state if state else FIPSCertificate.InternalState() @@ -459,7 +462,7 @@ def pandas_tuple(self) -> Tuple: self.web_data.module_type, self.web_data.level, self.web_data.embodiment, - self.web_data.validation_history[0] if self.web_data.validation_history else np.nan, + self.web_data.validation_history[0].date if self.web_data.validation_history else np.nan, self.web_data.date_sunset, self.heuristics.algorithms, self.heuristics.extracted_versions, @@ -543,22 +546,20 @@ def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: return cert @staticmethod - def extract_sp_metadata(cert: FIPSCertificate) -> FIPSCertificate: + def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) cert.pdf_data.st_metadata = metadata if metadata else dict() return cert @staticmethod - def find_keywords(cert: FIPSCertificate) -> Tuple[Optional[Dict], FIPSCertificate]: - # TODO: Replace the condition below - # if not cert.state.txt_state: - # return None, cert - - keywords = sec_certs.utils.extract.extract_keywords( - cert.state.policy_pdf_path.with_suffix(".pdf.txt"), fips_rules - ) - return keywords, cert + def extract_policy_pdf_keywords(cert: FIPSCertificate) -> FIPSCertificate: + keywords = sec_certs.utils.extract.extract_keywords(cert.state.policy_txt_path, fips_rules) + if not keywords: + cert.state.policy_extract_ok = False + else: + cert.pdf_data.keywords = keywords + return cert @staticmethod def analyze_tables( # noqa: C901 diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 3cc58e1a..c66a402e 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -89,7 +89,7 @@ def cc_dset(data_dir: Path, cve_dset: CVEDataset, tmp_path_factory) -> CCDataset cc_dset.process_protection_profiles() cc_dset._extract_data() cc_dset.auxillary_datasets.cve_dset = cve_dset - cc_dset._compute_heuristics(use_nist_cpe_matching_dict=False) + cc_dset._compute_heuristics() return cc_dset @@ -116,7 +116,7 @@ def test_find_related_cves( cc_dset: CCDataset, cpe_single_sign_on: CPE, cves: Set[CVE], random_certificate: CommonCriteriaCert ): random_certificate.heuristics.cpe_matches = {cpe_single_sign_on.uri} - cc_dset.compute_related_cves(use_nist_cpe_matching_dict=False) + cc_dset.compute_related_cves() assert {x.cve_id for x in cves} == random_certificate.heuristics.related_cves diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json index 5f0be2cd..68a38017 100644 --- a/tests/data/fips/certificate/fictional_cert.json +++ b/tests/data/fips/certificate/fictional_cert.json @@ -4,107 +4,27 @@ "cert_id": 3518, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-09-05", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/SeptConsolidated.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "active", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#C86" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#98", - "#C85" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#C86" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#C85", - "#C86" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#C85" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#C85" - ] - } - }, - "hw_versions": "8.0", - "fw_versions": "9.0.9.10", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2024-09-04", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index 3c8d21f7..99c7511e 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -7,7 +7,7 @@ "pdfs_converted": true, "certs_analyzed": false }, - "timestamp": "2022-11-17 16:06:06.266809", + "timestamp": "2022-11-23 12:12:45.225272", "sha256_digest": "not implemented", "name": "FIPSDataset dataset", "description": "No description provided", @@ -15,111 +15,31 @@ "certs": [ { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "184097a88a9b4ad9", - "cert_id": 3518, + "dgst": "f7df34ce0a0ad316", + "cert_id": 3093, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-09-05", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/SeptConsolidated.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "active", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#C86" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#98", - "#C85" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#C86" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#C85", - "#C86" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#C85", - "#C86" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#C85" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#C85" - ] - } - }, - "hw_versions": "8.0", - "fw_versions": "9.0.9.10", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2024-09-04", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, @@ -172,157 +92,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", - "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", + "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ed16d272044ed81f", - "cert_id": 2860, + "dgst": "cd0666cdd7ce0244", + "cert_id": 3095, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign HSM Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2017-03-08", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2017-10-03", - "validation_type": "Update", - "lab": "CYGNACOM SOLUTIONS INC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-04-10", - "validation_type": "Update", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMar2017.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "historical", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "DocuSign HSM Appliance is a high-performance cryptographic service provider. It performs high-speed cryptographic operations while protecting sensitive data. Its features include Triple-DES, AES, Triple-DES MAC, CCM, HMAC, RSA, ECDSA, SHA-1, SHA-256, SHA-384, SHA-512, public key database and certificate support, authenticated and encrypted communication with the module, secure storage of secret/private keys, software key medium and smartcard support, tamper-responsive enclosure, high level API requiring no cryptographic expertise, in-depth logging and auditing, and secure backup capabilities.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#4029", - "#4031", - "#4640", - "#4641", - "#5283", - "#5284" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1039", - "#1296", - "#1297", - "#1745", - "#1746", - "#857" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1205", - "#1565", - "#2030", - "#98" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#1143", - "#1378", - "#900" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2630", - "#2632", - "#3073", - "#3074", - "#3490", - "#3491" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#2630", - "#3074", - "#3491", - "#4029", - "#4641", - "#5284" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2069", - "#2533", - "#2822" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#3325", - "#3326", - "#3804", - "#3805", - "#4242", - "#4243" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2207", - "#2469", - "#2669" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#2207", - "#2469", - "#2669" - ] - } - }, - "hw_versions": "5.0", - "fw_versions": "5.0.0, 5.0.2 and 5.0.3", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, - "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -376,106 +175,37 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", - "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" + "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", + "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "e1ff7d9997726782", - "cert_id": 3090, + "dgst": "618102f275d5649a", + "cert_id": 3495, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Windows OS Loader", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-03-22", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-06-17", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.microsoft.com", - "vendor": "Microsoft Corporation", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "active", - "level": 1, - "caveat": "When operated in FIPS mode with module Boot Manager validated to FIPS 140-2 under Cert. #3089 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The BitLocker® Windows OS Loader loads the boot-critical driver and OS kernel image files.", - "tested_conf": [ - "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", - "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", - "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", - "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", - "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", - "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", - "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", - "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", - "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#4624", - "#4625", - "#5300", - "#5316" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2523", - "#2846" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#3790", - "#4253" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "10.0.15063 and 10.0.15063.728", - "mentioned_certs": { - "3089": 1 - }, + "sw_versions": null, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2023-03-21", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, @@ -528,283 +258,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", - "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" + "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", + "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ceab64951a30f684", - "cert_id": 3197, + "dgst": "24a05f4d1305d667", + "cert_id": 3493, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Cryptographic Primitives Library", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-10-22", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-07-17", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2020-09-08", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.microsoft.com", - "vendor": "Microsoft Corporation", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertOct2018.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode with modules Kernel Mode Cryptographic Primitives Library validated to FIPS 140-2 under Cert. #3196 operating in FIPS mode and Code Integrity validated to FIPS 140-2 under Cert. #3195 or Cert. #3644 operating in FIPS mode or Secure Kernel Code Integrity validated to FIPS 140-2 under Cert. #3096 or Cert. #3651 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The Cryptographic Primitives Library (bcryptprimitives.dll and ncryptsslp.dll) provides cryptographic services to Windows components and applications. It includes cryptographic algorithms in an easy-to-use cryptographic module via the Cryptography Next Generation (CNG) API. It can be dynamically linked into applications for the use of general-purpose FIPS 140-2 validated cryptography.", - "tested_conf": [ - "Azure Data Box Edge (x64) running on a Microsoft Azure Data Box with PAA [5] (single-user mode)", - "Surface Hub (x64) running on a Microsoft Surface Hub with PAA [1]", - "Windows 10 Education April 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [4]", - "Windows 10 Education Fall Creators Update (x64) running on a Microsoft Surface Pro with PAA [3]", - "Windows 10 Education October 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [5]", - "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [4]", - "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [4]", - "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [4]", - "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Studio 2 with PAA [4]", - "Windows 10 Enterprise April 2018 Update (x64) running on a Microsoft Surface Studio with PAA [4]", - "Windows 10 Enterprise April 2018 Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA [4]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Book 2 with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Book with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Laptop with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Pro with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on a Microsoft Surface Studio with PAA [3]", - "Windows 10 Enterprise Fall Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA [3]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [5]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [5]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [5]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Studio with PAA [5]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Samsung Galaxy Book 12\" with PAA [5]", - "Windows 10 Enterprise October 2018 Update (x64) running on an HP EliteBook x360 1030 G2 with PAA [5]", - "Windows 10 Home April 2018 Update (x86) running on a Dell Inspiron 660s without PAA [4]", - "Windows 10 Home Fall Creators Update (x86) running on a Dell Inspiron 660s without PAA [3]", - "Windows 10 Home October 2018 Update (x86) running on a Dell Inspiron 660s without PAA [5]", - "Windows 10 Mobile Fall Creators Update (ARMv7) running on a Microsoft Lumia 650 [2]", - "Windows 10 Mobile Fall Creators Update (ARMv7) running on a Microsoft Lumia 950 [2]", - "Windows 10 Mobile Fall Creators Update (ARMv7) running on a Microsoft Lumia 950 XL [2]", - "Windows 10 Mobile Fall Creators Update (ARMv7) running on an HP Elite x3 [2]", - "Windows 10 Pro April 2018 Update (x64) running on a Dell Latitude 12 Rugged Tablet with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Dell Latitude 5290 with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Go with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Laptop 2 with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Pro 6 with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [4]", - "Windows 10 Pro April 2018 Update (x64) running on an HP Slimline Desktop with PAA [4]", - "Windows 10 Pro Fall Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Dell Latitude 5285 with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Dell Latitude 5290 with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Microsoft Surface Laptop with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Microsoft Surface Pro with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on a Panasonic Toughbook with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on an HP Compaq Pro 6305 with PAA [3]", - "Windows 10 Pro Fall Creators Update (x64) running on an HP Slimline Desktop with PAA [3]", - "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 12 Rugged Tablet with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 5290 with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Book 2 with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Go with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Laptop with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Pro LTE with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on a Samsung Galaxy Book 10.6\" with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on an HP Elite x2 1013 G3 Tablet with PAA [5]", - "Windows 10 Pro October 2018 Update (x64) running on an HP Slimline Desktop with PAA [5]", - "Windows 10 S Fall Creators Update (x64) running on a Microsoft Surface Laptop with PAA [3]", - "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2016 running on a Dell PowerEdge R740 Server with PAA [5]", - "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with PAA [5]", - "Windows Server 2019 Core (x64) running on a Dell PowerEdge R740 Server with PAA [5]", - "Windows Server 2019 Datacenter Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with PAA [5]", - "Windows Server 2019 Datacenter Core (x64) running on a Dell PowerEdge R740 Server with PAA [5]", - "Windows Server Datacenter Core (x64) on Hyper-V on Windows Server running on a Dell Precision Tower 5810MT with PAA [3][4]", - "Windows Server Datacenter Core (x64) running on a Dell PowerEdge R630 Server with PAA [3]", - "Windows Server Datacenter Core (x64) running on a Dell PowerEdge R740 Server with PAA [3][4]", - "Windows Server Datacenter Core (x64) running on a Dell Precision Tower 5810MT with PAA [3]", - "Windows Server Standard Core (x64) on Hyper-V on Windows Server 2016 running on a Dell PowerEdge R740 Server with PAA [4]", - "Windows Server Standard Core (x64) on Hyper-V on Windows Server running on a Dell Precision Tower 5810MT with PAA [3][4]", - "Windows Server Standard Core (x64) running on a Dell PowerEdge R630 Server with PAA [3]", - "Windows Server Standard Core (x64) running on a Dell PowerEdge R740 Server with PAA [3][4]", - "Windows Server Standard Core (x64) running on a Dell Precision Tower 5810MT with PAA [3]" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#4897", - "#4898", - "#4899", - "#4900", - "#4901", - "#4902", - "#5847", - "#5860", - "#C211", - "#C347" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1496", - "#1498", - "#1507", - "#1509", - "#1511", - "#1513", - "#2110", - "#2111", - "#C211" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1730", - "#1731", - "#1732", - "#2435", - "#C211" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1301", - "#1302", - "#1303", - "#1479", - "#C211" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#1246", - "#1249", - "#1250", - "#1563", - "#C211" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#3267", - "#3268", - "#3269", - "#3858", - "#C211" - ] - }, - "KAS": { - "_type": "Set", - "elements": [ - "#146", - "#147", - "#148", - "#200", - "#C211" - ] - }, - "KBKDF": { - "_type": "Set", - "elements": [ - "#157", - "#158", - "#159", - "#242", - "#C347" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#4898", - "#4899", - "#4900", - "#5860", - "#C347" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2667", - "#2668", - "#2669", - "#2670", - "#2671", - "#2672", - "#3079", - "#3080", - "#C211" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#4009", - "#4010", - "#4011", - "#4633", - "#C211" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2556", - "#2557", - "#2558", - "#2862", - "#C211" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "10.0.15063.674 [1], 10.0.15254 [2], 10.0.16299 [3], 10.0.17134 [4] and 10.0.17763 [5]", - "mentioned_certs": { - "3196": 1, - "3195": 1, - "3644": 1, - "3096": 1, - "3651": 1 - }, - "historical_reason": "SP 800-56Arev3 transition", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -858,135 +341,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", - "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" + "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", + "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "51c94a510b50e68f", - "cert_id": 3141, + "dgst": "75ca28395bfc9ad1", + "cert_id": 2630, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-02-27", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-08-17", - "validation_type": "Update", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPSConsolidatedCertFeb2018.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "historical", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys. The module generates cryptographic keys whose strengths are modified by available entropy.", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#5058", - "#5448" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1615", - "#1894" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1864", - "#2133", - "#98" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#3363", - "#3378", - "#3606", - "#3607" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#2603", - "#2738" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2729", - "#2743", - "#2924", - "#2925" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#4108", - "#4123", - "#4367", - "#4368" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2603", - "#2616", - "#2738", - "#2739" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#2603", - "#2738" - ] - } - }, - "hw_versions": "8.0", - "fw_versions": "8.5 and 8.51.9.0", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, - "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -1040,276 +424,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", - "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" + "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", + "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ad1e075215267a28", - "cert_id": 2441, + "dgst": "2aec3694418f35d8", + "cert_id": 3488, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Red Hat Enterprise Linux 6.6 OpenSSL Module, Red Hat Enterprise Linux 7.1 OpenSSL Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2015-09-08", - "validation_type": "Initial", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-01-27", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-02-16", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-12-21", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - } - ], - "vendor_url": "http://www.redhat.com", - "vendor": "Red Hat®, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertList0057.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", - "exceptions": [ - "Physical Security: N/A" - ], - "embodiment": "Multi-chip standalone", - "description": "The OpenSSL FIPS Runtime Module is a general purpose cryptographic library designed to provide FIPS 140-2 validated cryptographic functionality for use with the high level API of the OpenSSL library.", - "tested_conf": [ - "Red Hat Enterprise Linux 6.6 running on HP ProLiant DL380p Gen8 with PAA", - "Red Hat Enterprise Linux 6.6 running on HP ProLiant DL380p Gen8 without PAA", - "Red Hat Enterprise Linux 6.6 running on IBM System x3500 M4 with PAA", - "Red Hat Enterprise Linux 6.6 running on IBM System x3500 M4 without PAA", - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380 Gen8 with PAA", - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380 Gen8 without PAA", - "Red Hat Enterprise Linux 7.1 running on IBM POWER8 Little Endian 8286-41A", - "Red Hat Enterprise Linux 7.1 running on IBM z13 with CP Assist for Cryptographic Functions (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#3104", - "#3105", - "#3106", - "#3107", - "#3108", - "#3109", - "#3110", - "#3111", - "#3112", - "#3113", - "#3114", - "#3119", - "#3634", - "#3635", - "#3636", - "#3637", - "#3638", - "#3639", - "#3640", - "#3641", - "#3642", - "#3651", - "#3696" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#374", - "#375", - "#376", - "#377", - "#380", - "#381", - "#654", - "#655", - "#656", - "#657", - "#658", - "#661", - "#662" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1003", - "#610", - "#611", - "#612", - "#613", - "#614", - "#615", - "#616", - "#617", - "#618", - "#619", - "#620", - "#621", - "#622", - "#623", - "#624", - "#625", - "#626", - "#629", - "#630", - "#631", - "#957", - "#958", - "#959", - "#960", - "#961", - "#962", - "#963", - "#964", - "#965", - "#966", - "#967", - "#968", - "#969", - "#970", - "#971", - "#982" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1013", - "#1014", - "#1015", - "#1016", - "#1023", - "#1038", - "#897", - "#898", - "#899", - "#903" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#560", - "#561", - "#562", - "#564", - "#755", - "#756", - "#757", - "#759", - "#775" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#1931", - "#1944", - "#1945", - "#1946", - "#1947", - "#1948", - "#1949", - "#1950", - "#1951", - "#1955", - "#1956", - "#1958", - "#2385", - "#2386", - "#2388", - "#2389", - "#2390", - "#2391", - "#2392", - "#2393", - "#2394", - "#2401", - "#2427" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1583", - "#1584", - "#1586", - "#1590", - "#1875", - "#1876", - "#1877", - "#1878", - "#1886", - "#1902" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#2547", - "#2563", - "#2564", - "#2565", - "#2566", - "#2567", - "#2568", - "#2569", - "#2570", - "#2574", - "#2575", - "#2577", - "#3052", - "#3053", - "#3054", - "#3055", - "#3056", - "#3057", - "#3058", - "#3059", - "#3060", - "#3061", - "#3069", - "#3095" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#1784", - "#1785", - "#1786", - "#1790", - "#2027", - "#2028", - "#2029", - "#2044", - "#2059" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "3.0, 4.0", - "mentioned_certs": {}, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -1363,8 +507,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", - "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" + "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", + "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" } }, { @@ -1373,111 +517,26 @@ "cert_id": 2665, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-06-21", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-07-25", - "validation_type": "Update", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertJune2016.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "historical", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "CVL": { - "_type": "Set", - "elements": [ - "#786", - "#787" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1137", - "#1138", - "#98" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2551", - "#2552", - "#2563", - "#2564" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#2161", - "#2564" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2005", - "#2006" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#3237", - "#3238", - "#3248", - "#3249" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2155", - "#2156", - "#2160", - "#2161" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#2155", - "#2156" - ] - } - }, - "hw_versions": "7.0 and 8.0", - "fw_versions": "8.0", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, - "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -1537,62 +596,30 @@ }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "75ca28395bfc9ad1", - "cert_id": 2630, + "dgst": "69d9200c309f5f97", + "cert_id": 2590, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Red Hat Enterprise Linux OpenSSH Server Cryptographic Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-05-02", - "validation_type": "Initial", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-06-17", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - } - ], - "vendor_url": "http://www.redhat.com", - "vendor": "Red Hat®, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMay2016.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode with module Red Hat Enterprise Linux 7.1 OpenSSL Module validated to FIPS 140-2 under Cert. #2441 operating in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", - "exceptions": [ - "Physical Security: N/A", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The OpenSSH Server cryptographic module provides the server-side component for an SSH protocol version 2 protected communication channel. OpenSSH is the standard SSH implementation and shipped with RHEL 7.1. Its cryptographic mechanisms use the OpenSSL library in FIPS 140-2 mode.", - "tested_conf": [ - "Red Hat Enterprise Linux 7.1 running on IBM Power8 Little Endian 8286-41A", - "Red Hat Enterprise Linux 7.1 running on IBM z13 with CP Assist for Cryptographic Functions (single-user mode)", - "Red Hat Enterprise Linux 7.1 running on ProLiant DL380p Gen8 with PAA", - "Red Hat Enterprise Linux 7.1 running on ProLiant DL380p Gen8 without PAA" - ], - "algorithms": { - "CVL": { - "_type": "Set", - "elements": [ - "#700", - "#701", - "#702" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "4.0", - "mentioned_certs": { - "2441": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -1646,114 +673,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", - "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" + "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", + "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0df59fd6c41cadd2", - "cert_id": 3711, + "dgst": "756a6524cdb2d4ea", + "cert_id": 3850, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Alteryx Cryptographic Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2020-09-21", - "validation_type": "Initial", - "lab": "ACUMEN SECURITY, LLC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2020-12-01", - "validation_type": "Update", - "lab": "ACUMEN SECURITY, LLC" - } - ], - "vendor_url": "http://www.alteryx.com", - "vendor": "Alteryx Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/September 2020_011020_0703.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode and built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy and operated in FIPS mode. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #3220.", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 3", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The Alteryx FIPS Module for OpenSSL is a general-purpose cryptographic module integrated in Alteryx’s products to provide FIPS 140-2 validated cryptography for the protection of sensitive information.", - "tested_conf": [ - "Windows Server 2016 running on HPE ProLiant DL60 Gen9 with Intel Xeon E5-2609 with PAA", - "Windows Server 2016 running on HPE ProLiant DL60 Gen9 with Intel Xeon E5-2609 without PAA (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#C1990" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#C1990" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "2.0.9, 2.0.10, 2.0.11, 2.0.12, 2.0.13, 2.0.14, 2.0.15 or 2.0.16", - "mentioned_certs": { - "3220": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -1807,129 +756,37 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", - "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" + "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", + "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "756a6524cdb2d4ea", - "cert_id": 3850, + "dgst": "184097a88a9b4ad9", + "cert_id": 3518, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2021-03-18", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2021-09-15", - "validation_type": "Update", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/March 2021_010421_0724_signed.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "active", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data. The DocuSign Signature Appliance also enables organization using AES based encryption keys for encrypting/decrypting data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#A1517", - "#A1518", - "#C1251", - "#C1252" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#A1518", - "#C1252" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#98", - "#A1517", - "#C1251" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#A1517", - "#A1518", - "#C1251", - "#C1252" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#A1518", - "#C1252" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [ - "#A1517" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#A1517", - "#C1251" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#A1517", - "#A1518", - "#C1251", - "#C1252" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#A1517", - "#C1251" - ] - } - }, - "hw_versions": "8.0", - "fw_versions": "9.3.9.20 and 9.4.9.20", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2026-03-17", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, @@ -1982,108 +839,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", - "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "2aec3694418f35d8", - "cert_id": 3488, + "dgst": "73e7f3f802b0e919", + "cert_id": 3176, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "ColorTokens OpenSSL FIPS Object Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-07-15", - "validation_type": "Initial", - "lab": "ACUMEN SECURITY, LLC" - } - ], - "vendor_url": "https://www.colortokens.com", - "vendor": "ColorTokens", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy and operated in FIPS mode. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 3", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The Module is a software cryptographic module that is built from the OpenSSL. The module is a software library that provides C-language application program interface (API) for use by other processes that require cryptographic functionality to various modules of the ColorTokens application.", - "tested_conf": [ - "Ubuntu 12.04 running on Intel Xeon E5-2430L(x86) with PAA (gcc Compiler Version 4.6.3)", - "Ubuntu 12.04 running on Intel Xeon E5-2430L(x86) without PAA (gcc Compiler Version 4.6.3) (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#3451" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#534" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#845" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#970" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#698" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2197" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1766" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#2847" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#1942" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "2.0.11", - "mentioned_certs": { - "2398": 1 - }, - "historical_reason": "186-2 transition", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2137,113 +922,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", - "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" + "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", + "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "73e7f3f802b0e919", - "cert_id": 3176, + "dgst": "ed16d272044ed81f", + "cert_id": 2860, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Digi ConnectCore Security Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-04-26", - "validation_type": "Initial", - "lab": "ACUMEN SECURITY, LLC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-06-12", - "validation_type": "Update", - "lab": "ACUMEN SECURITY, LLC" - } - ], - "vendor_url": "http://www.digi.com", - "vendor": "Digi International, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/AprilConsolidated2018.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 3", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The Digi ConnectCore Security Module provides cryptographic services for Digi International Inc. products.", - "tested_conf": [ - "Yocto Linux 2.4 running on ConnectCore 6/6UL SoM (ARMv7) without PAA (gcc Compiler Version 7.3.0), Yocto Linux 2.4 running on ConnectCore 6/6UL SoM (ARMv7) with PAA (gcc Compiler Version 7.3.0) (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#5323" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1788" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#2053" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1375" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#1397" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#3521" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2851" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#4275" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2686" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "1.0", - "mentioned_certs": { - "2398": 1 - }, - "historical_reason": "186-2 transition", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2297,110 +1005,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", - "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" + "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", + "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "618102f275d5649a", - "cert_id": 3495, + "dgst": "51c94a510b50e68f", + "cert_id": 3141, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Panzura CloudFS™ FIPS Cryptographic Module", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-07-19", - "validation_type": "Initial", - "lab": "ACUMEN SECURITY, LLC" - } - ], - "vendor_url": "http://www.panzura.com/", - "vendor": "Panzura, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security relevant modification to Cert. #2398.", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 3", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "Cryptographic module for use with Panzura products.", - "tested_conf": [ - "PZ Base OS 1.0 x86 on VMware ESXI 6.7 with AES-NI running on Dell PowerEdge R320 with Intel Xeon CPU E5-2430L with PAA", - "PZ Base OS 1.0 x86 on VMware ESXI 6.7 without AES-NI running on Dell PowerEdge R320 with Intel Xeon CPU E5-2430L without PAA", - "PZ Base OS 1.0 x86 running on Dell PowerEdge R320 with Intel Xeon CPU E5-2403 with PAA", - "PZ Base OS 1.0 x86 running on Dell PowerEdge R320 with Intel Xeon CPU E5-2403 without PAA (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#C659" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#C659" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "1.0", - "mentioned_certs": { - "2398": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2454,227 +1088,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", - "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" + "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", + "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "24a05f4d1305d667", - "cert_id": 3493, + "dgst": "5b00a7bb92c1cb19", + "cert_id": 2721, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "LogRhythm FIPS Object Module for OpenSSL", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-07-17", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2020-01-24", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.logrhythm.com", - "vendor": "LogRhythm", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/JulyConsolidated.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When built, installed, protected and initialized as assumed by the Crypto Officer role and as specified in the provided Security Policy. Appendix A of the provided Security Policy specifies the actual distribution tar file containing the source code of this module. There shall be no additions, deletions or alterations to the tar file contents as used during module build. The distribution tar file shall be verified as specified in Appendix A of the provided Security Policy. Installation and protection shall be completed as specified in Appendix A of the provided Security Policy. Initialization shall be invoked as per Section 4 of the provided Security Policy. Any deviation from specified verification, protection, installation and initialization procedures will result in a non FIPS 140-2 compliant module. This validation entry is a non-security-relevant modification to Cert. #2398.", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 3", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The LogRhythm FIPS Object Module for OpenSSL is a general purpose cryptographic module. It is designed for ease of use with the popular OpenSSL cryptographic library and toolkit and is available for use without charge for a wide variety of platforms. The basic validation can also be extended quickly and affordably to accommodate new platforms and many types of modifications.", - "tested_conf": [ - "AIX 6.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V10.1)", - "AIX 6.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", - "AIX 6.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V10.1)", - "AIX 6.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", - "AIX 7.1 32-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", - "AIX 7.1 32-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", - "AIX 7.1 32-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", - "AIX 7.1 64-bit running on IBM POWER 7 (PPC) (IBM XL C/C++ for AIX Compiler Version V13.1)", - "AIX 7.1 64-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", - "AIX 7.1 64-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", - "AIX 7.2 32-bit running on IBM Power7 (PPC) without PAA (IBM XL Compiler V13.1)", - "AIX 7.2 32-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", - "AIX 7.2 32-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", - "AIX 7.2 64-bit running on IBM Power7 (PPC) without PAA (IBM XL Compiler V13.1)", - "AIX 7.2 64-bit running on IBM Power8 (PPC) with PAA (IBM XL Compiler V13.1)", - "AIX 7.2 64-bit running on IBM Power8 (PPC) without PAA (IBM XL Compiler V13.1)", - "Android 5.0 32-bit running on Qualcomm APQ8084 (ARMv7) with PAA (gcc Compiler Version 4.9)", - "Android 5.0 32-bit running on Qualcomm APQ8084 (ARMv7) without PAA (gcc Compiler Version 4.9)", - "Android 5.0 64-bit running on SAMSUNG Exynos7420 (ARMv8) with PAA and Crypto Extensions (gcc Compiler Version 4.9)", - "Android 5.0 64-bit running on SAMSUNG Exynos7420 (ARMv8) without PAA and Crypto Extensions (gcc Compiler Version 4.9)", - "DataGravity Discovery Series OS V2.0 running on Intel Xeon E5-2420 (x86) with PAA (gcc Compiler Version 4.7.2)", - "DataGravity Discovery Series OS V2.0 running on Intel Xeon E5-2420 (x86) without PAA (gcc Compiler Version 4.7.2)", - "Debian 9 running on Intel Atom E3845 (x86) with PAA (gcc Compiler Version 6.3.0)", - "Debian 9 running on Intel Atom E3845 (x86) without PAA (gcc Compiler Version 6.3.0)", - "ExtremeXOS-Linux 3.1 running on Cavium Octeon II (MIPS) (gcc Compiler Version 4.9.2)", - "ExtremeXOS-Linux 3.18 32-bit running on Intel Atom C2558 (x86) with PAA (gcc Compiler Version 4.9.2)", - "ExtremeXOS-Linux 3.18 32-bit running on Intel Atom C2558 (x86) without PAA (gcc Compiler Version 4.9.2)", - "ExtremeXOS-Linux 3.18 running on Cavium Octeon II (MIPS) (gcc Compiler Version 4.9.2)", - "iOS 8.1 32-bit running on Apple A7 (ARMv8) with PAA (clang Compiler Version 600.0.56)", - "iOS 8.1 32-bit running on Apple A7 (ARMv8) without PAA (clang Compiler Version 600.0.56)", - "iOS 8.1 64-bit running on Apple A7 (ARMv8) with PAA and Crypto Extensions (clang Compiler Version 600.0.56)", - "iOS 8.1 64-bit running on Apple A7 (ARMv8) without PAA and Crypto Extensions (clang Compiler Version 600.0.56)", - "Linux 3.10 32-bit running on Intel Atom E3845 (x86) with PAA (gcc Compiler Version 4.8.1)", - "Linux 3.10 32-bit running on Intel Atom E3845 (x86) without PAA (gcc Compiler Version 4.8.1)", - "Linux 3.12 running on NXP T2080 (PPC) (gcc Compiler Version 4.9.2)", - "Microsoft Windows Server 2016 running on a Dell PowerEdge R610 with an Intel Xeon E5620 with PAA (x64) (Microsoft C/C++ Optimizing Compiler Version 19.16)", - "Microsoft Windows Server 2016 running on a Dell PowerEdge R610 with an Intel Xeon E5620 without PAA (x64) (Microsoft C/C++ Optimizing Compiler Version 19.16) (single-user mode)", - "SurfWare 7.2 running on TI c64 DSP (TMS320C6x Compiler Version 6.0.19)", - "TS-Linux 2.4 running on Arm920Tid (ARMv4) (gcc Compiler Version 4.3.2)", - "Ubuntu 12.04 running on Intel Xeon E5-2430L (x86) with PAA (gcc Compiler Version 4.6.3)", - "Ubuntu 12.04 running on Intel Xeon E5-2430L (x86) without PAA (gcc Compiler Version 4.6.3)", - "VxWorks 6.7 running on Intel Core 2 Duo (x86) (gcc Compiler Version 4.1.2)", - "VxWorks 6.9 running on Freescale P2020 (PPC) (gcc Compiler Version 4.3.3)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#3090", - "#3264", - "#3451", - "#3751", - "#3990", - "#4141", - "#4391", - "#4469", - "#C466" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1094", - "#1181", - "#372", - "#472", - "#534", - "#699", - "#814", - "#947", - "#C466" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1027", - "#1182", - "#1256", - "#1414", - "#1451", - "#607", - "#723", - "#845", - "#C466" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1040", - "#1085", - "#1124", - "#1170", - "#1195", - "#896", - "#933", - "#970", - "#C466" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#1050", - "#1091", - "#558", - "#620", - "#698", - "#801", - "#886", - "#952", - "#C466" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#1937", - "#2063", - "#2197", - "#2452", - "#2605", - "#2714", - "#2918", - "#2966", - "#C466" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1581", - "#1664", - "#1766", - "#1928", - "#2048", - "#2258", - "#2374", - "#2444", - "#C466" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#2553", - "#2702", - "#2847", - "#3121", - "#3294", - "#3411", - "#3620", - "#3681", - "#C466" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#1780", - "#1853", - "#1942", - "#2086", - "#2190", - "#2263", - "#2366", - "#2399", - "#C466" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "2.0.16", - "mentioned_certs": { - "2398": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2728,98 +1171,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", - "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" + "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", + "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "69d9200c309f5f97", - "cert_id": 2590, + "dgst": "0ab77c56eece2d24", + "cert_id": 3651, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "CoSign", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-03-24", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.arx.com", - "vendor": "ARX (Algorithmic Research)", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertMarch2016.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "historical", - "level": 3, - "caveat": "When operated in FIPS Mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys.", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "CoSign is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to CoSign from their PC for the purpose of signing documents and data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "CVL": { - "_type": "Set", - "elements": [ - "#697" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1028", - "#98" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2441", - "#2453" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1929" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#3109", - "#3122" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2074", - "#2087" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#2087" - ] - } - }, - "hw_versions": "7.0", - "fw_versions": "7.7", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, - "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2873,69 +1254,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", - "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" + "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", + "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "5b00a7bb92c1cb19", - "cert_id": 2721, + "dgst": "0df59fd6c41cadd2", + "cert_id": 3711, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Red Hat Enterprise Linux Libreswan Cryptographic Module v4.0", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-08-29", - "validation_type": "Initial", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-12-20", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - } - ], - "vendor_url": "http://www.redhat.com", - "vendor": "Red Hat®, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertAug2016.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "With module Red Hat Enterprise Linux NSS Cryptographic Module v4.0 validated to FIPS 140-2 under Cert. #2711 operating in FIPS mode and Red Hat Enterprise Linux 7.1 OpenSSL Module validated to FIPS 140-2 under Cert. #2441 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "Red Hat Enterprise Linux Libreswan Cryptographic Module v4.0 is a software only cryptographic module that provides the IKE protocol version 1 and version 2 key agreement services required for IPSec.", - "tested_conf": [ - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA", - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA", - "Red Hat Enterprise Linux 7.1 running on IBM Power8 Little Endian 8286-41A", - "Red Hat Enterprise Linux 7.1 running on IBM z13 (single-user mode)" - ], - "algorithms": { - "CVL": { - "_type": "Set", - "elements": [ - "#679", - "#680", - "#681" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "4.0", - "mentioned_certs": { - "2711": 1, - "2441": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -2989,162 +1337,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", - "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" + "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", + "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "9ca7b8be590113b8", - "cert_id": 2711, + "dgst": "e1ff7d9997726782", + "cert_id": 3090, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Red Hat Enterprise Linux NSS Cryptographic Module v4.0", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-08-15", - "validation_type": "Initial", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-12-19", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-01-30", - "validation_type": "Update", - "lab": "ATSEC INFORMATION SECURITY CORP" - } - ], - "vendor_url": "http://www.redhat.com", - "vendor": "Red Hat®, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertAug2016.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "Network Security Services (NSS) is a set of open source C libraries designed to support cross-platform development of security-enabled applications. NSS implements major Internet security standards. NSS is available free of charge under a variety of open source compatible licenses. See http://www.mozilla.org/projects/security/pki/nss/", - "tested_conf": [ - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA", - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA", - "Red Hat Enterprise Linux 7.1 running on IBM POWER8 Little Endian 8286-41A", - "Red Hat Enterprise Linux 7.1 running on IBM z13 (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#3604", - "#3605", - "#3606", - "#3607", - "#3608", - "#3609", - "#3610" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#625", - "#626", - "#627", - "#628", - "#629" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#935", - "#936", - "#937", - "#938", - "#940" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1001", - "#1002", - "#1003", - "#1004", - "#1005" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#738", - "#739", - "#740", - "#741", - "#742" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2299", - "#2300", - "#2301", - "#2303", - "#2305" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1853", - "#1854", - "#1855", - "#1856", - "#1857", - "#2031", - "#2032", - "#2033", - "#2034", - "#2035" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#2965", - "#2966", - "#2967", - "#2969", - "#2971" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2006", - "#2007", - "#2008", - "#2009", - "#2010" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "4.0", - "mentioned_certs": {}, - "historical_reason": "SP 800-56Arev3 transition", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -3198,160 +1420,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", - "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" + "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", + "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "cd0666cdd7ce0244", - "cert_id": 3095, + "dgst": "9ca7b8be590113b8", + "cert_id": 2711, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Cryptographic Primitives Library", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-03-22", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.microsoft.com", - "vendor": "Microsoft Corporation", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode with modules Kernel Mode Cryptographic Primitives Library validated to FIPS 140-2 under Cert. #3094 operating in FIPS mode and Code Integrity validated to FIPS 140-2 under Cert. #3093 operating in FIPS mode or Secure Kernel Code Integrity validated to FIPS 140-2 under Cert. #3096 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The Cryptographic Primitives Library (bcryptprimitives.dll and ncryptsslp.dll) provides cryptographic services to Windows components and applications. It includes cryptographic algorithms in an easy-to-use cryptographic module via the Cryptography Next Generation (CNG) API. It can be dynamically linked into applications for the use of general-purpose FIPS 140-2 validated cryptography.", - "tested_conf": [ - "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", - "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", - "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", - "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", - "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", - "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", - "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", - "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", - "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#4624" - ] - }, - "CKG": { - "_type": "Set", - "elements": [] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#1278", - "#1281" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1555" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1223" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#1133" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#3061" - ] - }, - "KAS": { - "_type": "Set", - "elements": [ - "#127" - ] - }, - "KBKDF": { - "_type": "Set", - "elements": [ - "#140" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#4626" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2521", - "#2522" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#3790" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2459" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "10.0.15063", - "mentioned_certs": { - "3094": 1, - "3093": 1, - "3096": 1 - }, - "historical_reason": "SP 800-56Arev3 transition", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -3405,95 +1503,37 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", - "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" + "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", + "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0ab77c56eece2d24", - "cert_id": 3651, + "dgst": "ad1e075215267a28", + "cert_id": 2441, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Secure Kernel Code Integrity", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2020-05-07", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2022-10-05", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.microsoft.com", - "vendor": "Microsoft Corporation", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/May 2020_010620_1115.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "active", - "level": 1, - "caveat": "When operated in FIPS mode with module Windows OS Loader validated to FIPS 140-2 under Cert. #3615 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "Secure Kernel Code Integrity (SKCI) running in the Virtual Secure Mode (VSM) of the Hyper-V hypervisor will only grant execute access to physical pages in the kernel that have been successfully verified. Executable pages will not have write permission outside of Hyper-V. Therefore, only verified code can be executed.", - "tested_conf": [ - "Azure Data Box Edge (x64) running on a Microsoft Azure Data Box with an Intel Xeon Silver without PAA[1]", - "Windows 10 Education October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Book 2 with an Intel Core i7 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Pro LTE with an Intel Core i5 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Microsoft Surface Studio with an Intel Core i7 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on a Samsung Galaxy Book 12\" with an Intel Core i5 without PAA[1]", - "Windows 10 Enterprise October 2018 Update (x64) running on an HP EliteBook x360 1030 G2 with an Intel Core i7 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 12 Rugged Tablet with an Intel Core i5 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Dell Latitude 5290 with an Intel Core i7 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Book 2 with an Intel Core i7 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Go with an Intel Pentium without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Laptop with an Intel Core i5 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Microsoft Surface Pro LTE with an Intel Core i5 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on a Samsung Galaxy Book 10.6\" with an Intel Core m3 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on an HP Elite x2 1013 G3 Tablet with an Intel Core i7 without PAA[1]", - "Windows 10 Pro October 2018 Update (x64) running on an HP Slimline Desktop with an Intel Pentium with PAA[1]", - "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2016 running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", - "Windows Server 2019 Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with an Intel Xeon E5 without PAA[1]", - "Windows Server 2019 Core (x64) running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", - "Windows Server 2019 Datacenter Core (x64) on Hyper-V on Windows Server 2019 running on a Dell Precision Tower 5810MT with an Intel Xeon E5 without PAA[1]", - "Windows Server 2019 Datacenter Core (x64) running on a Dell PowerEdge R740 Server with an Intel Xeon Gold without PAA[1]", - "Windows Server Core Datacenter 2019 RTM (x64) running on an HPE Edgeline EL8000 / ProLiant e910 Server Blade with PAA[2] (single-user mode)" - ], - "algorithms": { - "RSA": { - "_type": "Set", - "elements": [ - "#C2047", - "#C211", - "#C349" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#C2047", - "#C211" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "10.0.17763[1] and 10.0.17763.107[2]", - "mentioned_certs": { - "3615": 1 - }, + "sw_versions": null, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2025-05-06", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, @@ -3546,131 +1586,36 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", - "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" + "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", + "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "3b05cf67849835e3", - "cert_id": 2997, + "dgst": "ceab64951a30f684", + "cert_id": 3197, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "CryptoComply for NSS", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2017-08-18", - "validation_type": "Initial", - "lab": "ACUMEN SECURITY, LLC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-02-07", - "validation_type": "Update", - "lab": "ACUMEN SECURITY, LLC" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-03-29", - "validation_type": "Update", - "lab": "ACUMEN SECURITY, LLC" - } - ], - "vendor_url": "http://www.safelogic.com", - "vendor": "SafeLogic Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/AugConsolidatedCert.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "historical", - "level": 1, - "caveat": "When operated in FIPS mode. The module generates cryptographic keys whose strengths are modified by available entropy. This validation entry is a non-security relevant modification to Cert. #2711", - "exceptions": [ - "Roles, Services, and Authentication: Level 2", - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "SafeLogic's CryptoComply for NSS is designed to provide FIPS 140-2 validated cryptographic functionality and is available for licensing.", - "tested_conf": [ - "Red Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 with PAA\nRed Hat Enterprise Linux 7.1 running on HP ProLiant DL380p Gen8 without PAA\n (single-user mode)" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#3605", - "#3606", - "#3607", - "#3609" - ] - }, - "CVL": { - "_type": "Set", - "elements": [ - "#626", - "#627" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#936", - "#937" - ] - }, - "DSA": { - "_type": "Set", - "elements": [ - "#1002", - "#1003" - ] - }, - "ECDSA": { - "_type": "Set", - "elements": [ - "#739", - "#740" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2300", - "#2301" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#1854", - "#1855", - "#2034", - "#2035" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#2966", - "#2967" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2007", - "#2008" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "4.0", - "mentioned_certs": { - "2711": 1 - }, - "historical_reason": "Moved to historical list due to sunsetting", + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -3724,8 +1669,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", - "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" + "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", + "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" } }, { @@ -3734,96 +1679,26 @@ "cert_id": 2779, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "DocuSign Signature Appliance", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2016-10-25", - "validation_type": "Initial", - "lab": "CYGNACOM SOLUTIONS INC" - } - ], - "vendor_url": "http://www.docusign.com", - "vendor": "DocuSign, Inc.", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140ConsolidatedCertOct2016.pdf", - "module_type": "Hardware", - "standard": "FIPS 140-2", - "status": "historical", - "level": 3, - "caveat": "When operated in FIPS mode. This module contains the embedded module eToken 5105 validated to FIPS 140-2 under Cert. #1883 operating in FIPS mode. No assurance of the minimum strength of generated keys", - "exceptions": [ - "Mitigation of Other Attacks: N/A" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "The DocuSign Signature Appliance is a digital signature appliance that is connected to the organizational network and manages all signature keys and certificates of organization's end-users. End-users will connect securely to the appliance from their PC for the purpose of signing documents and data.", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, "tested_conf": null, - "algorithms": { - "CVL": { - "_type": "Set", - "elements": [ - "#787" - ] - }, - "DRBG": { - "_type": "Set", - "elements": [ - "#1203", - "#98" - ] - }, - "HMAC": { - "_type": "Set", - "elements": [ - "#2564", - "#2629" - ] - }, - "KTS": { - "_type": "Set", - "elements": [ - "#2161", - "#2564" - ] - }, - "PBKDF": { - "_type": "Set", - "elements": [] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2068" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#1465", - "#3249", - "#3324" - ] - }, - "Triple-DES": { - "_type": "Set", - "elements": [ - "#2161", - "#2206" - ] - }, - "Triple-DES MAC": { - "_type": "Set", - "elements": [ - "#2206" - ] - } - }, - "hw_versions": "8.0", - "fw_versions": "8.1", + "algorithms": null, + "hw_versions": null, + "fw_versions": null, "sw_versions": null, - "mentioned_certs": { - "1883": 1 - }, - "historical_reason": "Moved to historical list due to dependency on certificate #1883", + "mentioned_certs": null, + "historical_reason": null, "date_sunset": null, "revoked_reason": null, "revoked_link": null @@ -3883,101 +1758,31 @@ }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "f7df34ce0a0ad316", - "cert_id": 3093, + "dgst": "3b05cf67849835e3", + "cert_id": 2997, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": "Code Integrity", - "validation_history": [ - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2018-03-22", - "validation_type": "Initial", - "lab": "LEIDOS CSTL" - }, - { - "_type": "sec_certs.sample.fips.FIPSCertificate.ValidationHistoryEntry", - "date": "2019-06-17", - "validation_type": "Update", - "lab": "LEIDOS CSTL" - } - ], - "vendor_url": "http://www.microsoft.com", - "vendor": "Microsoft Corporation", - "certificate_pdf_url": "https://csrc.nist.gov/CSRC/media/projects/cryptographic-module-validation-program/documents/certificates/FIPS140-2-ConsolidatedMarch2018.pdf", - "module_type": "Software", - "standard": "FIPS 140-2", - "status": "active", - "level": 1, - "caveat": "When operated in FIPS mode with modules Windows OS Loader validated to FIPS 140-2 under Cert. #3090 operating in FIPS mode or Windows Resume validated to FIPS 140-2 under Cert. #3091 operating in FIPS mode", - "exceptions": [ - "Physical Security: N/A", - "Design Assurance: Level 2" - ], - "embodiment": "Multi-Chip Stand Alone", - "description": "Code Integrity (ci.dll) verifies the integrity of executable files, including kernel mode drivers, critical system components, and user mode cryptographic modules as they are loaded into memory from the disk.", - "tested_conf": [ - "Surface Hub (x64) running on a Microsoft Surface Hub with PAA", - "Windows 10 Education Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface 3 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Book with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro 4 with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Enterprise Creators Update (x64) running on a Microsoft Surface Studio with PAA", - "Windows 10 Enterprise Creators Update (x64) running on an HP Pro x2 612 G2 Detachable PC with LTE with PAA", - "Windows 10 Home Creators Update (x86) running on a Dell Inspiron 660s without PAA", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 650", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950", - "Windows 10 Mobile Creators Update (ARMv7) running on a Microsoft Lumia 950 XL", - "Windows 10 Mobile Creators Update (ARMv7) running on an HP Elite x3", - "Windows 10 Pro Creators Update (x64) on Hyper-V on Windows Server 2016 running on a Surface Pro 4 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Latitude 5285 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell PowerEdge R630 Server with PAA", - "Windows 10 Pro Creators Update (x64) running on a Dell Precision Tower 5810MT with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface 3 with LTE with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Laptop with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro 3 with PAA", - "Windows 10 Pro Creators Update (x64) running on a Microsoft Surface Pro with PAA", - "Windows 10 Pro Creators Update (x64) running on a Panasonic Toughbook with PAA (single-user mode)", - "Windows 10 Pro Creators Update (x64) running on an HP Compaq Pro 6305 with PAA", - "Windows 10 Pro Creators Update (x64) running on an HP Slimline Desktop with PAA", - "Windows 10 S Creators Update (x64) running on a Microsoft Surface Laptop with PAA" - ], - "algorithms": { - "AES": { - "_type": "Set", - "elements": [ - "#4624", - "#5300" - ] - }, - "RSA": { - "_type": "Set", - "elements": [ - "#2522", - "#2523", - "#2844", - "#2846" - ] - }, - "SHS": { - "_type": "Set", - "elements": [ - "#3790", - "#4253" - ] - } - }, + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "algorithms": null, "hw_versions": null, "fw_versions": null, - "sw_versions": "10.0.15063 and 10.0.15063.728", - "mentioned_certs": { - "3090": 1, - "3091": 1 - }, + "sw_versions": null, + "mentioned_certs": null, "historical_reason": null, - "date_sunset": "2023-03-21", + "date_sunset": null, "revoked_reason": null, "revoked_link": null }, @@ -4030,8 +1835,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", - "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" + "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", + "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" } } ] From 2871384595b73c085632a8c758764867f46aecfb Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 14:54:47 +0100 Subject: [PATCH 29/81] refactor algorithm extraction from policy tables --- sec_certs/cert_rules.py | 1 + sec_certs/dataset/fips.py | 47 ++++++----------- sec_certs/sample/fips.py | 89 ++++++++++++--------------------- sec_certs/utils/tables.py | 66 ++++++++++++------------ tests/fips/test_fips_dataset.py | 2 +- 5 files changed, 80 insertions(+), 125 deletions(-) diff --git a/sec_certs/cert_rules.py b/sec_certs/cert_rules.py index 764c24e7..44e0e911 100644 --- a/sec_certs/cert_rules.py +++ b/sec_certs/cert_rules.py @@ -196,6 +196,7 @@ } FIPS_ALGS_IN_TABLE = r"(?:#[CcAa]?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P\d+)" +FIPS_LIST_OF_TABLES = re.compile(r"^(?:(?:[Tt]able\s|[Ll]ist\s)(?:[Oo]f\s))[Tt]ables[\s\S]+?\f", re.MULTILINE) def _load(): diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 6292b6c3..79d0673d 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -2,7 +2,7 @@ import logging import shutil from pathlib import Path -from typing import Dict, Final, List, Set +from typing import Dict, Final, Set import numpy as np import pandas as pd @@ -73,11 +73,15 @@ def _extract_data_from_html_modules(self, fresh: bool = True) -> None: @serialize def _extract_data(self, fresh: bool = True) -> None: - # TODO: Update state of extraction properly, atm completely ignored. + if fresh: + for cert in self: + cert.state.policy_extract_ok = True + cert.state.module_extract_ok = True + self._extract_data_from_html_modules(fresh) self._extract_policy_pdf_metadata(fresh) self._extract_policy_pdf_keywords(fresh) - self.extract_certs_from_tables(fresh) + self._get_algorithms_from_policy_tables(fresh) def _extract_policy_pdf_keywords(self, fresh: bool = True) -> None: certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] @@ -206,38 +210,16 @@ def _process_algorithms(self): self.algorithms.get_certs_from_web() logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") - @serialize - def extract_certs_from_tables(self, fresh: bool = True, high_precision: bool = True) -> List[Path]: - """ - Function that extracts algorithm IDs from tables in security policies files. - :return: list of files that couldn't have been decoded - """ - # TODO: Refactor me - logger.info("Entering table scan.") - result = cert_processing.process_parallel( - FIPSCertificate.analyze_tables, - [(cert, high_precision) for cert in self.certs.values() if cert is not None and high_precision], - # TODO: Below is an old version with tables_done, txt_state attribute that we already deleted, rewrite without it - # FIPSCertificate.analyze_tables - # [ - # (cert, high_precision) - # for cert in self.certs.values() - # if cert is not None and (not cert.state.tables_done or high_precision) and cert.state.txt_state - # ], - config.n_threads // 4, # tabula already processes by parallel, so - # it's counterproductive to use all threads + def _get_algorithms_from_policy_tables(self, fresh: bool = True): + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + cert_processing.process_parallel( + FIPSCertificate.get_algorithms_from_policy_tables, + certs_to_process, + config.n_threads, use_threading=False, - progress_bar_desc="Searching tables", + progress_bar_desc="Extracting Algorithms from policy tables", ) - not_decoded = [cert.state.sp_path for done, cert, _ in result if done is False] - for state, cert, algorithms in result: - certificate = self.certs[cert.dgst] - # TODO: Fix me, attribute below deleted - # certificate.state.tables_done = state - certificate.pdf_data.algorithms = algorithms - return not_decoded - def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: # TODO: Refactor me for cert in self.certs.values(): @@ -370,6 +352,7 @@ def web_lookup(cert): ) def _compute_heuristics(self): + # TODO: Check in what state the cert must be in order to compute heuristics. Enforce it in the super method? logger.info("Computing various statistics from processed certificates.") super()._compute_heuristics() self._unify_algorithms() diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 614447f6..156b1b7e 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -1,6 +1,7 @@ from __future__ import annotations import copy +import itertools import re from dataclasses import dataclass, field from datetime import date @@ -17,9 +18,9 @@ import sec_certs.utils.extract import sec_certs.utils.helpers as helpers import sec_certs.utils.pdf -import sec_certs.utils.tables +import sec_certs.utils.pdf as pdf +import sec_certs.utils.tables as tables from sec_certs.cert_rules import FIPS_ALGS_IN_TABLE, fips_rules -from sec_certs.config.configuration import config from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics from sec_certs.sample.certificate import PdfData as BasePdfData @@ -276,10 +277,18 @@ def policy_is_ok_to_convert(self, fresh: bool = True) -> bool: return self.policy_download_ok if fresh else self.policy_download_ok and not self.policy_convert_ok def module_is_ok_to_analyze(self, fresh: bool = True) -> bool: - return self.module_download_ok if fresh else self.module_download_ok and not self.module_extract_ok + return ( + self.module_download_ok and self.module_extract_ok + if fresh + else self.module_download_ok and not self.module_extract_ok + ) def policy_is_ok_to_analyze(self, fresh: bool = True) -> bool: - return self.policy_convert_ok if fresh else self.policy_convert_ok and not self.policy_extract_ok + return ( + self.policy_convert_ok and self.policy_extract_ok + if fresh + else self.policy_convert_ok and not self.policy_extract_ok + ) def set_local_paths(self, policies_pdf_dir: Path, policies_txt_dir: Path, modules_html_dir: Path) -> None: self.state.policy_pdf_path = (policies_pdf_dir / str(self.dgst)).with_suffix(".pdf") @@ -548,8 +557,14 @@ def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" - _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) - cert.pdf_data.st_metadata = metadata if metadata else dict() + status, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) + + if metadata: + cert.pdf_data.st_metadata = metadata + else: + cert.pdf_data.st_metadata = dict() + cert.state.policy_extract_ok = False + cert.state.errors.append(status) return cert @staticmethod @@ -562,60 +577,18 @@ def extract_policy_pdf_keywords(cert: FIPSCertificate) -> FIPSCertificate: return cert @staticmethod - def analyze_tables( # noqa: C901 - tup: Tuple[FIPSCertificate, bool] - ) -> Tuple[bool, FIPSCertificate, Set[FIPSAlgorithm]]: - """ - Searches for tables in pdf documents of the instance. - - :param tup: certificate object, whether to use high precision results or approx. results - :return: True on success / False otherwise, modified cert object, List of processed tables. - """ - - def extract_algorithm_certificates(current_text): - set_items = set() - reg = r"(?:#?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P[CcAa]? ?\d+)" - for m in re.finditer(reg, current_text): - set_items.add(m.group()) - return set(map(FIPSAlgorithm, set_items)) - - cert, precision = tup - # TODO: Not sure what this was for, fixme - # if (not precision and cert.state.tables_done) or ( - # precision and cert.heuristics.unmatched_algs < config.cert_threshold - # ): - # return cert.state.tables_done, cert, set() - - cert_file = cert.state.policy_pdf_path - txt_file = cert_file.with_suffix(".pdf.txt") - with open(txt_file, "r", encoding="utf-8") as f: - tables = sec_certs.utils.tables.find_tables(f.read(), txt_file) - all_pages = precision and cert.heuristics.unmatched_algs > config.cert_threshold # bool value - - lst: Set = set() - if tables: + def get_algorithms_from_policy_tables(cert: FIPSCertificate): + if table_rich_page_numbers := tables.find_pages_with_tables(cert.state.policy_txt_path): + pdf.repair_pdf(cert.state.policy_pdf_path) try: - data = read_pdf(cert_file, pages="all" if all_pages else tables, silent=True) + tabular_data = read_pdf(cert.state.policy_pdf_path, pages=table_rich_page_numbers, silent=True) except Exception as e: - try: - logger.warn(e) - sec_certs.utils.pdf.repair_pdf(cert_file) - data = read_pdf(cert_file, pages="all" if all_pages else tables, silent=True) - - except Exception as ex: - logger.warn(ex) - return False, cert, lst - - # find columns with cert numbers - for df in data: - for col in range(len(df.columns)): - if "cert" in df.columns[col].lower() or "algo" in df.columns[col].lower(): - tmp = extract_algorithm_certificates(df.iloc[:, col].to_string(index=False)) - lst.update(tmp) - # Parse again if someone picks not so descriptive column names - tmp = extract_algorithm_certificates(df.to_string(index=False)) - lst.update(tmp) - return True, cert, lst + logger.warning(f"Error when parsing tables from {cert.dgst}: {e}") + cert.state.policy_extract_ok = False + + algorithms_str = set(itertools.chain.from_iterable([tables.get_algs_from_table(df) for df in tabular_data])) + algorithms = {FIPSAlgorithm(x) for x in algorithms_str} + cert.pdf_data.algorithms = cert.pdf_data.algorithms.union(algorithms) def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: pass diff --git a/sec_certs/utils/tables.py b/sec_certs/utils/tables.py index c397eea1..431c8346 100644 --- a/sec_certs/utils/tables.py +++ b/sec_certs/utils/tables.py @@ -1,27 +1,28 @@ import logging import re from pathlib import Path -from typing import List, Optional, Set, Union +from typing import Set + +from sec_certs.cert_rules import FIPS_LIST_OF_TABLES logger = logging.getLogger(__name__) -def parse_list_of_tables(txt: str) -> Set[str]: +def parse_list_of_tables(txt: str) -> Set[int]: """ - Parses list of tables from function find_tables(), finds ones that mention algorithms - :param txt: chunk of text - :return: set of all pages mentioning algorithm table + Parses list of tables in policy txt, returns page numbers of tables that mention algorithms """ rr = re.compile(r"^.+?(?:[Ff]unction|[Aa]lgorithm|[Ss]ecurity [Ff]unctions?).+?(?P\d+)$", re.MULTILINE) - pages = set() - for m in rr.finditer(txt): - pages.add(m.group("page_num")) - return pages + return {int(m.group("page_num")) for m in rr.finditer(txt)} -def find_tables_iterative(file_text: str) -> List[int]: +def get_table_rich_page_numbers_from_footer(file_text: str) -> Set[int]: + """ + Parses page numbers of policy txt pages that may contain tables with algorithm data + """ current_page = 1 pages = set() + for line in file_text.split("\n"): if "\f" in line: current_page += 1 @@ -30,34 +31,31 @@ def find_tables_iterative(file_text: str) -> List[int]: pages.add(current_page + 1) if current_page > 2: pages.add(current_page - 1) - if not pages: - logger.warning("No pages found") + for page in pages: if page > current_page - 1: - return list(pages - {page}) + return pages - {page} - return list(pages) + return pages -def find_tables(txt: str, file_name: Path) -> Optional[Union[List[str], List[int]]]: +def find_pages_with_tables(txt_filepath: Path) -> Set[int]: """ - Function that tries to pages in security policy pdf files, where it's possible to find a table containing - algorithms - :param txt: file in .txt format (output of pdftotext) - :param file_name: name of the file - :return: list of pages possibly containing a table - None if these cannot be found + Identifies pages in txt file that may contain tables. Return their page numbers. """ - # Look for "List of Tables", where we can find exactly tables with page num - tables_regex = re.compile(r"^(?:(?:[Tt]able\s|[Ll]ist\s)(?:[Oo]f\s))[Tt]ables[\s\S]+?\f", re.MULTILINE) - table = tables_regex.search(txt) - if table: - rb = parse_list_of_tables(table.group()) - if rb: - return list(rb) - return None - - # Otherwise look for "Table" in text and \f representing footer, then extract page number from footer - logger.info(f"parsing tables in {file_name}") - table_page_indices = find_tables_iterative(txt) - return table_page_indices if table_page_indices else None + with txt_filepath.open("r", encoding="utf-8") as handle: + txt = handle.read() + + # Parse page numbers from list of tables if available + # Else look for "Table" in text and \f representing footer, then extract page number from footer + if list_of_tables := FIPS_LIST_OF_TABLES.search(txt): + result = parse_list_of_tables(list_of_tables.group()) + else: + result = get_table_rich_page_numbers_from_footer(txt) + + return result if result else set() + + +def get_algs_from_table(dataframe_text: str) -> Set[str]: + reg = r"(?:#?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P[CcAa]? ?\d+)" + return {m.group() for m in re.finditer(reg, dataframe_text)} diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 7bc507be..df8eb009 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -92,7 +92,7 @@ def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path dataset.download_all_artifacts(set(certs)) dataset.convert_all_pdfs() dataset._extract_data() - dataset.extract_certs_from_tables(high_precision=True) + dataset._get_algorithms_from_policy_tables() dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) return dataset From 048f3f69bc62ce4e39fe2eb26ef746c4c193375a Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 15:09:17 +0100 Subject: [PATCH 30/81] delete InternalState.errors of cert objects --- sec_certs/sample/common_criteria.py | 19 ------------------- sec_certs/sample/fips.py | 6 ------ tests/data/cc/analysis/cc_full_dataset.json | 1 - .../data/cc/analysis/dependency_dataset.json | 3 --- .../dependency_vulnerability_dataset.json | 3 --- .../data/cc/analysis/vulnerable_dataset.json | 3 +-- tests/data/cc/certificate/fictional_cert.json | 1 - .../maintenances/Maintenance updates.json | 1 - tests/data/cc/dataset/toy_dataset.json | 3 --- 9 files changed, 1 insertion(+), 39 deletions(-) diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py index 4b21be19..79488f03 100644 --- a/sec_certs/sample/common_criteria.py +++ b/sec_certs/sample/common_criteria.py @@ -112,8 +112,6 @@ class InternalState(ComplexSerializableType): st_extract_ok: bool # Whether target extraction went OK report_extract_ok: bool # Whether report extraction went OK - errors: List[str] - st_pdf_hash: Optional[str] report_pdf_hash: Optional[str] st_txt_hash: Optional[str] @@ -134,7 +132,6 @@ def __init__( report_convert_ok: bool = False, st_extract_ok: bool = False, report_extract_ok: bool = False, - errors: Optional[List[str]] = None, st_pdf_hash: Optional[str] = None, report_pdf_hash: Optional[str] = None, st_txt_hash: Optional[str] = None, @@ -149,7 +146,6 @@ def __init__( self.report_convert_ok = report_convert_ok self.st_extract_ok = st_extract_ok self.report_extract_ok = report_extract_ok - self.errors = errors if errors else [] self.st_pdf_hash = st_pdf_hash self.report_pdf_hash = report_pdf_hash self.st_txt_hash = st_txt_hash @@ -166,7 +162,6 @@ def serialized_attributes(self) -> List[str]: "report_convert_ok", "st_extract_ok", "report_extract_ok", - "errors", "st_pdf_hash", "report_pdf_hash", "st_txt_hash", @@ -788,7 +783,6 @@ def download_pdf_report(cert: CommonCriteriaCert) -> CommonCriteriaCert: error_msg = f"failed to download report from {cert.report_link}, code: {exit_code}" logger.error(f"Cert dgst: {cert.dgst} " + error_msg) cert.state.report_download_ok = False - cert.state.errors.append(error_msg) else: cert.state.report_download_ok = True cert.state.report_pdf_hash = helpers.get_sha256_filepath(cert.state.report_pdf_path) @@ -812,7 +806,6 @@ def download_pdf_st(cert: CommonCriteriaCert) -> CommonCriteriaCert: error_msg = f"failed to download ST from {cert.st_link}, code: {exit_code}" logger.error(f"Cert dgst: {cert.dgst} " + error_msg) cert.state.st_download_ok = False - cert.state.errors.append(error_msg) else: cert.state.st_download_ok = True cert.state.st_pdf_hash = helpers.get_sha256_filepath(cert.state.st_pdf_path) @@ -837,7 +830,6 @@ def convert_report_pdf(cert: CommonCriteriaCert) -> CommonCriteriaCert: if not ok_result: error_msg = "failed to convert report pdf->txt" logger.error(f"Cert dgst: {cert.dgst} " + error_msg) - cert.state.errors.append(error_msg) else: cert.state.report_txt_hash = helpers.get_sha256_filepath(cert.state.report_txt_path) return cert @@ -858,7 +850,6 @@ def convert_st_pdf(cert: CommonCriteriaCert) -> CommonCriteriaCert: if not ok_result: error_msg = "failed to convert security target pdf->txt" logger.error(f"Cert dgst: {cert.dgst} " + error_msg) - cert.state.errors.append(error_msg) else: cert.state.st_txt_hash = helpers.get_sha256_filepath(cert.state.st_txt_path) return cert @@ -874,7 +865,6 @@ def extract_st_pdf_metadata(cert: CommonCriteriaCert) -> CommonCriteriaCert: response, cert.pdf_data.st_metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.st_pdf_path) if response != constants.RETURNCODE_OK: cert.state.st_extract_ok = False - cert.state.errors.append(response) else: cert.state.st_extract_ok = True return cert @@ -890,7 +880,6 @@ def extract_report_pdf_metadata(cert: CommonCriteriaCert) -> CommonCriteriaCert: response, cert.pdf_data.report_metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.report_pdf_path) if response != constants.RETURNCODE_OK: cert.state.report_extract_ok = False - cert.state.errors.append(response) else: cert.state.report_extract_ok = True return cert @@ -910,10 +899,6 @@ def extract_st_pdf_frontpage(cert: CommonCriteriaCert) -> CommonCriteriaCert: if response != constants.RETURNCODE_OK: cert.state.st_extract_ok = False - if not cert.state.errors: - cert.state.errors = [] - cert.state.errors.append(response) - return cert @staticmethod @@ -931,10 +916,6 @@ def extract_report_pdf_frontpage(cert: CommonCriteriaCert) -> CommonCriteriaCert if response != constants.RETURNCODE_OK: cert.state.report_extract_ok = False - if not cert.state.errors: - cert.state.errors = [] - cert.state.errors.append(response) - return cert @staticmethod diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 156b1b7e..44df4760 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -230,8 +230,6 @@ class InternalState(ComplexSerializableType): policy_txt_path: Path module_html_path: Path - errors: List[str] - def __init__( self, module_download_ok: bool = False, @@ -242,7 +240,6 @@ def __init__( policy_extract_ok: bool = False, policy_pdf_hash: Optional[str] = None, policy_txt_hash: Optional[str] = None, - errors: Optional[List[str]] = None, ): self.module_download_ok = module_download_ok self.policy_download_ok = policy_download_ok @@ -252,7 +249,6 @@ def __init__( self.policy_extract_ok = policy_extract_ok self.policy_pdf_hash = policy_pdf_hash self.policy_txt_hash = policy_txt_hash - self.errors = errors if errors else [] @property def serialized_attributes(self) -> List[str]: @@ -548,7 +544,6 @@ def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: if not ok_result: error_msg = "Failed to convert policy pdf->txt" logger.error(f"Cert dgst: {cert.dgst}" + error_msg) - cert.state.errors.append(error_msg) else: cert.state.policy_txt_hash = helpers.get_sha256_filepath(cert.state.policy_txt_path) @@ -564,7 +559,6 @@ def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: else: cert.pdf_data.st_metadata = dict() cert.state.policy_extract_ok = False - cert.state.errors.append(status) return cert @staticmethod diff --git a/tests/data/cc/analysis/cc_full_dataset.json b/tests/data/cc/analysis/cc_full_dataset.json index 4322f5a3..479f04b0 100644 --- a/tests/data/cc/analysis/cc_full_dataset.json +++ b/tests/data/cc/analysis/cc_full_dataset.json @@ -62,7 +62,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "f4ab8c62a2325cc634eef164a9707d2375f2e3d5c5ab9791b7c00a6034e64b62", "report_pdf_hash": "1954f42e857c02a156caf8fea7abd36ee0a5953fe0e319c4ec749af277fcfb4b", "st_txt_hash": "c8b4c5667a3f60edc845051e5a31a2d17b9d9a11df9e56dd89681d25e727a622", diff --git a/tests/data/cc/analysis/dependency_dataset.json b/tests/data/cc/analysis/dependency_dataset.json index eb6b7d7b..a1dc28f7 100644 --- a/tests/data/cc/analysis/dependency_dataset.json +++ b/tests/data/cc/analysis/dependency_dataset.json @@ -50,7 +50,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "16f1c7e32517d22f6062edf439f8c67eb0d202fadaaf1c54c1f96af7f74ad7ac", "report_pdf_hash": "2dc5ff15361621bb2bd7db79491b88a70049abe4ffbe8370de87f9c51f42fb50", "st_txt_hash": "81c53d1e5b1c2fcb129ce1053d13cd1308f7a556921f0b9024cedf75c6b2efb7", @@ -610,7 +609,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "8f288bc6133855bba6b78ccaeff86b46b4ce1db86aa1b5141c1073a74e6d13fd", "report_pdf_hash": "d77dffc3f2e4d95d6103de31b7ebbec54551c3b93f01415971ec3b9f5cb33e4f", "st_txt_hash": "926668bea7c427a4fcf82857bfc63420f3597b6bff39699927a58f335620eaac", @@ -1248,7 +1246,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "c72b032f6119e6dd64270296713d5626b1473bf4925018ce4dd106d76953213f", "report_pdf_hash": "eca6c3a665d8bd20394cf002bff0a8a1b451337f60c8184d458ffd5b31491085", "st_txt_hash": "179b07b4fc7402066a884edea494b28e324315108a5e0820184031f2e2062ad5", diff --git a/tests/data/cc/analysis/dependency_vulnerability_dataset.json b/tests/data/cc/analysis/dependency_vulnerability_dataset.json index dbf16833..7c2e82f9 100644 --- a/tests/data/cc/analysis/dependency_vulnerability_dataset.json +++ b/tests/data/cc/analysis/dependency_vulnerability_dataset.json @@ -60,7 +60,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "5bb93f7f3f08f30ba41abb003a2f8ce2609c385af82b863fbc0b19bd0c21a701", "report_pdf_hash": "ee1ebada2c91c5a000c8d112e9e3742d09cad4c920d3f3e2a9beb01f16c69bb6", "st_txt_hash": "66271d8bf0b581a2f189301438f2aee13ff3da0bb0bb180bcf518261eb695496", @@ -1366,7 +1365,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "4879ac5fdf9569ad88089df0122acd1c8a8f7252ed8f36aace14bbb0b805b758", "report_pdf_hash": "63e6ac157e08ed37f9861458c66c015663e17fb8936746d7ae487963bdd455c7", "st_txt_hash": "f7f7b8f31dddde3f0756cde8843061f01b606bdf266eca71dbcc56b3672d1db5", @@ -2317,7 +2315,6 @@ "report_convert_ok": true, "st_extract_ok": true, "report_extract_ok": true, - "errors": [], "st_pdf_hash": "1a5e4c1382e284da88d93aa5557c7728c14b3fce73d81e4cf731ab24cee9ffdd", "report_pdf_hash": "f10c85194adae814703781471f3a3de713383d8a9fbf5389fc7106958a8aaf90", "st_txt_hash": "90b8e48add278faea4668eccba591d3992bf782669cca1b0a63bf6f21b514cd9", diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json index 24441d7d..f3e9c643 100644 --- a/tests/data/cc/analysis/vulnerable_dataset.json +++ b/tests/data/cc/analysis/vulnerable_dataset.json @@ -43,8 +43,7 @@ "st_convert_ok": true, "report_convert_ok": true, "st_extract_ok": true, - "report_extract_ok": true, - "errors": [] + "report_extract_ok": true }, "pdf_data": { "_type": "sec_certs.sample.common_criteria.CommonCriteriaCert.PdfData", diff --git a/tests/data/cc/certificate/fictional_cert.json b/tests/data/cc/certificate/fictional_cert.json index 117f87d2..b1b225cb 100644 --- a/tests/data/cc/certificate/fictional_cert.json +++ b/tests/data/cc/certificate/fictional_cert.json @@ -49,7 +49,6 @@ "report_convert_ok": false, "st_extract_ok": false, "report_extract_ok": false, - "errors": [], "st_pdf_hash": null, "report_pdf_hash": null, "st_txt_hash": null, diff --git a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json index a14e39a7..5f52950a 100644 --- a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json +++ b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json @@ -29,7 +29,6 @@ "report_convert_ok": false, "st_extract_ok": false, "report_extract_ok": false, - "errors": [], "st_pdf_hash": "d42e4364d037ba742fcd4050a9a84d0e6300f93eb68bcfe8c61f72c429c9ceca", "report_pdf_hash": "80bada65614c1b037c13efa78996a8910700d0e05a3ca217286f76d7dacefe62", "st_txt_hash": null, diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json index cb0f0f41..13055120 100644 --- a/tests/data/cc/dataset/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -52,7 +52,6 @@ "report_convert_ok": false, "st_extract_ok": false, "report_extract_ok": false, - "errors": [], "st_pdf_hash": null, "report_pdf_hash": null, "st_txt_hash": null, @@ -140,7 +139,6 @@ "report_convert_ok": false, "st_extract_ok": false, "report_extract_ok": false, - "errors": [], "st_pdf_hash": null, "report_pdf_hash": null, "st_txt_hash": null, @@ -236,7 +234,6 @@ "report_convert_ok": false, "st_extract_ok": false, "report_extract_ok": false, - "errors": [], "st_pdf_hash": null, "report_pdf_hash": null, "st_txt_hash": null, From 4e582e13871307e50587305784b3f80e0f4fb5dd Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 16:39:56 +0100 Subject: [PATCH 31/81] deduplicate FIPSAlgorithm data structures --- sec_certs/dataset/fips.py | 33 +- sec_certs/sample/fips.py | 33 +- tests/data/fips/certificate/crt.json | 78 +++++ .../data/fips/certificate/fictional_cert.json | 5 - tests/data/fips/dataset/toy_dataset.json | 288 ++++++------------ tests/fips/test_fips_certificate.py | 2 +- tests/fips/test_fips_dataset.py | 2 +- 7 files changed, 191 insertions(+), 250 deletions(-) create mode 100644 tests/data/fips/certificate/crt.json diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 79d0673d..faede285 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -7,7 +7,6 @@ import numpy as np import pandas as pd from bs4 import BeautifulSoup, NavigableString -from graphviz import Digraph from sec_certs import constants from sec_certs.config.configuration import config @@ -81,7 +80,7 @@ def _extract_data(self, fresh: bool = True) -> None: self._extract_data_from_html_modules(fresh) self._extract_policy_pdf_metadata(fresh) self._extract_policy_pdf_keywords(fresh) - self._get_algorithms_from_policy_tables(fresh) + self._extract_algorithms_from_policy_tables(fresh) def _extract_policy_pdf_keywords(self, fresh: bool = True) -> None: certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] @@ -210,7 +209,7 @@ def _process_algorithms(self): self.algorithms.get_certs_from_web() logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") - def _get_algorithms_from_policy_tables(self, fresh: bool = True): + def _extract_algorithms_from_policy_tables(self, fresh: bool = True): certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] cert_processing.process_parallel( FIPSCertificate.get_algorithms_from_policy_tables, @@ -236,16 +235,6 @@ def _extract_policy_pdf_metadata(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - # TODO: Refactor me - def _unify_algorithms(self) -> None: - pass - # for certificate in self.certs.values(): - # certificate.heuristics.algorithms = set() - # if certificate.web_data.algorithms: - # certificate.heuristics.algorithms.update(certificate.web_data.algorithms) - # if certificate.pdf_data.algorithms: - # certificate.heuristics.algorithms.update(certificate.pdf_data.algorithms) - def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> bool: other_dgst = fips_dgst(other_id) other_cert = self.certs[other_dgst] @@ -351,24 +340,6 @@ def web_lookup(cert): self.certs[dgst].heuristics, "web_references", finder.predict_single_cert(dgst, keep_unknowns=False) ) - def _compute_heuristics(self): - # TODO: Check in what state the cert must be in order to compute heuristics. Enforce it in the super method? - logger.info("Computing various statistics from processed certificates.") - super()._compute_heuristics() - self._unify_algorithms() - - def _highlight_vendor_in_dot(self, dot: Digraph, current_dgst: str, highlighted_vendor: str) -> None: - current_cert = self.certs[current_dgst] - - if current_cert.web_data.vendor != highlighted_vendor: - return - - dot.attr("node", color="red") - if current_cert.web_data.status == "Revoked": - dot.attr("node", color="grey32") - if current_cert.web_data.status == "Historical": - dot.attr("node", color="gold3") - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame([x.pandas_tuple for x in self.certs.values()], columns=FIPSCertificate.pandas_columns) df = df.set_index("dgst") diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 44df4760..76df0636 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -36,7 +36,7 @@ class FIPSHTMLParser: def __init__(self, soup: BeautifulSoup): self._soup = soup - def build_web_data(self) -> FIPSCertificate.WebData: + def get_web_data_and_algorithms(self) -> Tuple[Set[FIPSAlgorithm], FIPSCertificate.WebData]: divs = self._soup.find_all("div", class_="panel panel-default") details_div, vendor_div, related_files_div, validation_history_div = divs @@ -48,15 +48,25 @@ def build_web_data(self) -> FIPSCertificate.WebData: assert validation_history_div.find("h4").text == "Validation History" details_dict = self._build_details_dict(details_div) + vendor_dict = self._build_vendor_dict(vendor_div) related_files_dict = self._build_related_files_dict(related_files_div) validation_history_dict = self._build_validation_history_dict(validation_history_div) - return FIPSCertificate.WebData( + + if "algorithms" in details_dict: + algorithms_data = details_dict.pop("algorithms") + algorithms = set() + for category, alg_ids in algorithms_data.items(): + algorithms |= {FIPSAlgorithm(x, algorithm_type=category) for x in alg_ids} + + else: + algorithms = set() + + return algorithms, FIPSCertificate.WebData( **{**details_dict, **vendor_dict, **related_files_dict, **validation_history_dict} ) - @classmethod - def _build_details_dict(cls, details_div: Tag) -> Dict[str, Any]: + def _build_details_dict(self, details_div: Tag) -> Dict[str, Any]: def parse_single_detail_entry(key, entry): normalized_key = DETAILS_KEY_NORMALIZATION_DICT[key] normalization_func = DETAILS_KEY_TO_NORMALIZATION_FUNCTION.get(normalized_key, None) @@ -323,7 +333,6 @@ class WebData(ComplexSerializableType): embodiment: Optional[str] = field(default=None) description: Optional[str] = field(default=None) tested_conf: Optional[List[str]] = field(default=None) - algorithms: Optional[Dict[str, Set[str]]] = field(default=None) hw_versions: Optional[str] = field(default=None) fw_versions: Optional[str] = field(default=None) sw_versions: Optional[str] = field(default=None) @@ -362,7 +371,6 @@ class PdfData(BasePdfData, ComplexSerializableType): """ keywords: Dict = field(default_factory=dict) - algorithms: Set[FIPSAlgorithm] = field(default_factory=set) clean_cert_ids: Dict[str, int] = field(default_factory=dict) st_metadata: Dict[str, Any] = field(default_factory=dict) @@ -490,8 +498,8 @@ def parse_html_module(cert: FIPSCertificate) -> FIPSCertificate: soup = BeautifulSoup(handle, "html5lib") parser = FIPSHTMLParser(soup) - cert.web_data = parser.build_web_data() - + algorithms, cert.web_data = parser.get_web_data_and_algorithms() + cert.heuristics.algorithms |= algorithms return cert @staticmethod @@ -552,7 +560,7 @@ def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" - status, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) + _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) if metadata: cert.pdf_data.st_metadata = metadata @@ -581,8 +589,7 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): cert.state.policy_extract_ok = False algorithms_str = set(itertools.chain.from_iterable([tables.get_algs_from_table(df) for df in tabular_data])) - algorithms = {FIPSAlgorithm(x) for x in algorithms_str} - cert.pdf_data.algorithms = cert.pdf_data.algorithms.union(algorithms) + cert.heuristics.algorithms |= {FIPSAlgorithm(x) for x in algorithms_str} def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: pass @@ -622,8 +629,8 @@ def clean_cert_ids(self) -> None: matches = copy.deepcopy(self.pdf_data.keywords["fips_cert_id"]["Cert"]) alg_set: Set[str] = set() - if self.web_data.algorithms is None: - raise RuntimeError(f"Algorithms were not found for cert {self.cert_id} - this should not be happening.") + # if self.web_data.algorithms is None: + # raise RuntimeError(f"Algorithms were not found for cert {self.cert_id} - this should not be happening.") # TODO : Refactor this, dictionary form changed # for algo in self.web_data.algorithms: diff --git a/tests/data/fips/certificate/crt.json b/tests/data/fips/certificate/crt.json new file mode 100644 index 00000000..1e8ad260 --- /dev/null +++ b/tests/data/fips/certificate/crt.json @@ -0,0 +1,78 @@ +{ + "_type": "sec_certs.sample.fips.FIPSCertificate", + "dgst": "184097a88a9b4ad9", + "cert_id": 3518, + "web_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", + "module_name": null, + "validation_history": null, + "vendor_url": null, + "vendor": null, + "certificate_pdf_url": null, + "module_type": null, + "standard": null, + "status": null, + "level": null, + "caveat": null, + "exceptions": null, + "embodiment": null, + "description": null, + "tested_conf": null, + "hw_versions": null, + "fw_versions": null, + "sw_versions": null, + "mentioned_certs": null, + "historical_reason": null, + "date_sunset": null, + "revoked_reason": null, + "revoked_link": null + }, + "pdf_data": { + "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", + "keywords": {}, + "clean_cert_ids": {}, + "st_metadata": {} + }, + "heuristics": { + "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", + "keywords": {}, + "algorithms": { + "_type": "Set", + "elements": [] + }, + "unmatched_algs": null, + "clean_cert_ids": null, + "extracted_versions": { + "_type": "Set", + "elements": [] + }, + "cpe_matches": null, + "verified_cpe_matches": null, + "related_cves": null, + "st_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + }, + "web_references": { + "_type": "sec_certs.sample.certificate.References", + "directly_referenced_by": null, + "indirectly_referenced_by": null, + "directly_referencing": null, + "indirectly_referencing": null + } + }, + "state": { + "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", + "module_download_ok": true, + "policy_download_ok": true, + "policy_convert_garbage": false, + "policy_convert_ok": true, + "module_extract_ok": false, + "policy_extract_ok": false, + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + } +} \ No newline at end of file diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json index 68a38017..1e8ad260 100644 --- a/tests/data/fips/certificate/fictional_cert.json +++ b/tests/data/fips/certificate/fictional_cert.json @@ -18,7 +18,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -31,10 +30,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index 99c7511e..7446e4ad 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -7,7 +7,7 @@ "pdfs_converted": true, "certs_analyzed": false }, - "timestamp": "2022-11-23 12:12:45.225272", + "timestamp": "2022-11-23 16:23:44.899168", "sha256_digest": "not implemented", "name": "FIPSDataset dataset", "description": "No description provided", @@ -15,8 +15,8 @@ "certs": [ { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "f7df34ce0a0ad316", - "cert_id": 3093, + "dgst": "184097a88a9b4ad9", + "cert_id": 3518, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -33,7 +33,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -46,10 +45,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -92,14 +87,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", - "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "cd0666cdd7ce0244", - "cert_id": 3095, + "dgst": "3b05cf67849835e3", + "cert_id": 2997, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -116,7 +111,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -129,10 +123,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -175,14 +165,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", - "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" + "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", + "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "618102f275d5649a", - "cert_id": 3495, + "dgst": "ad1e075215267a28", + "cert_id": 2441, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -199,7 +189,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -212,10 +201,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -258,14 +243,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", - "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" + "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", + "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "24a05f4d1305d667", - "cert_id": 3493, + "dgst": "e1ff7d9997726782", + "cert_id": 3090, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -282,7 +267,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -295,10 +279,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -341,14 +321,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", - "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" + "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", + "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "75ca28395bfc9ad1", - "cert_id": 2630, + "dgst": "9ca7b8be590113b8", + "cert_id": 2711, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -365,7 +345,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -378,10 +357,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -424,14 +399,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", - "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" + "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", + "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "2aec3694418f35d8", - "cert_id": 3488, + "dgst": "24a05f4d1305d667", + "cert_id": 3493, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -448,7 +423,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -461,10 +435,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -507,14 +477,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", - "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" + "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", + "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0e8b7a7f79b3098b", - "cert_id": 2665, + "dgst": "618102f275d5649a", + "cert_id": 3495, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -531,7 +501,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -544,10 +513,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -590,14 +555,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", - "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" + "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", + "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "69d9200c309f5f97", - "cert_id": 2590, + "dgst": "0df59fd6c41cadd2", + "cert_id": 3711, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -614,7 +579,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -627,10 +591,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -673,14 +633,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", - "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" + "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", + "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "756a6524cdb2d4ea", - "cert_id": 3850, + "dgst": "0ab77c56eece2d24", + "cert_id": 3651, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -697,7 +657,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -710,10 +669,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -756,14 +711,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", - "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" + "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", + "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "184097a88a9b4ad9", - "cert_id": 3518, + "dgst": "cd0666cdd7ce0244", + "cert_id": 3095, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -780,7 +735,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -793,10 +747,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -839,14 +789,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", - "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", + "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "73e7f3f802b0e919", - "cert_id": 3176, + "dgst": "ed16d272044ed81f", + "cert_id": 2860, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -863,7 +813,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -876,10 +825,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -922,14 +867,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", - "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" + "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", + "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ed16d272044ed81f", - "cert_id": 2860, + "dgst": "69d9200c309f5f97", + "cert_id": 2590, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -946,7 +891,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -959,10 +903,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1005,14 +945,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", - "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" + "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", + "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "51c94a510b50e68f", - "cert_id": 3141, + "dgst": "0e8b7a7f79b3098b", + "cert_id": 2665, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1029,7 +969,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1042,10 +981,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1088,14 +1023,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", - "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" + "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", + "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "5b00a7bb92c1cb19", - "cert_id": 2721, + "dgst": "ceab64951a30f684", + "cert_id": 3197, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1112,7 +1047,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1125,10 +1059,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1171,14 +1101,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", - "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" + "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", + "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0ab77c56eece2d24", - "cert_id": 3651, + "dgst": "73e7f3f802b0e919", + "cert_id": 3176, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1195,7 +1125,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1208,10 +1137,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1254,14 +1179,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", - "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" + "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", + "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0df59fd6c41cadd2", - "cert_id": 3711, + "dgst": "c333a75353784177", + "cert_id": 2779, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1278,7 +1203,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1291,10 +1215,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1337,14 +1257,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", - "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" + "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", + "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "e1ff7d9997726782", - "cert_id": 3090, + "dgst": "75ca28395bfc9ad1", + "cert_id": 2630, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1361,7 +1281,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1374,10 +1293,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1420,14 +1335,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", - "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" + "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", + "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "9ca7b8be590113b8", - "cert_id": 2711, + "dgst": "51c94a510b50e68f", + "cert_id": 3141, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1444,7 +1359,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1457,10 +1371,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1503,14 +1413,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", - "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" + "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", + "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ad1e075215267a28", - "cert_id": 2441, + "dgst": "756a6524cdb2d4ea", + "cert_id": 3850, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1527,7 +1437,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1540,10 +1449,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1586,14 +1491,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", - "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" + "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", + "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ceab64951a30f684", - "cert_id": 3197, + "dgst": "2aec3694418f35d8", + "cert_id": 3488, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1610,7 +1515,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1623,10 +1527,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1669,14 +1569,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", - "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" + "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", + "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "c333a75353784177", - "cert_id": 2779, + "dgst": "f7df34ce0a0ad316", + "cert_id": 3093, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1693,7 +1593,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1706,10 +1605,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1752,14 +1647,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", - "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" + "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", + "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "3b05cf67849835e3", - "cert_id": 2997, + "dgst": "5b00a7bb92c1cb19", + "cert_id": 2721, "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1776,7 +1671,6 @@ "embodiment": null, "description": null, "tested_conf": null, - "algorithms": null, "hw_versions": null, "fw_versions": null, "sw_versions": null, @@ -1789,10 +1683,6 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "algorithms": { - "_type": "Set", - "elements": [] - }, "clean_cert_ids": {}, "st_metadata": {} }, @@ -1835,8 +1725,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", - "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" + "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", + "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" } } ] diff --git a/tests/fips/test_fips_certificate.py b/tests/fips/test_fips_certificate.py index 0b4012c4..b612834c 100644 --- a/tests/fips/test_fips_certificate.py +++ b/tests/fips/test_fips_certificate.py @@ -15,7 +15,7 @@ def data_dir() -> Path: return Path(tests.data.fips.certificate.__path__[0]) -@pytest.fixture(scope="module") +@pytest.fixture def certificate(tmp_path_factory) -> FIPSCertificate: tmp_dir = tmp_path_factory.mktemp("dset") dset_json_path = Path(tests.data.fips.dataset.__path__[0]) / "toy_dataset.json" diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index df8eb009..32176f73 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -92,7 +92,7 @@ def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path dataset.download_all_artifacts(set(certs)) dataset.convert_all_pdfs() dataset._extract_data() - dataset._get_algorithms_from_policy_tables() + dataset._extract_algorithms_from_policy_tables() dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) return dataset From 0568ca492cc2fa02a9587dd7e2b5f994c2e9d016 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 16:43:51 +0100 Subject: [PATCH 32/81] remove graphviz requirement --- Dockerfile | 1 - cli.py | 4 ++-- docs/installation.md | 1 - requirements/requirements.in | 1 - requirements/requirements.txt | 2 -- sec_certs/utils/helpers.py | 11 ----------- 6 files changed, 2 insertions(+), 18 deletions(-) diff --git a/Dockerfile b/Dockerfile index c6d52ad0..467f239b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -18,7 +18,6 @@ RUN DEBIAN_FRONTEND="noninteractive" apt-get -y install tzdata RUN apt-get install build-essential libpoppler-cpp-dev pkg-config python3-dev -y RUN apt-get install libqpdf-dev -y RUN apt-get install default-jdk -y -RUN apt-get install graphviz -y RUN apt-get install tesseract-ocr tesseract-ocr-eng tesseract-ocr-deu tesseract-ocr-fra -y diff --git a/cli.py b/cli.py index fa54751e..e678e0e7 100644 --- a/cli.py +++ b/cli.py @@ -11,7 +11,7 @@ from sec_certs import constants from sec_certs.config.configuration import config from sec_certs.dataset import CCDataset, FIPSDataset -from sec_certs.utils.helpers import warn_if_missing_graphviz, warn_if_missing_poppler, warn_if_missing_tesseract +from sec_certs.utils.helpers import warn_if_missing_poppler, warn_if_missing_tesseract logger = logging.getLogger(__name__) @@ -153,7 +153,7 @@ def main( dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" - analysis_pre_callback = None if framework == "cc" else warn_if_missing_graphviz + analysis_pre_callback = None steps = [ ProcessingStep( diff --git a/docs/installation.md b/docs/installation.md index d9b32414..ae3db3f3 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -47,5 +47,4 @@ If you're not using Docker, you must install the dependencies as described below - [Java](https://www.java.com/en) is needed to parse tables in FIPS pdf documents, must be available from `PATH`. - Some imported libraries have non-trivial dependencies to resolve: - [pdftotext](https://github.com/jalan/pdftotext) requires [Poppler](https://poppler.freedesktop.org/) to be installed. We've experienced issues with older versions of Poppler (`0.x`), make sure to install `20.x` version of these libraries. - - [graphviz](https://pypi.org/project/graphviz/) requires `graphviz` to be on the path - [tesseract](https://github.com/tesseract-ocr/tesseract) is required for OCR of malformed PDF documents, together with data files for English, French and German. \ No newline at end of file diff --git a/requirements/requirements.in b/requirements/requirements.in index 4769bc99..74c60649 100644 --- a/requirements/requirements.in +++ b/requirements/requirements.in @@ -1,7 +1,6 @@ beautifulsoup4 billiard click -graphviz html5lib jsonschema lxml diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c5b41f1f..ad8bba1e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -64,8 +64,6 @@ fastjsonschema==2.15.3 # via nbformat fonttools==4.31.2 # via matplotlib -graphviz==0.19.1 - # via -r requirements.in html5lib==1.1 # via -r requirements.in idna==3.3 diff --git a/sec_certs/utils/helpers.py b/sec_certs/utils/helpers.py index e91f09da..402f946e 100644 --- a/sec_certs/utils/helpers.py +++ b/sec_certs/utils/helpers.py @@ -206,17 +206,6 @@ def warn_if_missing_poppler() -> None: logger.warning("Attempting to find poppler-cpp, but pkg-config was not found.") -def warn_if_missing_graphviz() -> None: - """ - Warns user if he misses a graphviz dependency - """ - try: - if not pkgconfig.installed("libcgraph", ">=2.0.0"): - logger.warning("Attempting to run pipeline that requires graphviz, but graphviz was not found.") - except EnvironmentError: - logger.warning("Attempting to find graphviz, but pkg-config was not found.") - - def warn_if_missing_tesseract() -> None: """ Warns user if he misses a tesseract dependency From d7603e1f13d14dda4bcdc1361d24be987670b275 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 23 Nov 2022 16:53:40 +0100 Subject: [PATCH 33/81] move AlgorithmDataset to AuxillaryDatasets class --- sec_certs/dataset/fips.py | 44 ++++++++++++++++++++++++++++++++------- 1 file changed, 36 insertions(+), 8 deletions(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index faede285..773a4248 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -1,8 +1,9 @@ +import datetime import itertools import logging import shutil from pathlib import Path -from typing import Dict, Final, Set +from typing import Dict, Final, Optional, Set, Union import numpy as np import pandas as pd @@ -10,6 +11,8 @@ from sec_certs import constants from sec_certs.config.configuration import config +from sec_certs.dataset.cpe import CPEDataset +from sec_certs.dataset.cve import CVEDataset from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset from sec_certs.model.dependency_finder import DependencyFinder @@ -21,13 +24,36 @@ logger = logging.getLogger(__name__) -class FIPSDataset(Dataset[FIPSCertificate, AuxillaryDatasets], ComplexSerializableType): +class FIPSAuxillaryDatasets(AuxillaryDatasets): + cpe_dset: Optional[CPEDataset] = None + cve_dset: Optional[CVEDataset] = None + algorithm_dset: Optional[FIPSAlgorithmDataset] = None + + +class FIPSDataset(Dataset[FIPSCertificate, FIPSAuxillaryDatasets], ComplexSerializableType): """ Class for processing of FIPSCertificate samples. Inherits from `ComplexSerializableType` and base abstract `Dataset` class. """ - def __init__(self, *args, **kwargs): - return super().__init__(*args, **kwargs) + def __init__( + self, + certs: Dict[str, FIPSCertificate] = dict(), + root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, + name: Optional[str] = None, + description: str = None, + state: Optional[Dataset.DatasetInternalState] = None, + auxillary_datasets: Optional[FIPSAuxillaryDatasets] = None, + ): + self.certs = certs + self._root_dir = Path(root_dir) + self.timestamp = datetime.datetime.now() + self.sha256_digest = "not implemented" + self.name = name if name else type(self).__name__ + " dataset" + self.description = description if description else datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S") + self.state = state if state else self.DatasetInternalState() + self.auxillary_datasets: FIPSAuxillaryDatasets = ( + auxillary_datasets if auxillary_datasets else FIPSAuxillaryDatasets() + ) LIST_OF_CERTS_HTML: Final[Dict[str, str]] = { "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, @@ -205,8 +231,10 @@ def process_auxillary_datasets(self) -> None: def _process_algorithms(self): logger.info("Processing FIPS algorithms.") self.algorithms_dir.mkdir(parernts=True, exist_ok=True) - self.algorithms = FIPSAlgorithmDataset({}, self.algorithms_dir, "algorithms", "sample algs") - self.algorithms.get_certs_from_web() + self.auxillary_datasets.algorithm_dset = FIPSAlgorithmDataset( + {}, self.algorithms_dir, "algorithms", "sample algs" + ) + self.auxillary_datasets.algorithm_dset.get_certs_from_web() logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") def _extract_algorithms_from_policy_tables(self, fresh: bool = True): @@ -292,13 +320,13 @@ def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) ): return False - if self.algorithms is None: + if self.auxillary_datasets.algorithm_dset is None: raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") if not FIPSDataset._match_with_algorithm(processed_cert, cert_candidate_id): return False - algs = self.algorithms.certs_for_id(cert_candidate_id) + algs = self.auxillary_datasets.algorithm_dset.certs_for_id(cert_candidate_id) for current_alg in algs: if current_alg.vendor is None or processed_cert.web_data.vendor is None: raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") From 35c5734e09de3314a1bb631a394369e296218b16 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 25 Nov 2022 12:08:55 +0100 Subject: [PATCH 34/81] Refactor FIPSAlgorithm objects --- cli.py | 27 +- sec_certs/dataset/common_criteria.py | 4 +- sec_certs/dataset/cpe.py | 3 +- sec_certs/dataset/cve.py | 13 +- sec_certs/dataset/dataset.py | 34 +- sec_certs/dataset/fips.py | 36 +- sec_certs/dataset/fips_algorithm.py | 225 ++++---- sec_certs/dataset/fips_iut.py | 2 +- sec_certs/dataset/fips_mip.py | 2 +- sec_certs/model/cpe_matching.py | 6 +- sec_certs/sample/fips.py | 44 +- sec_certs/sample/fips_algorithm.py | 65 ++- sec_certs/utils/helpers.py | 41 +- sec_certs/utils/parallel_processing.py | 2 +- sec_certs/utils/tqdm.py | 9 + tests/data/cc/analysis/cc_full_dataset.json | 1 + .../data/cc/analysis/vulnerable_dataset.json | 1 + .../maintenances/Maintenance updates.json | 1 + tests/data/cc/dataset/toy_dataset.json | 1 + tests/data/fips/algorithms.json | 490 ------------------ tests/data/fips/dataset/alg_dataset.json | 29 ++ tests/data/fips/dataset/toy_dataset.json | 1 + tests/fips/test_fips_algorithm_dataset.py | 66 ++- 23 files changed, 342 insertions(+), 761 deletions(-) create mode 100644 sec_certs/utils/tqdm.py delete mode 100644 tests/data/fips/algorithms.json create mode 100644 tests/data/fips/dataset/alg_dataset.json diff --git a/cli.py b/cli.py index e678e0e7..6ae117a0 100644 --- a/cli.py +++ b/cli.py @@ -20,19 +20,20 @@ class ProcessingStep: name: str processing_function_name: str - precondition: Optional[str] = field(default=None) + preconditions: List[str] = field(default_factory=list) precondition_error_msg: Optional[str] = field(default=None) pre_callback_func: Optional[Callable] = field(default=None) def run(self, dset: Union[CCDataset, FIPSDataset]) -> None: - if self.precondition and not getattr(dset.state, self.precondition): - err_msg = ( - self.precondition_error_msg - if self.precondition_error_msg - else f"Error, precondition to run {self.name} not met, exiting." - ) - print(err_msg) - sys.exit(1) + for condition in self.preconditions: + if not getattr(dset.state, condition): + err_msg = ( + self.precondition_error_msg + if self.precondition_error_msg + else f"Error, precondition to run {self.name} not met, exiting." + ) + print(err_msg) + sys.exit(1) if self.pre_callback_func: self.pre_callback_func() @@ -159,28 +160,28 @@ def main( ProcessingStep( "process-aux-dsets", "process_auxillary_datasets", - precondition="meta_sources_parsed", + preconditions=["meta_sources_parsed"], precondition_error_msg=f"Error: You want to process the auxillary datasets: {aux_dsets_to_handle} , but the data from cert. framework website was not parsed. You must use 'build' action first.", pre_callback_func=None, ), ProcessingStep( "download", "download_all_artifacts", - precondition="meta_sources_parsed", + preconditions=["meta_sources_parsed"], precondition_error_msg="Error: You want to download all artifacts, but the data from the cert. framework website was not parsed. You must use 'build' action first.", pre_callback_func=None, ), ProcessingStep( "convert", "convert_all_pdfs", - precondition="pdfs_downloaded", + preconditions=["pdfs_downloaded"], precondition_error_msg="Error: You want to convert pdfs -> txt, but the pdfs were not downloaded. You must use 'download' action first.", pre_callback_func=warn_missing_libs, ), ProcessingStep( "analyze", "analyze_certificates", - precondition="pdfs_converted", + preconditions=["pdfs_converted", "auxillary_datasets_processed"], precondition_error_msg="Error: You want to process txt documents of certificates, but pdfs were not converted. You must use 'convert' action first.", pre_callback_func=analysis_pre_callback, ), diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index b3df6703..a9eb1877 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -250,8 +250,8 @@ def _download_csv_html_resources(self, get_active: bool = True, get_archived: bo csv_urls, csv_paths = [x[0] for x in csv_items], [x[1] for x in csv_items] logger.info("Downloading required csv and html files.") - self._download_parallel(html_urls, html_paths) - self._download_parallel(csv_urls, csv_paths) + helpers.download_parallel(html_urls, html_paths) + helpers.download_parallel(csv_urls, csv_paths) @serialize def get_certs_from_web( diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index b67d678d..e66fb7ec 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -14,6 +14,7 @@ from sec_certs.sample.cpe import CPE, cached_cpe from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers +from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) @@ -204,7 +205,7 @@ def _adding_condition( # We only enrich if tuple (vendor, item_name) is not already in the dataset vendor_item_lookup = {(cpe.vendor, cpe.item_name) for cpe in self} vendor_item_version_lookup = {(cpe.vendor, cpe.item_name, cpe.version) for cpe in self} - for cpe in helpers.tqdm(all_cpes_in_cve_dset, desc="Enriching CPE dataset with new CPEs"): + for cpe in tqdm(all_cpes_in_cve_dset, desc="Enriching CPE dataset with new CPEs"): if _adding_condition(cpe, vendor_item_lookup, vendor_item_version_lookup): new_cpe = copy.deepcopy(cpe) new_cpe.start_version = None diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index 12489f7f..35952678 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -19,6 +19,7 @@ from sec_certs.sample.cve import CVE from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder from sec_certs.utils.parallel_processing import process_parallel +from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) @@ -79,7 +80,7 @@ def build_lookup_dict(self, use_nist_mapping: bool = True, nist_matching_filepat matching_dict = self.get_nist_cpe_matching_dict(nist_matching_filepath) cve: CVE - for cve in helpers.tqdm(self, desc="Building-up lookup dictionaries for fast CVE matching"): + for cve in tqdm(self, desc="Building-up lookup dictionaries for fast CVE matching"): # See note above, we use matching_dict.get(cpe, []) instead of matching_dict[cpe] as would be expected if use_nist_mapping: vulnerable_configurations = list( @@ -104,16 +105,12 @@ def download_cves(cls, output_path_str: str, start_year: int, end_year: int): logger.info(f"Identified {len(urls)} CVE files to fetch from nist.gov. Downloading them into {output_path}") with tempfile.TemporaryDirectory() as tmp_dir: outpaths = [Path(tmp_dir) / Path(x).name.rstrip(".zip") for x in urls] - responses = list(zip(*helpers.download_parallel(list(zip(urls, outpaths)), num_threads=config.n_threads)))[ - 1 - ] + responses = helpers.download_parallel(urls, outpaths) - for o, u, r in zip(outpaths, urls, responses): + for o, r in zip(outpaths, responses): if r == constants.RESPONSE_OK: with zipfile.ZipFile(o, "r") as zip_handle: zip_handle.extractall(output_path) - else: - logger.info(f"Failed to download from {u}, got status code {r}") @classmethod def from_nist_json(cls, input_path: str) -> "CVEDataset": @@ -229,7 +226,7 @@ def parse_values_cpe(field: Dict) -> List[CPE]: match_data = json.load(handle) mapping_dict = dict() - for match in helpers.tqdm(match_data["matches"], desc="parsing cpe matching (by NIST) dictionary"): + for match in tqdm(match_data["matches"], desc="parsing cpe matching (by NIST) dictionary"): key = parse_key_cpe(match) value = parse_values_cpe(match) mapping_dict[key] = value if value else [key] diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 8eec3795..a22e7317 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -9,14 +9,12 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Collection, Dict, Generic, Iterator, List, Optional, Pattern, Set, Type, TypeVar, Union, cast +from typing import Any, Dict, Generic, Iterator, List, Optional, Pattern, Set, Type, TypeVar, Union, cast import pandas as pd -import requests import sec_certs.constants as constants import sec_certs.utils.helpers as helpers -import sec_certs.utils.parallel_processing as cert_processing from sec_certs.config.configuration import config from sec_certs.dataset.cpe import CPEDataset from sec_certs.dataset.cve import CVEDataset @@ -24,6 +22,7 @@ from sec_certs.sample.certificate import Certificate from sec_certs.sample.cpe import CPE from sec_certs.serialization.json import ComplexSerializableType, serialize +from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) @@ -45,6 +44,7 @@ class DatasetInternalState(ComplexSerializableType): meta_sources_parsed: bool = False artifacts_downloaded: bool = False pdfs_converted: bool = False + auxillary_datasets_processed: bool = False certs_analyzed: bool = False def __bool__(self): @@ -262,6 +262,10 @@ def analyze_certificates(self, fresh: bool = True) -> None: "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." ) return + if not self.state.auxillary_datasets_processed: + logger.info( + "Attempting to run analysis of certifies while not having the auxillary datasets processed. Returning." + ) if fresh: logger.info("Analyzing certificates.") @@ -301,24 +305,6 @@ def _compute_dependencies(self, fresh: bool = True) -> None: def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - @staticmethod - def _download_parallel(urls: Collection[str], paths: Collection[Path], prune_corrupted: bool = True) -> None: - exit_codes = cert_processing.process_parallel( - helpers.download_file, list(zip(urls, paths)), config.n_threads, unpack=True - ) - n_successful = len([e for e in exit_codes if e == requests.codes.ok]) - logger.info(f"Successfully downloaded {n_successful} files, {len(exit_codes) - n_successful} failed.") - - for url, e in zip(urls, exit_codes): - if e != requests.codes.ok: - logger.error(f"Failed to download {url}, exit code: {e}") - - if prune_corrupted is True: - for p in paths: - if p.exists() and p.stat().st_size < constants.MIN_CORRECT_CERT_SIZE: - logger.error(f"Corrupted file at: {p}") - p.unlink() - def _prepare_cpe_dataset(self, download_fresh_cpes: bool = False) -> CPEDataset: logger.info("Preparing CPE dataset.") if not self.auxillary_datasets_dir.exists(): @@ -394,7 +380,7 @@ def filter_condition(cpe: CPE) -> bool: clf.fit([x for x in self.auxillary_datasets.cpe_dset if filter_condition(x)]) cert: CertSubType - for cert in helpers.tqdm(self, desc="Predicting CPE matches with the classifier"): + for cert in tqdm(self, desc="Predicting CPE matches with the classifier"): cert.compute_heuristics_version() cert.heuristics.cpe_matches = ( @@ -429,7 +415,7 @@ def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: labeled_cert_digests: Set[str] = set() logger.info("Translating label studio matches into their CPE representations and assigning to certificates.") - for annotation in helpers.tqdm(data, desc="Translating label studio matches"): + for annotation in tqdm(data, desc="Translating label studio matches"): cpe_candidate_keys = { key for key in annotation.keys() if "option_" in key and annotation[key] != "No good match" } @@ -507,7 +493,7 @@ def compute_related_cves( self.auxillary_datasets.cve_dset.filter_related_cpes(relevant_cpes) cert: Certificate - for cert in helpers.tqdm(cpe_rich_certs, desc="Computing related CVES"): + for cert in tqdm(cpe_rich_certs, desc="Computing related CVES"): if cert.heuristics.cpe_matches: related_cves = [ self.auxillary_datasets.cve_dset.get_cve_ids_for_cpe_uri(x) for x in cert.heuristics.cpe_matches diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 773a4248..3d2f1f63 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -18,6 +18,7 @@ from sec_certs.model.dependency_finder import DependencyFinder from sec_certs.sample.fips import FIPSCertificate from sec_certs.serialization.json import ComplexSerializableType, serialize +from sec_certs.utils import helpers from sec_certs.utils import parallel_processing as cert_processing from sec_certs.utils.helpers import fips_dgst @@ -81,6 +82,10 @@ def module_dir(self) -> Path: def algorithms_dir(self) -> Path: return self.auxillary_datasets_dir / "algorithms" + @property + def algorithm_dataset_path(self) -> Path: + return self.algorithms_dir / "algorithms.json" + def _extract_data_from_html_modules(self, fresh: bool = True) -> None: """ Extracts data from html module file @@ -167,7 +172,7 @@ def _download_html_resources(self) -> None: logger.info("Downloading HTML files that list FIPS certificates.") html_urls = list(FIPSDataset.LIST_OF_CERTS_HTML.values()) html_paths = [self.web_dir / x for x in FIPSDataset.LIST_OF_CERTS_HTML.keys()] - self._download_parallel(html_urls, html_paths) + helpers.download_parallel(html_urls, html_paths) def _get_all_certs_from_html_sources(self) -> Set[FIPSCertificate]: return set( @@ -206,6 +211,10 @@ def _set_local_paths(self) -> None: for cert in self.certs.values(): cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.module_dir) + super()._set_local_paths() + if self.auxillary_datasets.algorithm_dset: + self.auxillary_datasets.algorithm_dset.json_path = self.algorithm_dataset_path + @serialize def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = True) -> None: self.web_dir.mkdir(parents=True, exist_ok=True) @@ -225,17 +234,20 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru @serialize def process_auxillary_datasets(self) -> None: - self._process_algorithms() + self.auxillary_datasets.algorithm_dset = self._prepare_algorithm_dataset() super().process_auxillary_datasets() - def _process_algorithms(self): - logger.info("Processing FIPS algorithms.") - self.algorithms_dir.mkdir(parernts=True, exist_ok=True) - self.auxillary_datasets.algorithm_dset = FIPSAlgorithmDataset( - {}, self.algorithms_dir, "algorithms", "sample algs" - ) - self.auxillary_datasets.algorithm_dset.get_certs_from_web() - logger.info(f"Finished parsing. Have algorithm dataset with {len(self.algorithms)} algorithm numbers.") + def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSAlgorithmDataset: + logger.info("Preparing FIPSAlgorithm dataset.") + self.algorithms_dir.mkdir(parents=True, exist_ok=True) + + if not self.algorithm_dataset_path.exists() or download_fresh_algs: + alg_dset = FIPSAlgorithmDataset.from_web(self.algorithm_dataset_path) + alg_dset.to_json() + else: + alg_dset = FIPSAlgorithmDataset.from_json(self.algorithm_dataset_path) + + return alg_dset def _extract_algorithms_from_policy_tables(self, fresh: bool = True): certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] @@ -304,7 +316,7 @@ def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: @staticmethod def _match_with_algorithm(processed_cert: FIPSCertificate, cert_candidate_id: str) -> bool: for algo in processed_cert.heuristics.algorithms: - curr_id = "".join(filter(str.isdigit, algo.cert_id)) + curr_id = "".join(filter(str.isdigit, algo)) if curr_id == cert_candidate_id: return False return True @@ -326,7 +338,7 @@ def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) if not FIPSDataset._match_with_algorithm(processed_cert, cert_candidate_id): return False - algs = self.auxillary_datasets.algorithm_dset.certs_for_id(cert_candidate_id) + algs = self.auxillary_datasets.algorithm_dset.get_algorithms_by_id(cert_candidate_id) for current_alg in algs: if current_alg.vendor is None or processed_cert.web_data.vendor is None: raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index 244034e6..9edb5427 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -1,143 +1,138 @@ +import itertools import logging +import re from pathlib import Path -from typing import Any, Dict, List, Optional, Set, Union +from tempfile import TemporaryDirectory +from typing import Dict, Iterator, List, Set, Union, cast +import pandas as pd from bs4 import BeautifulSoup -import sec_certs.utils.extract -from sec_certs import constants as constants -from sec_certs.config.configuration import config -from sec_certs.dataset.dataset import Dataset -from sec_certs.sample.fips import FIPSCertificate -from sec_certs.sample.fips_algorithm import FIPSAlgorithm +from sec_certs import constants +from sec_certs.sample import FIPSAlgorithm from sec_certs.serialization.json import ComplexSerializableType -from sec_certs.utils import helpers as helpers -from sec_certs.utils import parallel_processing as cert_processing +from sec_certs.utils import helpers logger = logging.getLogger(__name__) -class FIPSAlgorithmDataset(Dataset, ComplexSerializableType): - certs: Dict[str, FIPSAlgorithm] - +class FIPSAlgorithmDataset(ComplexSerializableType): def __init__( - self, - certs: Dict[str, FIPSAlgorithm] = dict(), - root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, - name: str = "dataset name", - description: str = "dataset_description", + self, algs: Dict[str, FIPSAlgorithm] = dict(), json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH ): - super().__init__(certs, root_dir, name, description) - self._id_map: Dict[str, List[str]] = {} + self.algs = algs + self._json_path = Path(json_path) + self.alg_number_to_algs: Dict[str, Set[FIPSAlgorithm]] = dict() - def get_certs_from_web(self): - self.root_dir.mkdir(exist_ok=True) - algs_paths, algs_urls = [], [] + self._build_lookup_dicts() - # get first page to find out how many pages there are - res = helpers.download_file(constants.FIPS_ALG_SEARCH_URL + "1", self.root_dir / "page1.html") - if res != 200: - logger.error("Couldn't download first page of algo dataset") + @property + def serialized_attributes(self) -> List[str]: + return ["algs"] - with open(self.root_dir / "page1.html", "r") as alg_file: - soup = BeautifulSoup(alg_file.read(), "html5lib") - num_pages_elem = soup.select("span[data-total-pages]")[0].attrs + @property + def json_path(self) -> Path: + return self._json_path - num_pages = int(num_pages_elem["data-total-pages"]) + @json_path.setter + def json_path(self, new_json_path: Union[str, Path]) -> None: + self._json_path = Path(new_json_path) + self.to_json() - for i in range(2, num_pages + 1): - if not (self.root_dir / f"page{i}.html").exists(): - algs_urls.append(constants.FIPS_ALG_SEARCH_URL + str(i)) - algs_paths.append(self.root_dir / f"page{i}.html") + def __iter__(self) -> Iterator[FIPSAlgorithm]: + yield from self.algs.values() - # get the last page, always - algs_urls.append(constants.FIPS_ALG_SEARCH_URL + str(num_pages)) - algs_paths.append(self.root_dir / f"page{num_pages}.html") + def __getitem__(self, item: str) -> FIPSAlgorithm: + return self.algs.__getitem__(item) - logger.info(f"Downloading {len(algs_urls)} algo html files") - # TODO: Refactor me, the method download_html_page should definitely not be called! - cert_processing.process_parallel( - FIPSCertificate.download_html_page, list(zip(algs_urls, algs_paths)), config.n_threads - ) + def __setitem__(self, key: str, value: FIPSAlgorithm) -> None: + self.algs.__setitem__(key, value) - logger.info(f"Parsing {len(algs_urls)} algo html files") - self.parse_html() + def __len__(self) -> int: + return len(self.algs) - @staticmethod - def _extract_algorithm_information(elements, vendor, date, product, validation): - for elem in elements: - # td > a > (vendor or date) - attachments = elem.find_all("a") - - if len(attachments) == 0: - vendor = elem.text.strip() if "vendor-name" in elem["id"] else vendor - date = elem.text.strip() if "validation-date" in elem["id"] else date - continue - - for attachment in attachments: - product = elem.text.strip() if "product-name" in attachment["id"] else product - validation = elem.text.strip() if "validation-number" in attachment["id"] else validation - return vendor, date, product, validation - - def parse_html(self): - def split_alg(alg_string): - cert_type = alg_string.rstrip("0123456789") - cert_id = alg_string[len(cert_type) :] - return cert_type.strip(), cert_id.strip() - - for f in sec_certs.utils.extract.search_files(self.root_dir): - if not f.endswith("html"): - continue - - with open(f, "r", encoding="utf-8") as handle: - html_soup = BeautifulSoup(handle.read(), "html5lib") - - table = html_soup.find("table", class_="table table-condensed publications-table table-bordered") - tbody_contents = table.find("tbody").find_all("tr") - vendor = product = validation = date = "" - for tr in tbody_contents: - elements = tr.find_all("td") - vendor, date, product, validation = FIPSAlgorithmDataset._extract_algorithm_information( - elements, vendor, date, product, validation - ) - - alg_type, alg_id = split_alg(validation) - fips_alg = FIPSAlgorithm(alg_id, alg_type, vendor, product, date) - self.certs[fips_alg.dgst] = fips_alg - # And now rebuild the id map - self._build_id_map() - - def _build_id_map(self): - for cert in self.certs.values(): - self._id_map.setdefault(cert.cert_id, []) - self._id_map[cert.cert_id].append(cert.dgst) - - def _get_certs_by_name(self, name: str) -> Set[FIPSAlgorithm]: - raise NotImplementedError("Not meant to be implemented") - - def _set_local_paths(self) -> None: - pass + def __contains__(self, item: FIPSAlgorithm) -> bool: + if not isinstance(item, FIPSAlgorithm): + raise ValueError(f"{item} is not of FIPSAlgorithm class") + return item.dgst in self.algs.keys() and self.algs[item.dgst] == item + + def __eq__(self, other: object) -> bool: + return isinstance(other, FIPSAlgorithmDataset) and self.algs == other.algs @classmethod - def from_dict(cls, dct: Dict[str, Any]) -> "FIPSAlgorithmDataset": - dset: FIPSAlgorithmDataset = super().from_dict(dct) - dset._build_id_map() - return dset + def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH) -> "FIPSAlgorithmDataset": + with TemporaryDirectory() as tmp_dir: + htmls = FIPSAlgorithmDataset.download_alg_list_htmls(Path(tmp_dir)) + algs = set( + itertools.chain.from_iterable([FIPSAlgorithmDataset.parse_algorithms_from_html(x) for x in htmls]) + ) + return cls({x.dgst: x for x in algs}, json_path) - def convert_all_pdfs(self): - raise NotImplementedError("Not meant to be implemented") + @staticmethod + def download_alg_list_htmls(output_dir: Path) -> List[Path]: + first_page_path = output_dir / "page1.html" + ITEMS_PER_PAGE = "ipp=250" + + res = helpers.download_file(constants.FIPS_ALG_SEARCH_URL + "1&" + ITEMS_PER_PAGE, first_page_path) + if res != constants.RESPONSE_OK: + res = helpers.download_file(constants.FIPS_ALG_SEARCH_URL + "1&" + ITEMS_PER_PAGE, first_page_path) + if res != constants.RESPONSE_OK: + logger.error(f"Could not build Algorithm dataset, got server response: {res}") + raise ValueError(f"Could not build Algorithm dataset, got server response: {res}") + + n_pages = FIPSAlgorithmDataset.get_number_of_html_pages(first_page_path) + + urls = [constants.FIPS_ALG_SEARCH_URL + str(i) + "&" + ITEMS_PER_PAGE for i in range(2, n_pages + 1)] + paths = [output_dir / f"page{i}.html" for i in range(2, n_pages + 1)] + responses = helpers.download_parallel(urls, paths, progress_bar_desc="Downloading FIPS Algorithm HTMLs") + + failed_tuples = [ + (url, path) for url, path, resp in zip(urls, paths, responses) if resp != constants.RESPONSE_OK + ] + if failed_tuples: + failed_urls, failed_paths = zip(*failed_tuples) + responses = helpers.download_parallel(failed_urls, failed_paths) + if any([x != constants.RESPONSE_OK for x in responses]): + raise ValueError("Failed to download the algorithms HTML data, the dataset won't be constructed.") + + return paths - def download_all_artifacts(self, cert_ids: Optional[Set[str]] = None) -> None: - raise NotImplementedError("Not meant to be implemented") + @staticmethod + def get_number_of_html_pages(html_path: Path) -> int: + with html_path.open("r") as handle: + soup = BeautifulSoup(handle, "html5lib") + return int(soup.select("span[data-total-pages]")[0].attrs["data-total-pages"]) - def __getitem__(self, item: str) -> FIPSAlgorithm: - return self.certs.__getitem__(item) + @staticmethod + def parse_algorithms_from_html(html_path: Path) -> Set[FIPSAlgorithm]: + df = pd.read_html(html_path)[0] + df["alg_type"] = df["Validation Number"].map(lambda x: re.sub(r"[0-9\s]", "", x)) + df["alg_number"] = df["Validation Number"].map(lambda x: re.sub(r"[^0-9]", "", x)) + df["alg"] = df.apply( + lambda row: FIPSAlgorithm( + row["alg_number"], row["alg_type"], row["Vendor"], row["Implementation"], row["Validation Date"] + ), + axis=1, + ) + return set(df["alg"]) + + @classmethod + def from_json(cls, input_path: Union[str, Path]) -> "FIPSAlgorithmDataset": + dset = cast("FIPSAlgorithmDataset", ComplexSerializableType.from_json(input_path)) + dset._json_path = Path(input_path) + return dset + + def to_pandas(self) -> pd.DataFrame: + df = pd.DataFrame([x.pandas_tuple for x in self], columns=FIPSAlgorithm.pandas_columns) + df = df.set_index("dgst") + return df - def __setitem__(self, key: str, value: FIPSAlgorithm): - self.certs.__setitem__(key, value) + def _build_lookup_dicts(self) -> None: + for alg in self: + if alg.alg_number not in self.alg_number_to_algs: + self.alg_number_to_algs[alg.alg_number] = {alg} + else: + self.alg_number_to_algs[alg.alg_number].add(alg) - def certs_for_id(self, cert_id: str) -> List[FIPSAlgorithm]: - if cert_id in self._id_map: - return [self.certs[x] for x in self._id_map[cert_id]] - else: - return [] + def get_algorithms_by_id(self, alg_number: str) -> Set[FIPSAlgorithm]: + return self.alg_number_to_algs.get(alg_number, set()) diff --git a/sec_certs/dataset/fips_iut.py b/sec_certs/dataset/fips_iut.py index e8feaa76..9604c63d 100644 --- a/sec_certs/dataset/fips_iut.py +++ b/sec_certs/dataset/fips_iut.py @@ -9,7 +9,7 @@ from sec_certs.dataset.dataset import logger from sec_certs.sample.fips_iut import IUTSnapshot from sec_certs.serialization.json import ComplexSerializableType -from sec_certs.utils.helpers import tqdm +from sec_certs.utils.tqdm import tqdm @dataclass diff --git a/sec_certs/dataset/fips_mip.py b/sec_certs/dataset/fips_mip.py index 78455171..8927ee8a 100644 --- a/sec_certs/dataset/fips_mip.py +++ b/sec_certs/dataset/fips_mip.py @@ -9,7 +9,7 @@ from sec_certs.dataset.dataset import logger from sec_certs.sample.fips_mip import MIPSnapshot from sec_certs.serialization.json import ComplexSerializableType -from sec_certs.utils.helpers import tqdm +from sec_certs.utils.tqdm import tqdm @dataclass diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index d50be954..b5d5e7f0 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -8,9 +8,9 @@ from rapidfuzz import fuzz from sklearn.base import BaseEstimator -import sec_certs.utils.helpers as helpers from sec_certs import cert_rules, constants from sec_certs.sample.cpe import CPE +from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) @@ -66,7 +66,7 @@ def _build_lookup_structures(self, X: List[CPE]) -> None: self.vendors_ = set(self.vendor_to_versions_.keys()) self.vendor_version_to_cpe_ = dict() - for cpe in helpers.tqdm(sufficiently_long_cpes, desc="Fitting the CPE classifier"): + for cpe in tqdm(sufficiently_long_cpes, desc="Fitting the CPE classifier"): self.vendor_to_versions_[cpe.vendor].add(cpe.version) if (cpe.vendor, cpe.version) not in self.vendor_version_to_cpe_: self.vendor_version_to_cpe_[(cpe.vendor, cpe.version)] = {cpe} @@ -80,7 +80,7 @@ def predict(self, X: List[Tuple[str, str, str]]) -> List[Optional[Set[str]]]: :param List[Tuple[str, str, str]] X: tuples (vendor, product name, identified versions in product name) :return List[Optional[Set[str]]]: List of CPE uris that correspond to given input, None if nothing was found. """ - return [self.predict_single_cert(x[0], x[1], x[2]) for x in helpers.tqdm(X, desc="Predicting")] + return [self.predict_single_cert(x[0], x[1], x[2]) for x in tqdm(X, desc="Predicting")] def predict_single_cert( self, diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 76df0636..abb2ee83 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -26,7 +26,6 @@ from sec_certs.sample.certificate import PdfData as BasePdfData from sec_certs.sample.certificate import References, logger from sec_certs.sample.cpe import CPE -from sec_certs.sample.fips_algorithm import FIPSAlgorithm from sec_certs.serialization.json import ComplexSerializableType from sec_certs.serialization.pandas import PandasSerializableType from sec_certs.utils.helpers import fips_dgst @@ -36,7 +35,7 @@ class FIPSHTMLParser: def __init__(self, soup: BeautifulSoup): self._soup = soup - def get_web_data_and_algorithms(self) -> Tuple[Set[FIPSAlgorithm], FIPSCertificate.WebData]: + def get_web_data_and_algorithms(self) -> Tuple[Set[str], FIPSCertificate.WebData]: divs = self._soup.find_all("div", class_="panel panel-default") details_div, vendor_div, related_files_div, validation_history_div = divs @@ -57,8 +56,7 @@ def get_web_data_and_algorithms(self) -> Tuple[Set[FIPSAlgorithm], FIPSCertifica algorithms_data = details_dict.pop("algorithms") algorithms = set() for category, alg_ids in algorithms_data.items(): - algorithms |= {FIPSAlgorithm(x, algorithm_type=category) for x in alg_ids} - + algorithms |= {category + x for x in alg_ids} else: algorithms = set() @@ -85,7 +83,13 @@ def parse_single_detail_entry(key, entry): if "caveat" in entries: entries["mentioned_certs"] = FIPSHTMLParser.get_mentioned_certs_from_caveat(entries["caveat"]) - # TODO: Enhance algorithms with those parsed from description entry + + if "description" in entries: + algs = FIPSHTMLParser.get_algs_from_description(entries["description"]) + if "algorithms" in entries: + entries["algorithms"].update(algs) + else: + entries["algorithms"] = algs return entries @@ -128,6 +132,10 @@ def get_mentioned_certs_from_caveat(caveat: str) -> Dict[str, int]: ids_found[m.group("id")] = 1 return ids_found + @staticmethod + def get_algs_from_description(description: str) -> Set[str]: + return {m.group() for m in re.finditer(FIPS_ALGS_IN_TABLE, description)} + @staticmethod def parse_algorithms(algorithms_div: Tag) -> Dict[str, Set[str]]: rows = algorithms_div.find("tbody").find_all("tr") @@ -389,7 +397,7 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): # TODO: How are keywords, clean_cert_ids and algorithms attributes different from those in pdf data? keywords: Dict[str, Dict] = field(default_factory=dict) - algorithms: Set[FIPSAlgorithm] = field(default_factory=set) + algorithms: Set[str] = field(default_factory=set) unmatched_algs: Optional[int] = field(default=None) clean_cert_ids: Optional[Dict[str, int]] = field(default=None) @@ -461,8 +469,8 @@ def __init__( self.cert_id = cert_id self.web_data: FIPSCertificate.WebData = web_data if web_data else FIPSCertificate.WebData() self.pdf_data: FIPSCertificate.PdfData = pdf_data if pdf_data else FIPSCertificate.PdfData() - self.heuristics = heuristics if heuristics else FIPSCertificate.Heuristics() - self.state = state if state else FIPSCertificate.InternalState() + self.heuristics: FIPSCertificate.Heuristics = heuristics if heuristics else FIPSCertificate.Heuristics() + self.state: FIPSCertificate.InternalState = state if state else FIPSCertificate.InternalState() @property def pandas_tuple(self) -> Tuple: @@ -502,21 +510,6 @@ def parse_html_module(cert: FIPSCertificate) -> FIPSCertificate: cert.heuristics.algorithms |= algorithms return cert - @staticmethod - # TODO: This should probably get deleted - def download_html_page(cert: Tuple[str, Path]) -> Optional[Tuple[str, Path]]: - """ - Wrapper for downloading a file. `delay=1` introduced to avoid problems with requests at NIST.gov - - :param Tuple[str, Path] cert: tuple url, output_path - :return Optional[Tuple[str, Path]]: None on success, `cert` on failure. - """ - exit_code = helpers.download_file(*cert, delay=constants.FIPS_DOWNLOAD_DELAY) - if exit_code != requests.codes.ok: - logger.error(f"Failed to download html page from {cert[0]}, code: {exit_code}") - return cert - return None - @staticmethod def download_module(cert: FIPSCertificate) -> FIPSCertificate: if (exit_code := helpers.download_file(cert.module_html_url, cert.state.module_html_path)) != requests.codes.ok: @@ -588,8 +581,9 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): logger.warning(f"Error when parsing tables from {cert.dgst}: {e}") cert.state.policy_extract_ok = False - algorithms_str = set(itertools.chain.from_iterable([tables.get_algs_from_table(df) for df in tabular_data])) - cert.heuristics.algorithms |= {FIPSAlgorithm(x) for x in algorithms_str} + cert.heuristics.algorithms |= set( + itertools.chain.from_iterable([tables.get_algs_from_table(df) for df in tabular_data]) + ) def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: pass diff --git a/sec_certs/sample/fips_algorithm.py b/sec_certs/sample/fips_algorithm.py index 8b9658bb..83ac839b 100644 --- a/sec_certs/sample/fips_algorithm.py +++ b/sec_certs/sample/fips_algorithm.py @@ -1,49 +1,48 @@ -from dataclasses import dataclass, field -from functools import total_ordering -from typing import Optional +from dataclasses import dataclass +from datetime import date +from typing import ClassVar, List, Tuple from sec_certs import constants from sec_certs.serialization.json import ComplexSerializableType +from sec_certs.serialization.pandas import PandasSerializableType @dataclass(eq=True, frozen=True) -@total_ordering -class FIPSAlgorithm(ComplexSerializableType): +class FIPSAlgorithm(PandasSerializableType, ComplexSerializableType): """ Data structure for algorithm of `FIPSCertificate` """ - cert_id: str - algorithm_type: Optional[str] = field(default=None) - vendor: Optional[str] = field(default=None) - implementation: Optional[str] = field(default=None) - date: Optional[str] = field(default=None) + alg_number: str + algorithm_type: str + vendor: str + implementation_name: str + validation_date: date + + pandas_columns: ClassVar[List[str]] = [ + "dgst", + "alg_number", + "algorithm_type", + "vendor", + "implementation_name", + "validation_date", + ] @property - def dgst(self) -> str: - return f"{self.algorithm_type}{self.cert_id}" - - @property - def page_url(self) -> str: - return constants.FIPS_ALG_URL.format(self.algorithm_type, self.cert_id) - - def _compare_tuple(self): - # This is necessary to not have errors with comparing str with None. + def pandas_tuple(self) -> Tuple: return ( - self.cert_id, - self.algorithm_type if self.algorithm_type else "", - self.vendor if self.vendor else "", - self.implementation if self.implementation else "", - self.date if self.date else "", + self.dgst, + self.alg_number, + self.algorithm_type, + self.vendor, + self.implementation_name, + self.validation_date, ) - def __lt__(self, other): - if not isinstance(other, FIPSAlgorithm): - raise ValueError("Cannot compare.") - return self._compare_tuple() < other._compare_tuple() - - def __repr__(self) -> str: - return f"FIPSAlgorithm({self.dgst})" + @property + def dgst(self) -> str: + return f"{self.algorithm_type}{self.alg_number}" - def __str__(self) -> str: - return f"{self.algorithm_type} algorithm {self.cert_id} created by {self.vendor}" + @property + def page_url(self) -> str: + return constants.FIPS_ALG_URL.format(self.algorithm_type, self.alg_number) diff --git a/sec_certs/utils/helpers.py b/sec_certs/utils/helpers.py index 402f946e..6052faaf 100644 --- a/sec_certs/utils/helpers.py +++ b/sec_certs/utils/helpers.py @@ -5,28 +5,21 @@ from contextlib import nullcontext from datetime import datetime from functools import partial -from multiprocessing.pool import ThreadPool from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Union +from typing import Any, Collection, Dict, List, Optional, Set, Union import numpy as np import pkgconfig import requests -from tqdm import tqdm as tqdm_original import sec_certs.constants as constants from sec_certs.config.configuration import config +from sec_certs.utils import parallel_processing +from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) -# TODO: Once typehints in tqdm are implemented, we should use them: https://github.com/tqdm/tqdm/issues/260 -def tqdm(*args, **kwargs): - if "disable" in kwargs: - return tqdm_original(*args, **kwargs) - return tqdm_original(*args, **kwargs, disable=not config.enable_progress_bars) - - def download_file( url: str, output: Path, delay: float = 0, show_progress_bar: bool = False, progress_bar_desc: Optional[str] = None ) -> Union[str, int]: @@ -66,20 +59,20 @@ def download_file( return constants.RETURNCODE_NOK -def download_parallel(items: Sequence[Tuple[str, Path]], num_threads: int) -> Sequence[Tuple[str, int]]: - def download(url_output): - url, output = url_output - return url, download_file(url, output) - - pool = ThreadPool(num_threads) - responses = [] - with tqdm(total=len(items)) as progress: - for response in pool.imap(download, items): - progress.update(1) - responses.append(response) - pool.close() - pool.join() - return responses +def download_parallel( + urls: Collection[str], paths: Collection[Path], progress_bar_desc: Optional[str] = None +) -> List[int]: + exit_codes = parallel_processing.process_parallel( + download_file, list(zip(urls, paths)), config.n_threads, unpack=True, progress_bar_desc=progress_bar_desc + ) + n_successful = len([e for e in exit_codes if e == requests.codes.ok]) + logger.info(f"Successfully downloaded {n_successful} files, {len(exit_codes) - n_successful} failed.") + + for url, e in zip(urls, exit_codes): + if e != requests.codes.ok: + logger.error(f"Failed to download {url}, exit code: {e}") + + return exit_codes def fips_dgst(cert_id: Union[int, str]) -> str: diff --git a/sec_certs/utils/parallel_processing.py b/sec_certs/utils/parallel_processing.py index ec0ae5e8..f3526511 100644 --- a/sec_certs/utils/parallel_processing.py +++ b/sec_certs/utils/parallel_processing.py @@ -4,7 +4,7 @@ from billiard.pool import Pool -from sec_certs.utils.helpers import tqdm +from sec_certs.utils.tqdm import tqdm def process_parallel( diff --git a/sec_certs/utils/tqdm.py b/sec_certs/utils/tqdm.py new file mode 100644 index 00000000..77eeae94 --- /dev/null +++ b/sec_certs/utils/tqdm.py @@ -0,0 +1,9 @@ +from tqdm import tqdm as tqdm_original + +from sec_certs.config.configuration import config + + +def tqdm(*args, **kwargs): + if "disable" in kwargs: + return tqdm_original(*args, **kwargs) + return tqdm_original(*args, **kwargs, disable=not config.enable_progress_bars) diff --git a/tests/data/cc/analysis/cc_full_dataset.json b/tests/data/cc/analysis/cc_full_dataset.json index 479f04b0..3d63bf31 100644 --- a/tests/data/cc/analysis/cc_full_dataset.json +++ b/tests/data/cc/analysis/cc_full_dataset.json @@ -5,6 +5,7 @@ "meta_sources_parsed": true, "artifacts_downloaded": true, "pdfs_converted": true, + "auxillary_datasets_processed": true, "certs_analyzed": false }, "timestamp": "2022-11-04 16:28:04.282938", diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json index f3e9c643..71ce27e2 100644 --- a/tests/data/cc/analysis/vulnerable_dataset.json +++ b/tests/data/cc/analysis/vulnerable_dataset.json @@ -5,6 +5,7 @@ "meta_sources_parsed": true, "artifacts_downloaded": false, "pdfs_converted": false, + "auxillary_datasets_processed": true, "certs_analyzed": false }, "timestamp": "2021-04-16 15:05:18.386794", diff --git a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json index 5f52950a..3532ff03 100644 --- a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json +++ b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json @@ -5,6 +5,7 @@ "meta_sources_parsed": true, "artifacts_downloaded": true, "pdfs_converted": false, + "auxillary_datasets_processed": false, "certs_analyzed": false }, "timestamp": "2022-11-10 13:44:35.171285", diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json index 13055120..517e1f9e 100644 --- a/tests/data/cc/dataset/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -5,6 +5,7 @@ "meta_sources_parsed": true, "artifacts_downloaded": false, "pdfs_converted": false, + "auxillary_datasets_processed": false, "certs_analyzed": false }, "timestamp": "2020-11-16 17:04:14.770153", diff --git a/tests/data/fips/algorithms.json b/tests/data/fips/algorithms.json deleted file mode 100644 index 825f3009..00000000 --- a/tests/data/fips/algorithms.json +++ /dev/null @@ -1,490 +0,0 @@ -{ - "_type": "sec_certs.dataset.fips_algorithm.FIPSAlgorithmDataset", - "timestamp": "2022-07-11 17:17:38.998647", - "sha256_digest": "not implemented", - "name": "", - "description": "", - "n_certs": 60, - "certs": [ - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3415", - "vendor": "Apple Inc.", - "implementation": "Apple Secure Key Store CoreCrypto Module (Generic Software Implementation)", - "algorithm_type": "HMAC", - "date": "1/26/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2702", - "vendor": "MikroM GmbH", - "implementation": "OpenSSL Crypto Library", - "algorithm_type": "HMAC", - "date": "10/28/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2602", - "vendor": "Sage Microelectronics Corp", - "implementation": "SHA Library", - "algorithm_type": "SHS", - "date": "12/5/2014" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2701", - "vendor": "Cisco Systems, Inc.", - "implementation": "Cisco_SSL_Implementation-1", - "algorithm_type": "HMAC", - "date": "10/28/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Altera Canada", - "implementation": "AES-256 Core", - "algorithm_type": "AES", - "date": "3/8/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2600", - "vendor": "IBM Corporation", - "implementation": "Cryptographic Security Kernel", - "algorithm_type": "SHS", - "date": "12/5/2014" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto Kernel Module (Optimized SHA, A6X)", - "algorithm_type": "HMAC", - "date": "11/27/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2700", - "vendor": "VMware, Inc.", - "implementation": "VMware NSS Cryptographic Module", - "algorithm_type": "AES", - "date": "11/29/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3415", - "vendor": "Security First Corp.", - "implementation": "Secure Parser Library", - "algorithm_type": "SHS", - "date": "11/18/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2600", - "vendor": "Hewlett-Packard Development Company, L.P.", - "implementation": "Blade System Virtual Connect", - "algorithm_type": "AES", - "date": "8/16/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Seagate Technology, LLC.", - "implementation": "Karnak SHA in Hardware", - "algorithm_type": "SHS", - "date": "3/21/2014" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2602", - "vendor": "Apple Inc.", - "implementation": "Apple tvOS CoreCrypto Kernel Module v8.0 (Generic Software Implementation)", - "algorithm_type": "TDES", - "date": "12/22/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2702", - "vendor": "McAfee, Inc.", - "implementation": "RSA BSAFE Crypto-J", - "algorithm_type": "AES", - "date": "12/6/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto Kernel Module (Optimized SHA, A6)", - "algorithm_type": "HMAC", - "date": "11/27/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2600", - "vendor": "IBM Corporation", - "implementation": "IBM z/OS(R) Cryptographic Services System SSL - 31bit", - "algorithm_type": "RSA", - "date": "9/1/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2701", - "vendor": "RSA, The Security Division of EMC", - "implementation": "RSA BSAFE® Crypto-J JSAFE and JCE Software Module", - "algorithm_type": "SHS", - "date": "3/13/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3427", - "vendor": "Hewlett Packard Enterprise", - "implementation": "HP ESKM OpenSSL", - "algorithm_type": "AES", - "date": "6/11/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2700", - "vendor": "Axway Inc.", - "implementation": "Axway OpenSSL", - "algorithm_type": "HMAC", - "date": "10/21/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2701", - "vendor": "BlackBerry Certicom", - "implementation": "Security Builder GSE-J Crypto Core", - "algorithm_type": "TDES", - "date": "3/30/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2701", - "vendor": "Cavium, Inc.", - "implementation": "ngfips_rsa", - "algorithm_type": "RSA", - "date": "11/30/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3426", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto Module (KeyWrap A8 32 bit)", - "algorithm_type": "AES", - "date": "6/11/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3451", - "vendor": "OpenSSL Software Foundation, Inc.", - "implementation": "OpenSSL FIPS Object Module", - "algorithm_type": "AES", - "date": "7/2/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2702", - "vendor": "BlackBerry Certicom", - "implementation": "Security Builder GSE-J Crypto Core", - "algorithm_type": "TDES", - "date": "3/30/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2602", - "vendor": "RSA Security, Inc.", - "implementation": "RSA BSAFE® Crypto-J Software Module", - "algorithm_type": "AES", - "date": "8/16/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2601", - "vendor": "EFJohnson Technologies", - "implementation": "EFJ Communication Cryptographic Library", - "algorithm_type": "HMAC", - "date": "6/10/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2601", - "vendor": "Dell, Inc.", - "implementation": "Dell AppAssure Crypto Library", - "algorithm_type": "AES", - "date": "8/16/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2600", - "vendor": "Cisco Systems, Inc.", - "implementation": "IOS Common Cryptographic Module (IC2M) Algorithm Module", - "algorithm_type": "HMAC", - "date": "6/10/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2700", - "vendor": "Juniper Networks, Inc.", - "implementation": "Junos OS 17.4R1-S1 - Dataplane", - "algorithm_type": "TDES", - "date": "3/30/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3447", - "vendor": "Apple Inc.", - "implementation": "Apple OSX CoreCrypto Module (Optimized SHA nosse)", - "algorithm_type": "SHS", - "date": "12/2/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3447", - "vendor": "FireEye, Inc.", - "implementation": "FireEye Algorithms Implementation", - "algorithm_type": "AES", - "date": "7/2/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3426", - "vendor": "Apple Inc.", - "implementation": "Apple Secure Key Store CoreCrypto Module (VNG)", - "algorithm_type": "HMAC", - "date": "1/26/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Apple Inc.", - "implementation": "Apple CoreCrypto Kernel Module v9.0 for ARM (iOS12, A11 Bionic, Assembler_VNG)", - "algorithm_type": "DRBG", - "date": "9/21/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3447", - "vendor": "Red Hat, Inc.", - "implementation": "OpenSSL (no AVX2/AVX/AESNI/SSSE3, x86_64, 64-bit library)", - "algorithm_type": "HMAC", - "date": "2/9/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Canonical Ltd.", - "implementation": "OpenSSL using assembler for AES and SHA", - "algorithm_type": "RSA", - "date": "1/27/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2700", - "vendor": "Apple Inc.", - "implementation": "Apple OSX CoreCrypto Module (Generic, Xeon)", - "algorithm_type": "SHS", - "date": "3/13/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2602", - "vendor": "Mercury Systems", - "implementation": "FIPS-ALGORITHMS.1.5.0v", - "algorithm_type": "HMAC", - "date": "6/10/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3451", - "vendor": "Apple Inc.", - "implementation": "Apple OSX CoreCrypto Module (Optimized SHA nosse)", - "algorithm_type": "SHS", - "date": "12/2/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3464", - "vendor": "Hewlett-Packard Development Company, L.P.", - "implementation": "HPE Secure Encryption Engine v1.1", - "algorithm_type": "HMAC", - "date": "2/9/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Symantec Corporation", - "implementation": "Symantec PGP Cryptographic Engine", - "algorithm_type": "SHS", - "date": "3/7/2014" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3427", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto Module (Generic)", - "algorithm_type": "SHS", - "date": "12/2/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3427", - "vendor": "Forcepoint", - "implementation": "Forcepoint NGFW FIPS Java API", - "algorithm_type": "HMAC", - "date": "1/26/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2700", - "vendor": "Brocade Communications Systems LLC", - "implementation": "Brocade Fabric OS FIPS Cryptographic Module", - "algorithm_type": "RSA", - "date": "11/30/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2702", - "vendor": "DELPHI", - "implementation": "DELPHI RSA2048 Signature Verification Algorithm Implementation", - "algorithm_type": "RSA", - "date": "11/30/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2601", - "vendor": "Oracle Corporation", - "implementation": "Oracle Linux 7 GnuTLS C Implementation", - "algorithm_type": "TDES", - "date": "12/22/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Apple Inc.", - "implementation": "Apple CoreCrypto Kernel Module v9.0 for ARM (iOS12, A10X Fusion, Assembler_VNG)", - "algorithm_type": "DRBG", - "date": "9/21/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2600", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto v8 Kernel Module (Generic Software Implementation)", - "algorithm_type": "TDES", - "date": "12/15/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Canonical Ltd.", - "implementation": "OpenSSL using support from Power ISA 2.07 for AES and SHA", - "algorithm_type": "RSA", - "date": "1/27/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3426", - "vendor": "Apple Inc.", - "implementation": "Apple iOS CoreCrypto Module (Generic)", - "algorithm_type": "SHS", - "date": "12/2/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2702", - "vendor": "OpenSSL Validation Services, Inc.", - "implementation": "OpenSSL FIPS Object Module", - "algorithm_type": "SHS", - "date": "3/13/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2601", - "vendor": "Canon Inc.", - "implementation": "SHA256 Library on Canon MFP Security Chip", - "algorithm_type": "SHS", - "date": "12/5/2014" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2701", - "vendor": "VMware, Inc.", - "implementation": "VMware Cryptographic Module", - "algorithm_type": "AES", - "date": "11/29/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2352", - "vendor": "Juniper Networks, Inc.", - "implementation": "Junos FIPS Version Junos 15.1 X49 - Dataplane_CN7130", - "algorithm_type": "TDES", - "date": "1/19/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Juniper Networks, Inc.", - "implementation": "Junos FIPS Version Junos 15.1 X49 - Dataplane_CN7020", - "algorithm_type": "TDES", - "date": "1/19/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2601", - "vendor": "IBM Corporation", - "implementation": "IBM z/OS(R) Cryptographic Services System SSL - 64bit", - "algorithm_type": "RSA", - "date": "9/1/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2602", - "vendor": "Legion of the Bouncy Castle Inc.", - "implementation": "Bouncy Castle FIPS Java API", - "algorithm_type": "RSA", - "date": "9/1/2017" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "2351", - "vendor": "Samsung Electronics Co., Ltd", - "implementation": "Samsung OpenSSL Cryptographic Module", - "algorithm_type": "AES", - "date": "3/8/2013" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3464", - "vendor": "Apple Inc.", - "implementation": "Apple OSX CoreCrypto Module (Generic)", - "algorithm_type": "SHS", - "date": "12/9/2016" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3451", - "vendor": "Red Hat, Inc.", - "implementation": "OpenSSL (no AVX2/AVX/AESNI, x86_64, 64-bit library)", - "algorithm_type": "HMAC", - "date": "2/9/2018" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3464", - "vendor": "Certicom Corp.", - "implementation": "Security Builder Linux Kernel Crypto Core", - "algorithm_type": "AES", - "date": "7/10/2015" - }, - { - "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", - "cert_id": "3415", - "vendor": "Motorola Solutions Inc", - "implementation": "Motorola Solutions Subscriber µMace AES256", - "algorithm_type": "AES", - "date": "6/5/2015" - } - ] -} \ No newline at end of file diff --git a/tests/data/fips/dataset/alg_dataset.json b/tests/data/fips/dataset/alg_dataset.json new file mode 100644 index 00000000..95ef875a --- /dev/null +++ b/tests/data/fips/dataset/alg_dataset.json @@ -0,0 +1,29 @@ +{ + "_type": "sec_certs.dataset.fips_algorithm.FIPSAlgorithmDataset", + "algs": { + "AES5331": { + "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", + "alg_number": "5331", + "algorithm_type": "AES", + "vendor": "Juniper Networks, Inc.", + "implementation_name": "OpenSSL - EX4300", + "validation_date": "3/23/2018" + }, + "AES779": { + "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", + "alg_number": "779", + "algorithm_type": "AES", + "vendor": "NetLogic Microsystems, Inc.", + "implementation_name": "XLS Processor", + "validation_date": "5/13/2008" + }, + "AES2902": { + "_type": "sec_certs.sample.fips_algorithm.FIPSAlgorithm", + "alg_number": "2902", + "algorithm_type": "AES", + "vendor": "Hewlett-Packard Development Company, L.P.", + "implementation_name": "HP Secure Encryption Engine v1.0", + "validation_date": "7/10/2014" + } + } +} \ No newline at end of file diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index 7446e4ad..14b4f855 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -5,6 +5,7 @@ "meta_sources_parsed": true, "artifacts_downloaded": true, "pdfs_converted": true, + "auxillary_datasets_processed": true, "certs_analyzed": false }, "timestamp": "2022-11-23 16:23:44.899168", diff --git a/tests/fips/test_fips_algorithm_dataset.py b/tests/fips/test_fips_algorithm_dataset.py index 73f38b9a..9f1d0008 100644 --- a/tests/fips/test_fips_algorithm_dataset.py +++ b/tests/fips/test_fips_algorithm_dataset.py @@ -1,16 +1,66 @@ from pathlib import Path -from tempfile import TemporaryDirectory +from typing import Any, Dict import pytest +import tests.data.fips.dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset +from sec_certs.sample.fips_algorithm import FIPSAlgorithm -@pytest.mark.skip(reason="FIPSAlgorithmDataset yet to undergo refactoring.") +@pytest.mark.xfail(reason="May fail due to errors with NIST server.") @pytest.mark.slow -def test_get_certs_from_web(self): - with TemporaryDirectory() as tmp_dir: - web_path = Path(tmp_dir) / "web" - web_path.mkdir() - aset = FIPSAlgorithmDataset({}, web_path / "algorithms", "algorithms", "sample algs") - aset.get_certs_from_web() +def test_alg_dset_from_web(tmp_path): + dset = FIPSAlgorithmDataset.from_web(tmp_path) + assert dset + assert "TDES2840" in dset + assert "SHS1619" in dset + assert "RNG447" in dset + assert len(dset) > 30000 + + +@pytest.fixture(scope="module") +def alg_dset_path() -> Path: + return Path(tests.data.fips.dataset.__path__[0]) / "alg_dataset.json" + + +@pytest.fixture(scope="module") +def alg_dset(alg_dset_path: Path) -> FIPSAlgorithmDataset: + return FIPSAlgorithmDataset.from_json(alg_dset_path) + + +@pytest.fixture(scope="module") +def alg_dict() -> Dict[str, Any]: + return { + "alg_number": "2902", + "algorithm_type": "AES", + "vendor": "Hewlett-Packard Development Company, L.P.", + "implementation_name": "HP Secure Encryption Engine v1.0", + "validation_date": "7/10/2014", + } + + +def test_alg_dset_lookup_dict(alg_dset: FIPSAlgorithmDataset): + alg = alg_dset["AES2902"] + assert alg_dset.alg_number_to_algs["2902"] == {alg} + + +def test_alg_from_to_dict(alg_dict: Dict[str, Any]): + alg = FIPSAlgorithm.from_dict(alg_dict) + ret = alg.to_dict() + assert alg_dict == ret + other_alg = FIPSAlgorithm.from_dict(ret) + assert alg == other_alg + + +def test_to_pandas(alg_dset: FIPSAlgorithmDataset): + df = alg_dset.to_pandas() + assert df.shape == (len(alg_dset), len(FIPSAlgorithm.pandas_columns) - 1) + assert df.index.name == "dgst" + assert set(df.columns) == set(FIPSAlgorithm.pandas_columns) - {"dgst"} + + +def test_serialization_missing_path(): + dummy_dset = FIPSAlgorithmDataset() + with pytest.raises(ValueError): + dummy_dset.to_json() From 25d42fc4473f7fb2b5596cb157f412e64f818810 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 25 Nov 2022 12:15:58 +0100 Subject: [PATCH 35/81] update flake8 CI workflow --- .github/workflows/lint.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 1c9741da..c908adb6 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -7,10 +7,10 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - - name : Setup python + - name: Setup python uses: actions/setup-python@v2 with: - python-version: '3.9' + python-version: "3.9" - name: Install external dependencies run: sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python3-dev -y - name: Install python dependencies @@ -33,16 +33,16 @@ jobs: python-version: 3.8 - uses: isort/isort-action@master with: - requirementsFiles: "requirements/requirements.txt requirements/dev_requirements.txt" + requirementsFiles: "requirements/requirements.txt requirements/dev_requirements.txt" flake8-lint: runs-on: ubuntu-latest name: Flake8 steps: - name: Check out source repository - uses: actions/checkout@v2 + uses: actions/checkout@v3 - name: Set up Python environment - uses: actions/setup-python@v1 + uses: actions/setup-python@v4 with: python-version: "3.8" - name: flake8 Lint - uses: py-actions/flake8@v2 \ No newline at end of file + uses: py-actions/flake8@v2 From 67fc6676ba73f16ae83116a7fc5a7ba2723bbf3e Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 25 Nov 2022 12:20:36 +0100 Subject: [PATCH 36/81] update flake8 config --- .flake8 | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/.flake8 b/.flake8 index a8a34373..07ddab5e 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,6 @@ [flake8] max-line-length = 120 -exclude = +exclude = .git, __pycache__, build, @@ -13,10 +13,13 @@ max-complexity = 10 # Ignore complexity for CLI per-file-ignores = - cc_cli.py: C901, + cc_cli.py: C901, fips_cli.py: C901 -ignore = - E501, # line length, should be handleded by black - W503, # line break before binary operator, depracated - E203, # whitespace before :, not PEP8 compliant +ignore = + # line length, should be handleded by black + E501, + # line break before binary operator, depracated + W503, + # whitespace before :, not PEP8 compliant + E203, From 97dce4830e87a00786636dd6dadb202906b0b10a Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 25 Nov 2022 14:11:33 +0100 Subject: [PATCH 37/81] cleanup --- notebooks/cc/cert_id_eval.ipynb | 16 +++++++--------- notebooks/examples/common_criteria.ipynb | 18 ++++++++++-------- notebooks/examples/fips.ipynb | 21 ++++++--------------- 3 files changed, 23 insertions(+), 32 deletions(-) diff --git a/notebooks/cc/cert_id_eval.ipynb b/notebooks/cc/cert_id_eval.ipynb index 65d14225..ad1abde2 100644 --- a/notebooks/cc/cert_id_eval.ipynb +++ b/notebooks/cc/cert_id_eval.ipynb @@ -457,18 +457,11 @@ "print(f\"\\\\newcommand{{\\\\numCcRefEvalInReport}}{{{sum((manual_references.location == 'report') & (manual_references.reason != 'self'))}}}\")\n", "print(f\"\\\\newcommand{{\\\\numCcRefEvalInTarget}}{{{sum((manual_references.location == 'target') & (manual_references.reason != 'self'))}}}\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -482,7 +475,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.7" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, diff --git a/notebooks/examples/common_criteria.ipynb b/notebooks/examples/common_criteria.ipynb index e096e640..87502302 100644 --- a/notebooks/examples/common_criteria.ipynb +++ b/notebooks/examples/common_criteria.ipynb @@ -217,19 +217,16 @@ "source": [ "dset = CCDataset()\n", "dset.get_certs_from_web()\n", - "dset.process_protection_profiles()\n", - "dset.download_all_pdfs()\n", + "dset.process_auxillary_datasets()\n", + "dset.download_all_artifacts()\n", "dset.convert_all_pdfs()\n", "dset.analyze_certificates()" ] } ], "metadata": { - "interpreter": { - "hash": "6386d1612879d92d026c363e7667e428bc38d86c5a080d58c3d70e7cd43df67d" - }, "kernelspec": { - "display_name": "Python 3.8.1 ('certsvenv': venv)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -243,9 +240,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.8.13" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/notebooks/examples/fips.ipynb b/notebooks/examples/fips.ipynb index 28eb2b1a..6d9edee4 100644 --- a/notebooks/examples/fips.ipynb +++ b/notebooks/examples/fips.ipynb @@ -69,7 +69,7 @@ "outputs": [], "source": [ "# Get certificates from a single manufacturer\n", - "cisco_certs = [cert for cert in dset if \"Cisco\" in cert.web_scan.vendor]\n", + "cisco_certs = [cert for cert in dset if \"Cisco\" in cert.manufacturer]\n", "\n", "# Get certificates with some CVE\n", "vulnerable_certs = [cert for cert in dset if cert.heuristics.related_cves]\n", @@ -94,9 +94,9 @@ "# Select a certificate and print some attributes\n", "cert: FIPSCertificate = dset[\"542cacae1d41132a\"]\n", "\n", - "print(f\"{cert.web_scan.module_name=}\")\n", + "print(f\"{cert.web_data.module_name=}\")\n", "print(f\"{cert.heuristics.cpe_matches=}\")\n", - "print(f\"{cert.web_scan.level=}\")" + "print(f\"{cert.web_data.level=}\")" ] }, { @@ -134,19 +134,10 @@ "source": [ "dset = FIPSDataset()\n", "dset.get_certs_from_web()\n", + "dset.process_auxillary_datasets()\n", + "dset.download_all_artifacts()\n", "dset.convert_all_pdfs()\n", - "dset.pdf_scan()\n", - "dset.extract_certs_from_tables()\n", - "dset.finalize_results()\n", - "\n", - "# TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/210 and refactor this\n", - "# if not no_download_algs:\n", - "# aset.get_certs_from_web()\n", - "# logger.info(f\"Finished parsing. Have algorithm dataset with {len(aset)} algorithm numbers.\")\n", - "# dset.algorithms = aset\n", - "\n", - "# TODO: Resolve https://github.com/crocs-muni/sec-certs/issues/211 and uncomment\n", - "# dset.plot_graphs(show=False)" + "dset.analyze_certificates()" ] } ], From 4d0ae4064f0e4980c698a90286e757c0472db5f6 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 25 Nov 2022 14:28:06 +0100 Subject: [PATCH 38/81] clean-up, update docs, cli --- Dockerfile | 3 -- docs/installation.md | 2 - docs/quickstart.md | 2 +- requirements/requirements.in | 1 + requirements/requirements.txt | 22 +++++++---- sec_certs/__main__.py | 3 ++ cli.py => sec_certs/cli.py | 0 sec_certs/dataset/common_criteria.py | 3 +- sec_certs/dataset/dataset.py | 1 - sec_certs/dataset/fips.py | 6 +-- sec_certs/model/cpe_matching.py | 2 +- sec_certs/sample/fips.py | 59 ++++++---------------------- setup.py | 2 +- tests/fips/test_fips_dataset.py | 57 ++++++++++++++------------- 14 files changed, 69 insertions(+), 94 deletions(-) create mode 100644 sec_certs/__main__.py rename cli.py => sec_certs/cli.py (100%) diff --git a/Dockerfile b/Dockerfile index 467f239b..7585236f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -50,9 +50,6 @@ RUN \ pip3 install --no-cache notebook jupyterlab && \ pip3 install -e . -# Download spacy language model -RUN python3 -m spacy download en_core_web_sm - # #just to be sure that pdftotext is in $PATH ENV PATH /usr/bin/pdftotext:${PATH} diff --git a/docs/installation.md b/docs/installation.md index ae3db3f3..741bd72e 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -7,7 +7,6 @@ The tool can be installed from PyPi with ```bash pip install -U sec-certs -python3 -m spacy download en_core_web_sm ``` Note, that `Python>=3.8` is required. @@ -32,7 +31,6 @@ git clone https://github.com/crocs-muni/sec-certs.git python3 -m venv venv source venv/bin/activate pip install -e . -python3 -m spacy download en_core_web_sm ``` Alternatively, our Our [Dockerfile](https://github.com/crocs-muni/sec-certs/blob/main/Dockerfile) represents a reproducible way of setting up the environment. diff --git a/docs/quickstart.md b/docs/quickstart.md index 9b2f9ed7..de0c64f0 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -53,7 +53,7 @@ This script takes a long time to run (few hours) and will create `./cc_dset` or ## Run sec-certs from docker -If you installed the docker image (see [installation](installation.md)), use `docker run -it sec-certs` to run the container interactively. From there, you can run the `cc-certs` CLI or `fips-certs` CLI. Alternatively, you can serve a Jupyter notebook from the docker to use at your host machine and even write your scripts to some shared folder. Example of both use-cases follow. +If you installed the docker image (see [installation](installation.md)), use `docker run -it sec-certs` to run the container interactively. From there, you can run the `sec-certs` CLI. Alternatively, you can serve a Jupyter notebook from the docker to use at your host machine and even write your scripts to some shared folder. Example of that use-case follows. ### Persistent files with docker mounts diff --git a/requirements/requirements.in b/requirements/requirements.in index 74c60649..77192041 100644 --- a/requirements/requirements.in +++ b/requirements/requirements.in @@ -22,6 +22,7 @@ setuptools-scm ipykernel ipywidgets spacy +en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1.tar.gz pkgconfig seaborn pySankeyBeta diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ad8bba1e..53c0dbe0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -20,10 +20,8 @@ billiard==3.6.4.0 # via -r requirements.in bleach==5.0.0 # via nbconvert -blis==0.7.7 - # via - # spacy - # thinc +blis==0.7.9 + # via thinc catalogue==2.0.7 # via # spacy @@ -39,6 +37,8 @@ click==8.1.2 # via # -r requirements.in # typer +confection==0.0.3 + # via thinc cycler==0.11.0 # via matplotlib cymem==2.0.6 @@ -54,6 +54,8 @@ defusedxml==0.7.1 # via nbconvert distro==1.7.0 # via tabula-py +en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1.tar.gz + # via -r requirements.in entrypoints==0.4 # via # jupyter-client @@ -232,6 +234,7 @@ pycparser==2.21 # via cffi pydantic==1.8.2 # via + # confection # spacy # thinc pygments==2.11.2 @@ -293,14 +296,17 @@ smart-open==5.2.1 # via pathy soupsieve==2.3.1 # via beautifulsoup4 -spacy==3.3.0 - # via -r requirements.in -spacy-legacy==3.0.9 +spacy==3.4.3 + # via + # -r requirements.in + # en-core-web-sm +spacy-legacy==3.0.10 # via spacy spacy-loggers==1.0.2 # via spacy srsly==2.4.3 # via + # confection # spacy # thinc stack-data==0.2.0 @@ -309,7 +315,7 @@ tabula-py==2.3.0 # via -r requirements.in terminado==0.13.3 # via notebook -thinc==8.0.15 +thinc==8.1.5 # via spacy threadpoolctl==3.1.0 # via scikit-learn diff --git a/sec_certs/__main__.py b/sec_certs/__main__.py new file mode 100644 index 00000000..4bf567a3 --- /dev/null +++ b/sec_certs/__main__.py @@ -0,0 +1,3 @@ +from sec_certs.cli import main + +main() diff --git a/cli.py b/sec_certs/cli.py similarity index 100% rename from cli.py rename to sec_certs/cli.py diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index a9eb1877..7313ec95 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -675,6 +675,7 @@ def _compute_dependency_vulnerabilities(self): def _compute_heuristics(self, fresh: bool = True) -> None: logger.info("Computing various heuristics on CC certificates.") + self._compute_normalized_cert_ids() super()._compute_heuristics() self._compute_cert_labs() self._compute_sars() @@ -827,8 +828,8 @@ def from_json(cls, input_path: Union[str, Path]) -> "CCDatasetMaintenanceUpdates input_path = Path(input_path) with input_path.open("r") as handle: dset = json.load(handle, cls=CustomJSONDecoder) + dset._root_dir = Path(input_path).parent return dset - # TODO: This does not set the root_path properly def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame( diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index a22e7317..25245686 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -287,7 +287,6 @@ def _extract_data(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _compute_heuristics(self, fresh: bool = True) -> None: - self._compute_normalized_cert_ids() self.compute_cpe_heuristics() self.compute_related_cves() self._compute_dependencies() diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 3d2f1f63..eef88a8c 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -298,10 +298,8 @@ def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> ) def _clean_cert_ids(self, current_cert: FIPSCertificate) -> None: + # TODO: Refactor me current_cert.clean_cert_ids() - # TODO: Fix me, txt state no longer available - # if not current_cert.state.txt_state: - # return current_cert.heuristics.clean_cert_ids = { cert_id: count for cert_id, count in current_cert.pdf_data.clean_cert_ids.items() @@ -322,6 +320,7 @@ def _match_with_algorithm(processed_cert: FIPSCertificate, cert_candidate_id: st return True def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) -> bool: + # TODO: Refactor me candidate_dgst = fips_dgst(cert_candidate_id) if candidate_dgst not in self.certs or not cert_candidate_id.isdecimal(): return False @@ -350,6 +349,7 @@ def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) return True def _compute_dependencies(self, fresh: bool = True) -> None: + # TODO: Refactor me def pdf_lookup(cert): return set( filter( diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index b5d5e7f0..23a683ee 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -356,7 +356,7 @@ def simple_startswith(seeked_version: str, checked_string: str) -> bool: if not cpe_version: return False - just_numbers = r"(\d{1,5})(\.\d{1,5})" # TODO: The use of this should be double-checked + just_numbers = r"(\d{1,5})(\.\d{1,5})" # This assures that on cert version with at least two tokens, we don't match only one-token CPE. # E.g. cert with version 7.6 must not match CPE record of version 7 diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index abb2ee83..5275f726 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -38,14 +38,6 @@ def __init__(self, soup: BeautifulSoup): def get_web_data_and_algorithms(self) -> Tuple[Set[str], FIPSCertificate.WebData]: divs = self._soup.find_all("div", class_="panel panel-default") details_div, vendor_div, related_files_div, validation_history_div = divs - - # TODO: Move the assertions to tests - assert len(divs) == 4 - assert details_div.find("h4").text == "Details" - assert vendor_div.find("h4").text == "Vendor" - assert related_files_div.find("h4").text == "Related Files" - assert validation_history_div.find("h4").text == "Validation History" - details_dict = self._build_details_dict(details_div) vendor_dict = self._build_vendor_dict(vendor_div) @@ -382,39 +374,20 @@ class PdfData(BasePdfData, ComplexSerializableType): clean_cert_ids: Dict[str, int] = field(default_factory=dict) st_metadata: Dict[str, Any] = field(default_factory=dict) - # TODO: Is this meaningful? Cert id attribute got deleted. - # def __repr__(self) -> str: - # return str(self.cert_id) - - # def __str__(self) -> str: - # return str(self.cert_id) - @dataclass(eq=True) class Heuristics(BaseHeuristics, ComplexSerializableType): """ Data structure that holds data obtained by processing the certificate and applying various heuristics. """ - # TODO: How are keywords, clean_cert_ids and algorithms attributes different from those in pdf data? - keywords: Dict[str, Dict] = field(default_factory=dict) algorithms: Set[str] = field(default_factory=set) - unmatched_algs: Optional[int] = field(default=None) clean_cert_ids: Optional[Dict[str, int]] = field(default=None) - extracted_versions: Set[str] = field(default_factory=set) cpe_matches: Optional[Set[str]] = field(default=None) verified_cpe_matches: Optional[Set[CPE]] = field(default=None) related_cves: Optional[Set[str]] = field(default=None) - - st_references: References = field(default_factory=References) - web_references: References = field(default_factory=References) - - @property - def dgst(self) -> str: - return helpers.get_first_16_bytes_sha256(str(self.keywords)) - - def __str__(self) -> str: - return str(self.cert_id) + policy_references: References = field(default_factory=References) + module_references: References = field(default_factory=References) @property def dgst(self) -> str: @@ -423,7 +396,6 @@ def dgst(self) -> str: """ return fips_dgst(self.cert_id) - # TODO: Fix type errors, they exist because FIPS uses this as property to change variable names, while CC and abstract class have variables @property def manufacturer(self) -> Optional[str]: # type: ignore return self.web_data.vendor @@ -490,14 +462,14 @@ def pandas_tuple(self) -> Tuple: self.heuristics.cpe_matches, self.heuristics.verified_cpe_matches, self.heuristics.related_cves, - self.heuristics.web_references.directly_referenced_by, - self.heuristics.web_references.indirectly_referenced_by, - self.heuristics.web_references.directly_referencing, - self.heuristics.web_references.indirectly_referencing, - self.heuristics.st_references.directly_referenced_by, - self.heuristics.st_references.indirectly_referenced_by, - self.heuristics.st_references.directly_referencing, - self.heuristics.st_references.indirectly_referencing, + self.heuristics.module_references.directly_referenced_by, + self.heuristics.module_references.indirectly_referenced_by, + self.heuristics.module_references.directly_referencing, + self.heuristics.module_references.indirectly_referencing, + self.heuristics.policy_references.directly_referenced_by, + self.heuristics.policy_references.indirectly_referenced_by, + self.heuristics.policy_references.directly_referencing, + self.heuristics.policy_references.indirectly_referencing, ) @staticmethod @@ -576,13 +548,13 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): if table_rich_page_numbers := tables.find_pages_with_tables(cert.state.policy_txt_path): pdf.repair_pdf(cert.state.policy_pdf_path) try: - tabular_data = read_pdf(cert.state.policy_pdf_path, pages=table_rich_page_numbers, silent=True) + tabular_data = read_pdf(cert.state.policy_pdf_path, pages=list(table_rich_page_numbers), silent=True) except Exception as e: logger.warning(f"Error when parsing tables from {cert.dgst}: {e}") cert.state.policy_extract_ok = False cert.heuristics.algorithms |= set( - itertools.chain.from_iterable([tables.get_algs_from_table(df) for df in tabular_data]) + itertools.chain.from_iterable([tables.get_algs_from_table(df.to_string()) for df in tabular_data]) ) def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: @@ -609,13 +581,10 @@ def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> # continue def clean_cert_ids(self) -> None: + # TODO: Refactor me """ Removes algorithm mentions from the cert_id rule matches and stores them into clean_cert_id matches. """ - # self.state.file_status = True # TODO: Not sure what this was for - if not self.pdf_data.keywords: - return - if "Cert" not in self.pdf_data.keywords["fips_cert_id"]: self.pdf_data.clean_cert_ids = {} return @@ -625,8 +594,6 @@ def clean_cert_ids(self) -> None: alg_set: Set[str] = set() # if self.web_data.algorithms is None: # raise RuntimeError(f"Algorithms were not found for cert {self.cert_id} - this should not be happening.") - - # TODO : Refactor this, dictionary form changed # for algo in self.web_data.algorithms: # alg_set.add(algo.cert_id) diff --git a/setup.py b/setup.py index 5f0cc160..f532da9f 100644 --- a/setup.py +++ b/setup.py @@ -29,7 +29,7 @@ }, include_package_data=True, package_data={"sec_certs": ["settings.yaml", "settings-schema.json", "rules.yaml"]}, - entry_points={"console_scripts": ["sec-certs=cli:main"]}, + entry_points={"console_scripts": ["sec-certs=sec_certs.cli:main"]}, project_urls={ "Project homepage": "https://seccerts.org", "GitHub repository": "https://github.com/crocs-muni/sec-certs/", diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 32176f73..a7c9247b 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -278,15 +278,18 @@ def test_docusign_chunk(certs_to_process): certs = certs_to_process["docusign"] with TemporaryDirectory() as tmp_dir: dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) - assert set(dataset.certs[fips_dgst("3850")].heuristics.st_references.directly_referencing) == {"1883", "3518"} - assert set(dataset.certs[fips_dgst("3850")].heuristics.web_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2779")].heuristics.st_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2860")].heuristics.st_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2665")].heuristics.st_references.directly_referencing) == {"1883"} - assert not dataset.certs[fips_dgst("1883")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("3518")].heuristics.st_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("3141")].heuristics.st_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2590")].heuristics.st_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("3850")].heuristics.policy_references.directly_referencing) == { + "1883", + "3518", + } + assert set(dataset.certs[fips_dgst("3850")].heuristics.module_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2779")].heuristics.policy_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2860")].heuristics.policy_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2665")].heuristics.policy_references.directly_referencing) == {"1883"} + assert not dataset.certs[fips_dgst("1883")].heuristics.policy_references.directly_referencing + assert set(dataset.certs[fips_dgst("3518")].heuristics.policy_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("3141")].heuristics.policy_references.directly_referencing) == {"1883"} + assert set(dataset.certs[fips_dgst("2590")].heuristics.policy_references.directly_referencing) == {"1883"} @pytest.mark.skip(reason="FIPS tests to-be-refactored") @@ -295,24 +298,24 @@ def test_openssl_chunk(certs_to_process): certs = certs_to_process["referencing_openssl"] with TemporaryDirectory() as tmp_dir: dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) - assert set(dataset.certs[fips_dgst("3493")].heuristics.st_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3495")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("3711")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("3176")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("3488")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("3126")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("3126")].heuristics.web_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3269")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("3524")].heuristics.web_references.directly_referencing) == {"3220"} - assert set(dataset.certs[fips_dgst("3220")].heuristics.st_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3220")].heuristics.web_references.directly_referencing - assert not dataset.certs[fips_dgst("2398")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("3543")].heuristics.web_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("2676")].heuristics.web_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("3313")].heuristics.web_references.directly_referencing) == {"3220"} - assert not dataset.certs[fips_dgst("3363")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("3608")].heuristics.st_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("3158")].heuristics.web_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("3493")].heuristics.policy_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3495")].heuristics.policy_references.directly_referencing + assert not dataset.certs[fips_dgst("3711")].heuristics.policy_references.directly_referencing + assert not dataset.certs[fips_dgst("3176")].heuristics.policy_references.directly_referencing + assert not dataset.certs[fips_dgst("3488")].heuristics.policy_references.directly_referencing + assert not dataset.certs[fips_dgst("3126")].heuristics.policy_references.directly_referencing + assert set(dataset.certs[fips_dgst("3126")].heuristics.module_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3269")].heuristics.policy_references.directly_referencing + assert set(dataset.certs[fips_dgst("3524")].heuristics.module_references.directly_referencing) == {"3220"} + assert set(dataset.certs[fips_dgst("3220")].heuristics.policy_references.directly_referencing) == {"2398"} + assert not dataset.certs[fips_dgst("3220")].heuristics.module_references.directly_referencing + assert not dataset.certs[fips_dgst("2398")].heuristics.policy_references.directly_referencing + assert set(dataset.certs[fips_dgst("3543")].heuristics.module_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("2676")].heuristics.module_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("3313")].heuristics.module_references.directly_referencing) == {"3220"} + assert not dataset.certs[fips_dgst("3363")].heuristics.policy_references.directly_referencing + assert set(dataset.certs[fips_dgst("3608")].heuristics.policy_references.directly_referencing) == {"2398"} + assert set(dataset.certs[fips_dgst("3158")].heuristics.module_references.directly_referencing) == {"2398"} def test_dataset_to_json(toy_dataset: FIPSDataset, data_dir: Path, tmp_path: Path): From cb879f30cbcad3352d892d7edb37d3d8edf17570 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 29 Nov 2022 11:34:08 +0100 Subject: [PATCH 39/81] fix json objects for fips test --- sec_certs/dataset/common_criteria.py | 4 +- sec_certs/dataset/dataset.py | 8 +- sec_certs/dataset/fips.py | 4 +- tests/cc/test_cc_analysis.py | 6 +- tests/data/fips/certificate/crt.json | 6 +- .../data/fips/certificate/fictional_cert.json | 6 +- tests/data/fips/dataset/toy_dataset.json | 132 ++++++------------ 7 files changed, 59 insertions(+), 107 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 7313ec95..ea8f1d75 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -662,7 +662,7 @@ def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: for cert in self: cert.compute_heuristics_cert_id() - def _compute_dependency_vulnerabilities(self): + def _compute_transitive_vulnerabilities(self): logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") cve_dependency_finder = DependencyVulnerabilityFinder() cve_dependency_finder.fit(self.certs) @@ -686,7 +686,7 @@ def _compute_sars(self) -> None: for cert in self: cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) - def _compute_dependencies(self, fresh: bool = True) -> None: + def _compute_references(self, fresh: bool = True) -> None: def ref_lookup(kw_attr): def func(cert): kws = getattr(cert.pdf_data, kw_attr) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 25245686..dbb871b1 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -289,19 +289,19 @@ def _extract_data(self, fresh: bool = True) -> None: def _compute_heuristics(self, fresh: bool = True) -> None: self.compute_cpe_heuristics() self.compute_related_cves() - self._compute_dependencies() - self._compute_dependency_vulnerabilities() + self._compute_references() + self._compute_transitive_vulnerabilities() @abstractmethod def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def _compute_dependencies(self, fresh: bool = True) -> None: + def _compute_references(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: + def _compute_transitive_vulnerabilities(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _prepare_cpe_dataset(self, download_fresh_cpes: bool = False) -> CPEDataset: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index eef88a8c..c73f2775 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -307,7 +307,7 @@ def _clean_cert_ids(self, current_cert: FIPSCertificate) -> None: and cert_id != current_cert.cert_id } - def _compute_dependency_vulnerabilities(self, fresh: bool = True) -> None: + def _compute_transitive_vulnerabilities(self, fresh: bool = True) -> None: # TODO: Implement me pass @@ -348,7 +348,7 @@ def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) return False return True - def _compute_dependencies(self, fresh: bool = True) -> None: + def _compute_references(self, fresh: bool = True) -> None: # TODO: Refactor me def pdf_lookup(cert): return set( diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index c66a402e..0308084d 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -202,7 +202,7 @@ def test_single_record_dependency_heuristics(random_certificate: CommonCriteriaC def test_dependency_dataset(dependency_dataset: CCDataset): - dependency_dataset._compute_dependencies() + dependency_dataset._compute_references() test_cert = dependency_dataset["692e91451741ef49"] assert test_cert.heuristics.report_references.directly_referenced_by == {"BSI-DSZ-CC-0370-2006"} @@ -215,12 +215,12 @@ def test_dependency_dataset(dependency_dataset: CCDataset): def test_direct_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): - dependency_vulnerability_dataset._compute_dependency_vulnerabilities() + dependency_vulnerability_dataset._compute_transitive_vulnerabilities() assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.direct_dependency_cves == {"CVE-2013-5385"} def test_indirect_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): - dependency_vulnerability_dataset._compute_dependency_vulnerabilities() + dependency_vulnerability_dataset._compute_transitive_vulnerabilities() assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.indirect_dependency_cves == {"CVE-2013-5385"} diff --git a/tests/data/fips/certificate/crt.json b/tests/data/fips/certificate/crt.json index 1e8ad260..36cdcef3 100644 --- a/tests/data/fips/certificate/crt.json +++ b/tests/data/fips/certificate/crt.json @@ -35,12 +35,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -49,14 +47,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json index 1e8ad260..36cdcef3 100644 --- a/tests/data/fips/certificate/fictional_cert.json +++ b/tests/data/fips/certificate/fictional_cert.json @@ -35,12 +35,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -49,14 +47,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index 14b4f855..c4a4994f 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -51,12 +51,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -65,14 +63,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -129,12 +127,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -143,14 +139,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -207,12 +203,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -221,14 +215,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -285,12 +279,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -299,14 +291,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -363,12 +355,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -377,14 +367,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -441,12 +431,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -455,14 +443,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -519,12 +507,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -533,14 +519,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -597,12 +583,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -611,14 +595,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -675,12 +659,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -689,14 +671,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -753,12 +735,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -767,14 +747,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -831,12 +811,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -845,14 +823,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -909,12 +887,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -923,14 +899,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -987,12 +963,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1001,14 +975,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1065,12 +1039,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1079,14 +1051,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1143,12 +1115,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1157,14 +1127,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1221,12 +1191,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1235,14 +1203,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1299,12 +1267,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1313,14 +1279,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1377,12 +1343,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1391,14 +1355,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1455,12 +1419,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1469,14 +1431,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1533,12 +1495,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1547,14 +1507,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1611,12 +1571,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1625,14 +1583,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1689,12 +1647,10 @@ }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "keywords": {}, "algorithms": { "_type": "Set", "elements": [] }, - "unmatched_algs": null, "clean_cert_ids": null, "extracted_versions": { "_type": "Set", @@ -1703,14 +1659,14 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "st_references": { + "policy_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "web_references": { + "module_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, From 5895c85a92e238ed4fb7b9f5642e293729c4ce2e Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 29 Nov 2022 12:10:59 +0100 Subject: [PATCH 40/81] rename dependency -> references of transitive vulns --- notebooks/cc/references.ipynb | 13 +- notebooks/cc/vulnerabilities.ipynb | 40011 +--------------- sec_certs/dataset/common_criteria.py | 22 +- sec_certs/dataset/fips.py | 6 +- sec_certs/model/__init__.py | 6 +- ...pendency_finder.py => reference_finder.py} | 28 +- ....py => transitive_vulnerability_finder.py} | 48 +- sec_certs/sample/common_criteria.py | 6 +- tests/cc/test_cc_analysis.py | 28 +- tests/data/cc/analysis/cc_full_dataset.json | 4 +- ...cy_dataset.json => reference_dataset.json} | 14 +- ... => transitive_vulnerability_dataset.json} | 14 +- tests/data/cc/certificate/fictional_cert.json | 4 +- .../maintenances/Maintenance updates.json | 4 +- tests/data/cc/dataset/toy_dataset.json | 12 +- 15 files changed, 117 insertions(+), 40103 deletions(-) rename sec_certs/model/{dependency_finder.py => reference_finder.py} (91%) rename sec_certs/model/{dependency_vulnerability_finder.py => transitive_vulnerability_finder.py} (68%) rename tests/data/cc/analysis/{dependency_dataset.json => reference_dataset.json} (99%) rename tests/data/cc/analysis/{dependency_vulnerability_dataset.json => transitive_vulnerability_dataset.json} (99%) diff --git a/notebooks/cc/references.ipynb b/notebooks/cc/references.ipynb index 6017e18c..81a3f664 100644 --- a/notebooks/cc/references.ipynb +++ b/notebooks/cc/references.ipynb @@ -185,12 +185,12 @@ "print(f\"\\\\newcommand{{\\\\numCCActiveDirectReferencing}}{{{df_id_rich.loc[df_id_rich.status == 'active'].has_outgoing_direct_references.sum()}}}\")\n", "\n", "archived_cert_id_list = set(df_id_rich[df_id_rich.status == \"archived\"].cert_id)\n", - "def contains_archived_cert_dependency(referencing):\n", + "def contains_archived_cert_reference(referencing):\n", " if referencing is np.nan:\n", " return False\n", " \n", " return bool(archived_cert_id_list.intersection(referencing))\n", - "print(f\"\\\\newcommand{{\\\\numCCActiveDirectReferencingArchived}}{{{df_id_rich[df_id_rich.status == 'active'].directly_referencing.apply(contains_archived_cert_dependency).sum()}}}\")" + "print(f\"\\\\newcommand{{\\\\numCCActiveDirectReferencingArchived}}{{{df_id_rich[df_id_rich.status == 'active'].directly_referencing.apply(contains_archived_cert_reference).sum()}}}\")" ] }, { @@ -1219,7 +1219,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -1233,7 +1233,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, diff --git a/notebooks/cc/vulnerabilities.ipynb b/notebooks/cc/vulnerabilities.ipynb index 89c422a8..7dcd9b43 100644 --- a/notebooks/cc/vulnerabilities.ipynb +++ b/notebooks/cc/vulnerabilities.ipynb @@ -19,7 +19,7 @@ "- Weak correlations (in a sensible direction, i.e., negative correlations) between expert-selected SARs that could correspond to more secure products and number/severity of vulnerabilities. Same correlations exist on more coarser scale of EALs\n", "- Maintenannce updates very rarely used to address vulnerabilities. Instead, they often make claims about vulnerabilities not being applicable to the *certified version* of the product. The rationale is often mentioned in the non-public Impact Analysis Report\n", "- Approximately 50% of vulns. exist prior to certification, these are seldomly referenced in the certificatation documents (TODO: double-check). \n", - "- In combination with dependency map between certificates, the ecosystem is quite fragile, as a single vulnerability on average affects X certs directly and Y certs indirectly\n", + "- In combination with reference map between certificates, the ecosystem is quite fragile, as a single vulnerability on average affects X certs directly and Y certs indirectly\n", "- Some categories heavily underrepresented in the CPE/CVE landscape, e.g., smartcards" ] }, @@ -347,2523 +347,7 @@ { "data": { "image/png": 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T21:18:47.864895\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -2883,1389 +367,7 @@ { "data": { "image/png": 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25PPPPwcKvmBvvPEG3333ncm5Ozo6EhwczNSpU0lOTgYgJyeHefPmcfv2bdzc3Mq8PorauXOn9ufp6VnqeM899xx2dnYcPXoUKFifQUFBJh2hlOS1114jIyODNWvWaNvL1atXmT9/vsHND61atSI5OZlPPvkEnU5nsDO9F09PT4Nlu5dmzZpx/PhxbXv48ssv+eCDD0xelqNHj9KuXTsGDBiAp6cnBw4cMPodaNKkCRcvXuTcuXNAwR01JYmLi6NmzZqMGjWKVq1aaTtNY/ELlbRPuHz5Mi1btmTdunXa92vkyJF88cUXJcYo+p24l9K2k59//rnU5ahYsSJ5eXkopXj22WdxcnLSPrP//e9/dO/enbi4OJP2G3d75I4AgoKCil2wePfdd9m8eTODBw82+JXt7OxMQEAA69evp3HjxmRmZha7FfTLL780uHVsxowZzJo1C51Oh1KK4cOH4+HhwcKFC5k9ezYRERHk5OSg0+nw9/fXfuWYovBDWbhwIZUqVWLFihXY29szevRowsPDWblyJfb29jRu3Jj//ve/94zVs2dPfvrpJ7p27UrlypV58sknCQwM1L4MANOmTdNuEczNzaVVq1ZlOh1W6OrVq/Ts2RM7OzsWL15M9erVadWqFcHBwbz11lvY2dlRtWpVli9fjp2dHY0aNeLDDz9k9OjRrFixgk6dOvHpp59qxad58+acP39eO10watQoFixYQK9evcjLy+PFF19k8uTJACxcuJCwsDB0Oh05OTl0796dHj16GNz6a8yIESOoXLmydiSYnZ1N06ZNWblypckxCk8R3c3e3r7UnU5JHB0dWbZsGfPmzSM8PBwHBweWLVumndoojZ+fH3PmzClWXBwdHfn888/54IMP0Ol02NvbY29vz8iRI/H399fGK7xwuXjx4hJvc1y3bp3Br22Al19+uUw3UNSvX58JEyYwdOhQAFxcXJg3bx6XL182afr+/fszfvx4bTmaNGnCvn377lkca9asycKFCxk/fjwODg688sorJY7XokULtm3bRufOnalcuTJeXl7UrFmTK1eumJRbafuE0NBQ5s6dq32/mjdvri1/Ue3atWPBggXk5uYanV9p28k//vEPvv766xKX4+mnn+all16iS5cubN68mZUrVzJ37lzWrl3LnTt3ePvtt/H29ubFF18scb9RGjtl7DhUmCQ+Ph6dTqfdWy6EqZYsWYKvry8vvfTSw05F2BirOQUkxF+RUoo6derIzl88FHIEIIQQNkqOAIQQwkZJARBCCBv1yN0FBAX3jd+6davY/fdCCCFKp5QiNzeXxx57rPTmH+7ySBaAW7duceHChYedhhBCWCV3d3eqVatmdLxHsgAUPlno7u5u9B7qQnFxcfdsLKq8JL7Ef1TjW3PuEt+88XNycrhw4UKprRUU9UgWgMLTPo6OjiW2XVOasox7PyS+xH9U41tz7hLf/PFNPXUuF4GFEMJGSQEQQggbJQVACCFslBQAIYSwUVIAhBDCRkkBEEIIG2VSAYiKiqJr16506tSpxDcU7d+/H51OR7du3Zg8ebL2Iu7IyEhatmyJn58ffn5+LFmyxLzZCyGEuG9GnwNITExkyZIlREREaO+dbdasmfaezszMTGbPns2OHTuoXbs2ISEh7Nixg379+hEbG8vkyZO1d6GKB+/S3N4A1AAu7YXnQou/r/WvTjfu3m+70mwy/SU0AFGL/IyOEx8fT+fOnXnyySe1114C9O3bV3t37507d2jbti2+vr5Mnz5dG2fy5Mk0bdrU4OUv9xIREcFPP/3E/PnzCQ4OZs6cOaW+EW3KlCmMGTOGOnXqGPRPTExk2rRprFmzRpv/vd4pe7f09HQmTZrEypUrDeJYwokTJ1i+fDkbNmwgICCAMWPG0KxZM7PFX7p0Kc2bN6dJkyaEhobSv39/PD09mTJlCj///DOjR48mKirqvpavtHX/MBgtAMeOHcPHx4fq1asD4Ovry969exkzZgxQ8F7MwtcfZmZmcv36dZydnYGCd45euXKF1atX4+7uzvTp04u9UzYtLY20tDSDfqa+yk0Ia+Dq6sr7779f4ovkAQ4fPoynpyd79uxh/PjxBoXifhnbMZ04cYLRo0cX6+/m5nbfO+3U1FTtjXXlifMoiI6O1grK3Llztf47duzg9OnTODo60qNHj/uKXdq6fxiMFgC9Xo+Li4vW7erqyunTpw3GcXBw4NChQ0ycOBFXV1datmwJFLw2btiwYXh5ebF48WJmz57NokWLDKZdv349y5cvN+hXu3Ztli5dSlxcXJkWJiYmpkzjl5U1xq9RpNuSy2CN66c8TMknKSmJ7Ozse47/2Wef0bhxY27cuMHKlStp27YtANevX+fy5cv3nM+RI0eIjIykcuXK1K5dm0qVKhETE8PYsWOZPn06t2/fZu3ateTl5eHg4MDw4cOJjo4mISGBwMBAZsyYQWhoKPXq1ePKlSuMHDmSpUuXsnTpUq5fv86OHTvQ6/Xk5eXRq1cvfHx8OHToEGfPntVePxoWFkbv3r3ZvXs3CQkJDBw4kICAAMLCwli6dCmpqamsXr2a5ORk7O3t6devHy+//DLbtm3jxo0bJCQkkJycTLt27ejZs6fB8mVmZrJ69WpSUlK4efMmDRo0YOTIkVy4cIH09HRiYmJIT0/nwoULBi+NV0rx5ZdfEh0djb29Pe3bt6dLly4kJCTw2WefkZGRgaOjI4MGDeKZZ57hk08+IT09ncTERHr06MGpU6eYMGECISEhrF+/Xls+pRTdu3dnyJAh2npKSkqiV69epKWl4ejoyLBhw/j73//Oli1bOHPmDBkZGVSrVo2QkBAOHz5ssO71ej0bNmwgJyeHatWqMWTIEFxdXdm1axdHjhzBzs7O4P3P5ma0AJT0vpiSHjNu06YNJ06cYPHixcyaNYtFixaxYsUKbfjQoUPp2LFjsemCgoLo1auXQb+8vDySk5Px8PAw+RHomJiYUn9hmYO1xr9U5BWxllqGR3r9lPHUjqlMySc+Pp7U1FSmTJli8Ms+PDyc+vXrk5KSwpkzZ1ixYgXPP/88GzZsYNy4cQDUqlWLZ555ptT5JCYmsm3bNmbPnk3r1q0ZPnw4tWrVwtvbGycnJzw8PFixYgVjxoyhS5cu7N69m5ycHN577z2OHDnCv/71L+rWrYuTkxN+fn74+/sTHx/PqlWr8Pb2platWly/fp2wsDCefvppevfuTd++fXnmmWfQ6/VaXtWqVcPd3Z127doRGBjIxo0biY+Px8nJCW9vb95++218fX0ZPHgwV69e5Y033iAyMpInn3ySixcvMmXKFOrVq0fHjh2ZMGGCdgYB4JtvvqF58+aMHDmSnJwcunXrRqVKlbTGzry9vbX5372e9uzZw7Vr19i/fz/R0dGEh4czfPhwPvjgA8LCwnjppZe4ePEio0eP5ttvv6VWrVrUqlWLL7/8EoCff/5ZO60UERGBu7s7AQEB1K9fn3379hmsp379+tGnTx8GDhzIoUOHiIiI4N133+X27dtERUVRoUIFJk6cyJUrVwzWvaurK3369GHVqlU8+eSTHDlyhE8//ZS1a9cyevRojhw5gr29PWPGjKFu3bqlns67W3Z2dpl+OBstAG5ubpw8eVLr1uv1uLq6at03b94kLi5O+9Wv0+kICQkhPT2d7du3M2jQIKCgkNxdoQs5OzsbfOCFC5GcnGzyQgjxKLvXKaCvv/4aHx8fHn/8cTp06MD06dP57bffTHpF5C+//EKjRo14/PHHqVixIjqdjh9//NFgnDZt2jB79myOHDlCu3bt8PX1LTHWyy+/XGL/Xr16YW9vj5ubGw0bNuTUqVMmLLGhH3/8kTlz5gDw1FNP8fLLL2txmjVrRsWKFalVqxbVq1cnPT3dYH/QvXt3Tp8+zbp167h06RI3b94kMzPT6Dyjo6Pp0qULjo6OVKpUiZ07d3Lr1i3i4uKYMmWKNl5mZiY3btwAwMvLq8zLBnD27Fk+/fRToGB9t2nTBoBJkybx1Vdf8Z///Idff/2Vv//97wbTXb58matXrzJy5EitX0ZGBhUrVqRRo0b06dOHDh068Nprr5m0878fRgtA8+bNWbZsGSkpKVSuXJl9+/YRFhamDVdKMWHCBLZv386TTz7Jnj17aNy4MVWqVGHt2rU0atSIl19+mS+++IJOnTpZZCGEsFYRERHo9Xrat28PQIUKFfjyyy+ZPXu20Wnt7OzIz8/Xukv6gdW5c2caNWrEv//9b9avX8+hQ4e0nfHdSjvStre31/5XSmnv6Lj7zEBubu498yx6FkEppV3nu3u+ReMCbNiwgW+//Za+ffvSvHlzLly4UOJZiaKKrov4+Hgef/xxHB0d2bnz/24KSEhI0K5vVqpUyWjckhRdR3/88QdZWVmMGzeOQYMG4evrS4UKFYrlnZ+fT926dbV8Cs98AKxcuZJff/2Vw4cPs2DBAp588kmaNm16X/ndi9HbQN3c3AgJCSEwMJCePXvSvXt3vLy8CA4OJjY2lho1ahAWFsbw4cPp0aMHly9fZsKECdjb2/Phhx8ya9YsunTpwpkzZ5gwYYLZF0AIa3XmzBkSEhI4ePAg33//Pd9//z2rVq0iKiqKjIwMo9N7e3tz6tQpUlJSyM/PZ/fu3cXGeeeddzh9+jT9+/fn7bff5rfffgMKdlqm3Gyxa9culFJcu3aN2NhYPD09qVGjBn/88QdKKa5evcr58+eBgp3unTt3isXw8fFh27ZtAFy9epWff/6Zhg0bGp03wNGjR+nXrx89evTAzs6Oc+fOGRS90rzyyivs37+f3NxcsrOzGTp0KMnJyTzzzDPaDvfo0aPanVhFmbp+AF588UV27doFFNw0M336dKKjo2natClvvPEG9erV4+jRo1q8wtjPPfccqamp2hmW7du3M378eFJSUujSpQvu7u68/fbbeHp6auvY3ExqDlqn06HT6Qz63X2Fv2PHjiWe32/SpAk7duwoZ4pCWDe9Xl/sGsArr7yCUgp/f3+DX57NmjXj2WefJSoqCoCZM2caHHGvWbOGJk2aAAU3S0ybNo158+bx8ccfa7dm323EiBGEhoaycuVK7O3tmTx5MgBt27Zl2LBhrF279p65V6lShdDQUBwdHZk9ezY1a9akefPmbN++nc6dO/Pss89qp7Zq1arFk08+SUBAAO+//74WIzQ0lBkzZhAREQHAnDlzDE4j30tQUBCzZs3is88+47HHHqNRo0bEx8cXO51SVKdOnYiLi8Pf359bt24xdOhQnn32WT744ANmzZrF2rVrcXBwYMmSJSVe02zVqhUzZ85kwYIFRnMcNGgQW7ZsYdOmTVSuXJk5c+ZQrVo1xowZg06nw8HBgfr16xMfX3At6u51/9FHHzF37lyys7OpWrUqCxYsoGbNmvTv358+ffpQuXJlKleuXOw6qdmoR1BWVpY6efKkysrKMnmakydPWjAj643/xxx/gz9Lsdb181eIb825S3zzxi/rvlOaghBCCBslBUAIIWyUFAAhhLBRUgCEEMJGSQEQQggbJQVACCFslEnPAQhhzQqbxL6Xwuayy8IWm9YWfy1yBCCEBcXHx+Ph4cGUKVO0FyP5+fkZvFjpzp07tGzZ0uCBLyh4H0Dhw1OmiIiI0B70Cg4OJjExsdRxp0yZwrVr14r1T0xMJDg4+L7mn56ezqhRo4rFKS+9Xs/48ePp1q0bPXr0YPjw4Vy9ehUoaFq5UaNG+Pn50bNnTzp37szYsWO1J6nbt2/PhAkTDNb93W0BlSY2NpbQ0NBSh584cYKAgACzLN/DJEcAQliYvA/g/mVmZhIQEMBbb73FBx98gJ2dHV9//TWDBw9mz549AHh4eLBhwwZtmnHjxrF06VKmTp0KwMSJE+ncuXOZ5uvp6Ymnp2e583/UyRGAEA9ZREQEnTp1wsvLS2tTxlSRkZGMGzeO3r17c/DgQa1/+/btiY+P59y5c/Tt2xd/f3/eeOMNLl++zOrVq9Hr9QwbNowbN27Qvn173nnnHXx9fTl9+rTWMB3AwYMHmTp1KjqdTmtr6O4jDYCAgABOnDjBnDlz0Ov1jB49mvj4eC1OcnIyw4cPR6fT0atXLw4fPgzAsmXLmDZtGmFhYbRv356PP/642PLt2rULV1dX+vXrpzXZ0KNHD8aPH6+9erYob29vLl++XKb1CAVtFg0ZMgQ/Pz+OHj2q/cI/e/Ysr7/+OjqdjjfffJOEhAQAUlJSCA4OZty4cYwYMaLUfB5lUgCEsLDCtoDuPg1R2LhXSkoKR48epUOHDnTp0kVrj94UiYmJLFy4kBkzZrBlyxZu3bpVbJz169czePBgIiIiCAgI4Ndff2XYsGG4urqyevVqatQoeGVQ69at+fbbb6lZs6bB9Ldv3yYsLIy1a9cyb948kpKSSs1n2rRpuLq6GrwHBApeGOPj40NUVJT2y7yw1cvz588zZcoUvvrqK1avXl3s7YBnz54tsZnmzp0789hjjxXrn5mZyYEDB2jcuLHWLzw83GDdb99e8rWbGzduMGzYMHbu3GnQmuj48eMZNWqU9m709evXA/Dnn38yY8YMPvjgA5KTkzl27Fip6+ZRJaeAhLAweR/A/b8PoKRmlIuKi4vDz6/g/cx37tzBx8eHwYMHa8PLcgqo6HpISUkhKSmJdu3aATBgwACg4BRagwYNeOqpp9Dr9Tz//PPaewWsiRQAIR4ieR/Avd8H4OHhUeKF6NDQUO1lU0WvAZgiMTGRYcOGAQUFuvB6RdF3Ajg4OBh0Z2dno9frAcP1XVLu1kBOAQnxkMj7AIzr3Lkz165d46uvvtL6bd++nZ9++omnn37apBglcXNzY+fOnezcufOeF6urVavGE088wdGjRwHYuXMnH3300X3P91EjRwDiL8+U+/Ut+U5jeR/A/b8PoFKlSqxbt4558+axbt067OzsqFu3Lp999hmOjo4mxQgPDze4wFy5cuUyXWspfIdAeHg4NWrUIDw8nP/85z8mT/9IK1vL1A+GvA/AfOR9AH/9+Nacu8Q3b3x5H4AQQgiTSAEQQggbJdcAbEzfLSMNurf2K/7wjRDCNph0BFD4AESnTp0M2jAptH//fnQ6Hd26dWPy5MnaE3F//vknAwcOpHPnzowcObLEB1WEEEI8HEYLQGJiIkuWLGHTpk3s3LmTLVu2cPHiRW14ZmYms2fP5vPPP2fXrl1kZ2ezY8cOAN577z0GDBjA3r178fDwYOXKlZZbEiGEEGVitAAcO3YMHx8fqlevTpUqVfD19WXv3v9rN7dKlSp8//331K5dm8zMTK5fv46zszO5ublER0drTx76+/sbTFcoLS2N+Ph4g7/CtjaEEEJYjtFrAHq9HhcXF63b1dWV06dPG4zj4ODAoUOHmDhxIq6urrRs2ZIbN25QtWpV7Wk5FxeXEpunXb9+PcuXLzfoV7t2bZYuXUpcXFyZFiYmJqZM45eVNcav8QDn+aiunwUX732vu8bU8f6/SfWGGh0nKSmJd999l7p16xr0b9euHa+99hoAeXl5/POf/6Rp06ba060An3zyCS+++CJt2rQxOp+YmBgOHTrE2bNnGTFiBAsWLGDYsGFaWz9FffLJJ/Tu3dvguw0F7eGsXr2aSZMmGczflHWfmZnJxx9/zLhx4wzimOJe8W/cuMGmTZu4fPky9vb21KpVi8DAQFxcXHjnnXcYM2YM7u7u2vhJSUnMnDmTpUuX8u677+Lk5GTw1O7TTz/NiBEj7pnPpUuXOHDggPa0cFG//fYb27dvZ/r06UbzNwdLxTdaAFQJjzcXtsp3tzZt2nDixAkWL17MrFmzmDhxoknTBQUF0atXL4N+eXl5JCcn4+HhUeoj6kVZ8kEea45v7CUn5prnI71+yrhjN5Up+cTHx/PEE0/cszno77//nsaNGxMTE0N4eLj2wFitWrV45plnjM6ncN1cuXIFvV6Pt7c3W7duvec0f/zxBx4eHsUKE0DHjh0N5g+mL2tCQoI2bmEcY+712WZmZtKrVy/eeust+vbtqzUHvXjxYvbs2UPfvn25ePEib7zxhjbNihUr8Pf3p1mzZjg5Od1Xc9De3t68/vrrpQ6/c+cO1apVw9vb+5Ha9rOzs8v0w9noKSA3Nzet5T4oOCK4+ym+mzdv8sMPP2jdOp2O8+fPU7NmTTIyMrTHzZOSkkp8+s/Z2Zm6desa/D3xxBMmL4AQ1k6ag77/5qD9/f359ttvDZq1+Prrr+nTp0+Z1iNIc9Alat68OcePHyclJYXbt2+zb98+WrdurQ1XSjFhwgT+/PNPAPbs2UPjxo1xcHCgSZMm2kYTGRlpMJ0QtkKag7Zcc9BPPfUUzz77rNYU86+//kr16tV5/vnntXGlOejSGT0F5ObmRkhICIGBgeTm5tKnTx+8vLwIDg5m7NixeHp6EhYWxvDhw7Gzs6NevXq89957QEE7JpMnT+bjjz/mb3/7G4sXL7b4AgnxqJHmoC3bHHTv3r355ptvaNWqFTt37iz261+agy6dSQ+C6XQ6dDqdQb+7W9Dr2LFjief76tSpU+ZmWoWwJdIcdPmag37hhRfw9fVlyZIlZGRkcPDgQcaNG3fPfECagy4kTUEI8ZBIc9DGmdIcdOXKlWndujXh4eH4+PhQtWpVo3GlOegC0hSEEBYmzUFbvjnowovcX3zxRbEY0hx06ezUI3jcUngrk9wGWn6X5vY26J5cz/CLZ662gKx1/fwV4ltz7hLfvPHLuu+UU0BCCGGjpAAIIYSNkgIghBA2SgqAEELYKCkAQghho+Q2UCHMSDdup0F31CK/h5SJEMbJEYAQQtgoKQBCCGGjpAAIIYSNkgIghBA2SgqAEELYKCkAQghho6QACCGEjZICIIQQNkoKgBBC2CgpAEIIYaOkAAghhI0yqQBERUXRtWtXOnXqxMaNG4sNP3DgAH5+fvTo0YNRo0aRmpoKQGRkJC1btsTPzw8/Pz+WLFli3uyFEELcN6ONwSUmJrJkyRIiIiJwdHSkf//+NGvWTHv/aEZGBrNmzWL79u24ubnx0UcfsWzZMqZNm0ZsbCyTJ0+me/fuFl8QIYQQZWP0CODYsWP4+PhQvXp1qlSpgq+vL3v37tWG5+bmMmvWLNzc3ACoX78+//vf/wCIjY0lMjKSHj16MH78eO3IQAghxMNntADo9XpcXFy0bldXVxITE7XuGjVq0LFjRwCysrJYvXq11u3i4sI///lPdu7cyd/+9jdmz55dLH5aWhrx8fEGfwkJCeVeMCGEEPdm9BSQUqpYPzs7u2L90tPTGTVqFA0aNKBXr14ArFixQhs+dOhQrTDcbf369SxfvtygX+3atVm6dClxcXHGl+AuMTExZRq/rKwxfo0HOE9rXD+Wjn93TEvmb43rRuI//PhGC4CbmxsnT57UuvV6Pa6urgbj6PV6hgwZgo+PD1OnTgUKCsL27dsZNGgQUFBIKlYsPrugoCCtYBTKy8sjOTkZDw8PnJycTFqQmJgYvL29TRr3flhr/Et77z3cXPO01vVj9vib4g06C2NaMn+rWTcS3+Lxs7Ozy/TD2egpoObNm3P8+HFSUlK4ffs2+/bto3Xr1trwvLw8RowYQZcuXQgNDdWODqpUqcLatWs5deoUAF988QWdOnUqFt/Z2Zm6desa/D3xxBMmL4AQQoj7Y9IRQEhICIGBgeTm5tKnTx+8vLwIDg5m7NixJCQk8Ntvv5GXl8e3334LgIeHB3PnzuXDDz9k1qxZZGVl8cwzzxAeHm7xBRJCCGEak94JrNPp0Ol0Bv3WrFkDgKenJ+fOnStxuiZNmrBjx45ypiiEEMIS5ElgIYSwUVIAhBDCRkkBEEIIGyUFQAghbJQUACGEsFFSAIQQwkZJARBCCBslBUAIIWyUFAAhhLBRUgCEEMJGSQEQQggbJQVACCFslBQAIYSwUVIAhBDCRkkBEEIIGyUFQAghbJQUACGEsFFSAIQQwkZJARBCCBtl0juBhRDm0XfLSIPurf0+fkiZCCFHAEIIYbNMKgBRUVF07dqVTp06sXHjxmLDDxw4gJ+fHz169GDUqFGkpqYC8OeffzJw4EA6d+7MyJEjuXXrlnmzF0IIcd+MFoDExESWLFnCpk2b2LlzJ1u2bOHixYva8IyMDGbNmsXq1av5+uuvqV+/PsuWLQPgvffeY8CAAezduxcPDw9WrlxpuSURQghRJkYLwLFjx/Dx8aF69epUqVIFX19f9u7dqw3Pzc1l1qxZuLm5AVC/fn3+97//kZubS3R0NL6+vgD4+/sbTFcoLS2N+Ph4g7+EhARzLZ8QQohSGL0IrNfrcXFx0bpdXV05ffq01l2jRg06duwIQFZWFqtXryYgIIAbN25QtWpVKlYsmIWLiwuJiYnF4q9fv57ly5cb9KtduzZLly4lLi6uTAsTExNTpvHLyhrj13iA87TG9WPp+HfHLCm+ueZpjetG4j/8+EYLgFKqWD87O7ti/dLT0xk1ahQNGjSgV69eJe7sS5ouKCiIXr16GfTLy8sjOTkZDw8PnJycjKUIFKwgb29vk8a9H9Ya/1Lxgy4D5pqnta4fs8ffFG/QWRhTi39xbYnDy8Nq1o3Et3j87OzsMv1wNloA3NzcOHnypNat1+txdXU1GEev1zNkyBB8fHyYOnUqADVr1iQjI4O8vDzs7e1JSkoqNh2As7Mzzs7OxRYiOTnZ5IUQQghRdkavATRv3pzjx4+TkpLC7du32bdvH61bt9aG5+XlMWLECLp06UJoaKj2K9/BwYEmTZqwe/duACIjIw2mE0II8XCZdAQQEhJCYGAgubm59OnTBy8vL4KDgxk7diwJCQn89ttv5OXl8e233wLg4eHB3LlzmTlzJpMnT+bjjz/mb3/7G4sXL7b4AgkhhDCNSU8C63Q6dDqdQb81a9YA4Onpyblz50qcrk6dOmzYsKGcKQohhLAEeRJYCCFslBQAIYSwUVIAhBDCRkkBEEIIGyUFQAghbJQUACGEsFFSAIQQwkZJARBCCBslr4QUwoIuze0NFLTKemkvUK94e1hCPCxyBCCEEDZKCoAQQtgoKQBCCGGjpAAIIYSNkovAQggAdON2GnRHLfJ7SJmIB0WOAIQQwkZJARBCCBslBUAIIWyUFAAhhLBRUgCEEMJGSQEQQggbZdJtoFFRUXz88cfk5uYyaNAgBg4cWOJ4kyZNolmzZvj7+wMQGRnJwoULqVWrFgBt27YlJCTETKmXrO+WkQbdW/t9bNH5CSGEtTJaABITE1myZAkRERE4OjrSv39/mjVrRr169QzGmTlzJsePH6dZs2Za/9jYWCZPnkz37t0tk70QQoj7ZrQAHDt2DB8fH6pXrw6Ar68ve/fuZcyYMdo4UVFRdOjQQRunUGxsLFeuXGH16tW4u7szffp0Hn/8cYNx0tLSSEtLM+iXl5d3n4sjhBDCVEYLgF6vx8XFRet2dXXl9OnTBuMMHToUgJiYGIP+Li4uDBs2DC8vLxYvXszs2bNZtGiRwTjr169n+fLlBv1q167N0qVLiYuLK9PCFJ1/af3ulzljWSr+rE3xBt0f1bT8PC0R668Y35LztETud8e09nUv8UtmtAAopYr1s7OzMyn4ihUrtP+HDh1Kx44di40TFBREr169DPrl5eWRnJyMh4cHTk5OJs0rJiYGb29vuLjWoL+3t7dJ05sc30LMFr9IATDG5taPpeM/hPVvqdwLY1rNupf4ZGdnl+mHs9G7gNzc3EhOTta69Xo9rq7GX2qRnp7OunXrtG6lFBUrFq83zs7O1K1b1+DviSeeMDF9IYQQ98toAWjevDnHjx8nJSWF27dvs2/fPlq3bm00cJUqVVi7di2nTp0C4IsvvqBTp07lz1gIIYRZGD0F5ObmRkhICIGBgeTm5tKnTx+8vLwIDg5m7NixeHp6ljidvb09H374IbNmzSIrK4tnnnmG8PBwsy+AEEKI+2PScwA6nQ6dTmfQb82aNcXGmz9/vkF3kyZN2LFjRznSE0IIYSnyJLAQQtgoKQBCCGGjpAAIIYSNkgIghBA2SgqAEELYKCkAQghho6QACCGEjZICIIQQNkoKgBBC2CgpAEIIYaOkAAghhI0yqS0gIR4V8s5nIcxHjgCEEMJGSQEQQggbJQVACCFslFwDEI+0S3N7A1ADuLQXqGf8daRCCNNYfQGQHYQQQtwfOQUkhBA2SgqAEELYKCkAQghho0wqAFFRUXTt2pVOnTqxcePGUsebNGkSERERWveff/7JwIED6dy5MyNHjuTWrVvlz1gIIYRZGC0AiYmJLFmyhE2bNrFz5062bNnCxYsXi40zYsQI9u7da9D/vffeY8CAAezduxcPDw9Wrlxp3uyFEELcN6MF4NixY/j4+FC9enWqVKmCr69vsR19VFQUHTp0oEuXLlq/3NxcoqOj8fX1BcDf37/YdABpaWnEx8cb/CUkJJR3uYQQQhhh9DZQvV6Pi4uL1u3q6srp06cNxhk6dCgAMTExWr8bN25QtWpVKlYsmIWLiwuJiYnF4q9fv57ly5cb9KtduzZLly4lLi7O6ALUMDL87pzKy5yxHkZ8S8/TEvn/lT5fS87TErnfHdPat32JXzKjBUApVayfnZ2d0cCmThcUFESvXr0M+uXl5ZGcnIyHhwdOTk73nM+l4gcVBry9vY3maoqYmBizxbJo/E3xZRr9UV8/Vvf5PoT1b6ncC2NazbYv8cnOzjbph3MhowXAzc2NkydPat16vR5XV+MPW9WsWZOMjAzy8vKwt7cnKSmpxOmcnZ1xdnY26JednU1ycrIp+QshhLhPRq8BNG/enOPHj5OSksLt27fZt28frVu3NhrYwcGBJk2asHv3bgAiIyNNmk4IIcSDYbQAuLm5ERISQmBgID179qR79+54eXkRHBxMbGzsPaedOXMmW7dupWvXrpw8eZJ33nnHXHkLIYQoJ5PaAtLpdOh0OoN+a9asKTbe/PnzDbrr1KnDhg0bypGeEEIIS7H6xuCsjW7cToPuqEV+DykTIYStk6YghBDCRskRwENWtDnr50K3P9yEHrKiR0gf1XxIiQhhA+QIQAghbJQUACGEsFFyCkiUi5zCEsJ6yRGAEELYKCkAQghho6QACCGEjZICIIQQNkoKgBBC2CgpAEIIYaOkAAghhI2yuucA/upNBfTdMtKge2u/jx9SJiX7q69/IWyJHAEIIYSNkgIghBA2SgqAEELYKCkAQghho6QACCGEjZICIIQQNsqkAhAVFUXXrl3p1KkTGzduLDb87Nmz9O7dG19fX0JDQ7lz5w4AkZGRtGzZEj8/P/z8/FiyZIl5sxdCCHHfjD4HkJiYyJIlS4iIiMDR0ZH+/fvTrFkz6tWrp40zYcIE5syZQ8OGDZk6dSpbt25lwIABxMbGMnnyZLp3727RhRBCCFF2Ro8Ajh07ho+PD9WrV6dKlSr4+vqyd+9ebfi1a9fIysqiYcOGAPj7+2vDY2NjiYyMpEePHowfP57U1FTLLIUQQogyM3oEoNfrcXFx0bpdXV05ffp0qcNdXFxITEzU/h82bBheXl4sXryY2bNns2jRIoP4aWlppKWlGfTLy8u7v6URQljMo/6Uuig7owVAKVWsn52dnUnDV6xYofUbOnQoHTt2LDbu+vXrWb58uUG/2rVrs3TpUuLi4oylZ1RMTEy5Y1gi1qMyT3PHf9DrSD5f88YpLWZJ8a1p3Uv8khktAG5ubpw8eVLr1uv1uLq6GgxPTk7WupOSknB1dSU9PZ3t27czaNAgoKBQVKxYfHZBQUH06tXLoF9eXh7Jycl4eHjg5ORkOMGmeJMWrJC3t3eZxi9NTEyMeWJZOv+HHL/c60g+3zKzVO6FMbX4F9eWOLy8zJa/xCc7O7tMP5yNXgNo3rw5x48fJyUlhdu3b7Nv3z5at26tDa9Tpw5OTk5ahYqMjKR169ZUqVKFtWvXcurUKQC++OILOnXqVCy+s7MzdevWNfh74oknTF4AIYQQ98ekI4CQkBACAwPJzc2lT58+eHl5ERwczNixY/H09GThwoVMmzaNW7du8dJLLxEYGIi9vT0ffvghs2bNIisri2eeeYbw8PAHsUxCCCFMYFJz0DqdDp1OZ9BvzZo12v8NGjRg27ZtxaZr0qQJO3bsKGeKQgghLMHq3gcghCXJnS7ClkhTEEIIYaPkCEAI8ZdQ9G11UYv8HlIm1kOOAIQQwkbJEYCwKfIrUYj/IwVACFGiS3N7A1ADuLQXqOd6z/GF9ZFTQEIIYaPkCEDYNPmVK2yZHAEIIYSNkgIghBA2Sk4BCbOSJ2mFsB5yBCCEEDZKjgCEEDZBjk6LkyMAIYSwUVIAhBDCRkkBEEIIGyUFQAghbJRcBBZC/CXJU97GSQEoQlqLFELYCjkFJIQQNkqOAISwEnJ0KszNpCOAqKgounbtSqdOndi4cWOx4WfPnqV37974+voSGhrKnTt3APjzzz8ZOHAgnTt3ZuTIkdy6dcu82QshhLhvRgtAYmIiS5YsYdOmTezcuZMtW7Zw8eJFg3EmTJjA9OnT+fbbb1FKsXXrVgDee+89BgwYwN69e/Hw8GDlypWWWQohhBBlZvQU0LFjx/Dx8aF69eoA+Pr6snfvXsaMGQPAtWvXyMrKomHDhgD4+/uzdOlSXn/9daKjo1mxYoXW/80332TChAkG8dPS0khLSzPoV3gEkZOTUyyf6o/ZG3TnVX7coNu5YlWD7uzsbGOLeM/4d09f1limxLd0/hL/rxPf2rfNe7GG/IfO3W/QvTa0033HKitT4xfuM5VSJo1vp4yMuWrVKjIzMwkJCQHgq6++4vTp04SFhQHwyy+/EB4ezubNmwG4cuUKw4YNY8OGDfTp04fDhw8DBTv1hg0bEhcXZxB/2bJlLF++3KCfu7s7s2bNMmkBhBBCGHJ3d6datWpGxzN6BFBSfbCzszM63Nh0hYKCgujVq5dBv/z8fBwdHalevXqJ0xSVkJDAgAED2LRpE0888YTR8ctK4kv8RzW+Necu8c0fXylFbm4ujz3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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -6161,2889 +407,7 @@ { "data": { "image/png": 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T21:18:59.560866\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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HS9O7du0KALC2tkbNmjWRkpKCevXqqdXz8ccfAwBiY2ORmZmJbt26Sct169YNf/31F9q2batLs6q0QaVKlQAAJ06cQGpqqtQ3MDs7GzVr1oRMJkPfvn0RHh6OFi1aYNeuXZg0aZJKPWfOnCm2ds+vTp060tXquLg46Zt5njZt2sDc3BwA0KxZM6SkpODmzZswMjKCg4MDAMDNzU3lNmR+tra2iIiIwNWrV3HhwgWcPn0aK1euxOLFi9GlSxd4e3tj+/btcHR0xLZt2zB58mQYGhrCxcUFXl5e+Oyzz9ChQwe4u7ur1X38+HFER0ejT58+AHKvLrx8+VKa7uTkBADSe92xY0cAubcUz58/DwBwdXXFmDFj4OjoiA4dOmDEiBEat6M4vHor+tKlSxgxYoRaP8I///wTW7ZsgUwmg4mJCby8vLB+/XqMHDkSAKSrKu+99x5sbGxgbW0NAKhbty5SUlIQGxuLe/fuYerUqVKdGRkZ+Oeff/Dhhx+qxZW33+enbX97VUV4f11cXFCvXj1s2LABcXFxOH/+PFq1aiVN79y5MwwMDGBubo769esjJSUF//zzDzp06CBdMR0wYABOnTqlsY00kclkAABTU9MCt7Nfv34IDw+Hi4sLdu3ahX79+qnUc+nSJTRu3BhNmzYFAHTr1g3dunXDyZMn8eTJE5XzoEwmw40bN9CxY0eMHDkS8fHxaN++Pb777jtYWFi8NuaC9t+//voLPXr0kLqL9O7dG7Nnz9b43MGrNJ2fz5w5AycnJ+nc06dPH2zYsAEAMGnSJJw+fRpr1qxBbGwsEhMTkZ6eLtWXtz+bmJigX79+2L59O/z9/REeHo6NGzdqjOHAgQNSf+ns7Gw0b94cs2bNUqszPT0dly5dws8//wwAsLCwQO/evfHnn3/C1dVVpc4TJ04gOjoaO3bsAJB7DALAzZs3YWBggM8++wwAYGdnJ90t1OTKlSt47733pO4OjRs3xkcffYTz589DJpOhefPmeOeddwDknqOPHDmivbFf8ernk6Z4zc3N4ezsjL1792Lo0KHSldT8zpw5AxcXF1StWhUApDtT8+fPR1xcHLy8vKR5U1JS8Pz5c3Tv3h3BwcE4duwY2rdvjwkTJqjF16pVK1StWhVbt27F/fv3ce7cOVSpUkWa3rFjR+nqv42NDVJSUhAVFQUbGxvpyuqAAQOkq/+vcnBwwIkTJ/C///0PFy9exPHjx7Fs2TKsX78eLVu21Lj/WFtbw9bWFp6enujUqRM6deokfRbmp+3zPk9eflGvXj3UqlULtra2AHLPXykpKTqfJ/NjYlwM0tPTsXv3bpiamkoPjrx48QKbNm3C8OHDpZM3kHuQPHz4EIaGhhrLlUolHBwcpL46ABAfHw8rKyvY2tri0KFDiIyMxJkzZ7Bs2TJs3boVnTt31lj+3nvvSXV88MEHyMnJQVxcnMrttMzMTIwZMwY//PCDWuKsVCqRnZ0tvTY1NZX+lslkEEJobA8zMzNp+VcJIZCTk1Nge2oSHR0t3dZSKpWYOnUqHB0dAQBpaWnIzMwEkHvi79WrF/r164fU1FS1BLw42z1/+169ehXffPMNhg4dig4dOuCTTz7BzJkzpel5J03g/9pOUxsaGakfkjk5OZg5cya+++472NnZwc7ODsOGDcPy5cuxbds2dOnSBe7u7li4cCHOnj2L9PR06Tb3ggULcPPmTURGRmLNmjXYsWMHVqxYoVK/UqnE8OHDMWjQIABAVlYWUlJSpOmv3i7N++KUn5+fH/r27YtTp05h165dWL16NXbt2qX2BaIkfPTRR2jQoAGio6NVTpia9uf8+17+7dC0TQqFApaWlipJTFJSktbkJ2+/z0/b/pa/311FeX83b96M7du3w9vbG+7u7qhWrZpKQqfLMWBoaKixbbWJjo5G3bp1UaVKlQK308XFBXPnzsXt27dx4cIFhISEqNTz6vskhMCNGzegUCjQqFEjlQchExISUKNGDRgbG+Po0aM4c+YMzp49i379+mHZsmX46KOPCrUN+fdfTefUvHPmq22V/9wMaD4/F9S+EyZMgEKhQPfu3fHZZ58hPj5eZd78+/OAAQPQr18/tGnTBo0bN0bdunU1bsurfYxflf+z4dVtffX4zF++ePFi6ZiRy+WQyWR4+PChynsG5CbLDRs21Ljugj6PjI2NNe6funr180lTvEDuF7S8LhAffPCB2oWlV/dDuVwOuVwOpVIJDw8P6UKPUqlEYmIiqlatCi8vL3Tu3BmnT5/GX3/9hbCwMOzdu1flPHXixAnMnj0bw4YNQ9euXdGwYUPs3btXmq7LsanpswkAnj59iqVLlyIoKAgff/wxPv74Y4waNQrTpk3D7t270bJlS637z8aNGxEdHY0zZ85gzpw5aNu2LQIDA1XqL+jzHlA9f2k6dwG6nSfz46gUxWDfvn2oXr06/vrrLxw7dgzHjh3DH3/8gfT0dKSmpuLMmTNITEwEAGzduhU//vgj2rZtq7G8Xbt2OH36NG7fvg0AOHnyJHr27InMzEx89913OHDgAFxdXTF9+nSYm5sjPj5ea3l+JiYmGDFiBKZOnYqkpCQAuR8cc+bMwcuXL2FtbY2OHTti69atyM7OhlKpxKZNm9ChQ4fXbr+hoaHGE1qDBg1gbGwsPVSSkJCAQ4cOoX379oVq37t372L58uXSAzaffvopNm3ahKysLCiVSgQFBUnfZK2trWFvb4/vv/8effv2VaurONs9vwsXLkgJTZs2bXD06FEoFIoCt8vGxgZCCOmJ5qNHj6okLHmMjIwQGxuL5cuXSx+GOTk5uH//Ppo1awYAqFy5Mnr27ImpU6dKVxWSk5Ph6OiIatWqYejQoRg/fjxu3LgBQPU9+/TTT7Fjxw68ePECQG6fxLz+ibrIyclBly5dkJ6ejoEDB2L69Om4ffv2G30BehN3795FbGysdLUvT95+IoRAVlYWtm/fXqh9r0GDBjA1NZUS4/j4eLi5uUlPy2vb7/PTtr/lV1He31OnTsHT0xP9+vVDgwYNcOzYsdceA+3bt8fp06fx+PFjALmj5ugqISEBCxYsUDkvaNtOU1NTuLq6IiAgAN26dUPlypVV6rK3t8ft27dx69YtALnH4qRJk/Dhhx8iLi4OFy5cAABcu3YNzs7OSExMxIIFC7B8+XJ8/vnnmDZtGj744APExsbqHH+e/Pvvp59+igMHDkijnezcuVPqG1y9enVp30tOTsbFixdfW3fHjh0REREhJVf5v+SdOnUKo0ePRo8ePSCTyXD58mWt79d//vMffPjhh5gzZw4GDhxY6G18lbm5Oezt7aWRU1JTU7F7927p+Hx1/123bp10HH/99dfYuHEjGjZsCJlMJj0Ee/XqVQwZMgRKpVLjsWlvb4+7d+/iypUrAIBbt27hwoULaNOmTZG25cqVK9iyZQuGDBlSYLwApDtNy5YtU7trAeQeD0eOHJH24aVLl2LdunXo0KEDfv/9d+k8kn99Xl5euHbtGnr37o1Zs2ZBLperfY6cPn0anTt3xqBBg9CiRQv88ccfrz02P/74Y/z777+4fv06AGh9gLJq1aqIjIzEr7/+KiXSL1++RHx8vHT+0rT/XL9+HW5ubmjUqBF8fX0xdOhQrecvbZ/3uijoPKkNrxgXgy1btmDYsGEq38YtLS0xePBgHD9+HJMmTcLw4cMBALVr18acOXNgbW2ttTw4OBgTJkyAEELqQG5mZoZvvvkG06ZNw7Zt22BoaIjPP/8cbdq0Qa1atTSWv2rUqFGoXLmy9PBGZmYm2rRpg+XLlwMAvv76a8ybNw+9evVCTk4OWrZsiaCgoNduv5OTEwYNGiTVk8fY2BjLly/HDz/8gKVLl0KhUGD06NFo164dzp07p7W+jIwMeHh4AMjtAmJqaooJEyZIt8y++eYbzJs3D56enlAoFGjatCkCAgKk5fv164dx48Zp/EbYpEmTYmv3/Nzc3HD48GH06NEDxsbGcHBwQEpKinSC08TY2BjLli3DjBkzsHDhQjRt2lTlimd+ixcvxo8//ghnZ2dUrlwZQgh8/vnnKrd4e/fuje3bt0sPN9aoUQNff/01hg4dikqVKsHQ0BA//PADAKBTp07SLc4RI0YgISEB/fv3h0wmw7vvvqt2Ra0gRkZGmDp1KiZOnAgjIyPIZDLMmTPntQ/mvKn8+weQe0UhODgYDRo0kD44gNwnpX/44Qe4u7sjOzsbHTt2xKhRo3Rej4mJCZYvX47Zs2fjp59+Qk5ODsaNG4fWrVsD0L7f56dtf3tVRXh/v/zyS3z//ffYtWsXDA0N0bx5c9y8ebPAuvPaZ8iQIahSpQpatmxZ4PxDhgyBgYGBdK7t06eP9CBsv379CtzOfv36YePGjZgxY4ZavbVq1cKCBQvg7+8PhUIBc3NzhIaGokaNGliyZAnmz5+PzMxMCCEwf/581KlTB0OGDEFAQADc3NxgYmKCJk2aaByC8lUF7b8NGjTA0KFDpQQv72EsAwMDDB48GBMnToSzszPq1q2rU0Ln6OiIGzduoE+fPrC0tIStra30sKCfnx9Gjx6NqlWronLlyvjkk09w7949rXXlJV55V+6KasGCBQgODsauXbuQlZUlDc0JAJ9//jn8/Pzwww8/YNq0aZg9e7Z0HLdv3x7Dhw+HsbExli5dijlz5mD+/PnSaxMTE3Tu3Bnz5s1Tuapeo0YNLF68GLNmzUJGRgZkMhnmzp2LBg0a4O+//9Y57nv37ql8Ppmbm2PBggXSLXxt8ebp16+f9IXqVY6OjtKIDkDund5Zs2bB3NwcI0aMwJdffgmZTAZzc3OEhYVBJpNh4sSJmDNnDhYtWgQDAwOMGTNG7Yq+l5cXJk6cCHd3dxgaGuLjjz+WHlLVpkaNGliwYAEmTpwIY2NjrQ9TGxkZYe3atfjxxx+xYcMGmJmZQSaTwdPTU+Xi1Kv7j62tLbp3744+ffrAzMwMlSpVkq4W53//Xvd5/zoFnSe1kYnC3C8gojJJCIE1a9bg4cOHKl04qGLg+0v6lJe8/+c//5H66BPpqrztP7xiTFQBdO3aFTVq1Ciw3xSVX3x/SV9evHiBzp07o2XLloXqgkMElM/9h1eMiajEvHjxAl5eXli5cqXa7b1r164hMDAQL168wMcff4yZM2dqfcCDiIioNPDhOyIqEZcvX8bAgQO1PpA0adIkBAUF4dChQxBCYPv27aUbIBER0SuYGBNRidi+fTumT58OKysrtWkPHz5ERkaG9JR27969ERERUcoREhERqeJ9Sz3LG9LFxMREbUxGovJs9uzZWqclJiaq/Axu7dq1kZCQoDZf3jie+QkhYGZmhho1avCYIdIBP2eIdMfEWM+ysrIQExMDOzs7lQHa87t69SqaN29eypGVP2wn3em7rTQ92qDpA3v9+vUICwtTKatVqxaWLFnyRmPGEpWEvCH8yip+zhQftpPuymtbMTEuB/J+UpIKxnbSnb7bytraWvqhGQB48uSJxi4XQ4YMgaenp0qZQqFAUlJSgR/yUVFRZT5ZKQvYTrqr6G2l73NCecF20l15bSsmxkRU6urUqQNTU1Mp2di9ezc6deqkNp+lpSUsLS1VyjIzM1WSaiIiouLCh++IqNSMGDEC0dHRAHJ/+Wru3Lno3r07Xr58CR8fHz1HR0REbzteMSaiEnXs2DHp7zVr1kh/29raYseOHfoIiYiISCMmxsXIx8cHT58+lX6kIDg4GPb29nqOioiIiIh0wcS4mAghcOfOHZw4cYK/3kVERERUDjGDKyZ37tyBTCbDiBEj8PTpU/Tv3x9ffPGFyjyaxmRVKBSlGSYRERERacHEuJjI5XI4ODhgxowZyMjIgI+PDxo0aIAOHTpI8xQ0JmtMTEyB9UdFRZVI3GWVRXVrZGSrjnVbyViG1GfqPwKRN6+xuRWu3Lhf4Lz0f7TtUxV5SCoiojfxTJ4BeVoWjM2tEBefe4HLsooJqltW0nNkVNyYGBeTVq1aoVWrVgAAMzMz9O3bFydPnlRJjDkmq+7i4uVYFxGtUubr2QI2DetqnTc1NRUWFhYFzku53sZ9iojoTcnTsrAqXP1zholxxcPEuJhcvHgR2dnZcHBwAJDb5/jVvsYck5WIiIio7OI4xsUkNTUV8+fPR2ZmJl68eIHw8HA4OTnpOywiIiqHwsLC4OrqCldXV8yfPx8AMGXKFHTr1g0eHh7w8PDAkSNH9BwlUcXDK8bFpHPnzrh8+TJ69eoFpVKJQYMGSV0riIiIdBUZGYlTp04hPDwcMpkMw4cPx5EjRxATE4ONGzdq/Pl0IioeTIyL0fjx4zF+/Hh9h0FEROVY7dq1ERAQABMTEwBAo0aN8OjRIzx69AhBQUF49OgRnJycMGbMGBgY8MYvUXFiYkxERFSGNG7cWPo7NjYWBw4cwObNm3H+/HkEBwfDzMwMvr6+2LFjB/r376+yLIcFJSoaJsZERERl0K1bt+Dr6wt/f380bNgQy5Ytk6YNHjwYu3fvVkuMOSxoyTA2t0JqaioASP+nyFMQ9eiWPsMq88rjsKBMjImIiMqYqKgojB07FlOnToWrqytu3LiB2NhYODs7A9A88hHAYUFLSly8HBYWFirDtVW1rIr6TerpObKyq7zuU0yMiYiIypD4+HiMHj0aoaGhKkOAzpkzB+3atYOZmRm2bdumlgADHBaUqKiYGBMREZUha9euRWZmJkJCQqQyLy8vjBw5EgMHDkROTg66desGNzc3PUZJVDExMSYiIipDAgMDERgYqHGat7d3KUdD9HbhOC9ERERERGBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREAJsZEVEL27duHHj16wMnJCZs2bVKbfvXqVfTp0wc9e/aEr68v5HK5HqIkIiL6P0yMiajYJSQkIDQ0FJs3b8aePXuwbds2/PvvvyrzzJ49G2PHjsXevXvRoEEDrF27Vk/REhER5WJiTETFLjIyEu3atUO1atVgZmYGZ2dnREREqMyjVCqRlpYGAHj58iUqVaqkj1CJiIgkRvoOgIgqnsTERNSuXVt6bWVlhStXrqjMExAQgGHDhmHOnDmoXLkytm/frlaPXC5X62KhUChKJmgiInrrMTEmomInhFArk8lk0t8ZGRmYNm0a1q9fj5YtW+KXX36Bv78/Vq9erbLM+vXrERYWplJWq1YtLFmyBDExMQXGEBUVVYQteHuwnXSnra1at25dypEQUUlhYkxExc7a2hoXL16UXicmJsLKykp6ffPmTZiamqJly5YAgAEDBmDx4sVq9QwZMgSenp4qZQqFAklJSbCzs4OpqanG9UdFRTFZ0QHbSXdsK6K3A/sYE1Gxa9++Pc6cOYPk5GS8fPkShw8fRqdOnaTp9evXx+PHj3Hnzh0AwNGjR9GiRQu1eiwtLVG3bl2Vf++8806pbQcREb1deMW4BMybNw/Pnj1DSEiIvkMh0gtra2v4+fnBx8cH2dnZ6Nu3L1q2bIkRI0Zg7NixaNGiBebOnYvx48dDCIGaNWtizpw5+g6biIjeckyMi9mZM2cQHh6Ozz77TN+hEOmVu7s73N3dVcrWrFkj/e3o6AhHR8fSDouIiEgrJsbF6Pnz5wgNDcWoUaNw/fp1tel8wp6IiIio7GJiXIy+//57+Pn5IT4+XuN0PmGvO2NzK6SmpqqUpchTEPXoVoHz5v2vbd7SZFHdGhnZ6qMzVDKWIfVZgl5jsKhuzSfsiYiIXsHEuJj89ttvePfdd+Hg4IBdu3ZpnIdP2OsuLl4OCwsLlbKqllVRv0k9rfOmpqZKy2ibtzTFxcuxLiJardzXswVsGtbVawxeXd976/YpIiKi12FiXEwOHDiAJ0+ewMPDAykpKUhPT8ecOXMwdepUaR5LS0tYWlqqLJeZmYmkpKTSDpeIiMqwsLAwHDx4EEBuf/zJkycjMjISc+fORWZmJrp37w4/Pz89R0lU8TAxLia//PKL9PeuXbtw/vx5laSYiIhIF5GRkTh16hTCw8Mhk8kwfPhw7N+/HwsWLMCGDRvw7rvvwtfXFydPnuQDrETFjOMYExERlSG1a9dGQEAATExMYGxsjEaNGiE2Nhb169dHvXr1YGRkBHd3d0REROg7VKIKh1eMS0Dv3r3Ru3dvfYdBRETlUOPGjaW/Y2NjceDAAQwePBi1a9eWyq2srJCQoP4QL0c/IioaJsZERERl0K1bt+Dr6wt/f38YGRnh7t27KtNlMpnaMhz9qGRw9KM3Ux5HP2JiTEREVMZERUVh7NixmDp1KlxdXXH+/HmVB7UTExNhZWWlthxHPyoZHP2o8MrrPsXEmIiIqAyJj4/H6NGjERoaCgcHBwCAvb097t69i7i4ONStWxf79+9Hnz591Jbl6EdERcPEmIiIqAxZu3YtMjMzERISIpV5eXkhJCQE3377LTIzM+Ho6AgXFxc9RklUMTExJiIiKkMCAwMRGBiocdrevXtLORqitwuHayMiIiIiAhNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjrTZs2IAXL17oOwwiIiIiKiVMjLW4efMmnJ2dMW3aNERHR+s7HCIiIiIqYUyMtZg1axYOHToEOzs7zJw5E3369MGOHTuQmZmp79CIiIiIqAQwMS6Aubk5XFxc4ObmhufPn2Pz5s1wcXFBRESEvkMjIiIiomJmpO8AyqrIyEhs374dZ86cgbOzM5YtWwZbW1vcu3cPgwYNgouLi75DJCIiIqJixCvGWgQHB+Ojjz7CH3/8geDgYNja2gIA3nvvPfTv31/P0RGVffv27UOPHj3g5OSETZs2qU2/c+cOBg8ejJ49e+Krr75CSkqKHqIkIiL6P0yMtdi7dy+qVasGCwsLPHnyBOvWrYNSqQQAjB07Vs/REZVtCQkJCA0NxebNm7Fnzx5s27YN//77rzRdCIGvv/4aI0aMwN69e9G0aVOsXr1ajxETERExMdZq1qxZOHHiBADAwMAAUVFRmDNnjn6DIionIiMj0a5dO1SrVg1mZmZwdnZW6Zt/9epVmJmZoVOnTgCAUaNGwdvbW1/hEhERAWAfY63+/vtv7N+/HwBQs2ZNLF68GB4eHnqOiqh8SExMRO3ataXXVlZWuHLlivT63r17qFWrFvz9/fHPP//AxsYGQUFBavXI5XLI5XKVMoVCUXKBExHRW42JsRbZ2dnIysqCiYkJACAnJ0fPERGVH0IItTKZTCb9nZOTg/Pnz2Pjxo1o0aIFFi1ahJCQEISEhKgss379eoSFhamU1apVC0uWLEFMTEyBMURFRRVhC94ebCfdaWur1q1bl3IkRFRSmBhr8dlnn+Grr76Ch4cHZDIZ9u/fD0dHxwKXWbx4MQ4dOgSZTIa+ffti2LBhpRQtUdlibW2NixcvSq8TExNhZWUlva5duzbq16+PFi1aAADc3Nw09t0fMmQIPD09VcoUCgWSkpJgZ2cHU1NTjeuPiopisqIDtpPu2FZEbwcmxlpMnjwZmzZtwtGjR2FkZAQnJyd4eXlpnf/8+fM4e/Ys9u7di5ycHPTo0QOOjo5o2LBhKUZNVDa0b98eS5cuRXJyMipXrozDhw9j1qxZ0vRWrVohOTkZ169fh62tLY4dO4bmzZur1WNpaQlLS0uVsszMTCQlJZX4NhAR0duHibEWhoaG8PHxgY+Pj07zt2nTBr/++iuMjIyQkJAAhUIBMzOzEo6SqGyytraGn58ffHx8kJ2djb59+6Jly5YYMWIExo4dixYtWmDZsmUIDAzEy5cv8c4772D+/Pn6DpuIiN5yTIy1OHDgABYsWICUlBSV/pKXLl3SuoyxsTGWLFmCn3/+GS4uLrC2tlaZzgeJ6G3i7u4Od3d3lbI1a9ZIf9vb22PHjh2lHRYREZFWTIy1WLx4MQICAtCsWTOVh4ZeZ+zYsRgxYgRGjRqF7du3Y8CAAdI0PkikO2NzK6SmpqqUpchTEPXoVoHz5v2vbd7SpGkbgNKNTVsMAB8kIirrXrx4AS8vL6xcuRJ169bFlClTEBUVhcqVKwMAxowZAycnJz1HSVSxMDHWwtLSEt26ddN5/tu3byMrKwtNmzZF5cqV0a1bN9y4cUNlHj5IpLu4eDksLCxUyqpaVkX9JvW0zpuamioto23e0qRpG4DSjU1bDAATYKKy7PLlywgMDERsbKxUFhMTg40bN6o8yEpExYs/8KGFvb09Tp48qfP8Dx48QGBgILKyspCVlYWjR4+qJR6WlpaoW7euyr933nmnuEMnIqJybvv27Zg+fbqUBKenp+PRo0cICgqCu7s7lixZIv0aKxEVH14x1uLkyZPYuHEjjI2NYWxsDCEEZDKZ1j7Gjo6OuHz5Mnr16gVDQ0N069YNrq6upRw1ERFVBLNnz1Z5/fTpU7Rr1w7BwcEwMzODr68vduzYgf79+6vMx2dZiIqGibEW69atK/QyY8eO1TgWKxERUVHUq1cPy5Ytk14PHjwYu3fvVkuM+SxLyeCzLG+mPD7LwsRYizp16iAiIgLXrl3DqFGjcPToUbi5uek7LCIiegvduHEDsbGxcHZ2BpD765JGRuof4XyWpWTwWZbCK6/7FPsYa7F69Wps2bIFERERyMjIQFhYmMq3dSIiotIihMCcOXOQkpKC7OxsbNu2TeOIFHyWhahomBhr8fvvv2PNmjWoXLkyqlevju3bt2P//v36DouIiN5Ctra2GDlyJAYOHAhXV1c0bdqUdzGJSgC7UmhhZGQEExMT6bWlpaXG21ZEREQl5dixY9Lf3t7e8Pb21mM0RBUfMz0t3n33XZw4cQIymQxZWVlYu3Yt6tSpo++wiIiIiKiEMDHWIigoCJMnT8aNGzfw4Ycfwt7eHgsWLNB3WERERERUQpgYa2FtbY3169fj5cuXUCgUMDc313dIRERERFSCmBhr8csvv2gsHzZsWClHQkRERESlgYmxFjdv3pT+zsrKQlRUFNq2bavHiIiIiIioJDEx1mLu3Lkqr5OTkzF58mQ9RUNEREREJY3jGOuoRo0aePjwob7DICIiIqISwivGWuTvYyyEQExMDGrWrKnHiIiIiIioJDEx1iJ/H2Mgd1xjdqUgIiIiqriYGGvxah9jIiIiIqrYmBhrMXjwYMhkMq3Tf/3111KMhoiIiIhKGhNjLezs7HD79m30798fxsbG2LNnD3JycuDq6qrv0IiIiIioBDAx1uLSpUvYvHkzDA0NAQAdO3ZE//794ezsrOfIiIiIiKgkcLg2LZKTk5GVlSW9TktLQ0ZGhh4jIiIiIqKSxCvGWri5uaF///5wcnKCEAIHDx6Ej4+PvsMiIiIiKpCBARAXL1cps6xiguqWlfQUUfnBxFiLcePGoVmzZjh79ixMTU0RHByMNm3a6DssIiIiogKlvczBrweuqZT5erZgYqwDdqUogLW1NRo3bozx48fD2NhY3+EQERERUQliYqzFzp07MWXKFPz0009ITU3FN998g+3bt+s7LKJyY9++fejRowecnJywadMmrfOdOHECXbp0KcXIiIiINGNirMXGjRuxbds2mJubo2bNmti1axfWr1+v77CIyoWEhASEhoZi8+bN2LNnD7Zt24Z///1Xbb6kpCTMmzdPDxESERGpY2KshYGBAczNzaXX7777rjR0GxEVLDIyEu3atUO1atVgZmYGZ2dnREREqM0XGBiIMWPG6CFCIiIidXz4Totq1arh2rVr0q/f7d27F1WrVtVzVETlQ2JiImrXri29trKywpUrV1Tm+fXXX9GsWTPY29trrUcul0MuV32yWqFQFG+wRPRWeibPgDwtS6WMIzcQE2Mtpk6dinHjxuHevXv49NNPYWpqiuXLlxe4TFhYGA4ePAgAcHR0xOTJk0sjVKIyRwihVpb/J9Zv3ryJw4cPY926dXj8+LHWetavX4+wsDCVslq1amHJkiWIiYkpMIaoqKhCRv12YjvpTltbtW7dupQjoeIgT8vCqvBolTKO3EBMjLXIyMjAnj17EBsbC4VCgQYNGhQ4MkVkZCROnTqF8PBwyGQyDB8+HEeOHIGTk1MpRk1UNlhbW+PixYvS68TERFhZWUmvIyIi8OTJE/Tp0wfZ2dlITEzEoEGDsHnzZpV6hgwZAk9PT5UyhUKBpKQk2NnZwdTUVOP6o6KimKzogO2kO7YV0duBfYy1mDhxIgwNDdGoUSPY2Ni8dri22rVrIyAgACYmJjA2NkajRo3w6NEjlXnkcjkePHig8q+gq2VE5VX79u1x5swZJCcn4+XLlzh8+DA6deokTR87diwOHTqEPXv2YPXq1bCyslJLigHA0tISdevWVfn3zjvvlOamEOnNixcv4ObmhgcPHgDIvQDj7u6Obt26ITQ0VM/REVVMvGKsRZMmTbBv3z60bt0aZmZmUnm1atU0zt+4cWPp79jYWBw4cABbt25Vmedtvi1sUd0aGdnqt9crGcuQ+ixBrdzY3AqpqakqZSnyFEQ9ulXgvHn/a5u3qLEVZl5N2wAA2TlZuHLj/muXLw7aYgBK9rawtbU1/Pz84OPjg+zsbPTt2xctW7bEiBEjMHbsWLRo0aLI6yCqyC5fvozAwEDExsYCyL2LOXXqVGzYsAHvvvsufH19cfLkSTg6Ouo3UKIKhomxFkePHlV7il4mk+HatWtalsh169Yt+Pr6wt/fH++//77KtLf5tnBcvBzrIqLVyn09W8CmYV2N81tYWKiUVbWsivpN6mmdNzU1VVpG27xFja2w8766DQCQozDA1qP3Xrt8cdAWA1Dy/SLd3d3h7u6uUrZmzRq1+erWrYtjx46VaCxE5c327dsxffp06VmVK1euoH79+qhXL/e85u7ujoiICCbGRMWMibEW0dHqyc/rREVFYezYsZg6dSpcXV3VpltaWsLS0lKlLDMzE0lJSW8cJxERVTyzZ89Wea1ppJeEBPW7TBzJhahomBi/IigoCLNmzQIAJCcno0aNGjotFx8fj9GjRyM0NBQODg4lGSIREb1lXjfSS563ucteYRW1y56mbnFAyXWN00Rbd7mcnBydt60klceRXJgYvyL/ieOrr75CeHi4TsutXbsWmZmZCAkJkcq8vLwwcODAYo+RiIjeLtbW1ip3F18d6SXP29xlr7CK2mVPU7c4oOS6xmmirbuckZGRzttWUsrrPsXE+BX5v5Vr+oauTWBgIAIDA0siJCIiesvZ29vj7t27iIuLQ926dbF//3706dNHbT522SMqGibGBdB0m4qIiKi0mZqaIiQkBN9++y0yMzPh6OgIFxcXfYdFVOEwMX6FUqlESkoKhBBQKBTS33m0DddGRERU3PKP2OLg4IC9e/fqMRqiio+J8Stu3ryJdu3aSclw27ZtpWm6DNdGREREROUTE+NXXL9+Xd8hEBEREZEe8CehiYiIiIjAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLAxJiIiIiICABgpO8AiKhi2rdvH1asWIHs7GwMHToU3t7eKtP/+OMPLF26FEII1K1bF3PnzkXVqlX1FC0RUdnwTJ4BeVqWWrllFRNUt6ykh4jeLkyMiajYJSQkIDQ0FLt27YKJiQm8vLzQtm1bfPDBBwCAFy9eYMaMGdi5cyesra2xePFiLF26FIGBgXqOnIhIv+RpWVgVHq1W7uvZgolxKWBXimL24sULuLm54cGDB/oOhUhvIiMj0a5dO1SrVg1mZmZwdnZGRESEND07OxszZsyAtbU1AKBJkyaIj4/XV7hEREQAeMW4WF2+fBmBgYGIjY3VdyhEepWYmIjatWtLr62srHDlyhXpdfXq1fH5558DADIyMrB69WoMHjxYrR65XA65XK5SplAoSihqorLPx8cHT58+hZFR7sd3cHAw7O3t9RwVUcXBxLgYbd++HdOnT8fkyZM1TueHPL0thBBqZTKZTK0sNTUV33zzDWxtbeHp6ak2ff369QgLC1Mpq1WrFpYsWYKYmJgCY4iKiipk1G8ntpPutLVV69atS2X9QgjcuXMHJ06ckBJjIipePLKK0ezZswucXtE+5C2qWyMjWz0BqmQsQ+qzBJUyY3MrpKamqs2bnZOFKzfuq5UbGZuqzZ8iT0HUo1tq8+avO+9/bfUWJjZN6yvMdmjaBgDIyclRKy9MvNpoej+0xQCU7Ie8tbU1Ll68KL1OTEyElZWVyjyJiYn46quv0K5dO0ydOlVjPUOGDFFLmBUKBZKSkmBnZwdTU1ONy0VFRZVaslKesZ10Vxba6s6dO5DJZBgxYgSePn2K/v3744svvtBrTEQVDRPjUlTRPuTj4uVYF6H5AQGbhnXV5rWwsFCbN0dhgK1H76mV+/RoqjZ/VcuqqN+knsY4LCwskJqaKi2jrd7CxKZpfYXZDk3bAABGRkZq5YWJVxtN74e2GICSvcrVvn17LF26FMnJyahcuTIOHz6MWbNmSdMVCgVGjRqF7t2745tvvtFaj6WlJSwtLVXKMjMzkZSUVGKxE5VVcrkcDg4OmDFjBjIyMuDj44MGDRqgQ4cOKvPwziTRm2NiXIr4IU9vC2tra/j5+cHHxwfZ2dno27cvWrZsiREjRmDs2LF4/Pgx/vnnHygUChw6dAgAYGdn99q7LkRvs1atWqFVq1YAADMzM/Tt2xcnT55USYwr2p3JwtJ058zS3AzyF+lq8xb1zqSmu38F1aGr4riLqSm2osYFaG7fgu5s6rv70ZtgYkxEJcLd3R3u7u4qZWvWrAEAtGjRAtevX9dHWETl1sWLF5GdnQ0HBwcAuX2OX+1rXNHuTBaWtjtnJXFnUtPdv4LqKMw2FPUupqbYihpX3vpebV9tdzbL6z7F4dqIiIjKgdTUVMyfPx+ZmZl48eIFwsPD4eTkpDKPpaUl6tatq/LvnXfe0VPEROUPrxiXgGPHjuk7BCIiqmA6d+6My5cvo1evXlAqlRg0aJDUtYKIigcTYyIionJi/PjxGD9+vL7DIKqw2JWCiIiIiAi8YkxERESkF8/kGZCnZamUZeco9RTNmzEwyH0o71UW1a31EE3RMTEmIiIi0gN5WhZWhauPolGepL3Mwa8HrqmVe3V9Tw/RFB27UhARERERgYkxEREREREAJsZERERERACYGBMRERERAeDDd0REREQlStPoE0D5G4HibcDEmIiIiKgEaRp9Aih/I1C8DdiVgoiIiIgITIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIAHJWCiIiIqNgYGABx8XKVsrI8LJumoeQsq5igumUlPUWkX0yMiYiIiIpJ2ssc/HrgmkpZWR6WTdNQcr6eLd7axJhdKYiIiIiIwMSYiIiIiAgAE2MiIiIiIgBMjImIiIiIAPDhOyIiIipFmkZBAN7ukRB0UdTRLjQtr62Owsxb0TAxJiIiolKjaRQE4O0eCUEXRR3tQtPy2uoozLwVDbtSEBERERGBiXGx2rdvH3r06AEnJyds2rRJ3+EQ6dXrjodr166hT58+cHZ2xrRp05CTk6OHKInKF37OEJUsJsbFJCEhAaGhodi8eTP27NmDbdu24d9//9V3WER6ocvxMGnSJAQFBeHQoUMQQmD79u16ipaofODnDFHJY2JcTCIjI9GuXTtUq1YNZmZmcHZ2RkREhL7DItKL1x0PDx8+REZGBj788EMAQO/evXm8EL0GP2eISh4fvismiYmJqF27tvTaysoKV65cUZlHLpdDLld9yjPv9nFWlvoTuvllZmYWU6TFR5GTDTMTmcbyV+PVNq9SoXu5pnrz1y0qGUjLaKu3MLEVdTsKs22FiVcbTbFpq1coFQXWa2JiAplMfTldve54eHV67dq1kZCQoFZPRTtmyiK2k+5K8pjRRUX5nCnMObc46i7tz5mS+jwob/OW9OdMSZEJIYS+g6gIVq5ciZcvX8LPzw8A8NtvvyE6OhrBwcHSPEuXLkVYWJjKcjY2NpgxY0Zphkr0WnZ2djA1NX3j5V93PFy6dAk//vgjtmzZAgCIi4uDr6+v2tUvHjNUXhT1mNEFP2eoIimNY+ZN8IpxMbG2tsbFixel14mJibCyslKZZ8iQIfD09FQpUyqVMDExQbVq1TR+c3r8+DEGDRqEzZs345133imZ4CsAtpPudGkrExOTIq3jdceDtbU1kpKSpNdPnjxRO14AHjMlie2ku9I4ZnTBzxn9YjvprqwcM2+CiXExad++PZYuXYrk5GRUrlwZhw8fxqxZs1TmsbS0hKWlZaHqNTQ0RFJSEgwNDcvkN6uygu2ku9Joq9cdD3Xq1IGpqSmioqLQunVr7N69G506dVKrh8dMyWE76a6stBU/Z/SL7aS78txWTIyLibW1Nfz8/ODj44Ps7Gz07dsXLVu21HdYRHqh7XgYMWIExo4dixYtWmDBggUIDAxEWloamjVrBh8fH32HTVSm8XOGqOQxMS5G7u7ucHd313cYRGWCpuNhzZo10t+2trbYsWNHaYdFVK7xc4aoZHG4NiIiIiIiMDEu8ywtLTFmzJhC9xl727CddFfR26qib19xYTvprqK3VUXfvuLCdtJdeW4rDtdGRERERAReMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIAgJG+A6Dy4cGDB3BycoKNjQ0AQKlUwtjYGD4+PujVq9drlw8LC4OtrS0+//xznde5dOlSbNq0CdbW1pDJZFAoFKhZsyamT5+OBg0aFCr+wMBAeHl5wc7OrlDL5ckf/+LFi1G/fn2dtpuosIpyrOl6nDVp0gRnzpxBjRo1iitsKqOKeu7+7bffkJWVBW9v7zda/4kTJ3D58mWMGzcOR48exZkzZxAYGPhGdb2qSZMmsLGxgYGBAYQQAAAPDw989dVXb1zntGnT4Orqivbt2xdLjCXFzc0NQUFBaNu2rUp5dnY2QkND8ddff0Emk0EIAVdXV/j6+kImkxV6Pfnfv7cFE2PSWaVKlbBnzx7p9cOHDzF06FBUrlwZzs7OBS577tw5fPDBB4VeZ48ePfD9999Lrzds2IDvvvsOu3btKlQ9kZGRGDBgQKHXnyd//G/TCYL0402PtTc9zqhiK8q5OyoqCo0bN37jdUdHRyMlJQUA0LVrV3Tt2vWN69Jk/fr10he85ORkjBo1CjKZDF9++eUb1Td79uziDK/UrV+/Hg8ePEB4eDiMjIyQmpqKIUOGoHr16m/0GZj//XtbMDGmN1anTh2MHTsWa9euhbOzM+7evYvg4GCkp6cjMTERtra2WLRoEXbs2IGYmBjMnz8fhoaG+OCDDzTOZ2pq+tp1Ojg4YOHChQCAwYMHo2rVqrhz5w4GDhwIJycnzJgxAw8fPoQQAr169cLw4cMRGhqKxMRETJw4EfPnz0fDhg0xe/Zs3Lx5E9nZ2XBwcMDkyZNhZGSEy5cv44cffsDLly9hbGyMyZMn486dOyrxHz16FI0bN8ZXX32FixcvYv78+dL848ePR6dOnbBr1y4cOXIEBgYGiIuLg7GxMebNmyddtSEqjPzHmo2NTbEcZ4sWLUJ0dDSUSiXGjx+Pzp07Iz09HTNmzEBsbCxSUlJQpUoVLFiwAA0bNsThw4exYsUKyGQyGBoaYvLkyfjkk0+Qmpqq9XhasmQJjhw5AmNjY1SvXh1z586FlZWVnluTXj13Z2VlYcGCBbhw4QIUCgWaNWuGwMBAnDlzBseOHcPp06dRqVIleHt7Y8WKFTh8+DCUSiXq1KmD6dOnw9raGk+ePMH06dNx584dGBgYwMvLC/b29ti6dSsUCgUsLCxQv359HDp0CKtWrcLjx481nq8fPHiAoUOHwtHREZcvX0ZKSgr8/PzQo0eP125XjRo1EBAQgLFjx2LYsGGQyWQa401PT4eXlxf++usvmJiYQKFQoHPnzvj5558xc+ZMeHt7w8XFBcePH8eiRYugVCphZmaGmTNnwtbWFpcuXcKCBQvw8uVLyGQyfPvtt+jcuTOePHkCf39/PHv2DADg6OiI8ePHq8W5Y8cObNu2DdnZ2UhJScGIESMwaNCgAj83/v33X0ydOhUvX75Ew4YNkZ6errENnjx5guzsbGRlZcHIyAgWFhaYP38+lEolHj16BFdXV/z555+wsLCAEAIuLi5YvHgx7t27p3Z8m5iYqLx/fn5++O2337BlyxYolUpUq1YNQUFBaNSoEQICAmBqaoro6GgkJSWhe/fuqFGjBo4fP44nT57ghx9+gIODAy5evIiQkBAolUoAgK+v72u/nJU6QaSD+/fviw8//FCt/ObNm8Le3l4IIURISIjYvXu3EEKIrKws4ebmJiIiIoQQQnzxxRfi4MGDr50vvyVLloiZM2dKr7Ozs8XcuXOFr6+vVOeUKVOk6d7e3uLnn38WQgghl8uFu7u72L9/vxBCiM6dO4srV64IIYQICAgQv/76qxBCiJycHDFx4kSxevVqkZWVJTp06CCOHz8uhBAiOjpauLm5CYVCoRK/v7+/+Omnn0RycrJwcHAQ//vf/6S2aNOmjbh3757YuXOnaN26tYiPjxdCCBEcHCwmT56se4PTW+t1x1pxHGc2NjZi1apVQgghbty4Idq0aSOePn0qDh48KGbNmiWtMygoSAQHBwshhOjatav4+++/hRBC/PXXX2Lp0qVCCO3H06NHj8RHH30kMjMzhRBCrF27Vhw5cqT4Gop0osu5e+nSpSIkJEQolUohhBD//e9/xfTp04UQ/3e+E0KI8PBwMX78eJGdnS2EEGLr1q1i+PDhQgghRo8eLebNmyeEyD3/urq6itjYWJXz+M6dO8XIkSOFENrP1/fv3xc2Njbi2LFjQgghIiIixGeffaZx22xsbMTTp09VytLS0qTyguL19vaWjpUTJ04ILy8vIcT/HUNPnjwRrVu3Fv/8848QQohDhw6Jr776Sjx//lx069ZN3L9/XwghxOPHj0WnTp3Ew4cPRVhYmAgKCpLiGD9+vJDL5SrxvXjxQvTv318kJycLIYT4+++/pfenoM8NDw8PsX37diGEEBcvXhRNmjQRZ8+eVWuT+Ph44enpKVq0aCG++OILsXDhQnH16lVp+tdffy02btwohBAiMjJS9O/fXwih/fjO//6dO3dODBo0SKSnp0vzde/eXQiRu5/069dPZGVlicTERGFjYyOdF9atWyeGDRsmhBDCx8dH+ly+du2amDFjhoZ3Vr94xZiKRCaToVKlSgCASZMm4fTp01izZg1iY2ORmJio8VutrvMBwIEDBxAVFQUgt+9U8+bNMWvWLGn6xx9/DABIT0/HpUuX8PPPPwMALCws0Lt3b/z5559wdXVVqfPEiROIjo7Gjh07AAAZGRkAgJs3b8LAwACfffYZAMDOzg779u3Tuu1XrlzBe++9B3t7ewBA48aN8dFHH+H8+fOQyWRo3rw53nnnHQBAs2bNcOTIkQJakqhgecdacR1nAwcOBADY2NigUaNG+Pvvv+Hi4oJ69ephw4YNiIuLw/nz59GqVSsAgKurK8aMGQNHR0d06NABI0aMAKD9eLK2toatrS08PT3RqVMndOrUCQ4ODiXaRqS7/OfuEydOIDU1FZGRkQByz7U1a9ZUW+b48eOIjo5Gnz59AOT2V3758iWA3O5qkyZNApB7/t2/f7/WdRd0vra3t4exsTEcHR0B5J47nz9/XqjtAgBTU9MC4+3Xrx/Cw8Ph4uKCXbt2oV+/fir1XLp0CY0bN0bTpk0BAN26dUO3bt1w8uRJPHnyBKNHj1ZZ540bN9CxY0eMHDkS8fHxaN++Pb777jtYWFio1FulShWsXLkSJ0+eRGxsLK5fv65yXGr63Hj27Blu3Lgh9Qlv3bq11u4t77zzDnbt2oV///0X586dw7lz5zBgwAAEBATA29sb3t7e+PHHH+Ht7Y1t27ZJ5wFtx3d+J06cQFxcHLy8vKSylJQU6f3p3LkzjI2NUbt2bZiZmaFjx44AgPfee0+ap3v37ggODsaxY8fQvn17TJgwQeN26BMTYyqS6OhoqXvAhAkToFAo0L17d3z22WeIj4+XHojIT9f5APU+xq8yMzMDkHvCe7UOpVKJnJwctWWUSiUWL16MRo0aAQDkcjlkMhkePnyo9nDCzZs30bBhQ43rzrsVlJ8QAjk5OTA2NpY+dABID0EQvam8Y624jjMDg/8blEgIASMjI2zevBnbt2+Ht7c33N3dUa1aNTx48AAA4Ofnh759++LUqVPYtWsXVq9ejV27dmk9ngwMDLBx40ZER0fjzJkzmDNnDtq2bVtsD15R0eQ/dyuVSkydOlVKRtPS0pCZmam2jFKpxPDhwzFo0CAAQFZWltT/1MjISOX8ef/+fVSvXl3jul93vjY2Npb2z8I+MBYdHY26deuiSpUqBcbr4uKCuXPn4vbt27hw4QJCQkJU6jE0NFRZtxACN27cgEKhQKNGjfDbb79J0xISElCjRg0YGxtLDxiePXsW/fr1w7Jly/DRRx9J8z5+/BgDBgxA//790bp1a6nLRh5Nnxt5ceRvMyMjzenb/Pnz0a9fP3zwwQf44IMP4O3tjT179mDNmjXw9vZG+/bt8fLlS5w5cwYXL17EvHnzAGg/vvNTKpXw8PCQvgAplUokJiaiatWqAAATExOV+TXF6OXlhc6dO+P06dP466+/EBYWhr1796p9gdAnDtdGb+zu3btYvny59JDDqVOnMHr0aPTo0QMymQyXL1+GQqEAkHuSyTvpFTTfmzI3N4e9vT02bdoEAEhNTcXu3bulJ4vzr//TTz/FunXrIIRAVlYWvv76a2zcuBENGzaETCbD6dOnAQBXr17FkCFDoFQqVZbPY29vj7t37+LKlSsAgFu3buHChQto06ZNkbaF6FX5j7XiOs7Cw8MB5O7ncXFxsLe3x6lTp+Dp6Yl+/fqhQYMGOHbsGBQKBXJyctClSxekp6dj4MCBmD59Om7fvo2cnBytx9P169fh5uaGRo0awdfXF0OHDsWNGzdKv/FIzavn7k8//RSbNm1CVlYWlEolgoKCpGc5Xj137tixAy9evAAALF68GJMnTwaQ+/zHzp07AUB64Cs2NlbjufN15+s3lZCQgAULFqhsl7Z4TU1N4erqioCAAHTr1g2VK1dWqcve3h63b9/GrVu3AABHjx7FpEmT8OGHHyIuLg4XLlwAAFy7dg3Ozs5ITEzEggULsHz5cnz++eeYNm0aPvjgA8TGxqrUGxMTgxo1auCbb75Bx44dpaS4oM/AatWqoXnz5lIyfvXqVdy8eVPjvMnJyVi8eLF0ZVwIgbt376JZs2YAcpPtQYMGYdq0aXBzc4OpqWmBx3f+969Dhw74/fffkZiYCADYsmULhgwZ8rq3RYWXlxeuXbuG3r17Y9asWZDL5WXu4T5eMSadZWRkwMPDA0Du1SZTU1NMmDBB6nrg5+eH0aNHo2rVqqhcuTI++eQT3Lt3D0DuLZZ58+YhOzu7wPmKYsGCBQgODsauXbuQlZUFd3d39O7dGwDw+eefw8/PDz/88AOmTZuG2bNnw93dHdnZ2Wjfvj2GDx8OY2NjLF26FHPmzMH8+fOl1yYmJirx56lRowYWL16MWbNmISMjAzKZDHPnzkWDBg3w999/F3l76O1V0LFWXMfZ/fv30atXL8hkMixcuBDVqlXDl19+ie+//x67du2CoaEhmjdvjps3b8LIyAhTp07FxIkTpSuDc+bMgYmJSYHHU/fu3dGnTx+YmZmhUqVKvFqsJ687d3/zzTeYN28ePD09oVAo0LRpUwQEBAAAOnXqJHVfGzFiBBISEtC/f3/IZDK8++670pXW77//HjNmzIC7uzuEEPD19YWdnR2ys7Px7bffwtjYGM2bN5di0na+fvjwYaG2bciQITAwMIChoSEAoE+fPtLQcv369dMab970jRs3YsaMGWr11qpVCwsWLIC/vz8UCgXMzc0RGhqKGjVqYMmSJZg/fz4yMzMhhMD8+fNRp04dDBkyBAEBAXBzc4OJiQmaNGkCNzc3lXo7dOiAHTt2wMXFBZUrV0bLli1Ro0YNxMXFFbidCxcuxJQpU7B161a89957Wu9kTp8+HaGhoejZsydMTEyQk5ODdu3aqdx59fT0xLx586RRKgo6vh0cHKT3LygoCCNGjMCXX34JmUwGc3NzhIWFFeqq/sSJEzFnzhwsWrQIBgYGGDNmDOrWravz8qVBJnh/l4iIiOit8PvvvyM8PBw//fSTvkMpk3jFmIiIiOgtMHjwYCQlJWHp0qX6DqXM4hVjIiIiIiLw4TsiIiIiIgBMjImIiIiIADAx1jshhPRkKxG9Ho8ZosLhMUOkOybGepaVlYWYmBhkZWVpnefq1aulGFH5xXbSXXluKx4zxYftpLvy3FY8ZooP20l35bWtmBiXA3k/sUoFYzvprqK3VUXfvuLCdtJdRW+rir59xYXtpLvy2lZMjImIiIiIwMSYiIiIiAgAE2MiIiIiIgBMjImIiIiIADAxJiIiIiICwMSYiIiIiAgAYKTvAIhKwjN5BuRp6mN2WlYxQXXLSnqIiKjsyDs+jM2tEBcvB8Bjg6gi0fQZyGNcN0yMqUKSp2VhVXi0WrmvZwueGOitl3d8pKamwsLCAgCPDaKKRNNnII9x3bArBRERERERmBgTEREREQFgYkxEpSAsLAyurq5wdXXF/PnzAQCRkZFwd3dHt27dEBoaqucIiYiImBgTUQmLjIzEqVOnEB4ejt27d+Pq1avYv38/pk6diuXLl+PAgQOIiYnByZMn9R0qERG95ZgYE1GJql27NgICAmBiYgJjY2M0atQIsbGxqF+/PurVqwcjIyO4u7sjIiJC36ESEdFbjqNSEFGJaty4sfR3bGwsDhw4gMGDB6N27dpSuZWVFRISEtSWlcvlkMvlKmUKhaLkgiUiorcaE2MiKhW3bt2Cr68v/P39YWRkhLt376pMl8lkasusX78eYWFhKmW1atXCkiVLEBMTU+D6oqKiih50BWVsboXU1FQAkP5Pkacg6tEtfYZV5mnbp1q3bl3KkRBRSWFiTEQlLioqCmPHjsXUqVPh6uqK8+fPIykpSZqemJgIKysrteWGDBkCT09PlTKFQoGkpCTY2dnB1NRU6/qYrGgXFy+HhYWFyjjGVS2ron6TenqOrOziPkX0dmBiTEQlKj4+HqNHj0ZoaCgcHBwAAPb29rh79y7i4uJQt25d7N+/H3369FFb1tLSEpaWliplmZmZKkk1ERFRcWFiTEQlau3atcjMzERISIhU5uXlhZCQEHz77bfIzMyEo6MjXFxc9BglERERE2MiKmGBgYEIDAzUOG3v3r2lHA1R+RAWFoaDBw8CABwdHTF58mRERkZi7ty5yMzMRPfu3eHn56fnKIkqHg7XRkREVIZw7G8i/WFirMGLFy/g5uaGBw8eAAC2bdsGNzc3uLu7Y8qUKcjKylJbZvfu3fj000/h4eEBDw8P/pIXERG9kaKM/S2Xy/HgwQOVf48fP9bDVhCVT+xK8YrLly8jMDAQsbGxAIC7d+9i7dq12LVrF6pUqYKAgABs3rwZQ4cOVVkuOjoaAQEBcHNzK/2giYiowijK2N8c4rDklYd2yj8kYx59DMlYHoc4ZGL8iu3bt2P69OmYPHkyAMDExAQzZsyAubk5AMDGxgaPHj1SWy46OhpxcXFYvXo1bGxsEBQUhKpVq5Zq7EREVHG8ydjfHOKwZJWXdsobkjG/0h6Ssby01auYGL9i9uzZKq/r1KmDOnXqAACSk5OxadMmzJ07V2252rVrY+TIkWjZsiUWLlyI4OBg/Pe//1WZh7/iRUREunjTsb85xCFR0TAx1lFCQgKGDx+OPn36oG3btmrTly1bJv09fPhwfP7552rz8BZXyctrJ023kQD+uld+5fEWF9HboChjfxNR0TAx1sHt27cxYsQIfPHFF/jyyy/VpqempmLnzp1Sv2MhBIyM1JuWt7hKVv520nQbCeCve+XhPkVUdnHsbyL9YWL8Gi9evMBXX30FPz8/eHh4aJzHzMwMP/30E1q1agV7e3ts3LgRTk5OavPxFhcREb0Ox/4m0h8mxq+xY8cOJCUl4eeff8bPP/8MAOjSpQvGjRuHadOmoUuXLujatSsWLVqEGTNmICMjA++//z7mz5+v58iJiIiIqDCYGGtx7NgxAMDQoUPVhmbLk/9BvY8//hjh4eGlERoRERERlQD+wAcREREREXjFmKjEPJNnQJ6m/iuJllVMUN2ykh4iIiIiooIwMSYqIfK0LKwKj1Yr9/VswcSYiIioDGJXCiIiIiIiMDEmIiIiIgLAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLA4dqIiIgk2sYft6hurYdoiKi0MTEmIiL6/7SNP+7V9T09RENEpY1dKYiIiIiIwMSYiIiIiAgAE2MiIiIiIgBMjImIiIiIAPDhOyIiIqJC0zaCiWUVE1S3rKSHiKg4MDEmIiIiKiRtI5j4erZgYlyOsSsFERERERGYGBMRERERAWBiTEREREQEgIkxEREREREAJsYavXjxAm5ubnjw4AEAIDIyEu7u7ujWrRtCQ0M1LvPo0SN4e3vDxcUFX3/9NdLS0kozZKIy79XjasqUKejWrRs8PDzg4eGBI0eO6DlCIiJ62zExfsXly5cxcOBAxMbGAgAyMjIwdepULF++HAcOHEBMTAxOnjypttzMmTMxaNAgREREwM7ODsuXLy/lyInKrlePKwCIiYnBxo0bsWfPHuzZswdOTk76C5CIiAhMjNVs374d06dPh5WVFQDgypUrqF+/PurVqwcjIyO4u7sjIiJCZZns7GxcuHABzs7OAIDevXurzUP0Nnv1uEpPT8ejR48QFBQEd3d3LFmyBEqlUs9REhHR247jGL9i9uzZKq8TExNRu3Zt6bWVlRUSEhJU5nn27BnMzc1hZJTbnLVr11abBwDkcjnkcrlKmUKhKK7QicqsV4+rp0+fol27dggODoaZmRl8fX2xY8cO9O/fX2U+HjNERFSamBi/hhBCrUwmkxV6HgBYv349wsLCVMpq1aqFJUuWICYmpsA4oqKidAn3rZfXTsbmVkhNTVWbniJPQdSjW8W+Xovq1sjIVt0PjIxNSzWGwtK2T7Vu3brE112vXj0sW7ZMej148GDs3r1bLTHmMVMy8h8fef+Xlf1S37SdOwD9HjNEVDqYGL+GtbU1kpKSpNeJiYnS7eA8NWrUwIsXL6BQKGBoaIgnT56ozQMAQ4YMgaenp0qZQqFAUlIS7OzsYGpqqjGGqKgonnh1kL+d4uLlsLCwUJunqmVV1G9Sr9jXHRcvx7oI1V9A8unRtFRjKAx971M3btxAbGys1P1ICCHdccmPx0zJyDs+UlNTpX20LOyXZYG2cwdQ+gnwixcv4OXlhZUrV6Ju3bqYMmUKoqKiULlyZQDAmDFj2DefqJgxMX4Ne3t73L17F3Fxcahbty7279+PPn36qMxjbGyMjz/+GAcOHIC7uzt2796NTp06qdVlaWkJS0tLlbLMzEyVxJvobSCEwJw5c9CuXTuYmZlh27ZtagkwwGOG3l6XL19GYGCgxgdWNV14IaLiwYfvXsPU1BQhISH49ttv0aNHDzRs2BAuLi4AgGnTpuHo0aMAgOnTp2P79u3o0aMHLl68iPHjx+sxaqKyzdbWFiNHjsTAgQPh6uqKpk2bws3NTd9hEZUZfGCVSD94xViLY8eOSX87ODhg7969avPkf6CoTp062LBhQ6nERlRe5T+uvL294e3trcdoiMouPrBKpB8VNjHesGEDPD09YW5uru9QiIiIioQPrBY/TQ9NVzKWIfWZ+qhS+RXnQ95vGkNBywOaH/7WxwO25fGB1QqbGN+8eRPOzs747LPP4OXlhRYtWug7JCIiojfCB1aLn6aHpn09W8CmYV2tyxT3Q95vEsPrlgc0P/xd2g/Yltd9qsL2MZ41axYOHToEOzs7zJw5E3369MGOHTuQmZmp79CIiIgKJe+B1ZSUFGRnZ2Pbtm0aR6SwtLRE3bp1Vf698847eoiYqHyqsIkxAJibm8PFxQVubm54/vw5Nm/eDBcXF/4qHRFVOM/kGYiLl6v8eybP0HdYVEz4wCpR6aiwXSkiIyOxfft2nDlzBs7Ozli2bBlsbW1x7949DBo0SBpZgoioIpCnZWFVuPot2eqWlfQUERUHPrBKVLoqbGIcHByMQYMGYdasWSr9bN577z21hxWIiIiIiCpsV4q9e/eiWrVqsLCwwJMnT7Bu3TppzMexY8fqOToqTnm3kI3NraRbyNk5HN+TiIiICqfCXjGeNWsW0tLS0LNnTxgYGCAqKgoPHjxAYGCgvkOjYpZ3Czn/z9v69Giq56iIiIiovKmwifHff/+N/fv3AwBq1qyJxYsXw8PDQ89REREREVFZVWG7UmRnZyMrK0t6nZOTo8doiIiIiKisq7BXjD/77DN89dVX8PDwgEwmw/79++Ho6KjvsIiIiIiojKqwifHkyZOxadMmHD16FEZGRnBycoKXl5e+wyIiIiKiMqrCJsaGhobw8fGBj4+PvkMhIiIionKgwibGBw4cwIIFC5CSkgIhhFR+6dIlPUZFRERERGVVhU2MFy9ejICAADRr1gwymUzf4RARERFRGVdhE2NLS0t069ZN32EQERERUTlRYYdrs7e3x8mTJ/UdBhERERGVExX2ivHJkyexceNGGBsbw9jYGEIIyGQy9jEmIiIiIo0qbGK8bt06fYdARfBMngF5WpZKmWUVE1S3rKSniIiIiKiiq7CJcZ06dRAREYFr165h1KhROHr0KNzc3PQdFulInpaFVeHRKmW+ni2YGBMREVGJqbB9jFevXo0tW7YgIiICGRkZCAsLw7Jly/QdFhERERGVURU2Mf7999+xZs0aVK5cGdWrV8f27duxf//+N6rrt99+g4eHh/SvdevWCA4OVpknLCwMnTt3lubZtGlTcWwGEREREZWSCtuVwsjICCYmJtJrS0tLGBm92eb269cP/fr1AwDcunULo0ePxpgxY1TmiYmJwcKFC9GqVas3D5qIiIiI9KbCJsbvvvsuTpw4AZlMhqysLKxduxZ16tQpcr0zZsyAn58fatSooVIeExODNWvW4P79+/jkk0/g7+8PU1NTlXnkcjnkcrlKmUKhKHJMRERERFR0FTYxDgoKwuTJk3Hjxg18+OGHsLe3x4IFC4pUZ2RkJDIyMtC9e3eV8rS0NDRt2hT+/v6oU6cOAgICsHz5cvj5+anMt379eoSFhamU1apVC0uWLEFMTEyB646KiipS7OWNsbkVUlNTVcpS5CmIenSrwHnz/s/JyVFbvqA6SiLe0o6hsLTtU61bty7lSIiIiMqGCpsYW1tbY/369Xj58iUUCgXMzc2LXOfWrVsxbNgwtfIqVapgzZo10usvv/wSU6dOVUuMhwwZAk9PT5UyhUKBpKQk2NnZqV1hzhMVFfXWJStx8XJYWFiolFW1rIr6TeppnTc1NVVaxsjISG35guooiXhLO4bCeBv3KSIiotepsInxL7/8orFcU2Kri6ysLFy4cAEhISFq0x49eoTIyEj07dsXACCE0Nif2dLSEpaWliplmZmZSEpKeqOYiIiIiKj4VNjE+ObNm9LfWVlZiIqKQtu2bd+4vhs3buD999+HmZmZ2rRKlSrhxx9/RNu2bVG3bl1s2rQJTk5Ob7wuIiIiIip9FTYxnjt3rsrr5ORkTJ48+Y3ru3//Pt555x2VshEjRmDs2LFo0aIFgoOD8fXXXyM7OxsfffTRG1+ZJiIiIiL9qLCJ8atq1KiBhw8fvvHyPXr0QI8ePVTK8vcrdnZ2hrOz8xvXT0RERET6VWET4/x9jIUQiImJQc2aNfUYEVHpeybPgDwtS63corq1HqIhIiIq2ypsYpy/jzGQO65xUbpSEJVH8rQsrAqPViv36vpeqcfy4sULeHl5YeXKlahbty4iIyMxd+5cZGZmonv37mqjuBAREZW2CpsYv9rHmIj05/LlywgMDERsbCwAICMjA1OnTsWGDRvw7rvvwtfXFydPnoSjo6N+AyUiordahU2MBw8eDJlMpnX6r7/+WorREL3dtm/fjunTp0t3ba5cuYL69eujXr3c8Zzd3d0RERGhlhjz1yKJiKg0VdjE2M7ODrdv30b//v1hbGyMPXv2ICcnB66urvoOjeitM3v2bJXXiYmJqF27tvTaysoKCQkJasvx1yJ1V9RfiywLv8hoUd0aGdlCrbySsQypz9T3j5KgqR3zlPavRbL7EVHpq7CJ8aVLl7B582YYGhoCADp27Ij+/ftz5AiiMkAI9eRH0x0e/lqk7or6a5Fl4RcZ4+LlWBeh3ife17MFbBrWLbUYNP1iJVC6P5fO7kdE+mGg7wBKSnJyMrKy/u9p/LS0NGRkZOgxIiLKY21trfKLj4mJibCyslKbz9LSEnXr1lX59+p44kQVUV73o7zjIn/3IyMjI6n7EREVrwp7xdjNzQ39+/eHk5MThBA4ePAgfHx89B0WEQGwt7fH3bt3ERcXh7p162L//v3o06ePvsMiKjPetPsR++UTFU2FTYzHjRuHZs2a4ezZszA1NUVwcDDatGmj77CICICpqSlCQkLw7bffIjMzE46OjnBxcdF3WERllq7dj9gvX3eF6Zef1//d2NwKV27cBwAYGZtq7I9emP76mmLIzsmS1pGfpbkZ5C/SVcq0xZCTk6PztpWk0u6XXxwqbGIM5N6ubdy4MXr37o2rV6/qOxyit96xY8ekvx0cHLB37149RkNUfuja/Yj98nVX2H756yKiVfrl+/RoqrE/emH662uKIUdhgK1H76nN69OjqVq5thiMjIx03raSUl73qQrbx3jnzp2YMmUKfvrpJ6SmpuKbb77B9u3b9R0WERFRoeXvfqRQKLB//3506tRJbT72yycqmgqbGG/cuBHbtm2Dubk5atasiV27dmH9+vX6DouIiKjQ8nc/6tGjBxo2bMjuR0QloMJ2pTAwMIC5ubn0+t1335WGbqPyycAg97bTq7JzlHqI5s1p2g7LKiaobllJbd5n8gzI07J0mpeIKh52PyIqXRU2Ma5WrRquXbsmPZywd+9eVK1aVc9RUVGkvczBrweuqZX79Giqh2jenKbt8PVsoTHZladlYVV4tE7zEhERUdFU2MR46tSpGDduHO7du4dPP/0UpqamWL58ub7DIiIiIqIyqsImxhkZGdizZw9iY2OhUCjQoEEDGBsb6zssIiIiogpDU5c/IHeIu/KowibGEydOxMGDB9GoUSN9h0JERERUIWnq8gcAXl3f00M0RVdhR6Vo0qQJ9u3bh0ePHuH58+fSPyIiIiIiTSrsFeOjR4+q/Y68TCbDtWvqD28REREREVXYxDg6Wv2yPhERERGRNhWuK0VQUJD0d3Jysh4jISKi4pA39nf+f8/kGfoOi4gqoAp3xTgmJkb6+6uvvkJ4eHix1Ovj44OnT5/CyCi3yYKDg2Fvby9Nj4yMxNy5c5GZmYnu3bvDz8+vWNZLRPS2K8zY30RERVHhEmMhhMa/i1rnnTt3cOLECSkxzi8jIwNTp07Fhg0b8O6778LX1xcnT56Eo6NjsayfiIiIiEpehUuM88v71buiunPnDmQyGUaMGIGnT5+if//++OKLL6TpV65cQf369VGvXj0AgLu7OyIiItQSY7lcDrlc9aeAFQpFscRIREREREVT4RJjpVKJlJQUCCGgUCikv/NUq1at0HXK5XI4ODhgxowZyMjIgI+PDxo0aIAOHToAABITE1G7dm1pfisrKyQkJKjVs379eoSFhamU1apVC0uWLFHpAqJJVFRUoeMuz4zNrZCamqpSlpOTo1b2anne/9rmTZGnIOrRLZ1isKhujYxs1bsOlYxlSH2m/t6+abyvi0tTvYXZBk3L59G2T7Vu3VqnuomIiCqaCpcY37x5E+3atZOS4bZt20rT3nS4tlatWqFVq1YAADMzM/Tt2xcnT56UEmNNXTY0Xa0eMmQIPD09VcoUCgWSkpJgZ2cHU1NTjeuPiop665KVuHg5LCwsVMqMjIzUyvKXp6amStO1zVvVsirqN6mncwzrIlRHN/H1bAGbhnWLJV5d4tJUb2G3QVMMABNgIiKiV1W4xPj69evFXufFixeRnZ0NBwcHALmJcP6+xtbW1khKSpJeJyYmwsrKSq0eS0tLWFpaqpRlZmaqLEtERERE+lHhhmsrCampqZg/fz4yMzPx4sULhIeHw8nJSZpub2+Pu3fvIi4uDgqFAvv370enTp30GDERERERFVaFu2JcEjp37ozLly+jV69eUCqVGDRoEFq1agUPDw+sXr0a1tbWCAkJwbfffovMzEw4OjrCxcVF32ETERERUSEwMdbR+PHjMX78eJWyPXv2SH87ODhg7969pRwVERERERUXdqUgIiIiIgKvGBMREVE580yeAXlallq5ZRUT/iJiIWlqy7e5HZkYExERUbkiT8vCqvBotXL+VHjhaWrLt7kd2ZWCiIiIiAhMjImIiIiIADAxJiIiIiICwD7G9Ao+0FB+aXrvsnOUeoqGiIio/GFiTCr4QEP5pem98+nRVE/REBERlT/sSkFEREREBCbGREREREQA2JWCiPTIx8cHT58+hZFR7qkoODgY9vb2eo6KiIjeVkyMiUgvhBC4c+cOTpw4ISXGRERE+sRPIyLSizt37kAmk2HEiBF4+vQp+vfvjy+++ELfYRGVabzLQlSymBgTkV7I5XI4ODhgxowZyMjIgI+PDxo0aIAOHTqozCOXy1WWUygUpR0qUZnAuyxEJY9HFhHpRatWrdCqVSsAgJmZGfr27YuTJ0+qJMbr169HWFiYynK1atXCkiVLEBMTU2D9UVFRxR+0BhbVrZGRLdTKKxnLkPosoVRiAABjcyukpqaqlKXIUxD16FaB8+b9r23e0qRpGwAgJydH520rqRgA7ftU69atiz0OTXiXhajkMTEmIr24ePEisrOz4eDgACD3atirV8GGDBkCT09PlTKFQoGkpCTY2dnB1NRUY91RUVGllqzExcuxLkLz2N82DeuWSgx5cVhYWKiUVbWsivpN6mmdNzU1VVpG27ylSdM2AICRkZHO21ZSMQCllwBrw7ssRCWPiTER6UVqaiqWLFmCrVu3Ijs7G+Hh4Zg5c6bKPJaWlrC0tFQpy8zMRFJSUmmGSlQmVJS7LMVB25X9wtxJKOpdFk13MgAgOycLV27cVynTdgdJUwza6tVUXph5ddm2182rSVm+y/ImmBgTkV507twZly9fRq9evaBUKjFo0CDpQ5+I1FWUuyzFQduV/cLcSSjqXRZNdzIAIEdhgK1H76mUabuDpCkGbfVqKi/MvK/bNl3m1aQs32V5E0yMSa+eyTMgT8tSK8/OUZbI+gwMcg/i/ExNDJGZpX6rsaRioP8zfvx4jB8/Xt9hEJULvMtCVPKYGJNeydOysCpcvX+mT4+mJbK+tJc5+PXANbV1vVpWkjEQEb0J3mUhKnlMjHUUFhaGgwcPAgAcHR0xefJktek7d+6Uvqn3798f3t7epR4nERFVXLzLQlSymBjrIDIyEqdOnUJ4eDhkMhmGDx+OI0eOwMnJSZonJiYGCxcu5Ld3IiIionKKibEOateujYCAAJiYmAAAGjVqhEePHqnMExMTgzVr1uD+/fv45JNP4O/vr/UhByKiikjTMwOWVUxQ3bLSWxUDEZVfTIx10LhxY+nv2NhYHDhwAFu3bpXK0tLS0LRpU/j7+6NOnToICAjA8uXL4efnp1IPx5ckoopM0zMDvp4tSjUpLQsxUPml6QFtgA9jv02YGBfCrVu34OvrC39/f7z//vtSeZUqVbBmzRrp9ZdffompU6eqJcblYXzJ4hgbsjjW96bjNb5ufMnSHAeytMeXLEwMQPkcX5KIqCRpekAb4MPYbxMmxjqKiorC2LFjMXXqVLi6uqpMe/ToESIjI9G3b18AmseWBMrH+JLFMTZkcazvTcZr1GV8ydIcB7K0x5csTAwAE2AiIqJXMTHWQXx8PEaPHo3Q0FBpYPX8KlWqhB9//BFt27ZF3bp1sWnTJpUH8/JwfEkiIiKisouJsQ7Wrl2LzMxMhISESGVeXl44duwYxo4dixYtWiA4OBhff/01srOz8dFHH2HYsGF6jJiIiIiICouJsQ4CAwMRGBioVj5w4EDpb2dnZzg7O5dmWERERERUjAz0HQARERERUVnAK8ZUKjSNLQqUvyFwOJQPERFRxcXEmEqFprFFgfI3BA6H8iEiosKqKBdVNF3kKm/b8DpMjImIiIhKUEW5qKLpIld524bXYR9jIiIiIiIwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiABwVAoionKlpMYE1zaclGUVE1S3rFTm6i0Ob8PQU0RUOEyMiYjKkZIaE1zbcFK+ni2KlMCWVL3F4W0YeoqICoddKYiIiIiIwMSYiIiIiAgAu1KQjjT1EywLfQQrisL8XGhF+WlRIiKisoaJMelEUz/BstBHsKIozM+FVpSfFiUiIipr2JWCiIiIiAhMjImIiIiIALArBRGRzkp73Nuirq84+qNrqsPUxBCZWYoi1UtvL21jcWvar/gsC5U2JsZERDoq7XFvi7q+4uiPrqkOnx5N2c+d3lhBY3HzWRbSN3alICIiIiICE2MiIiIiIgBMjHW2b98+9OjRA05OTti0aZPa9GvXrqFPnz5wdnbGtGnTkJOTo4coicqX1x1XRKSKxwxRyWJirIOEhASEhoZi8+bN2LNnD7Zt24Z///1XZZ5JkyYhKCgIhw4dghAC27dv11O0ROWDLscVEf0fHjNEJY+JsQ4iIyPRrl07VKtWDWZmZnB2dkZERIQ0/eHDh8jIyMCHH34IAOjdu7fKdCJS97rjiohU8ZghKnkclUIHiYmJqF27tvTaysoKV65c0Tq9du3aSEhIUKtHLpdDLlcd9iivy0VWlvrQNfllZma+UeyFpcjJhpmJTK1cqVAvV+Rk6xxXYerVVv66eUUlA2l6cdZbEecVSkWB752JiQlkMvXlitPrjiug7B0zmvZjbW1cmOOjONZX3o4ZTe1THOcfTXUUZl4eMyWvpN7ngubXNY6yfMwUdd638Zh5EzIhhNB3EGXdypUr8fLlS/j5+QEAfvvtN0RHRyM4OBgAcOnSJfz444/YsmULACAuLg6+vr5q3+SXLl2KsLAwlTIbGxvMmDGj5DeCqBDs7Oxgampaout43XEF8Jih8oPHDFHhlMYx8yZ4xVgH1tbWuHjxovQ6MTERVlZWKtOTkpKk10+ePFGZnmfIkCHw9PRUKVMqlTAxMUG1atU0fnN6/PgxBg0ahM2bN+Odd94pjs2pkNhOutOlrUxMTEo8jtcdVwCPmZLEdtIdjxkC2E6FUVaOmTfBxFgH7du3x9KlS5GcnIzKlSvj8OHDmDVrljS9Tp06MDU1RVRUFFq3bo3du3ejU6dOavVYWlrC0tKyUOs2NDREUlISDA0Ny+Q3q7KC7aS7stJWrzuuAB4zJYntpLuy0lY8ZvSL7aS78txWTIx1YG1tDT8/P/j4+CA7Oxt9+/ZFy5YtMWLECIwdOxYtWrTAggULEBgYiLS0NDRr1gw+Pj76DpuoTNN2XBGRZjxmiEoeE2Mdubu7w93dXaVszZo10t+2trbYsWNHaYdFVK5pOq6ISDseM0Qli8O1ERERERGBiXGZZ2lpiTFjxhS6z9jbhu2ku4reVhV9+4oL20l3Fb2tKvr2FRe2k+7Kc1txuDYiIiIiIvCKMRERERERACbGREREREQAmBgTEREREQFgYkxEREREBICJMRERERERACbGREREREQAmBgTEREREQFgYkxEREREBICJMRERERERACbGREREREQAmBgTEREREQHQITHu0qULoqOjpdfHjx/H4MGD0atXL7i6umL8+PGIj48vUhC7du1C79690bNnT7i6umLatGlITU0tUp0F+e2337Bp0yat08PDwzFgwAB4eHigR48eCAoKglwuL/J6W7VqhQcPHhS5nsLw9fXFrl27NE5bs2YNPDw80LNnT7i5uWHevHnIysoqsVjCwsLwxx9/aJymUCjwyy+/oHfv3lK7//jjj0WOJzk5GU2aNNE47cGDB2jatCk8PDykf05OThg8eDDu37//2rpfPTY0uXLlCr7//vtCxx0cHIylS5eqle/atQutW7eW4nV3d8fgwYNx5cqVQq8jT0JCAry8vN54+dISHR2NLl26aJx2+/ZtjBw5Eu7u7nB3d8cXX3yBixcvAsh9n1u1aqUy/4EDB9C2bVucOXNGY32BgYGIiYnRObYmTZqgS5cuEEKolIeFhaFJkyav3U8qivv37+Pbb7/VOC07Oxvz58+Hu7s7evbsCXd3d6xcuVKtzXR14sQJLF68uCjh0lviwYMHaNKkCby9vdWmTZkyBU2aNEFycrLW5QMCArB27VqN0zw8PCCXy7Fr1y74+voCAKZNm4bIyEgAhT+XkP4V6orxvn378OOPP+KHH37A7t27sX//fjRt2hQ+Pj5vnMBcuXIFy5Ytw88//4y9e/di7969MDQ0xIwZM96oPl1ERUUhIyND47SVK1fit99+w7Jly7Bnzx7s2bMHRkZGGDVqVInFow8HDx7EH3/8gW3btmHv3r3YuXMn7ty5g7CwsBJb57lz55CTk6Nx2owZM/D3339j/fr12LNnD3bs2IG7d+9i2rRpJRYPAFSqVEl6n/fs2YPDhw/DxsYGoaGhxVL/v//+i4SEhGKpK8/HH38sxbtv3z58/fXXGDlyJB4+fPhG9VlbW2Pr1q3FGmNpGzt2LPr164d9+/Zh3759GDduHHx9ffH8+XO1ebdu3YqQkBCsW7cODg4OGuuLjIwsdMImhJCS8bzXBw4cQNWqVQtVT3n26NEj3L17V+O09evX48GDBwgPD8fevXuxefNmHD58GNu3b3+jdUVHRyMlJaUo4dJbxNTUFLGxsSrnyfT0dERFRRWp3j179sDS0lKlbPbs2Wjfvj2ANzuXkH4ZFWbm0NBQzJo1C/Xr1wcAyGQyjBw5Ev/5z3+QlZWFlJQU+Pv749mzZwAAR0dHjB8/vsA6nzx5AiGElKgaGhpi3LhxuHXrFgBg6dKluHfvHu7fv4/ExES0bNkSHTp0wO7du/HgwQNMmjQJbm5uSEpKwvfff4+nT5/iyZMnqFOnDhYtWoSaNWuiS5cuaNmyJW7cuIEJEybg2LFjOH36NCpVqqTyDTI9PR2rVq1CeHg4atWqBQAwNjbG5MmTceTIEWRlZUEul+u8nurVq2PWrFmQyWRo0aIFlEolACAtLQ1TpkxBXFwcDAwM0Lx5cwQHB8PAQPV7yvHjx7Fq1SpkZWUhOTkZvXr1wvjx43Hu3DmEhoaiXr16uHXrFrKysvD999+jXbt2SEhIQEBAABITE/Gf//wHT58+1druCoUCGRkZqFSpEkxNTREUFCR9aw4ICICpqSmio6ORlJSE7t27o0aNGjh+/DiePHmCH374AQ4ODrh79y6Cg4ORnp6OxMRE2NraYtGiRTA1NYWdnR26du2K69evw93dHTExMZg/fz4MDQ3h5OQkxXL//n3s27cPp06dgrm5OQDAzMwMM2fOxN9//w0AOq9nwYIFiI+PR2hoKCpXrgw7O7vX7NWqMjMz8eTJE9SsWRMAkJWVhQULFuDChQtQKBRo1qwZAgMDpTgBQKlUYs6cObh8+TLS0tIghMAPP/yA//znP1iyZAlSU1MxZcoUzJ07F8eOHcOKFSuQnZ2NSpUqwd/fH61atcKLFy8wbdo0XL9+HVZWVjA0NETr1q11irl9+/ZwcnLCli1bMHHiRCQkJCA4OBjx8fHIzs6Gq6srRo0ahYULF+LFixfSFew///wTS5cuRWhoKNzd3fH3338jJycHP/74I06cOAFDQ0O0atUK06dPh4mJCVasWIHDhw9DqVSiTp06mD59OqytrXH48GGsWLECMpkMhoaGmDx5Mj755BOVGNPT0zFjxgzExsYiJSUFVapUwYIFC9CwYUMMHjwYH374IS5duoT4+Hi0bt0a8+bNg4GBATZv3oz169fD3NwcNjY2WtvgyZMnSE9Pl15/8sknWLRoEQwNDbFx40ZkZmYCAFavXo3NmzfD3NwcjRs3xvTp03Hp0iVkZGQgJSUF77zzDp4/f46kpCR89913yMrKwrRp03D06FHcvHkT9+/fR5MmTbBu3ToYGRmhRYsWGDp0KABALpcjLCwMVatWxc2bN1GpUiW8//77yMjIwLZt2zB16lQ8f/4c6enpsLKyQnZ2Nvz9/eHk5ISlS5fi4cOHePLkCR4+fIgaNWogNDQU1tbWWs8DeduzY8cOVKlSBR9//DGOHj2KY8eOFbjfdunSBW5ubjhx4gSeP3+Ob7/9FpcuXcLVq1dhZGSEFStWwNraWut+9ODBAwwdOhSOjo64fPkyUlJS4OfnB2dnZwQGBiIhIQFfffWV2hW2J0+eIDs7G1lZWTAyMoKFhQXmz58PpVKJR48ewdXVFX/++ScsLCwghICLiwsWL16Me/fuqe1fJiYm2Lp1KxQKBSwsLODn54fffvsNW7ZsgVKpRLVq1RAUFIRGjRrpfC67ePEiQkJCpPO0r68vnJ2ddToGqewzNDRE9+7dsW/fPulC1+HDh9G1a1f8/PPPAIBt27Zhw4YNMDAwQK1atRAUFIQGDRoAyL2gdujQIbx48QIdOnSAv78/jIyM0KRJE7U7T4MHD4a3tzeuXbuGxMRETJw4EbNmzYKvr6/GfdzW1rZ0G4MKJl6jc+fO4sqVKyI5OVnY2NiI9PR0rfOGhYWJoKAgIYQQaWlpYvz48UIulxdYf1ZWlpgwYYJo2rSp6NWrl5g5c6Y4fvy4UCqVQgghlixZIjp37izkcrl4+fKl+OSTT8TcuXOFEEIcOXJEdOvWTQghxLp168SqVauEEEIolUoxfPhwsXbtWmkbwsLCpHX6+/uLn376SS2W6Oho0a5duwLj1XU9mZmZon379iIyMlIIIcS+ffuEjY2NuH//vggPDxdffvmlEEKInJwcMW3aNBEbG6uyHqVSKb744gtx9+5dIYQQjx8/Fk2bNhVPnz4VZ8+eFU2bNhX//POPEEKItWvXCm9vbyGEEN98840IDQ0VQggRGxsrPvzwQ7Fz50617ZDL5WLYsGGiefPmon///mLu3Lni/PnzKm3Ur18/kZWVJRITE4WNjY349ddfpTYYNmyYEEKIkJAQsXv3biFE7nvp5uYmIiIihBBC2NjYiPDwcKnOL774Qhw8eFAtloiICNGnT58C213X9Tx58kS0bt1a3Lp1SwghxMqVK4WNjY3GOu/fvy9sbW1Fz549hZubm3BwcBAuLi5i4cKF4sWLF0IIIZYuXSpCQkKk/fG///2vmD59uhDi/46NS5cuiW+//VYoFAohhBCrVq0Svr6+Qgghdu7cKUaOHCmEEOLu3bvCzc1NJCcnCyGEuHnzpujQoYNIS0sTs2fPFpMnTxZKpVI8ffpUdOrUSSxZskQt5vz15bdx40YxYsQIIYQQgwcPFkePHhVCCJGRkSEGDx4sfv/9d3Hv3j3Rtm1bkZmZKYQQYty4cWL79u3i/v374sMPPxRCCLF+/Xrh7e0tXr58KRQKhRg3bpwIDw8X4eHhYvz48SI7O1sIIcTWrVvF8OHDhRBCdO3aVfz9999CCCH++usvsXTpUrX4Dh48KGbNmiW9DgoKEsHBwUKI3P1i7NixQqFQiNTUVPHpp5+KM2fOiH/++Uc4ODiIxMREaZnOnTtrfC/37dsnPv74Y9GhQwcxduxYsWHDBvHs2TMhRO5xbWNjI4KDg4WNjY3o27ev2LJli7hw4YJwcXERd+7cEW5ubiI4OFhERUWJmzdvCltbW3H+/Hnxyy+/iM6dO4tff/1VpKaminbt2olx48aJ1atXCyFy973169cLGxsbMXv2bGFjYyPu3bsnFAqFaNOmjQgJCREdO3YU9vb24u7du2Lw4MFi5cqV4siRI2L//v3Czc1NCJF7nuvatatITU0VQgjh6+srFi9eXOB54M8//xTOzs4iJSVFKJVKMWXKFKl9XrffzpkzRwghxO+//y5sbW3FtWvXhBC5548VK1YUuB/dv39f2NjYiGPHjgkhco/fzz77TAghxNmzZ4Wrq6vG9yg+Pl54enqKFi1aiC+++EIsXLhQXL16VZr+9ddfi40bNwohhIiMjBT9+/cvcP9asmSJmDlzphBCiHPnzolBgwZJn09//fWX6N69uxBC93OZj4+P2L9/vxBCiGvXrokZM2Zo3A4qf/LOcdHR0dJ+IYQQQ4YMETdu3BA2Njbi999/F59//rl4+vSpECL3XNu9e3ehVCqFv7+/8PT0FGlpaSIzM1N88cUXYtOmTUKI3HPA06dPVc7N+T/r8j4nhNC+j1PZovMV47yrmXnfpjXp2LEjRo4cifj4eLRv3x7fffcdLCwsCqzX2NgY//3vfzF58mScO3cOFy5cgL+/PxwcHLBo0SIAuVfE8uqxsrJCx44dAQDvvfeedKt0yJAhuHjxIn755RfExsbi1q1bsLe3l9bz8ccf67SNBW1fYdZz8+ZNGBkZSbdq3dzcpCt1rVu3RmhoKAYPHoz27dtjyJAh0lX4PDKZDCtXrsSJEyewf/9+3L59G0IIvHz5EgDwn//8B02bNgUANGvWDOHh4QByb9v4+/sDAOrXr4+2bdtq3A4LCwv8/PPPuH//Ps6ePYvz589j5MiRGDRoECZNmgQA6Ny5M4yNjVG7dm2YmZlpbPdJkybh9OnTWLNmDWJjY5GYmKhy5a642l3X9URFRcHGxgYffPABAGDAgAFYuHCh1nrzulIAwF9//YVJkyahQ4cOqFKlCoDcfoypqalSf7Hs7GzpanKeVq1aoWrVqti6dSvu37+Pc+fOScvnd/r0aSQmJkpXGIHc9/nevXs4c+YMpk6dCplMhho1aqhcUddVpUqVkJ6ejgsXLiAlJUXqf5meno7r16+jR48esLW1xbFjx+Dg4IAzZ85g9uzZ0h0eIHf/8fDwQKVKlQBAOgbHjRuH6Oho9OnTB0DueSBvX3R1dcWYMWPg6OiIDh06YMSIEWqxubi4oF69etiwYQPi4uJw/vx5lX6/nTt3hoGBAczNzVG/fn2kpKTgn3/+QYcOHVC7dm0Aue/lqVOnNG67m5sbnJycEBUVhQsXLmDnzp1YsWIFtm3bhmrVqknbtmjRIvj5+WHKlCn44IMPYGhoiC+//BLJycnIzMxU6Qrx+PFj9O7dGyEhIdi0aRPWrFkDhUKBa9euwdjYWJrP2dkZs2fPho2NDSwtLfHPP/+gdu3ayMrKwrvvvgtDQ0O8//77GD16ND766CM8e/YM//vf/6Q7DHnatGkj3Ylo1qwZUlJSCjwPnDx5Ei4uLtJtXG9vb5w9exbA6/fbbt26AQDq1auHWrVqSVes3nvvPaSkpBS4H7Vs2RLGxsZwdHSUYtXUZeVV77zzDnbt2oV///0X586dw7lz5zBgwAAEBATA29sb3t7e+PHHH+Ht7Y1t27Zh4MCBAHTbv06cOIG4uDiVvvIpKSlSXLqcy7p3747g4GAcO3YM7du3x4QJE167TVS+2NnZwcDAADExMahZsybS0tKkO1F//fUXevTogRo1agAAevfujdmzZ0vPBXl4eMDMzAwA0LNnT5w8eRKDBg0q1Pq17eNUtuicGFetWhXvv/8+Ll++LPWdyTNu3Dh8/fXXaNmyJY4ePYozZ87g7Nmz6NevH5YtW4aPPvpIa707duxA9erV0bVrV/Ts2RM9e/bE119/jS5duki39U1MTFSDNlIP+8cff8SVK1fQp08ftG3bFjk5OSr9evJ26IJ88MEHyMnJQVxcnEqimpmZiTFjxuCHH37Ar7/+qtN6ZDKZWr+ivLjr1auHI0eO4Ny5czh79iyGDRuGwMBAuLi4SPOmp6fD09MTn3/+OT7++GP06dMHf/zxh1RnXuLy6rpeXa+mtgJyH7xr3bo1PvroI9SrVw/9+vXDxYsXMWLECCkx1qXdJ0yYAIVCge7du+Ozzz5DfHx8odu9ZcuWuHPnDl68eKHSRSEhIQFBQUFYsmQJAgICdFpPQds/bdo06SEILy8v6cMxT8eOHTFs2DBMmDABBw8ehIWFBZRKJaZOnSolAWlpadJt+TwnTpzA7NmzMWzYMHTt2hUNGzbE3r171bZTqVSqfOEDgPj4eFhZWQGAStyGhoavbbf8YmJiYGNjA6VSCSEEtm7disqVKwPIfQDR1NQUANCvXz/s3r0bT58+hZOTE6pUqaKSGL/6HiclJUGpVEKpVGL48OHSB0Fe1ykA8PPzQ9++fXHq1Cns2rULq1evxq5du1S6Bm3evBnbt2+Ht7c3/h97dx4f0/X/D/w12ZEMQhJqq9pJKqVFLLGVSCQisQWV2LVFSYtERKOxa75URPrBR1u1lIhYqj5BKZ8Sayhir0rsWSyZiKyT8/vDL/eTMZNISDKTzOv5eHjInLnLe87ce+c9Z849x83NDTVq1FC5EVXT8fzqe1lYndy6dQs7d+7EjBkz0LlzZ3Tu3BnTpk3DmDFjsH//fjg5OcHExARGRkZ48uQJ7OzsMGPGDERFRWH37t1YsmQJYmJikJGRAQ8PD4wePRqOjo5o0KAB5HI5jI2N8fHHH+PEiRMICgrCu+++C5lMJu2/YJJsZWWFPXv2IDs7G9bW1jAwMIBMJkNwcDD++ecfzJ8/H1WqVEHbtm0xfvx4fPPNN0XWQVHXASMjo0Lr53XHbcFzu2D8Bdcv7Dh6+vQpjI2Npfe3YF0UZdmyZRgyZAiaNm2Kpk2bYuTIkdi9ezfWrVuHkSNHonPnzsjIyMCJEydw9uxZLF26FEDhx9er8bq7u0vXrry8PCQlJUn9u4tzLfPy8kLPnj1x/Phx/PnnnwgLC8OePXte27hDFcuAAQOwZ88eWFpawt3dXSp/tSsj8PKanH9fzKvXn8I+W4tS2DFOuqVEN99NmTIFCxcuREJCAoCXIwmEh4fj2rVreO+99xASEoLw8HB8/PHHmDNnDpo2bYr4+PiiAzAwQEhICB49eiSVxcfHo169eiW6aeXYsWPw8fHBwIEDUatWLcTExECpVGpc1tDQUONNYCYmJpgwYQICAgKQkpIC4GUCsGjRImRkZMDGxqbY+2nevDmEEDh69CgA4NChQ1IisWXLFsyePRtdu3bFzJkz0bVrV6lPdb6EhAQ8f/4c06dPR69evXD69GlkZ2e/tmW1W7du2LZtG4CXN8KcOnVK43KZmZn4v//7P5WWntu3b6N169ZFbv9Vx44dw+TJk+Hi4gKZTIYLFy6UuN5tbGzg5uaGgIAAPH/+HADw/PlzzJs3DzVq1ICZmVmx9/Phhx/i77//xrVr1wBA5QN04cKF0k1rhX1THzt2LORyOUJDQwEAXbt2xebNm6W6nzt3rloL9PHjx9GzZ0+MGDECdnZ2+P3336XYCr7mTp064fjx47h16xYA4OjRoxgwYACysrLQrVs3REZGIi8vD6mpqTh06FDhlf6Ko0eP4siRIxg2bBjMzc1hb2+PH3/8EcDLfq/Dhw+XttenTx9cvnwZERERGDp0qNq2HBwcsHfvXun1zps3D7/99hu6du2KyMhI6f1ZuXIlZs2ahdzcXPTq1QsvXrzA8OHDERQUhFu3bqm9z8eOHYOHhweGDBmCxo0b4/Dhw4UeJ/k6d+6M48ePS9eG/F9FXlW7dm1EREQgOjpaKsvvJ5x/PBsZGcHQ0BDr16/H/Pnz0bRpU4wZMwY+Pj4YNGgQkpKS0L17d1y7dg1Hjx5FUlKS1CLeqVMnbNmyBXl5eWjZsiU+++wzbNq0SWMsNWvWxPnz57F582a88847AF621vr6+iIpKQmdOnWCr6+v9B6/rg6Kug50794dBw4ckEbwiYyMlNYrznFblNcdR4UxNDRETk6OxueePHmClStXSvUqhFC55shkMowYMQJz5syBq6srTE1Nizy+Cp5bXbp0wW+//YakpCQAwC+//AIfH59iv17gZWJ89epVeHp6Yv78+VAoFLy5rxJyd3dHdHQ09u3bB1dXV6m8a9eu2Ldvn9Qgt2PHDtSoUUNqJPvtt9+QnZ2NrKwsREVFwdHRsVj7K3icajrGSfeU6CuPm5sbhBD48ssvkZubi6ysLLRp0wYbNmyAiYkJfHx84O/vD1dXV5iYmKBFixbSgefu7o4FCxbAzs5OZZuenp7IyMjAhAkTkJ2dDZlMhnfffRf//ve/S9RqNnnyZCxbtgzh4eEwNDREu3btcOfOHY3LOjo6Yv78+QAgDa+S79NPP0WVKlUwbtw4AC9bizt06IDw8PAS7cfY2BirV6/GvHnzsHz5crRq1Ur6KXPgwIE4ffo0XFxcUKVKFbzzzjvw9vZWWb9Fixbo0aMHnJ2dIZfL0bBhQzRt2hQJCQlqrR8FBQUFYfbs2XB2dkadOnUK7dT/+eefQyaTwcvLCzKZDHl5ebC1tVVpzSwOX19fTJ48GdWrV0eVKlXw0UcfFVrvPXv2xNKlS5GTkwMPDw+1uMPDw+Hl5QVDQ0NkZ2fj448/loZ+Ku5+LC0tERISghkzZsDY2FjtJrDXMTY2xty5czF+/HgMGTIEn3/+OZYuXQoPDw8olUq0atUK/v7+Kut4eXlhxowZcHNzg6GhIT788EPpJrUPPvgA3333HSZPnozVq1cjODgYX375pdTi9/3336Nq1aqYOnUqgoKCpBuDirrR7OzZs1JLh0wmg7W1NdavXy91OQgJCcH8+fPh5uaG7OxsuLq6YsCAAQBefvlzcXFBTEwM3n//fbVte3l54f79+/D09IQQAh06dMCoUaNgYGCAxMREDB06FDKZDHXr1sWSJUtgZGSEgIAAzJgxA0ZGRpDJZFi0aJHaMTp27Fh8/fXXiIqKgqGhIdq0aYMbN24U+V60aNECM2fOhI+PD6pVq6YxXuDlr1kbNmzA//3f/2HZsmWoUqUKTExMMG7cODg4OEgt056enti3bx9atmyJpUuXYuDAgQCA6dOnQy6XY9euXahTpw5u3LgBZ2dnzJ49GwsWLMDSpUvh5OSElJQUuLm5oXPnzhg/frzGWAwMDNCrVy9cuXJF+iXA2NgYnTt3xvbt25GSkoJjx46hTp06sLW1RWpqqvRlo7A6KOw60K1bNwwdOhTDhg2DmZkZmjVrJrXuFue4fZ3CjqOihpxs1qwZDA0NMXjwYGzfvl2lNTkoKAgrVqzAgAEDYGJigtzcXHTq1EllOEMPDw8sXboUw4YNA4Aijy8HBwdMnTpVOmcnTJiAsWPHQiaTwdzcHGFhYcVuzQaAGTNmYNGiRfjuu+9gYGCAKVOmoH79+iWqM9J9NjY2aNKkCSwsLKRuVgDQsWNHjB49Gj4+PsjLy4OlpSXWrFkjtSTXr18fw4cPx4sXL9CnTx+1z7DCfPzxx/D19cWCBQvQtWtXtWOcdI9MvPp7fxlZsWIFnJycStwiSUT0tnJzczFlyhQMGDAALi4uJVr3zp07GDVqFKKjo6XEUxdcunQJ58+fl75U//jjj7hw4UKJv9zqkt9++w07d+7Ev//9b22HQlQmeIzrvpJ3knkDQgjUq1ePSTERlbu///4bw4cPh6Ojo0o//uJYuXIlIiIiMGfOHJ1KigGgcePGWLduHSIiIqRW/PxfwiqiUaNGISUlRePENkSVAY/xiqHcWoyJiIiIiHRZiW6+IyIiIiKqrJgYExERERGBibHWCSGQlZXFudSJionnDFHJ8JwhKj4mxlqWnZ2NuLg4ZGdnF7rM5cuXyzGiiov1VHwVua54zpQe1lPxVeS64jlTelhPxVdR64qJcQWQmZmp7RAqBNZT8VX2uqrsr6+0sJ6Kr7LXVWV/faWF9VR8FbWumBgTEREREYGJMRERERERACbGxRYWFob+/fujf//+WLZsmdrzV69exaBBg+Dk5IQ5c+ZIc6MTEREV5fnz53B1dZWm2962bRtcXV3h5uaG2bNnS32D+TlDVPaYGBdDTEwMjh07hp07d2LXrl24fPkyDh48qLLMzJkzMXfuXOzfvx9CCERERGgpWiIiqiguXLiA4cOHIz4+HgBw+/ZtrF+/Hlu3bsWePXuQl5eHLVu2AODnDFF5YGJcDFZWVvD394eJiQmMjY3RpEkTPHjwQHr+/v37yMzMhL29PQDA09MT0dHRWoqWiIgqioiICAQFBcHa2hoAYGJignnz5sHc3BwymQzNmzfHgwcP+DlDVE6MtB1ARdCsWTPp7/j4eOzbtw9bt26VypKSkmBlZSU9trKyQmJiotp2FAoFFAqFSplSqSyDiImIqCJYuHChyuN69eqhXr16AIAnT55g8+bNWLx4MT9niMoJE+MSuHnzJiZNmgQ/Pz+8++67UrmmQdNlMpla2YYNGxAWFqZSVrt2bYSGhiIuLq7IfcfGxr5Z0BWURU0bZOao1quZsQxpT9U/CAp6XT296XYro8Lqqn379uUcCZXEU0UmFOnq49HKq5mgptxMCxFRWUhMTMT48eMxaNAgdOzYEefOnVNbhp8z2qFL9aTpMw3Qnc+1ivg5w8S4mGJjY/HFF18gICAA/fv3V3nOxsYGKSkp0uPk5GTpZ7GCfHx84OHhoVKmVCqRkpICW1tbmJqaFrpvXT6IykLCQwV+ir6kUjbJww7N36tf6DrFqac32W5lpI/HVGWhSM/Gmp2X1MonedgxMa4kbt26hQkTJuCTTz7B2LFjAfBzRlfoWj1p+kwDdONzTdfqqriYGBfDw4cPMXnyZKxYsQIODg5qz9erVw+mpqbSQbBr1y44OjqqLSeXyyGXy1XKsrKyVC52RESkv54/f45x48bB19cX7u7uUjk/Z4jKBxPjYli/fj2ysrKwZMkSqczLywuHDx/GF198ATs7O4SEhCAwMBDp6elo3bo1vL29tRgxERFVRJGRkUhJScEPP/yAH374AQDQq1cvTJs2jZ8zROWAiXExBAYGIjAwUK18+PDh0t8tW7ZEZGRkeYZFRESVxOHDhwEAo0ePxujRozUuw88ZorLH4dqIiIiIiMDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjItKi3bt3o3///ujfvz+WLl2q7XCIiEjPMTEmIq3IyMjAwoULsXHjRuzevRtnz55FTEyMtsMiIiI9ZqTtAIhIPymVSuTl5SEjIwNVq1ZFbm4uTE1NVZZRKBRQKBRq6xEREZUFJsZEpBXm5uaYNm0anJ2dYWZmhg4dOqBdu3Yqy2zYsAFhYWEqZbVr10ZoaCji4uKK3H5sbGypx6wLjM2tkZaWplaeqkhF7IObxdqGRU0bZOYIGJtb4+L1uwAAM2MZ0p4mlmqslU1hx1T79u3LORIiKitMjIlIK65du4YdO3bgjz/+gIWFBWbMmIH169dj/Pjx0jI+Pj7w8PBQWU+pVCIlJQW2trZqLcz5YmNjK22ykvBQAQsLC7Xy6vLqaNSiQbG38VP0JaSlpUnbmuRhh+bv1S/VWCuTynxMEdH/sI8xEWnFsWPH4ODggFq1asHExASenp44ffq0yjJyuRz169dX+VenTh0tRUxERJUdE2Mi0oqWLVsiJiYGL168gBAChw8fhp2dnbbDIiIiPcauFESkFV27dsWVK1fg6ekJY2Nj2NnZYeLEidoOi4iI9BgTYyLSmokTJzIZJiIincGuFERERFr0/PlzuLq64t69ewCAmJgYuLm5oW/fvlixYoW03NWrVzFo0CA4OTlhzpw5yM3N1VbIRJUWE2MiIiItuXDhAoYPH474+HgAQGZmJgICAhAeHo59+/YhLi4OR48eBQDMnDkTc+fOxf79+yGEQEREhBYjJ6qcmBgTERFpSUREBIKCgmBtbQ0AuHjxIho1aoQGDRrAyMgIbm5uiI6Oxv3795GZmQl7e3sAgKenJ6Kjo7UYOVHlxD7GREREWrJw4UKVx0lJSbCyspIeW1tbIzExUa3cysoKiYnqE7Jwtkiit8PEmIiISEcIIdTKZDJZoeWv4myRZU+X6qk0ZsIsSxVxtkgmxkRERDrCxsYGKSkp0uOkpCRYW1urlScnJ0vdLwribJFlS9fqqTRmwiwrulZXxcU+xkRERDqibdu2uH37NhISEqBUKrF37144OjqiXr16MDU1lVrgdu3aBUdHR7X1OVsk0dthizEREZGOMDU1xZIlSzB16lRkZWWhe/fu6NevHwAgJCQEgYGBSE9PR+vWreHt7a3laIkqHybGREREWnb48GHpbwcHB+zZs0dtmZYtWyIyMrI8wyLSO+xKQUREREQEJsZERERERACYGBMRERERAWBiTEREREQEgDffERFVCgYGL8c0LUhezQQ15WZaioiIqOJhYlwCz58/h5eXF/71r3+hfv36Ks+FhYVhx44dkMvlAIChQ4di5MiR2giTiPRQekYuft53VaVskocdE2MiohJgYlxMFy5cQGBgIOLj4zU+HxcXh+XLl+ODDz4o38CIiIiIqFSwj3ExRUREICgoSOMUnMDLxHjdunVwc3NDcHAwsrKyyjlCIiIiInobbDEupoULFxb6XHp6Olq1agU/Pz/Uq1cP/v7+CA8Ph6+vr8pyCoUCCoVqH0ClUlkm8RIRERFRyTAxLgXVqlXDunXrpMdjx45FQECAWmK8YcMGhIWFqZTVrl0boaGhiIuLK3IfsbGxpRdwBWBsbo20tDSVslRFKmIf3CxyvdfV05tutzIqrK7at29fzpEQERHpBibGpeDBgweIiYnB4MGDAQBCCBgZqVetj48PPDw8VMqUSiVSUlJga2sLU1NTjduPjY3Vu2Ql4aECFhYWKmXV5dXRqEWDQtcpTj29yXYrI308poiIiF6HiXEpMDMzw7fffouOHTuifv362Lx5M/r06aO2nFwul0atyJeVlYWUlJTyCpWIiIiICsGb797ChAkTcOnSJVhaWiI4OBifffYZ+vXrByEExowZo+3wiIiIiKgE2GJcQocPH5b+Ltiv2MnJCU5OTtoIiYiIiIhKAVuMiYiIiIjAxJiIiIiICAATYyIiIiIiAEyMiYiIiIgAMDEmIiIiIgLAxJiItOjw4cPw9PREv379sGDBAm2HQ6Qzdu/ejf79+6N///5YunQpAODq1asYNGgQnJycMGfOHOTm5mo5SqLKR+8S440bN+L58+faDoNI7929exdBQUEIDw/Hr7/+iitXruDo0aPaDotI6zIyMrBw4UJs3LgRu3fvxtmzZxETE4OZM2di7ty52L9/P4QQiIiI0HaoRJWO3o1jfOPGDTg5OaFHjx7w8vKCnZ2dtkMi0ksHDx6Ei4sL6tSpAwBYsWKF2rToCoUCCoVCpUypVJZbjETaoFQqkZeXh4yMDFStWhW5ubkwMjJCZmYm7O3tAQCenp4IDQ3FiBEjVNblOUP0dvQuMZ4/fz78/Pzw66+/4ptvvoEQAsOHD4ebm5vahzIRlZ2EhAQYGxtj3LhxSE5ORs+ePTF9+nSVZTZs2ICwsDCVstq1ayM0NBRxcXFFbj82Nra0Q9YJxubWSEtLUyvPzc1VK09VpCL2wc0it5H/f2HL0v8Udky1b9++VPdjbm6OadOmwdnZGWZmZujQoQOMjY1hZWUlLWNlZYXExES1dXnOlD1dqqfCrge6cj6X1zlTmvQuMQZeXnT69euHrKwsbNy4EVu2bMHq1avh5+eHfv36aTs8Ir2gVCpx9uxZbNy4EVWrVsXnn3+OnTt3wtPTU1rGx8cHHh4eauulpKTA1ta20C+zsbGxOn3hfRsJDxWwsLBQKzcyMlIrry6vjkYtGhS6jbS0NGmdwpall8rzmLp27Rp27NiBP/74AxYWFpgxYwaOHz+utpxMJlMr4zlTtnStngq7HujC+axrdVVcepcYx8TEICIiAidOnICTkxNWr16Nli1b4s6dOxgxYgQTY6JyUrt2bTg4OMDS0hIA0Lt3b1y8eFElMZbL5ZDL5SrrZWVlISUlpVxjJSpPx44dg4ODA2rVqgXgZbeJ9evXqxz3ycnJsLa2VluX5wzR29G7m++Cg4PRrl07/P777wgODkbLli0BAA0bNsTQoUO1HB2R/ujZsyeOHTsGhUIBpVKJP//8E23atNF2WERa17JlS8TExODFixcQQuDw4cPo0KEDTE1NpZ+md+3aBUdHRy1HSlT56F2L8Z49exAdHQ0LCwskJyfjt99+g7e3NwwMDPDFF19oOzwivdG2bVuMHz8eI0aMQE5ODrp06YJBgwZpOywirevatSuuXLkCT09PGBsbw87ODhMnTkSfPn0QGBiI9PR0tG7dGt7e3toOlajS0bvEeP78+UhPT8eAAQNgYGCA2NhY3Lt3D4GBgdoOjUjvDB48GIMHD9Z2GEQ6Z+LEiZg4caJKWcuWLREZGamliIj0g94lxufPn8fevXsBALVq1cLKlSvh7u6u5aiIiIiISNv0ro9xTk4OsrOzpcecOYiIiIiIAD1sMe7RowfGjRsHd3d3yGQy7N27F927d9d2WERERESkZXqXGM+aNQubN2/GoUOHYGRkhD59+sDLy0vbYRERERGRluldYmxoaAhvb2/ezUtEREREKvQuMd63bx9CQkKQmpoKIYRUfu7cOS1GRURERETapneJ8cqVK+Hv74/WrVtrnE6TiIiIiPST3iXGcrkcffv21XYYRERERKRj9G64trZt2+Lo0aPaDoOIiIiIdIzetRgfPXoUmzZtgrGxMYyNjSGEgEwmYx9jIiIiIj2nd4nxTz/9pO0QiIiIiEgH6V1Xinr16uHSpUuIiIiApaUlzp8/j3r16mk7LCIiIiLSMr1LjNeuXYtffvkF0dHRyMzMRFhYGFavXq3tsIiIiIhIy/QuMf7tt9+wbt06VKlSBTVr1kRERAT27t2r7bCIiIiISMv0LjE2MjKCiYmJ9Fgul8PISO+6WhMRERHRK/QuI6xbty6OHDkCmUyG7OxsrF+/nn2MiYiIiEj/EuO5c+di1qxZuH79Ouzt7dG2bVuEhIRoOywiIiIi0jK9S4xtbGywYcMGZGRkQKlUwtzcXNshEREREZEO0LvE+Mcff9RYPmbMmHKOhIiIiIh0id7dfHfjxg3pX1xcHDZs2IBr164Va93nz5/D1dUV9+7dU3vu6tWrGDRoEJycnDBnzhzk5uaWduhERKQnDh8+DE9PT/Tr1w8LFiwAAMTExMDNzQ19+/bFihUrtBwhUeWkdy3GixcvVnn85MkTzJo167XrXbhwAYGBgYiPj9f4/MyZM7FgwQLY29sjICAAERERGDFiRGmETEREeuTu3bsICgrC9u3bUatWLfj4+ODo0aMICgrCxo0bUbduXUyaNAlHjx5F9+7dtR0uUaWidy3Gr7K0tMT9+/dfu1xERASCgoJgbW2t9tz9+/eRmZkJe3t7AICnpyeio6PVllMoFLh3757Kv0ePHr31ayAiosrj4MGDcHFxQZ06dWBsbIwVK1agSpUqaNSoERo0aAAjIyO4ubnxc4aoDOhdi3HBPsZCCMTFxaFWrVqvXW/hwoWFPpeUlAQrKyvpsZWVFRITE9WW27BhA8LCwlTKateujdDQUMTFxRW5/9jY2NfGWJkYm1sjLS1NpSxVkYrYBzeLXO919fSm262MCqur9u3bl3MkRFRQQkICjI2NMW7cOCQnJ6Nnz55o1qyZyueMtbU1P2e0RJfqSdNnGqA7n2sV8XNG7xLjGzduqDyuW7dusbpSFEUIoVYmk8nUynx8fODh4aFSplQqkZKSAltbW5iammrcfmxsrE4fRGUh4aECFhYWKmXV5dXRqEWDQtcpTj29yXYrI308pogqCqVSibNnz2Ljxo2oWrUqPv/8c1SpUkVtOX7OlD9dqydNn2mAbnyu6VpdFZfeJcav9jEuDTY2NkhJSZEeJycna+xyIZfLIZfLVcqysrJU1iUiIv1Wu3ZtODg4wNLSEgDQu3dvREdHw9DQUFomKSmJnzNEZUDvEuNRo0Zp/Jad7+effy7xNuvVqwdTU1Pp29GuXbvg6Oj4NmESEZGe6tmzJ/z8/KBQKFCtWjX8+eef6NevH9auXYuEhATUr18fe/fuxaBBg7QdKlGlo3eJsa2tLW7duoWhQ4fC2NgYu3fvRm5uLvr371/ibU2YMAFffPEF7OzsEBISgsDAQKSnp6N169bw9vYug+iJiKiya9u2LcaPH48RI0YgJycHXbp0wfDhw/Hee+9h6tSpyMrKQvfu3dGvXz9th0pU6ehdYnzu3Dls2bJF+kmqW7duGDp0KJycnIq1/uHDh6W/161bJ/3dsmVLREZGlm6wRESklwYPHozBgwerlDk4OGDPnj1aiohIP+jdcG1PnjxBdna29Dg9PR2ZmZlajIiIli5dCn9/f22HQUREek7vWoxdXV0xdOhQ9OnTB0II/Oc//2G3ByItOnHiBHbu3IkePXpoOxQiItJzepcYT5s2Da1bt8bJkydhamqK4OBgdOjQQdthEemlZ8+eYcWKFfj00081Ts2uUCigUChUypRKZXmFR0REekbvEmPg5fBqzZo1g6enJy5fvqztcIj01tdffw1fX188fPhQ4/OcrEBdYQP65+bmFnvymoLbyP9fVyYE0GUVcbICIioZvUuMd+zYgR9++AFZWVno06cPPv/8c/j6+mLo0KHaDo1Ir2zfvh1169aFg4MDoqKiNC7DyQrUFTagv5GRUbEnr8nfRlpamrSOLkwIoMsq8zFFRP+jdzffbdq0Cdu2bYO5uTlq1aqFqKgobNiwQdthEemdffv24fjx43B3d0doaCgOHz6MRYsWqSwjl8tRv359lX916tTRUsRERFTZ6V2LsYGBAczNzaXHdevWVZlNiIjKx48//ij9HRUVhdOnTyMgIECLERERkb7TuxbjGjVq4OrVq9Lsd3v27EH16tW1HBURERERaZvetRgHBARg2rRpuHPnDrp27QpTU1OEh4drOywivebp6QlPT09th0FERHpO7xLjzMxM7N69G/Hx8VAqlWjcuDGMjY21HRYRERERaZnedaWYMWMGDA0N0aRJEzRv3pxJMREREREB0MPEuEWLFvj111/x4MEDPHv2TPpHRERERPpN77pSHDp0CNHR0SplMpkMV69e1VJERERERKQL9C4xvnTpkrZDICIiIiIdpDddKebOnSv9/eTJEy1GQkRERES6SG8S47i4OOnvcePGaTESIiIiItJFepMYCyE0/k1EREREBOhRYlxQ/qx3RERERET59CYxzsvLQ2pqKp49ewalUin9zeHaiIhIVy1duhT+/v4AgKtXr2LQoEFwcnLCnDlzkJubq+XoiCofvUmMb9y4gU6dOqFTp064ceMGOnbsKD12cHDQdnhEREQqTpw4gZ07d0qPZ86ciblz52L//v0QQiAiIkKL0RFVTnozXNu1a9e0HQIREVGxPHv2DCtWrMCnn36Ka9eu4f79+8jMzIS9vT0AwNPTE6GhoRgxYoR2AyWqZPQmMSYiIqoovv76a/j6+uLhw4cAgKSkJFhZWUnPW1lZITExUW09hUIBhUKhUqZUKss2WKJKhIkxERGViqeKTCjSs1XK5NVMUFNu9sbrl3QblcH27dtRt25dODg4ICoqCoDm0ZQ03Ui+YcMGhIWFqZTVrl0boaGhKsOWahIbG/sWUesPXaonY3NrpKWlqZWnKlIR++CmFiJSVVhdtW/fvpwjKT4mxkREVCoU6dlYs1N1dtFJHnbFTmo1rV/SbVQG+/btQ3JyMtzd3ZGamooXL15AJpMhJSVFWiY5ORnW1tZq6/r4+MDDw0OlTKlUIiUlBba2tjA1NdW4z9jYWJ1OVnSFrtVTwkMFLCws1Mqry6ujUYsG5RJDYV9oszIUaP5e/XKJoTQxMSYiItIhP/74o/R3VFQUTp8+jcWLF8PV1VVKzHbt2gVHR0e1deVyOeRyuUpZVlaWSlJNVJoK+0Lr1buhFqJ5e0yMiYiIKoCQkBAEBgYiPT0drVu3hre3t7ZDIqp0mBgTERHpKE9PT3h6egIAWrZsicjISC1HRFS56c04xkRERERERWFiTEREREQEJsZERERERACYGBMRERERAWBiTEREREQEgIlxsf36669wcXFBnz59sHnzZrXnw8LC0LNnT7i7u8Pd3V3jMkRERESkuzhcWzEkJiZixYoViIqKgomJCby8vNCxY0c0bdpUWiYuLg7Lly/HBx98oMVIiYiIiOhNscW4GGJiYtCpUyfUqFEDVatWhZOTE6Kjo1WWiYuLw7p16+Dm5obg4GBkZWVpKVoiIiIqa08VmUh4qFD791SRqe3Q6C2wxbgYkpKSYGVlJT22trbGxYsXpcfp6elo1aoV/Pz8UK9ePfj7+yM8PBy+vr4q21EoFFAoFCplSqWybIMnIiKiUlfYVMiTPOxQU26mhYioNDAxLgYhhFqZTCaT/q5WrRrWrVsnPR47diwCAgLUEuMNGzYgLCxMpax27doIDQ1FXFxckTHExsa+SegVlrG5NdLS0lTKUhWpiH1ws8j1XldPb7rdyqiwumrfvn05R0JERKQbmBgXg42NDc6ePSs9TkpKgrW1tfT4wYMHiImJweDBgwG8TKSNjNSr1sfHBx4eHiplSqUSKSkpsLW1hampqcb9x8bG6l2ykvBQAQsLC5Wy6vLqaNSiQaHrFKee3mS7lZGuHFNhYWH4z3/+AwDo3r07Zs2apeWIiIhIn7GPcTF07twZJ06cwJMnT5CRkYEDBw7A0dFRet7MzAzffvst7t69CyEENm/ejD59+qhtRy6Xo379+ir/6tSpU54vhUhnxMTE4NixY9i5cyd27dqFy5cv4+DBg9oOi4iI9BhbjIvBxsYGvr6+8Pb2Rk5ODgYPHoz3338fEyZMwBdffAE7OzsEBwfjs88+Q05ODtq1a4cxY8ZoO2winWZlZQV/f3+YmJgAAJo0aYIHDx6oLMN++UREVJ6YGBeTm5sb3NzcVMoK9it2cnKCk5NTeYdFVGE1a9ZM+js+Ph779u3D1q1bVZZhv3x1mvrJA0Bubm6x+88X3Eb+/zm52bh4/a7asnLzqlA8f6FSZmYsQ9rTxGLFVpI+/IW9Nk2xFRZDWWK/fKLKj4kxEWnVzZs3MWnSJPj5+eHdd99VeY798tVp6icPAEZGRsXuP5+/jbS0NGmdXKUBth66o7ast0srtfJJHnZo/l79YsVWkj78hb02TbEVFkNZqczHFBH9DxNjItKa2NhYfPHFFwgICED//v3VnpfL5ZDL5SplWVlZSElJKa8QiYhIjzAxJiKtePjwISZPnowVK1bAwcFB2+EQERExMSYi7Vi/fj2ysrKwZMkSqczLywvDhw/XYlRERKTPmBgTkVYEBgYiMDBQ22EQERFJOI4xERGRjgkLC0P//v3Rv39/LFu2DMDLsb/d3NzQt29frFixQssRElVOTIyJiIh0iKbJb/bu3YuAgACEh4dj3759iIuLw9GjR7UdKlGlw8SYiIhIhxSc/MbY2BhNmjRBfHw8GjVqhAYNGsDIyAhubm6Ijo7WdqhElQ77GBMREekQTZPfjBo1ClZWVlK5tbU1EhPVJzjhbJFEb4eJMRERkQ4qOPmNkZERbt++rfK8TCZTW4ezRZa9/HoqbKbGksz2+LZ0OQagYs4WycSYiIhIx7w6+c3p06dVJrZJSkqCtbW12nqcLbJsFaynwmZqLMlsj2+rrGJ4qsiEIj1brVxezQQ15WbFigHQ7QS4MEyMiYiIdIimyW/atm2L27dvIyEhAfXr18fevXsxaNAgtXU5WySVBkV6NtbsvKRWPsnDTi0xrmyYGBMREemQwia/WbJkCaZOnYqsrCx0794d/fr102KURJUTE2MiIiIdUtTkN3v27CnnaIj0C4drIyIiIiICE2MiIiIiIgBMjImIiIiIADAxJiIiIiICwJvviIiKTdPYnqYmhsjKVp9ZTNN4n6Wxv5zcvGKvb2DwcozRV5VkG+W5XV1Q2PitFjVttBAN0ZvRdI6WxjVJHzAxJiIqJk1je3q7tMLP+66qLVsa430Wtr/iSs/I1RhbSbZRntvVBYWN3+rVu6EWoiF6M5rOUX0Yg7g0sCsFERERERGYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREATvBBREREpBWaZlrkDHXaxcSYiIiISAs0zbTIGeq0i10piIiIiIjAxJiIiIiICAC7UhARERGVGgMDIOGhQqWM/YYrDrYYF9Ovv/4KFxcX9OnTB5s3b1Z7/urVqxg0aBCcnJwwZ84c5ObmaiFKoorldecVEaniOaP70jNysWbnJZV/r95gR7qLLcbFkJiYiBUrViAqKgomJibw8vJCx44d0bRpU2mZmTNnYsGCBbC3t0dAQAAiIiIwYsQILUZNpNuKc14R0f/wnCl7mkaJAMq3xVdTizMAmJoYIitbqVKWk5tXLjHpEybGxRATE4NOnTqhRo0aAAAnJydER0djypQpAID79+8jMzMT9vb2AABPT0+EhoaqJcYKhQIKherBnt+ynJ1d9LfJrKysUnglFYcyNwdVTWRqZa+rh9c9/6bbrYyKes0mJiaQyWSFPl8aXndeAbp3zmg6fvKU6mX5y75tDCXZn6by1y0rzAyk50tzu6++huLWg6bXWxrbLYnCYhB5Sp4zOiT1eRaev8hRKzcxNkB2jmqyaF7VGNXNTYu13aep6diy/7pa+QinFqhq+vL9za+nkhyvIi8H/9x9rLZsrjJPbdm05xmI+uOW2rKePZuolXv2bPLWMWiqM01xFbaNwpfV/jnzJmRCCKHtIHTdmjVr8OLFC/j6+gIAtm/fjosXL2L+/PkAgPPnz2PZsmX45ZdfAAAJCQmYOHEi9u/fr7KdVatWISwsTKWsefPmmDdvXtm/CKISsLW1halp8T5I3tTrziuA5wxVHDxniEqmPM6ZN8EW42LQ9N2h4Lec1z2fz8fHBx4eHipleXl5MDExQY0aNTSu8+jRI4wYMQJbtmxBnTp13iR8vcB6Kr7i1JWJiUmZx1Gc84bnTNlhPRUfzxkCWE8loSvnzJtgYlwMNjY2OHv2rPQ4KSkJ1tbWKs+npKRIj5OTk1WezyeXyyGXy0u0b0NDQ6SkpMDQ0FAnv1npCtZT8elKXb3uvAJ4zpQl1lPx6Upd8ZzRLtZT8VXkuuKoFMXQuXNnnDhxAk+ePEFGRgYOHDgAR0dH6fl69erB1NQUsbGxAIBdu3apPE9E6l53XhGRKp4zRGWPLcbFYGNjA19fX3h7eyMnJweDBw/G+++/jwkTJuCLL76AnZ0dQkJCEBgYiPT0dLRu3Rre3t7aDptIpxV2XhGRZjxniMoeE+NicnNzg5ubm0rZunXrpL9btmyJyMjI8g6LqELTdF4RUeF4zhCVLXal0HFyuRxTpkwpcZ8xfcN6Kr7KXleV/fWVFtZT8VX2uqrsr6+0sJ6KryLXFYdrIyIiIiICW4yJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIwrtXv37qFFixYYOXKk2nOzZ89GixYt8OTJkyK3sWrVKgQHBwMAtm/fjs2bNwMAfvnlF6xdu1bjOv7+/ujWrRvc3d1V/iUmJr7lK1I3duxY6TVMmDABf//9d6lsd9WqVejUqZMUe//+/fHpp5/i9u3bb7zNS5cu4YsvviiV+IqS/75v375dpXz9+vXw9/d/7foF3+fSdOrUKbi6upb6djWZNGkSoqKi1MpXrVoFBwcHJCcnq5S7urri1KlTr91uweOtNPn7+2P9+vWvXS4qKgrt27eXjssBAwagV69emDVrFrKysopc9969e/jggw9eu483ff8Lq8NXrwcuLi746quv1N6Dkjh06BAWLFjwxuuXl6LOuSNHjmDYsGEYMGAA+vfvj2nTpuHRo0dvtJ+0tDR4e3u/Tajl4pdffsGAAQPg4uKC/v37Y+bMmXjw4EG57b+4n2Fv4q+//sKoUaPg5uYGV1dXjB8/Hjdv3nzj7ZXVtYZez0jbAVDZMjU1RXx8PO7fv4969eoBAF68eIHY2NgSbys2NhbNmjUDAAwfPrzIZUePHo1x48aVPOASOn78uPT3unXrSnXbLi4u+Prrr6XHu3btwujRo/Hbb7/B3Ny8xNuzs7NDaGhoaYZYKAMDAyxduhQffvghGjduXKJ1C77PldHz58/h5+eH9evXQyaTlWjdgsebtnz44YdYs2aN9DgrKwvDhw/Hzp074eXl9dbbL4v3v+D1QAiBNWvWYPz48YiKioKhoWGJt9e7d2/07t27VGMsT4mJifDz80NUVJR0Xf7+++8xffp0bN26tcTbS01NxaVLl0o7zFK1dOlSXLt2DWvWrEHdunWRl5eHPXv2YNiwYdi+fTvq1KlT5jGU5DOsJLKzszFp0iT88MMPaNOmDQBg9+7dmDBhAg4dOvRGx7guXGv0FRPjSs7Q0BDOzs749ddf8emnnwIADhw4gN69e+OHH34A8LIlb/78+di7d6/GxwBw8OBBHD58GMePH4eZmRmePHmCp0+fqiSOxeHv749mzZpJH5IFH/fq1QseHh44ceIEHj58CGdnZ8yaNQsAEBkZiR9//BEGBgaoWbMmli5dKiWZPj4+WLt2LUaOHImVK1fCzs4O27Ztw8aNG2FgYIDatWtj7ty5aNy4Mfz9/WFubo7r16/j0aNHeO+997B8+XJUq1bttbEPHDgQe/bswa+//orhw4fj1q1bWLhwIZ49ewalUolRo0Zh8ODB+Oqrr9C6dWvpNf7yyy84deoUhg8fLtVreno6FixYgHPnzsHQ0BAff/wxfH19kZOTg5CQEJw5cwZKpRKtW7dGYGAgzM3NsWXLFmzduhXGxsYwNTVFcHAwmjZtqjFWMzMzjBkzBl999RW2bt0KExMTleezs7M17ufEiRPS+5ydnY01a9YgJiYGVatWxddff41bt25JLS59+/ZFeHg4hBAIDg7Gs2fPIJPJMHbsWAwcOBCnTp3CwoULUbVqVbx48QIzZ86U9n/27FnMnDkT//d//4d27dqpxPbHH39gzZo1yM7OxpMnTzBw4EBMnz4dp06dwooVK9CgQQPcvHkT2dnZ+Prrr9GpUyckJibC398fSUlJeOedd/D48eNC38cBAwbgwoUL+OGHHzR+eSvsfZ09e7Z0vDk5OSE+Ph4hISHIyclBx44dERAQgMGDByM2NhaLFy9GZGRkkcfhs2fPcPfuXfTo0UNl/4sXL8a1a9cQHh5erOPy2bNneP78OapXrw7gZdIVHByMhw8fIicnR/q1o6CUlBR8/fXXePz4MZKTk1GvXj189913OHfunMp5PnLkSHz//fc4cOAA8vLyUK9ePQQFBcHGxgZ///03AgICkJGRgffeew8vXrx4bawAIJPJ8Omnn2Lnzp04fvw4HB0dce7cOYSEhCAjIwMymQxTp05Fz5494eXlhdGjR6Nfv34AgJCQEAgh0KRJE+zfvx9r1qxBcnIygoKC8M8//8DAwABeXl7w9vZGWloaFi5ciBs3biAnJwcODg6YNWsWjIyMEBoaioMHD8LY2Bg1a9bE4sWLYW1trRLn7du3ERwcjBcvXiApKQktW7bEd999B1NTU9jZ2WHixIk4fvw4kpKS4O3tjdGjRyMnJwcLFixATEwMatWqhVq1asHCwkKtDp4+fYqcnByVOvPx8UGrVq0AAGPGjEG/fv0wbNgwAC+T5qdPn2LChAnw8/PD06dPAQDdu3fH9OnTMXv2bGRmZsLd3R1RUVGIj4/XeAyfOnUKy5cvh7W1NW7evIkqVapg6tSp2LhxI27fvo2+ffsiICAA6enpmD17NhISEmBgYIA2bdogODgYBgZv9iPzo0ePsHXrVhw5ckQ6Tg0MDDBw4EDExcVhzZo1CAoKQq9evfDxxx/j7NmzSEtLw5gxYzBixAgAwOHDh/H9998jJycHZmZm8PPzwwcffIBVq1bhr7/+QlJSElq0aAF/f/9iHdsFP8OK+uxZu3YtIiMjUa1aNXz44Yc4dOgQDh8+rPL6MjIykJaWpvJ+DhgwAObm5lAqlQgKCoKlpSW+/PJLAMCePXuwf/9+LFu2TGM9z5kzRzom1q5dCwMDA43n9L179+Dj44NOnTrhr7/+Qm5uLmbNmoVt27bhn3/+ga2tLZYvX468vDzMnz8f586dg7GxMerXr4/FixcX6/qilwRVWnfv3hX29vbi0qVLwtnZWSr38fER169fF82bNxePHz8WJ0+eFP3795eeL/g4NDRUfPPNN0IIIfz8/MS///1vtfJX+fn5ia5du4oBAwZI/yIiItS28erjnj17iiVLlgghhHj06JGws7MTd+7cEVevXhUdO3YUDx48EEII8eOPP4q5c+cKIYT0GvLXv3jxooiJiREff/yxVL5jxw7h7Ows8vLyhJ+fnxg2bJjIysoS2dnZYuDAgSIyMlLtNRT2+pYsWSLmzZsncnJyhIuLi4iLixNCCKFQKISzs7M4f/68OHHihHB1dZXWGTx4sDh+/LhKvS5atEj4+vqK3NxckZWVJUaOHClOnjwpVq1aJZYsWSLy8vKEEEL83//9nwgKChK5ubmiTZs2IjExUQghxM6dO8XWrVs11n/++65UKsWIESOkOv33v/8t/Pz8hBCi0P28+p6MGjVKHD58WAghRN++fUXnzp3F8+fPxc2bN4Wzs7PIyckRvXv3Fvv375fet27duolz586JkydPipYtW4p79+4JIf53XJ04cUJ8/PHH4urVq2qx5+XliU8++UTcvn1b2l6rVq2k47RVq1biypUrQggh1q9fL0aOHCmEEOLzzz8XK1asEEIIER8fL+zt7cWOHTsKfV+vXbsm2rVrJ71//fv3FydPnizyfRXif8fbvXv3hIODg8jLyxMnT54UXbp0EV9++aUQQoilS5eKtWvXvvY49PHxkeLy8/MT69atE/PmzROTJ08WWVlZGt/bHTt2iHbt2okBAwaIfv36iY4dO4phw4aJX375RVpm1KhR4tChQ0IIITIzM8WoUaPEb7/9Jh0XQgjx008/iTVr1kh1Pn78eLF+/Xq193/nzp1i+vTpIicnRwghxNatW8X48eOFEEK4u7tL5/XZs2dFixYtxMmTJ9VifvWczzd16lSxbt068ezZM9G3b19x9+5dIcTL99zR0VHcv39fREZGiokTJwohhMjNzRXdunUTt2/fFjt27JDKJ0+eLJYuXSq9X/379xfx8fHC399f/Pzzz9K6M2bMEGvXrhUPHjwQ7dq1k+p4/fr14uDBg2rxLVmyROzatUsIIUR2drZwdXUV0dHRQoiXx8HGjRuFEEJcunRJ2NraiszMTPHTTz8Jb29vkZWVJdLT04WHh4d0zr1q8eLFok2bNsLZ2VnMmTNH7N27V6rngwcPikGDBgkhhFAqlaJnz57i1q1bIiwsTLr2paeni+nTpwuFQqHy3hZ1DOefQ5cvXxZCCDFu3Djpevj48WPRpk0b8ejRI7Fz504xduxYqe7mzJkj4uPjNb6O4oiOjhaenp4anzt06JBwc3MTQry8hs+dO1fk5eWJhw8fio4dO4pr166J27dvC1dXV/HkyRMhhBA3btwQXbp0Eenp6SI0NFQ4OTlJdVfcY7vgNb6wz57//ve/wsnJSaSmpoq8vDwxe/Zs0bNnT42v44cffhDvv/++6NWrl5gxY4bYvn27ePHihRBCiCtXroguXbpIMY4YMUL897//LbKeC362FXVON2/eXPz+++9CCCG+/vpr0bNnT5GWliYyMzNFly5dRGxsrDhz5ozo16+fdL1ftmyZiI2NLclbqFfYYqwHbG1tYWBggLi4ONSqVQvp6elo3rx5me7zTbtS5P88amNjg1q1aiE1NRVnzpxB165dUbduXWnbRfnzzz/h4uICS0tLAICnpycWLlyIe/fuAQC6desmtaA2b94cqampxY5PJpPBzMwM8fHxuHPnDgICAqTnMjMzceXKFQwfPhxZWVm4dOkSqlSpgidPnsDBwQGnT5+Wlo2JicHs2bNhaGgIQ0NDbNq0CQDw7bffIi0tDTExMQCAnJwc1KpVC4aGhujXrx+8vLzQo0cPdOnSBW5ubkXGamBggG+//RYeHh7o2rWrynNHjhzRuJ9X9enTB//973/RsGFD2NjYoHnz5jhz5gyuX7+Ovn37Ij4+HllZWejbty+Al+9b37598eeff6Jjx46oW7eu9FMx8LLl6NNPP8Xw4cPRsmVLjfX7r3/9C0eOHMHevXtx69YtCCGQkZEBAHjnnXekVrXWrVtj586dUn36+fkBABo1aoSOHTsWWTctWrTA9OnT8dVXX6n0RS7qfbW3t5fK6tWrhzp16uDSpUv4888/MXHiRKxduxZCCBw6dAjr1q3D1q1bizwO27dvrxLTTz/9hMePH2PXrl1qLfwF5XelyMvLQ3h4OH799VfpvHnx4gXOnDmD1NRUrFy5Uiq7du0a3n//fWkbPj4+OHv2LH788UfEx8fj5s2baNu2rdq+/vjjD1y6dAmDBg0CAOTl5SEjIwNPnz7F9evXMXDgQOm1lLT7hUwmQ5UqVfDXX38hOTkZkydPVnnu+vXrcHZ2xrJly5CcnIwrV66gUaNGePfdd3Hu3Dlp2ZiYGOnXCAsLC+mXriNHjuDSpUuIjIwE8PJ9BF4eoy1btoSHhwccHR3h6OgIBwcHtfhmzpyJ48ePY926dYiPj0dSUpJKi2B+nbdp0wbZ2dl48eIFTpw4AVdXV5iYmMDExARubm64fv26xtfv7++PSZMm4fTp0zhz5gyWLVuGjRs3YvPmzejZsycWLFiAa9euITExEfXr18d7772Hbt26YeLEiXj48CE6d+6Mr776ChYWFirXsKKO4SZNmqB+/fpo3bo1AKBhw4awsLCAiYkJLC0tUa1aNaSmpqJ9+/ZYsWIFRo0ahc6dO8PHxweNGjUqwburLjc3V2N5dna2SpemESNGQCaToU6dOujWrRuOHz8OU1NTJCUlqVz7ZTIZ7ty5AwCwt7eHkdHLdKa4x/arNH32HD16FP369YNcLgcAjBw5EidPntS4/pgxYzBkyBCcOXMGZ86cwbp167Bu3TpERkaiVatWqF+/Po4cOYLGjRsjKSkJXbt2xb17915bz687p42NjdGrVy8AL9/PDz74QOrqZ21tLb2fhoaGGDJkCLp27QonJyeV6wGpYmKsJwYMGIA9e/bA0tIS7u7uKs/JZDIIIaTHOTk5xd7upUuXEBgYKD3evXt3kcu/bl+mpqZqyxoaGqpcODMzM3H//n00adJE4z4Kbr9gWf6F2czMrNB4Xic/SVAqlZDL5SqvNyUlBRYWFpDJZBg8eDB2794NY2NjDB48WK0vq5GRkUrZw4cPYWZmhry8PAQEBKB79+4AgPT0dOmmqpCQENy4cQMxMTHSBff7778vMt533nkH8+bNg5+fn5TEAChyPwX16dMHI0eOxLvvvosuXbpALpfj2LFjuHTpEubNm4e8vDy1dQrWddWqVVWeMzQ0xNq1a/H555/D2dlZ7eL84sULeHh44OOPP8aHH36IQYMG4ffff5feo8Leu1ffx/wPyaKMGjUKx44dw8KFC6Wyot5XTXXz3//+F8ePH8eaNWuwd+9e7Nu3D2ZmZmjYsOFrj8NX6+ajjz5Cu3btMHv2bGzbtg3GxsYq5+qrN5sZGBhgypQpOH/+PObMmYO1a9ciLy8PQghs3boVVapUAQA8efIEpqam0s/vwMsvYBcvXsSgQYPQsWNH5Obmaow3Ly8P48ePl37Ozs7ORmpqqnTslrTOC9bD5cuX8cknnyAtLQ1NmjRRuVk0MTERlpaWMDY2hpOTE/bu3Yvz589jyJAhatt69Vy6e/cuatasiby8PKxcuVK6TigUCshkMhgYGGDTpk24dOkSTpw4gUWLFqFjx44q1zEA+PLLL6FUKuHs7IwePXrg4cOHKq83/1qlqS7yFda39NChQ3j27BkGDRoEJycnODk5wdfXFz169MCVK1dgZ2cHLy8vREZGIikpSeo7/v777+PQoUM4ceIETp48iSFDhmD16tUq3UCKOob/+usvtS9dmt63Bg0a4ODBgzh16hROnjyJMWPGIDAwUOrSUlL29vZISEhAcnIyrKysVJ47deqUyk2hBePJy8uDgYEB8vLy4ODggO+++0567uHDh7C2tsbBgwdVzqXiHtuv0vTZY2RkpLJuYe9nbGwszp8/j/Hjx6Nnz57o2bMnvvzyS7i5ueH48ePo168fRo4ciR07duDdd9/F0KFDIZPJilXPrzunjY2NVY5/Y2Njtfjyj4dz587h5MmTmD59utT9h9RxVAo94e7ujujoaOzbt09tZABLS0s8ePAAjx8/hhACv//+u8ZtGBoaqn3rt7Ozw+7du6V/r1OzZk3ExcUBeHlynz179rXrdOzYESdOnEBSUhIAYOvWrfj2228Ljalr167Yt2+fdEfvjh07UKNGjbdu8di+fTvu3bsHZ2dnNG7cGKamptJrfvjwIVxdXaXX5uHhgcOHD2P//v3w9PRU25aDgwN27tyJvLw8ZGdn44svvpBaxjdv3ozs7Gzk5eVh7ty5WL58OZ48eYLu3bujRo0aGD16NKZPn15oS9SrnJ2d4ejoiA0bNqjUkab9AKp1WqdOHdSsWRNbt25Fly5d0LVrVxw4cADPnj1Dq1at0LhxYxgbG+PAgQMAXiY0+/fvR+fOnTXGYmVlhXbt2sHPzw8zZ86UWoLzJSQk4Pnz55g+fTp69eqF06dPSzEWpVu3bti2bRsA4MGDB8UaYQJ42Z/36NGjSEhIAIDXvq8F66ZPnz749ddfoVQqYW1tjS5duuDbb7+Fk5OTVMclOQ5tbW3xySefwMLCAmFhYQCgcm7Z2dlpXC8oKAgnTpzA77//DnNzc9jb2+PHH38E8DIZHD58OA4dOqSyzrFjx+Dj44OBAweiVq1aiImJgVKpVHuNXbt2RWRkJJ4/fw4AWLlyJWbNmoUaNWqgTZs2UjJ7+fJl3Lhxo1h1rlQqsXr1atSsWRMfffSRlDSdOXMGAHD16lU4OTlJ5/vQoUMRFRWF8+fPS3VbkIODA3bs2AHg5egMPj4+iI+PR9euXfHTTz9BCIHs7Gx89tln2LRpE65duwZXV1c0adIEkyZNwujRozWeS8eOHcPkyZPh4uICmUyGCxcuSHVUmG7dumHXrl3IyspCVlYW9u3bp3G5atWqYfny5Sqj6Ny7dw+mpqZo2LAhAGDIkCH4/fffcfnyZfTp0wfAyy/H4eHh+PjjjzFnzhw0bdoU8fHxMDIyglKphBDitcdwcWzZsgWzZ89G165dMXPmTHTt2vWtRliwsbHBqFGj8OWXX6qMTrRjxw4cOHAAEyZMkMp27doF4OV5nN8HvVOnTjh+/Dhu3boFADh69CgGDBig8ct8cY/t4ujevTsOHDiAtLQ0AJB+fXiVpaUlvv/+e5XPs+TkZGRkZEi/zjo5OeHq1as4cOCA9AtMUfWcH2txz+mi/PHHHxg9ejQ++OADTJ06FQMHDsS1a9eKvb6+YYuxnrCxsUGTJk1gYWGBGjVqqDzXtGlTeHl5YdCgQbCyslK7GSifo6Mj5s+f/1ZxjBo1CjNmzICTkxPq16+PDh06vHadFi1aYObMmRg/fjyAl8nVokWLALxMTkaMGIHw8HBp+S5dumD06NHw8fFBXl4eLC0tsWbNmhLfOLJv3z7ExsZCJpMhLy8PjRs3xs8//yy1LISHh2PhwoX497//jdzcXEybNk36edzKygqtW7dGbm4ubGxs1LY9ZcoULFy4EO7u7lAqlXBxcUHfvn3h6OiIpUuXwsPDA0qlEq1atZJuGPzss88wevRomJmZwdDQsETDVQUGBqqMRPL5559r3A+g+j5PmjQJffr0wQ8//IDWrVvDwMAAZmZm+PjjjwG8bJ0IDw/HggULsGrVKiiVSkyePBmdOnUqMjn18PDA/v37sWTJEnzzzTdSeYsWLdCjRw84OztDLpejYcOGaNq0KRISEorsXhAUFITZs2fD2dkZderU0dhNQxNLS0ssWbJEOrZMTEyKfF8LHm/5H3j5P8N37doV4eHhUvL2JsehTCbDokWLMHDgQHTv3l3txkRNGjZsiAkTJmDx4sXo1q0bQkJCMH/+fLi5uSE7Oxuurq4YMGCA1IUDACZPnoxly5YhPDwchoaGaNeunfSzdMH3f8KECUhMTJRauOrWrYslS5YAAJYvX47Zs2dj69ataNiwId57771CY/zpp5+wZ88eyGQyKJVK2NnZSUNlWVpaIjQ0FMuWLUNWVhaEEFi2bJnUBcfW1hZGRkZwcnJSadXL9/XXX2PevHlwc3ODEAKTJk2Cra0t5syZg4ULF8LNzQ05OTno3Lkzxo8fD2NjYzg7O2PQoEGoWrUqzMzM1FqLAcDX1xeTJ09G9erVUaVKFXz00UdSHRXGy8sLd+7cgaura5Ffgjp16oS5c+fCz88PaWlpMDQ0hJWVFcLDw6Wb02rVqgVbW1s0adJEagX08fGBv7+/1F2jRYsWcHV1haGhIVq3bg1nZ2f88ssvhR7Dxf3COHDgQJw+fRouLi6oUqUK3nnnnbceDu6rr77C9u3b8dlnnyE7OxvZ2dmws7PD1q1bVbpb3bt3D56ensjMzERgYKB0XAUHB+PLL7+UWnK///57tV9dgOIf28Xh4OCAoUOHYtiwYTAzM0OzZs2kVtuCGjdujNWrV2PFihV49OgRTE1NYWFhgeDgYCl+ExMTODk5ISUlRepeVVQ9F7zWFOecLoqjoyP++9//wtXVFVWrVkX16tXf+rO8MpOJkvyOTERERGXuyZMnGDx4MDZv3izdX1HZ9erVSxpZSBdcunQJ58+fl5LVH3/8ERcuXFDp0lFcL168wCeffIKgoKBi9Xkm7WFXCiIiIh0SEREBFxcXeHt7601SrIsaN26Ms2fPwtXVFW5ubjhx4oQ0bGNJ/Pnnn+jRowc6duzIpLgCYIsxERERERHYYkxEREREBICJsdYJIaQbTojo9XjOEJUMzxmi4mNirGXZ2dmIi4tDdnZ2octcvny5HCOquFhPxVeR64rnTOlhPRVfRa4rnjOlh/VUfBW1rpgYVwD5MzZR0VhPxVfZ66qyv77SwnoqvspeV5X99ZUW1lPxVdS6YmJMRERERAQmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBAIy0HQDR23qqyIQiPRvG5tZIeKgAAMirmaCm3EzLkRFRSeSfy6/i+UykGc+Z0sfEmCo8RXo21uy8hLS0NFhYWAAAJnnY8aJAVMHkn8uv4vlMpBnPmdLHrhRERERERGBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBiTEREREQEgIkxEREREREAJsZERERERACYGBMRERERAWBi/MaeP38OV1dX3Lt3DwAQExMDNzc39O3bFytWrNBydERERERUUkyM38CFCxcwfPhwxMfHAwAyMzMREBCA8PBw7Nu3D3FxcTh69Kh2gyQiogrh1YaW2bNno2/fvnB3d4e7uzsOHjwIgA0wROWBifEbiIiIQFBQEKytrQEAFy9eRKNGjdCgQQMYGRnBzc0N0dHRWo6SiIh03asNLQAQFxeHTZs2Yffu3di9ezf69OnDBhiicmKk7QAqooULF6o8TkpKgpWVlfTY2toaiYmJauspFAooFAqVMqVSWTZBEhGRzstvaJk1axYA4MWLF3jw4AHmzp2LBw8eoE+fPpgyZYpKAwwAqQGme/fu2gyfqNJhYlwKhBBqZTKZTK1sw4YNCAsLUymrXbs2QkNDERcXV+Q+YmNj3y7ISszY3BppaWkAIP2fqkhF7IOb2gxL5xV2TLVv376cIyHSX682tDx+/BidOnVCcHAwqlatikmTJiEyMhJVq1ZlAwxROWBiXApsbGyQkpIiPU5KSpK6WRTk4+MDDw8PlTKlUomUlBTY2trC1NRU4/ZjY2OZrBQh4aECFhYWSEtLg4WFBQCgurw6GrVooOXIdBePKSLd1KBBA6xevVp6PGrUKOzatQv9+vVTW5YNMNqhS/VUsGGoIF1pHKqIDTBMjEtB27Ztcfv2bSQkJKB+/frYu3cvBg0apLacXC6HXC5XKcvKylJJqomISH9dv34d8fHxcHJyAvDyF0kjIyM2wOgIXaun/IahV+lC45Cu1VVxMTEuBaampliyZAmmTp2KrKwsdO/eXeO3eyIioqIIIbBo0SJ06tQJVatWxbZt2+Dh4cEGGKJywsT4LRw+fFj628HBAXv27NFiNEREVNG1bNkSEydOxPDhw5Gbm4u+ffvC1dUVANgAQ1QOmBgTUZnbvXs31q5dCwBwdHSEn58frl69isDAQDx//hwffvghvvnmGxgZ8ZJE+qlgQ8vIkSMxcuRItWXYAENU9jiOMRGVqYyMDCxcuBAbN27E7t27cfbsWcTExGDmzJmYO3cu9u/fDyEEIiIitB0qERHpOSbGRFSmlEol8vLykJGRgdzcXOTm5sLIyAiZmZmwt7cHAHh6emqcFEehUODevXsq/x49elTOr4CIiPQFf7ckojJlbm6OadOmwdnZGWZmZujQoQOMjY1VxmS1srLSOCYrh54qe7pUTxx6ioi0jYkxEZWpa9euYceOHfjjjz9gYWGBGTNm4Pjx42rLaRqTlUNPlS1dqycOPUVE2sbEmIjK1LFjx+Dg4IBatWoBeNltYv369SrDRyUnJ2sck5VDTxERUXliH2MiKlMtW7ZETEwMXrx4ASEEDh8+jA4dOsDU1FT6aXrXrl1wdHTUcqRERKTv2GJMRGWqa9euuHLlCjw9PWFsbAw7OztMnDgRffr0QWBgINLT09G6dWt4e3trO1QiItJzTIyJqMxNnDgREydOVClr2bIlIiMjtRQRERGROnalICIiIiICE2MiIiIiIgBMjImIiIiIADAxJiIiIiICwMSYiIiIiAgAE2MiIiIiIgAcro2IiIiI3tBTRSYU6dlq5RY1bbQQzdtjYkxEREREb0SRno01Oy+plXv1bqiFaN4eu1IQERFp0fPnz+Hq6op79+4BALZt2wZXV1e4ublh9uzZyM5+2RoXFhaGnj17wt3dHe7u7ti8ebM2wyaqlNhiTEREpCUXLlxAYGAg4uPjAQC3b9/G+vXrERUVhWrVqsHf3x9btmzB6NGjERcXh+XLl+ODDz7QbtBElRhbjImIiLQkIiICQUFBsLa2BgCYmJhg3rx5MDc3h0wmQ/PmzfHgwQMAQFxcHNatWwc3NzcEBwcjKytLm6ETVUpMjImIiLRk4cKF+PDDD6XH9erVQ+fOnQEAT548webNm9G7d2+kp6ejVatW8PPzw86dO6FQKBAeHq62PYVCgXv37qn8e/ToUbm9HqKKjl0piIiIdExiYiLGjx+PQYMGoWPHjgCAdevWSc+PHTsWAQEB8PX1VVlvw4YNCAsLUymrXbs2QkNDERcXV+Q+Y2NjSyn6yk2X6snY3BppaWlq5amKVMQ+uKnVGIDC66p9+/ZlGdJbYWJcinbv3o21a9cCABwdHeHn56fliIiIqKK5desWJkyYgE8++QRjx44FADx48AAxMTEYPHgwAEAIASMj9Y9wHx8feHh4qJQplUqkpKTA1tYWpqamGvcZGxur08mKrtC1ekp4qICFhYVaeXV5dTRq0UCrMQC6nQAXholxKcnIyMDChQsRHR0NuVyO4cOHIyYmRvpJjIiI6HWeP3+OcePGwdfXF+7u7lK5mZkZvv32W3Ts2BH169fH5s2b0adPH7X15XI55HK5SllWVhZSUlLKPHaiyoCJcSlRKpXIy8tDRkYGqlatitzcXLVv5gqFAgqFQm09IiIiAIiMjERKSgp++OEH/PDDDwCAXr16Ydq0aQgODsZnn32GnJwctGvXDmPGjNFytESVDxPjUmJubo5p06bB2dkZZmZm6NChA9q1a6eyDPt+lY2C/Zvy/y/P/lUVVUXs+0VUWR0+fBgAMHr0aIwePVrjMk5OTnBycirHqIj0DxPjUnLt2jXs2LEDf/zxBywsLDBjxgysX78e48ePl5Zh36+ykd+/KS0tTernVJ79qyoiHlNERETqmBiXkmPHjsHBwQG1atUCAHh6emLLli0qiTH7fhEREVFZMzB42WhUkLyaCWrKzbQUUcXBxLiUtGzZEt9++y1evHiBKlWq4PDhw7Czs9N2WERERFQJPFVkQpGerVKWk5uncdn0jFz8vO+qStkkDzsmxsXAxLiUdO3aFVeuXIGnpyeMjY1hZ2eHiRMnajssIiIiqgQU6dlYs/OSSpm3SystRVN5MTEuRRMnTmQyTERERFRBcUpoIiIiIiIwMSYiIiIiAsDEGBs3bsTz58+1HQYRERERaZneJ8Y3btyAk5MT5syZg0uXLr1+BSIiIiKqlPQ+MZ4/fz72798PW1tbfPPNNxg0aBAiIyORlZWl7dCIKo3Dhw/D09MT/fr1w4IFCwAAMTExcHNzQ9++fbFixQotR0hERMTEGMDL6Zz79esHV1dXPHv2DFu2bEG/fv0QHR2t7dCIKry7d+8iKCgI4eHh+PXXX3HlyhUcPXoUAQEBCA8Px759+xAXF4ejR49qO1QiItJzej9cW0xMDCIiInDixAk4OTlh9erVaNmyJe7cuYMRI0agX79+2g6RqEI7ePAgXFxcUKdOHQDAihUrkJCQgEaNGqFBg5fTdru5uSE6Ohrdu3fXZqhERBrlT65hbG4tzSjHmeQqJ71PjIODgzFixAjMnz8fFhYWUnnDhg0xdOhQLUZGVDkkJCTA2NgY48aNQ3JyMnr27IlmzZrByspKWsba2hqJiYlq6yoUCigUqtOaKpXKMo+ZiKig/Mk10tLSpFyBM8lVTnqfGO/ZswfR0dGwsLBAcnIyfvvtN3h7e8PAwABffPGFtsMjqvCUSiXOnj2LjRs3omrVqvj8889RpUoVteVkMpla2YYNGxAWFqZSVrt2bYSGhiIuLq7I/cbGxr5d4HpCl+rJ2NwaaWlpauWpilTEPriphYhUFVZX7du3L+dIiKis6H1iPH/+fKSnp2PAgAEwMDBAbGws7t27h8DAQG2HRvTW8n/+e5VFTZtyi6F27dpwcHCApaUlAKB3796Ijo6GoaGhtExSUhKsra3V1vXx8YGHh4dKmVKpREpKCmxtbWFqaqpxn7GxsUxWikHX6inhoULll7t81eXV0ahFAy1E9D+6VldEVDb0PjE+f/489u7dCwCoVasWVq5cCXd3dy1HRVQ68n/+e5VX74blFkPPnj3h5+cHhUKBatWq4c8//0S/fv2wdu1aJCQkoH79+ti7dy8GDRqktq5cLodcLlcpy8rKQkpKSnmFT0REekTvR6XIyclBdvb/WtRyc3O1GA1R5dO2bVuMHz8eI0aMgIuLC9555x0MHz4cS5YswdSpU+Hi4oL33nuPN7qS3nr+/DlcXV1x7949AIUPZXj16lUMGjRIGnufn1dEpU/vW4x79OiBcePGwd3dHTKZDHv37uWd8USlbPDgwRg8eLBKmYODA/bs2aOliIh0w4ULFxAYGIj4+HgAQGZmJgICArBx40bUrVsXkyZNwtGjR9G9e3fMnDkTCxYsgL29PQICAhAREYERI0Zo9wUQVTJ632I8a9Ys9OnTB4cOHcKRI0fQp08ffPnll9oOi4iI9EBERASCgoKkPvYXL16UhjI0MjKShjK8f/8+MjMzYW9vDwDw9PTkWPtEZUDvW4wNDQ3h7e0Nb29vbYdCRER6ZuHChSqPk5KSNA5l+Gq5lZUVhzgkKgN6nxjv27cPISEhSE1NhRBCKj937pwWoyIiIn1U8HMon0wmK7T8VRzisGwUHEow///yHkZQ03CGubm5Goc41FReVvEWNswiUDGHONT7xHjlypXw9/dH69atNV5kiIiIyouNjY3KqCv5Qxm+Wp6cnMwhDstR/lCCBSf4KO9hBDUNZ2hkZKRxiENN5WUVb2HDLAK6nQAXRu8TY7lcjr59+2o7DCKiCk/TuNmcNrdk2rZti9u3b6sNZVivXj2YmppKCeyuXbvg6Oiotj6HOCR6O3qfGLdt21a645eIiN6cpnGzOW1uyZiamkpDGWZlZaF79+7SUIYhISEIDAxEeno6WrduzXtjiMqA3ifGR48exaZNm2BsbAxjY2MIISCTydjHmIiIys3hw4elvwsbyrBly5aIjIwsz7CI9I7eJ8Y//fSTtkMgIiIiIh2g9+MY16tXD5cuXUJERAQsLS1x/vx51KtX7422dfjwYXh6eqJfv35YsGBBKUdKRERERGVJ7xPjtWvX4pdffkF0dDQyMzMRFhaG1atXl3g7d+/eRVBQEMLDw/Hrr7/iypUrOHr0aBlETERERERlQe8T499++w3r1q1DlSpVULNmTURERGDv3r0l3s7Bgwfh4uKCOnXqwNjYGCtWrEDbtm3LIGIiIiIiKgt638fYyMgIJiYm0mO5XA4jo5JXS0JCAoyNjTFu3DgkJyejZ8+emD59usoynJGIiIiISHfpfWJct25dHDlyBDKZDNnZ2Vi/fv0b9TFWKpU4e/YsNm7ciKpVq+Lzzz/Hzp074enpKS3DGYnKhi7MSKSrKtuMRERERGVJ7xPjuXPnYtasWbh+/Trs7e3Rtm1bhISElHg7tWvXhoODAywtLQEAvXv3xsWLF1USY85IVDZ0YUYiXVXZZiQioopP00QwACeDId2g94mxjY0NNmzYgIyMDCiVSpibm7/Rdnr27Ak/Pz8oFApUq1YNf/75J3r37q2yDGckIiIifadpIhiAk8GQbtD7xPjHH3/UWD5mzJgSbadt27YYP348RowYgZycHHTp0gWDBg0qjRCJiEpVfoudsbk1Eh6+vO+BrXVEREyMcePGDenv7OxsxMbGomPHjm+0rcGDB2Pw4MGlFRoRUZnIb7Er2P2IrXVE+klT1xZ9/qKs94nx4sWLVR4/efIEs2bN0lI0REREROVHU9cWff6irPfjGL/K0tIS9+/f13YYRERERFTO9L7FuGAfYyEE4uLiUKtWLS1GRERERETaoPeJccE+xsDLcY3ZlYKIiIhI/+h9YvxqH2MiIiIi0k96nxiPGjUKMpms0Od//vnncoyGiEg7DAwgDd1WkD7fnU5E+kfvE2NbW1vcunULQ4cOhbGxMXbv3o3c3Fz0799f26EREZWb9Ixc/Lzvqlq5Pt+dTkT6R+8T43PnzmHLli0wNDQEAHTr1g1Dhw6Fk5OTliMjIiJ9tH37dmzatEl6fO/ePbi7uyMjIwOxsbGoUqUKAGDKlCno06ePtsKkUsAxhHWP3ifGT548QXZ2tnShSU9PR2ZmppajIiKifJq6eVTm5GHIkCEYMmQIAODmzZuYPHkypkyZAh8fH2zatAnW1tZajpBKC8cQ1j16nxi7urpi6NCh6NOnD4QQ+M9//gNvb29th0VERP+fpm4e+pI8zJs3D76+vjAzM8ODBw8wd+5cPHjwAH369MGUKVNgYKA6HYFCoYBCofolQqlUlmfIRBWa3ifG06ZNQ+vWrXHy5EmYmpoiODgYHTp00HZYVAlo+okMqNwtXa+zdOlSPH36FEuWLMHVq1cRGBiI58+f48MPP8Q333wDIyO9vySRlhV23lrUtCn3WGJiYpCZmQlnZ2fcvXsXnTp1QnBwMKpWrYpJkyYhMjISQ4cOVVlnw4YNCAsLUymrXbs2QkNDERcXV+T+YmNjS/01aGJsbo20tDS18lRFKmIf3CyXGEqqYMz5/5dGvJrqorDtalo2NzdXY11qKi/Jdkvy2gp7P4HCj6n27dsXa9vawE8hADY2NmjWrBk8PT1x+fJlbYdDlYSmn8gA/WnpetWJEyewc+dO9OjRAwAwc+ZMLFiwAPb29ggICEBERARGjBih3SBJ7xV23nr1bljusWzduhVjxowBADRo0ACrV6+Wnhs1ahR27dqllhj7+PjAw8NDpUypVCIlJQW2trYwNTXVuK/Y2NhyS1YSHipgYWGhVl5dXh2NWjQolxhKKj/mtLQ0KfaaNaojT15dbdmSNH5oqovC6kHTskZGRhrrUlN5SbZbkveisPcT0O0EuDB6nxjv2LEDP/zwA7KystCnTx98/vnn8PX1VbvYENGbe/bsGVasWIFPP/0U165dw/3795GZmQl7e3sAgKenJ0JDQ5kY6xFNLbM5uXlaikb3ZGdn48yZM1iyZAkA4Pr164iPj5duDBdCaPyFRS6XQy6Xq5RlZWUhJSWl7IPWMxzJpXLS+8R406ZN2LZtGz755BPUqlULUVFRGD9+PBNjolL09ddfw9fXFw8fPgQAJCUlwcrKSnreysoKiYmJauuxv2Tlpall1tullZai0T3Xr1/Hu+++i6pVqwJ4mQgvWrQInTp1QtWqVbFt2za1lmEient6nxgbGBjA3Nxcely3bl1p6DYienvbt29H3bp14eDggKioKAAvP+RfpWminYrQX7Ii0tRfsrC+im/b17C8+0u+LV3pL3n37l3UqVNHetyyZUtMnDgRw4cPR25uLvr27QtXV9dS3y+RvtP7xLhGjRq4evWq9KG8Z88eVK+u3meIiN7Mvn37kJycDHd3d6SmpuLFixeQyWQqP+0mJydrHIKqIvSXrIg09ZcsrK/i2/Y1LO/+km9LV/pLuri4wMXFRaVs5MiRGDlyZLnFQKSP9D4xDggIwLRp03Dnzh107doVpqamCA8P13ZYRJXGjz/+KP0dFRWF06dPY/HixXB1dZUS2F27dsHR0VFtXfaXJCKi8qT3iXFmZiZ2796N+Ph4KJVKNG7cGMbGxtoOi6jSCwkJQWBgINLT09G6dWuOH05ERFqn94nxjBkz8J///AdNmjTRdihElZ6npyc8PT0BvOwzGRkZqeWIiIiI/sfg9YtUbi1atMCvv/6KBw8e4NmzZ9I/IiIiItIvet9ifOjQIURHR6uUyWQyXL2qPjYhEREREVVeep8YX7qkPsPR2yo47S0RERFVDIVNCc7JZ/SH3ibGc+fOxfz58wEAT548gaWlZals99Vpb4mIiKhiKGxKcE4+oz/0to9xwckBxo0bVyrbLDjtLRERERFVLHrbYlxw5i1Ns3C9iVenvX0Vp7clIiIi0l16mxgXpGkq2pLSNO3tqzi9bdnQNL1tWU0XWxKFTS2bk5uNi9fvqpSZGcuQ9jSx3GIAynd6WyIioopAbxPjvLw8pKamQggBpVIp/Z2vRo0aJdqepmlvFy1ahICAAGkZTm9bNjRNb1tW08W+SVyvylUaYOuhOyplkzzs0Py9+uUWA8AEmIiI6FV6mxjfuHEDnTp1kpLhjh07Ss+9yXBtmqa9LZgUA5zeloiIiEiX6W1ifO3aNW2HQEREREQ6RG8T47JUcNpbIiIiIqoY9Ha4NiIiIiKigpgYExERERGBXSmIiIh0jre3Nx4/fgwjo5cf08HBwbhz5w6+//575OTkYPTo0Rg5cqSWoySqfJgYExER6RAhBP755x8cOXJESowTExPh6+uLqKgomJiYwMvLCx07dkTTpk21HC1R5cLEmIiISIf8888/kMlkmDBhAh4/foyhQ4eiWrVq6NSpkzTGvpOTE6KjozFlyhSVdTnDKtHbYWJMRFQJPFVkQpGerVImr2aCmnIzLUVEb0qhUMDBwQHz5s1DZmYmvL294ezsDCsrK2kZa2trXLx4UW3dijDDamEzcuryjKW5ublqM6wWLCuoJK9D0/4KW1/TsoXFoKm8JNt929eQryLOsMrEmIioElCkZ2PNzksqZZM87JgYV0AffPABPvjgAwBA1apVMXjwYCxevBiffvqpynIymUxt3Yoww2phM3Lq8oylRkZGajOs5pe9qiSvQ9P+Cltf07KFxaCpvCTbfdvXkE+XE+DCMDEmIiLSIWfPnkVOTg4cHBwAvOxzXK9ePZVZUpOSkmBtba22LmdYJXo7HK6NiIhIh6SlpWHZsmXIysrC8+fPsXPnTnz77bc4ceIEnjx5goyMDBw4cACOjo7aDpXKgIHBy1bYV//l5OZpOzS9wBZjIiIiHdKzZ09cuHABAwcORF5eHkaMGIH27dvD19cX3t7eyMnJweDBg/H+++9rO1QqA+kZufh531W1cm+XVlqIRv8wMaYKQ9PNRQD4LZqIKp3p06dj+vTpKmVubm5wc3PTTkBEeoKJMVUYmm4uAvgtmoj+h6NzENHbYGJMRESVBkfnIKK3wZvviIiIiIjAxJiIiIiICAATYyIiIiIiAEyMiagchIWFoX///ujfvz+WLVsGAIiJiYGbmxv69u2LFStWaDlCIiIi3nxHRGUsJiYGx44dw86dOyGTyTB+/Hjs3bsXISEh2LhxI+rWrYtJkybh6NGj6N69u7bDJSI9oWkEEw7/SUyMiahMWVlZwd/fHyYmJgCAJk2aID4+Ho0aNUKDBg0AvByfNTo6molxBcExxaky0DSCCYf/JCbGRFSmmjVrJv0dHx+Pffv2YdSoUbCyspLKra2tkZiYqLauQqGAQqFQKVMqlWUXLBULxxQnosqKiTERlYubN29i0qRJ8PPzg5GREW7fvq3yvEwmU1tnw4YNCAsLUymrXbs2QkNDERcXV+T+YmNj3z7oCsTY3BppaWkqZamKVMQ+uFnksvn/5+bmqq1f2DY07auwbeTkZuPi9btqyxoZm6otW1gMmsqL89pet6wmhb02oPBjqn379sXaNhHpPibGRFTmYmNj8cUXXyAgIAD9+/fH6dOnkZKSIj2flJQEa2trtfV8fHzg4eGhUqZUKpGSkgJbW1uYmpoWuj99S1YSHipgYWGhUlZdXh2NWjQodNm0tDRpHSMjI7X1C9uGpn0Vto1cpQG2Hrqjtqy3Syu1ZQuLQVP5615bcZbVpLDXBjABJtIHTIxLUVhYGP7zn/8AALp3745Zs2ZpOSIi7Xv48CEmT56MFStWwMHBAQDQtm1b3L59GwkJCahfvz727t2LQYMGqa0rl8shl8tVyrKyslSSaiIiotLCxLiUaLrz/uDBg+jTp4+2QyPSqvXr1yMrKwtLliyRyry8vLBkyRJMnToVWVlZ6N69O/r166fFKImIiJgYlxpNd94/ePBAZRneSET6KDAwEIGBgRqf27NnTzlHQ0REVDgmxqVE0533W7duVVmGNxK9neLc8JP/f2E3/JgZy5D2VH30g7JQkhuUSnJzUGnEAPBGIiIiolcxMS5lBe+8f/fdd1We441Eb+d1N/wUvJGosBt+JnnYofl79cs8VqBkNyiV5Oag0ogBYAJMRJWPpjG25dVMUFNupqWIqKJhYlyKXr3z/lW8kYiIypOBwcsvR6/iRBy6T9PN3LNnz0ZsbCyqVKkCAJgyZQrvY3mFpjG2J3nYMTGmYmNiXEo03XlPRKRN6Rm5+HnfVbVyTsSh2wq7mTsuLg6bNm3SOLQhEZUOJsalpLA774cPH67FqIiIqKIp7GbuBw8eYO7cuXjw4AH69OmDKVOmwMDAQGVd3uRN9HaYGJeSou68JyKqqDR1x2BXjLKl6WbuLVu24PTp0wgODkbVqlUxadIkREZGYujQoSrrVoSbvAu7Kbg0bkJ+0xkg8xVn9sU3mS2yJPGWZAZIzhZZ+pgYExFRoTR1x2BXjPJR8Gbu9957D6tXr5aeGzVqFHbt2qWWGFeEm7wLuym4NG5CfpMZIAt63eyLbzpbpKabAoGXXzLfZgZIzhZZ+pgYExER6ZhXb+a+fv064uPj4eTkBAAQQsDISP0jnDd56yZNNwUC/JKpiwxevwgRERGVl/ybuUNCQqQRjoQQWLRoEVJTU5GTk4Nt27ZxRAqiMsAWYyIiIh1S2M3cEydOxPDhw5Gbm4u+ffvC1dVVi1ESVU5MjImIiHRIUTdzjxw5spyjIdIvTIyJiIio0uJEN1QSTIyJiIio0uJEN1QSvPmOiIiIiAhsMSYiIiIqNZwUp2JjYqzHNA04Lq9mgppyM63GAACmJobIyladxrS8LyyaYtMUF1D+9UZERLqJk+JUbEyM9ZimAccnediVa4JX1KDn2r6waIpNU1xA+dcbERERlT4mxkRERFShFDXFMtHbYGJMREREFQqnWKaywlEpiIiIiIjAxJiIiIiICAC7UhARVSjsW1lyhc18xtFkiDQr7JzRhRGjyhoTYyKiCoR9K0uusJnPOJoMkWZFzRao7RGjyhoTYyKit1BYCy5bI3UfJ2IgolcxMSYieguFteCyNVL3cSIGInoVE2MionLC1mUi0pbC+g3zVxJVTIyJiMoJW5eJCqcpceOXxtJTVL9h+h8mxqXo119/xffff4+cnByMHj0aI0eO1HZIRDqtop0zmlp8y7K1pbz3R7qvop0zJaEpcftskB1HYaFyxcS4lCQmJmLFihWIioqCiYkJvLy80LFjRzRt2lTboRHppIp4zmhq8S3L1pby3h/pNl0+Z8qqmxBbOam8MTEuJTExMejUqRNq1KgBAHByckJ0dDSmTJkiLaNQKKBQqP5MlJubCwDIzla/oBSUlZVVugEDUObmoKqJTK2sLPZVkhgAIE+pXq6prGC5MDOQni9s2ZK8Pk2xlWS7JXltZVXvhcUg8pRF7s/ExAQymfp6pUmXz5nU51l4/iJHrTxXmVcmx4TIy8E/dx+/1f7K6px5k+2Wx7LlXWeV/ZypJq+tVp/mVY1R3dy0WLE9TU3Hlv3X1co/cW6Bp6npqvFoeI+Atz8mSmMblfmcKe9ldeGceRMyIYTQdhCVwZo1a/DixQv4+voCALZv346LFy9i/vz50jKrVq1CWFiYynrNmzfHvHnzyjNUoteytbWFqWnxPhDfFM8Zqkx4zhCVTHmcM2+CLcalRNP3i1e/Cfn4+MDDw0OlLC8vDyYmJqhRo4bGb06PHj3CiBEjsGXLFtSpU6d0g65EWE/FV5y6MjExKfM4eM5oF+up+HjOEMB6KgldOWfeBBPjUmJjY4OzZ89Kj5OSkmBtba2yjFwuh1wuL9F2DQ0NkZKSAkNDQ538ZqUrWE/Fpyt1xXNGu1hPxacrdcVzRrtYT8VXkevKQNsBVBadO3fGiRMn8OTJE2RkZODAgQNwdHTUdlhEOovnDFHJ8JwhKntsMS4lNjY28PX1hbe3N3JycjB48GC8//772g6LSGfxnCEqGZ4zRGWPiXEpcnNzg5ubm7bDIKoweM4QlQzPGaKyxa4UOk4ul2PKlCkl7jOmb1hPxVfZ66qyv77SwnoqvspeV5X99ZUW1lPxVeS64nBtRERERERgizEREREREQAmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBYGJMRERERASAiTEREREREQAmxkREREREAJgYExEREREBYGJMRERERASAiXGF8ssvv2DAgAFwcXFB//79MXPmTDx48EB6fvv27di8eTMAYNWqVQgODi7V/fv7+6Nbt25wd3eHu7s7XFxc8NVXXyE5OfmNt3no0CEsWLCgFKMsG+vXr4e/v7/G544cOYJhw4ZhwIAB6N+/P6ZNm4ZHjx690X7S0tLg7e39NqHqjXv37qFVq1bS8eju7o4BAwYgMjLyrbft6uqKU6dOvdG6b/IePnz4EK6urhgwYADOnz//Rvt9tT7c3Nzg6emJXbt2ScusXLlS5bEmBc/JI0eOYOXKla/d96hRoxAdHa1WnpOTg2XLlsHNzQ0DBgyAm5sb/vWvf0EIAQCYM2cOYmJiiv8iS2Ds2LF48uRJmWybSm7BggXSsWlrawsnJyfpcWZm5ltvv7jH6qsmTZqEqKgojc8lJibC399fOn6HDBmC33///W1DfWthYWFSHMU5p6lkjLQdABXP0qVLce3aNaxZswZ169ZFXl4e9uzZg2HDhmH79u2oU6cOYmNj0axZszKNY/To0Rg3bhwAQAiBNWvWYPz48YiKioKhoWGJt9e7d2/07t27tMMsN4mJifDz80NUVBTq1asHAPj+++8xffp0bN26tcTbS01NxaVLl0o7zErLzMwMu3fvlh4nJibC1dUVtra2aNmypVZiepP38NSpU6hduzZ++umnt9r3q/Vx//59jB49GlWqVIGTkxOmTZv22m0UPCcvXbqE1NTUN45nw4YNuHfvHnbu3AkjIyOkpaXBx8cHNWvWxLBhw7Bw4cI33vbrHD9+vMy2TSUXGBgo/d2rVy+EhITAzs6u1Lb/tsfqq548eQIvLy9MmzYNixcvhkwmw7Vr1zBmzBhUqVIFXbp0KbV9ldSpU6fQtGlTACjWOU0lw8S4Anj06BG2bt2KI0eOoHr16gAAAwMDDBw4EHFxcVizZg06d+6Mw4cP4/jx4zAzMwMA/PPPPxg1ahSSk5NRu3ZtLF++HNbW1khMTERwcDAePnyInJwc9O/fH59++inu3buHkSNHokmTJrh//z42btwIa2vrQuOSyWT49NNPsXPnThw/fhyOjo44d+4cQkJCkJGRAZlMhqlTp6Jnz57w8vLC6NGj0a9fPwBASEgIhBBo0qQJ9u/fjzVr1iA5ORlBQUH4559/YGBgAC8vL3h7eyMtLQ0LFy7EjRs3kJOTAwcHB8yaNQtGRkYIDQ3FwYMHYWxsjJo1a2Lx4sVqMd++fRvBwcF48eIFkpKS0LJlS3z33XcwNTWFnZ0dJk6ciOPHjyMpKQne3t4YPXo0cnJysGDBAsTExKBWrVqoVasWLCws1Org6dOnyMnJwYsXL6QyHx8ftGrVCgAwZswY9OvXD8OGDQPwMml++vQpJkyYAD8/Pzx9+hQA0L17d0yfPh2zZ89GZmYm3N3dERUVhfj4eCxcuBDPnj2DUqnEqFGjMHjwYJw6dUp6P2/evIkqVapg6tSp2LhxI27fvo2+ffsiICAA6enpmD17NhISEmBgYIA2bdogODgYBgaV88ciGxsbNGrUCPHx8bhy5QoiIyORkZEBc3NzbNy4EatXr8Zvv/0GQ0NDNG7cGHPnzoWVlRX+/vtvBAQEICMjA++99570ft67dw9ubm5SK+6rj9esWSMlfY0aNcKSJUvU3sPVq1cXeYyePHkS3333HdLS0jBq1Chs3LgR27Ztw8aNG2FgYIDatWtj7ty5aNy4Mfz9/fHs2TPcvXsXPXr0wMyZM4usj3r16uGLL77A+vXr4eTkBH9/fzRr1gzjxo3D0aNHERISAgMDA7Rq1QoxMTHYsmULTp8+jf379+Pzzz/H1q1boVQqYWFhgUmTJmHevHmIj49HamoqqlWrhpCQELz33nuF7j85ORk5OTnIzs6GkZERLCwssGzZMuTl5QF42dI8cuRI9OvXD1FRUVi7di3MzMzQqVMn/Pzzz7hy5QpWrVqF+/fvIzk5Gffv34elpSVWrFgBGxsb/PHHH1izZg2ys7Px5MkTDBw4UDqPgJfn4tq1azFy5EisXLlSSsR69eqFlStXombNmmrXvHv37mm8hiUnJ2s8Z+ntrVq1Cn/99ReSkpLQokULNGrUCE+fPsXXX38tPZ//+MCBA/j+++8hk8lgaGiIWbNmwcTEROVY9fX1xfbt2/HLL78gLy8PNWrUwNy5c9GkSROpFTgpKQnvvPMOHj9+rDGmLVu2oF27dhg4cKBU1rJlS6xatUr6LDh79iyWLVuGjIwMGBsbY/r06XB0dERUVBQOHDiAzMxM3L9/H3Xr1sXIkSOxadMmxMfHY8yYMRg7diyioqLw22+/IS8vD4mJibCxscGSJUtgY2Ojcm4A/ztXHj9+jLi4OCxbtgyGhoY4dOiQdE4X9nmmVCqxbNkyHD58GBYWFnj//fdx69YtbNy4sWzf2IpKkM6Ljo4Wnp6eGp87dOiQcHNzE0II4efnJ/79738LIYQIDQ0VvXr1Eo8fPxZCCPHZZ5+JsLAwIYQQo0aNEocOHRJCCJGZmSlGjRolfvvtN3H37l3RvHlzcebMGY37Krj9gqZOnSrWrVsnnj17Jvr27Svu3r0rhBDi0aNHwtHRUdy/f19ERkaKiRMnCiGEyM3NFd26dRO3b98WO3bskMonT54sli5dKoQQQqFQiP79+4v4+Hjh7+8vfv75Z2ndGTNmiLVr14oHDx6Idu3aiaysLCGEEOvXrxcHDx5Ui2/JkiVi165dQgghsrOzhaurq4iOjhZCCNG8eXOxceNGIYQQly5dEra2tiIzM1P89NNPwtvbW2RlZYn09HTh4eEh/Pz8NNbL4sWLRZs2bYSzs7OYM2eO2Lt3r8jJyRFCCHHw4EExaNAgIYQQSqVS9OzZU9y6dUuEhYWJuXPnCiGESE9PF9OnTxcKhULcvXtX2NvbCyGEyMnJES4uLiIuLk6qE2dnZ3H+/Hlx8uRJ0apVK3H58mUhhBDjxo0Tw4YNE1lZWeLx48eiTZs24tGjR2Lnzp1i7NixUt3NmTNHxMfHa3wdFU3Busp37tw58dFHH4kHDx6IHTt2iI8++kikpaUJIYSIjIwUw4YNE+np6UKIl+dIft24u7uLiIgIIYQQZ8+eFS1atBAnT55U20fBx7///rvo27evePbsmRBCiEWLFonw8HCVZYp7jBY8D2JiYsTHH38snbs7duwQzs7OIi8vT/j5+QkfH59i14cQQty4cUO0bdtWCPG/c/jJkyeiQ4cO4urVq0IIIaKiokTz5s3F3bt3VWIJDQ0V33zzjRBCiP/85z9i/vz50nbnzp0rgoODhRBCfPLJJ+I///mP2r4fPnwoPDw8hJ2dnfjkk0/E8uXLpWO24Ho3b94UDg4O4uHDh0IIIVatWiWaN28uxdC7d2/pfZw0aZJYuXKlyMvLE5988om4ffu2EOLl9aZVq1ZSvTVv3lz6u2fPnuLixYvSfvMfv3rNK+oaVtg5SyX36vsRGhoqnJycpOtmwePu1ce9e/cW58+fF0II8eeff4pVq1apLXPq1CkxYsQI8eLFC2k5Z2dnIYQQn3/+uVixYoUQQoj4+Hhhb28vduzYoRbjpEmTxKZNmwp9DU+ePBEODg7ir7/+EkK8PM86dOgg7ty5I3bs2CHat28vHjx4IJRKpXBxcRFTp04VSqVSXL16VdjZ2QmlUil27Ngh7O3txT///COEEOLbb78VU6dOFUKon1MFHxf8u+DncmGfZ7/88osYOXKkyMzMFFlZWWLs2LHik08+KfS16Tu2GFcQubm5Gsuzs7Mhk8k0PtelSxdYWloCePlN98mTJ3jx4gXOnDmD1NRUqT/WixcvcO3aNbz//vswMjKCvb19iWKTyWSoUqUK/vrrLyQnJ2Py5Mkqz12/fh3Ozs5YtmwZkpOTceXKFTRq1Ajvvvsuzp07Jy0bExMjtYBZWFhg7969AF72Hbt06ZLUdzS/P5qNjQ1atmwJDw8PODo6wtHREQ4ODmrxzZw5E8ePH8e6desQHx+PpKQklRbe/J+N27Rpg+zsbLx48QInTpyAq6srTExMYGJiAjc3N1y/fl3j6/f398ekSZNw+vRpnDlzBsuWLcPGjRuxefNm9OzZEwsWLMC1a9eQmJiI+vXr47333kO3bt0wceJEPHz4EJ07d8ZXX30FCwsLlZ8C4+PjcefOHQQEBEhlmZmZuHLlCpo0aYL69eujdevWAICGDRvCwsICJiYmsLS0RLVq1ZCamor27dtjxYoVGDVqFDp37gwfHx80atSoBO+ubstvmQUApVKJmjVr4ttvv0XdunUBAC1atIC5uTkA4L///S88PT1RtWpVAIC3tzf+9a9/ISUlBdevX5dahtq3b1+sLkknTpxAv379pF9x8lsp7927Jy1T3GO0oD///BMuLi7Suevp6YmFCxdK223fvn2x6iafTCaTfkXKd/bsWTRp0kTqbuLh4fHavv79+vVDgwYNsHHjRiQkJOD06dP44IMPilynTp06iIqKwt9//41Tp07h1KlTGDZsGPz9/TFy5EhpuWPHjqFLly6oU6cOAOCTTz7BqlWrpOc7dOggvY+tW7dGamoqZDIZ/vWvf+HIkSPYu3cvbt26BSEEMjIyil85gMo1r6hrWGHnLJUOe3t7GBm9PiXp378/pkyZgu7du6NLly6YMGGC2jJHjhxBQkICvLy8pLLU1FQ8e/YMMTEx8PPzAwA0atQIHTt21LgfmUwm9YXX5OLFi2jYsCHatm0LAGjWrBnatWuH06dPQyaTwc7OTroO1a9fH127doWBgQEaNGiArKws6Tjt0qULGjduDAAYOnSodD17U5o+z44ePQp3d3eYmpoCAIYNG8bW4iIwMa4A7O3tkZCQpciLlQAANz5JREFUgOTkZFhZWak8d+rUqUI/nApeZPJP8ry8PAghsHXrVlSpUgXAy75UpqamePr0KUxMTIp1cconhMDly5fxySefIC0tDU2aNMH27dul5xMTE2FpaQljY2M4OTlh7969OH/+PIYMGaIx3oJJ/t27d1GzZk3k5eVh5cqVaNKkCQBAoVBAJpPBwMAAmzZtwqVLl3DixAksWrQIHTt2VOnLBgBffvkllEolnJ2d0aNHDzx8+FDlgpd/scjft6aLYWH9pw8dOoRnz55h0KBBcHJygpOTE3x9fdGjRw9cuXIFdnZ28PLyQmRkJJKSkqQL9fvvv49Dhw7hxIkTOHnyJIYMGYLVq1er/MSuVCohl8tV+oympKTAwsICf/31F0xMTNTq71UNGjTAwYMHcerUKZw8eRJjxoxBYGCg9PNcRfdqn9pX5SfBgPr7mpeXp/KFs+Dz+XX56odjTk6O9LehoaHK8apQKKBQKFT2UdxjtCBNx58QQoq14GsqjkuXLqF58+YqZYaGhmr7eV33mi1btiAiIgIjR46Em5sbatSoofIlQJNly5ZhyJAhaNq0KZo2bYqRI0di9+7dWLdunUpi/Go8r55vBRP7/PfkxYsX8PDwwMcff4wPP/wQgwYNwu+//15oMlOwPDs7W/q74DVPqVQWeQ3TdM62a9euyDqg4il4XBd13vn6+mLw4ME4duyY1P3m1Zvn8vLy4O7uLjW05OXlISkpCdWrV1fbdmGfd/b29vjrr7/wySefqJRv3boVGRkZePfdd9XWyT9PjY2Ni3V9BlSP9by8PJXHhdVBUTR9nr2678rala60sHYqgPz+Rl9++SUSExOl8h07duDAgQPSN2ZDQ8NCW5bzmZubw97eHj/++COAlx/mw4cPx6FDh0ocl1KpxOrVq1GzZk189NFHUgJ/5swZAMDVq1fh5OSEpKQkAC+/DUdFReH8+fNwcnJS256DgwN27NgBANJNOvHx8ejatSt++uknCCGQnZ2Nzz77DJs2bcK1a9fg6uqKJk2aYNKkSRg9erTGVt1jx45h8uTJcHFxgUwmw4ULF6BUKot8bd26dcOuXbuQlZWFrKws7Nu3T+Ny1apVw/Lly/H3339LZffu3YOpqSkaNmwIANKdzJcvX0afPn0AvOxjHR4ejo8//hhz5sxB06ZNER8fDyMjIyiVSggh0LhxY5iamkqJX/7IBXFxcUXGXtCWLVswe/ZsdO3aFTNnzkTXrl1x8+bNYq9fmXTt2hVRUVHSrwUbN27ERx99hNq1a6NNmzZSMnT58mXcuHEDACCXy5GTkyO9vwcPHpS217lzZxw8eBDPnz8H8LIf5E8//aTyHhb3GH01zn379kkjKuzYsQM1atR4o5b+27dvIzw8HGPHjlUpb9euHeLj43Ht2jUAwP79+6UvnAUVvKYcO3YMHh4eGDJkCBo3bozDhw+/9jx68uQJVq5cKbWOCSFw+/Zt6ZeOgq/5xIkT0vWtYGJamISEBDx//hzTp09Hr169cPr0aWRnZ0v9lwvGbmlpKZ03+a3CmhR1DSvsnKXSV7NmTVy+fFn6AnTs2DEAL3857dWrF168eIHhw4cjKCgIt27dQm5ursr73aVLF/z222/SZ88vv/wCHx8fAC+v7du2bQMAPHjwoNDRZ4YNG4bTp09jz549UoIaFxeH0NBQNG/eHG3btsXt27dx8eJFAMDNmzdx5swZdOjQoUSv9eTJk9Jxv3XrVvTs2ROA6jF7584dletGcT7rC+revTv27NmD7Oxs5ObmYufOnSWKUd+wxbiC+Oqrr7B9+3Z89tlnyM7ORnZ2Nuzs7LB161ZpNARHR0fMnz//tdsKCQnB/Pnz4ebmhuzsbGmYqNe1/gDATz/9hD179kAmk0GpVMLOzg5r164F8PJEDg0NxbJly5CVlQUhBJYtWybFZ2trCyMjIzg5OUnfagv6+uuvMW/ePLi5uUEIgUmTJsHW1hZz5szBwoUL4ebmhpycHHTu3Bnjx4+HsbExnJ2dMWjQIFStWhVmZmYaW+J8fX0xefJkVK9eHVWqVMFHH32EO3fuFPk6vby8cOfOHbi6uhaZlHTq1Alz586Fn58f0tLSYGhoCCsrK4SHh0s/sdeqVQu2trZo0qQJjI2NAby8Kcjf31/qrtGiRQu4urrC0NAQrVu3hrOzM3755ReEh4dj4cKF+Pe//43c3FxMmzYN7du3L/ZQYgMHDsTp06fh4uKCKlWq4J133tHb4eAGDx6Mhw8fYsiQIcjLy0OjRo0QEhICAFi+fDlmz56NrVu3omHDhtINZRYWFpg5cyYmTJgAS0tLlZb27t274++//8bw4cMBAE2bNsX8+fNRpUoVlfewOMdoQV26dMHo0aPh4+ODvLw8WFpaYs2aNcVq5SnYtcTAwACmpqb48ssv0aNHD5XlatSogeXLl8PPzw8GBgbSuZn/K1I+BwcHTJ06FcbGxhg7diy+/vpraQSaNm3aSF8gChMUFIQVK1ZgwIABMDExQW5uLjp16iTdVJWvcePGmD17NsaNGwcTExO0atVKLZZXtWjRAj169ICzszPkcjkaNmyIpk2bIiEhAQ0bNkSfPn0wYsQIhIeHY8aMGZg3bx62bduGNm3aoE2bNhq3WdQ1rLBzlkrfgAED8Oeff6Jv376wsbHBBx98ILV8BgQEYMaMGdIvjIsWLYKJiYnKsTp37lxMmDABY8eOhUwmg7m5OcLCwiCTyRAUFITZs2fD2dkZderUKXT0mho1amDjxo349ttvpfOvSpUqWLhwoTQixcqVKzF//nxkZmZCJpNh8eLFaNy4cYmGXLSxscHMmTORnJyMpk2bSsOsfvbZZ/D398fRo0fx3nvv4cMPP5TW6dmzJ5YuXVrsVmRPT0/cvn0bAwcORNWqVVG/fv3Xnl/6TCaK6kRDRG/tyZMnGDx4MDZv3iz1OSPSpufPnyM8PBxTp05FlSpVcPnyZUyaNAl//vlnofcslKW7d+9i9+7d+Pzzz2FgYIADBw5g3bp1xWo5JqqooqKipFGZytKxY8fw+PFj6UvzggULYGpq+tpRbfQVW4yJylBERASWL1+OTz/9lEkx6Qxzc3MYGxtj8ODBMDIygpGREb777jutJMXAy5v0kpKS4ObmBkNDQ1hYWGDRokVaiYWosmnWrBnWr1+P9evXQ6lUomXLlpg3b562w9JZbDEmIiLSEc+fP4eXlxf+9a9/oX79+irPXb16FYGBgXj+/Dk+/PBDfPPNNyW6WZqIXo833xEREemACxcuYPjw4YXe1Ddz5kzMnTsX+/fvhxACERER5RsgkR5gYqxlQgjpJg8iej2eM1RZRUREICgoSOOMo/fv30dmZqY05rKnpyeio6PVllMoFLh3757Kv7t37+Lx48c8Z4iKgb/BaFl2djbi4uJga2urcaQG4OXwUYXdRU3/w3oqvopcVzxnSg/rqfjKo64WLlxY6HNJSUkq49hbWVmpDN+Zb8OGDQgLC1Mpq127NkJDQzm8HOmMkk5UVJ6YGFcA+TO9UdFYT8VX2euqsr++0sJ6Kj5t15Wm1l5NN0v6+PjAw8NDpUypVCIlJaXIL5OxsbE6nazoCtZT8VXUumJiTEREpONsbGyQkpIiPU5OTtbY5UIul0Mul6uUZWVlqaxLRIVjH2MiIiIdV69ePZiamiI2NhYAsGvXLjg6Omo5KqLKh4kxERGRjpowYQIuXboE4OWspYsXL4azszMyMjL0dhZLorLErhREVGY4JitRyR0+fFj6e926ddLfLVu2RGRkpDZCItIbbDEmojLBMVmJiKiiYWJMRGWiNMZkJSIiKk/83ZKIykRpjMmqUCigUChUypRKZekFSUREVAATY6rwnioyoUjPhrG5NRIevkyi5NVMUFNupuXIqDDFHZO1qMkK4uLiitxH/t37Zc2ipg0yc9Rfj9y8KhTPX7y2DADMjGVIe6r+xeBt4yjOdvPrqbDXURqxVSSF1YNFTZtCj6mKOFYrEWnGxJgqPEV6NtbsvIS0tDRYWFgAACZ52DEx1mHFHZO1IkxWkPBQgZ+iL6mVe7u0wtZDd15bBrw8Xpu/V1+t/G3jeN12C9ZTYa+jNGKrSAqrB6/eDZkAE+kBJsZEVO4Kjsnavn37Qsdk5WQFRERUnnjzHRGVG47JSkREuowtxkRUpjgmKxERVRRsMSYiIiIiAhNjIiIiIiIATIyJiIiIiAAwMSYiIiIiAsDEmIiIiIgIABNjIiIiIiIATIzfyO7du9G/f3/0798fS5cuBQBcvXr1/7V371FNXfkewL+Rl1oIiELsqMNqbdWxvDrOrYAdqLdFKBIVZDk+BnDGoq21dJjbqkuxWqzVOsylitUrXuujSitVFGGcSEerMw7ObaHOKNZnrfgoz8ES5JmEff9wmRqTSFIDB5LvZy1Wyc4+J7+zm5/5cbL3OZg6dSqioqKwdOlSaLVaiaMkIiIiImuwMLZSS0sLVq1ahY8++ggFBQUoLS1FSUkJ3nzzTSxbtgyHDx+GEAJ5eXlSh0pEREREVmBhbCWdToeOjg60tLRAq9VCq9XC2dkZra2tCA4OBgDEx8dDpVJJGygRERERWYV3vrOSu7s7Xn/9dbz44ovo27cvnnnmGbi4uMDHx0ffx8fHB9XV1UbbqtVqqNVqgzadTtflMRMRERFR51gYW+n8+fPYt28fPv/8c3h4eOCNN97A3//+d6N+MpnMqG3Hjh3YsGGDQdugQYOwfv16lJeXP/B1y8rKHi5wO+bi7ovGxkYA0P+3Qd2Asu8uSRlWj2fuPTVmzJhujoSIiKhnYGFspRMnTiA0NBQDBw4EcGfaxNatW1FXV6fvU1tbC19fX6Ntk5OTERcXZ9Cm0+lQV1cHf39/uLm5mXzNsrIyFisPUFGphoeHBxobG+Hh4QEA8JR7wm/kMIkj67n4niIiIjLGOcZWGjVqFEpKStDc3AwhBI4ePYpnnnkGbm5u+jNwBw4cQHh4uNG2crkcQ4cONfgZPHhwdx8CEREREZnAM8ZWevbZZ/H1118jPj4eLi4uCAgIwNy5cxEZGYn09HQ0NTVh9OjRSEpKkjpUIiIiIrICC+MfYe7cuZg7d65B26hRo7B3716JIiIiIiKih8WpFEREREREYGFMRERERASAhTEREREREQAWxkRERD1CYWEhYmJiEBkZid27dxs9f/bsWUydOhWTJk3CvHnzjG4YRUQPj4Ux9Ui31K2oqFQb/NxSt0odFhFRl6iurkZWVhZyc3NRUFCAPXv24PLlywZ9Vq1ahdTUVBw8eBCPPfYYtm7dKlG0RPaLV6WgHknd1I7N+88YtM2LC8AAeV+JIiIi6jolJSUICQmBl5cXACAqKgoqlQoLFizQ9+no6EBTUxMAoKWlBZ6enkb7UavVRmeSdTpd1wVOZGdYGBMREUmspqYGPj4++se+vr44ffq0QZ/FixfjN7/5Dd59913069cPeXl5RvvZsWMHNmzYYNA2aNAgrF+/HuXl5Q+Mwdxt4skQx8ly5saqJ995lYUxERGRxIQQRm0ymUz/e2trK5YuXYodO3YgMDAQ27Ztw6JFi5CTk2OwTXJyMuLi4gzadDod6urq4O/vDzc3N5Ovz9vEW4bjZLneOlYsjImIiCSmUChQWlqqf1xTUwNfX1/944sXL8LNzQ2BgYEAgF/96ldYt26d0X7kcjnkcrlBW1tbG+rq6roociL7wsV3RNQluMKeyHJhYWE4efIk6uvr0dLSguLiYoSHh+uf9/PzQ1VVFa5cuQIAOHLkCAICAqQKl8husTAmIpvjCnsi6ygUCqSlpSEpKQlTpkxBbGwsAgMDkZKSgjNnzsDT0xOrV6/G7373OyiVSuzbtw/vvvuu1GET2R1OpSAim7PVCnsiR6JUKqFUKg3atmzZov89IiICERER3R0WkUNhYUxENmerFfa89BQREXUnFsZEZHO2WmHfGy495eLui8bGRqN2rVZr1G6qDQAa1A0o++6SzeOwZL93x8nccdgitt7E3DgAvfPSU0RkHRbGRGRztlph3xsuPVVRqYaHh4dRu7Ozs1G7qTYA8JR7wm/kMJvH0dl+7x0nc8dhi9h6E3PjALAAJnIEXHxHRDZnqxX2crkcQ4cONfgZPHhwtx0HERE5Fp4xJiKbu3eFvUajQUJCgn6FfWpqKgICAvQr7IUQGDhwIFfYExGR5FgYE1GX4Ap7IiLqbTiVgoiIiIgILIyJiIiIiACwMCYiIiIiAsDCmIiIiIgIAAtjIiIiIiIAvCoFUZe5pW6FuqndqF3+iCsGyPtKEBERERE9CAtjoi6ibmrH5v1njNrnxQWwMCYiIuqBOJWCiIiIiAgsjImIiIiIALAwJiIiIiICwMKYiIiIiAgAC+Mf5ejRo4iPj0d0dDTeeecdAEBJSQmUSiUmTJiArKwsiSMkIiIiImuxMLbS9evXsXz5cmzcuBGFhYX4+uuvcfz4cSxZsgQbN27EoUOHUF5ejuPHj0sdKhERERFZgZdrs9Jnn32GmJgYDB48GACQlZWFiooK+Pn5YdiwYQAApVIJlUqFiIgIKUMlMnstZY8BCgmiISIi6tlYGFupoqICLi4umDNnDmprazF+/Hg8+eST8PHx0ffx9fVFdXW10bZqtRpqtdqgTafTdXnM5LjMXUt5+vM/lSAaIiKins2hC+OPPvoIcXFxcHd3t3gbnU6H0tJSfPTRR+jfvz/mz5+Pfv36GfWTyWRGbTt27MCGDRsM2gYNGoT169ejvLz8ga9bVlZmcYz2wMXdF42NjQZtDeoGlH136YF97/7XXN/uZOoYgO6NzVwMgPn31JgxY7oyJCIioh7LoQvjixcvIioqCs899xymT5+OgICATrcZNGgQQkND4e3tDQB4/vnnoVKp4OTkpO9TU1MDX19fo22Tk5MRFxdn0KbT6VBXVwd/f3+4ubmZfM2ysjKHK1YqKtXw8PAwaPOUe8Jv5DCzfRsbG/XbmOvbnUwdA9C9sZmLAWABTEREdD+HXny3cuVKHD58GP7+/nj77bcxdepU7N27F21tbWa3GT9+PE6cOAG1Wg2dToe//e1viI6OxrfffouKigrodDoUFRUhPDzcaFu5XI6hQ4ca/Nydq0xERERE0nLowhgA3N3dER0djdjYWHz//ffIzc1FdHQ0VCqVyf5BQUF46aWXMHPmTMTExOAnP/kJZsyYgTVr1uC1115DTEwMHn/8cURHR3fzkRARERHRw3DoqRQlJSXIy8vDyZMnERUVhQ8++ACjRo3CtWvXMHPmTLPFbUJCAhISEgzaQkNDcfDgwe4Im4iIiIi6gEOfMc7IyMDPf/5z/OUvf0FGRgZGjRoFAPjpT3+KadOmSRwdERE5ksLCQsTExCAyMhK7d+82ev7KlStITEzEpEmTMGfOHDQ0NEgQJZF9c+jC+ODBg/Dy8oKHhwdqa2uxfft2dHR0AABSU1Mljo6IiBxFdXU1srKykJubi4KCAuzZsweXL1/WPy+EwCuvvIKUlBQcPHgQP/vZz5CTkyNhxET2yaGnUqxcuRJNTU2YNGkS+vTpg7KyMty4cQPp6elSh0ZERA6kpKQEISEh8PLyAgBERUVBpVJhwYIFAICzZ8+if//++oXdL7/8stF18QFeL5/oYTl0YXzq1CkUFRUBAAYOHIh169Zh8uTJEkdFRESOpqamxuhGUadPn9Y/vnbtGgYNGoRFixbh66+/xogRI7Bs2TKj/fB6+V2P42S53ni9fIcujDUaDdrb2+Hq6goA0Gq1EkdERESOSAhh1HbvjaK0Wi2++OIL7Nq1CwEBAXj//fexZs0arFmzxmAbXi+/a3GcLNdbx8qhC+PnnnsOc+bMweTJkyGTyVBUVISIiAipwyKyC4WFhdi0aRM0Gg1mz56NWbNmGTx/5coVLF++HA0NDfDx8cF///d/w9PTU6JoiaSlUChQWlqqf3z/jaJ8fHzg5+envxFVbGysybUwcrkccrncoK2trQ11dXVdFDmRfXHoxXcLFy5EZGQkjhw5gmPHjiEyMhK///3vpQ6LqNfjQiIi64SFheHkyZOor69HS0sLiouLDW4U9fTTT6O+vh7nz58HABw9ehRPPfWUVOES2S2HPmPs5OSEpKQkJCUlSR0KkV3hQiIi6ygUCqSlpSEpKQkajQYJCQkIDAxESkoKUlNTERAQgA8++ADp6eloaWnB4MGDsXbtWqnDJrI7Dl0YHzp0CJmZmWhoaDCY3/XVV19JGBXZuz59gIpKw2JP/ogrBsj7ShSR7TnSQiIXd180NjYatWu1WqN2U20A0KBuQNl3l2wehyX7vTtO5o7DFrH1JubGAej6hURKpRJKpdKgbcuWLfrfg4KCsHfvXpu8FhGZ5tCF8bp167B48WKMHj3aYJEDUVdqatFi56FzBm3z4gLsqjB2pIVEFZVqeHh4GLU7OzsbtZtqAwBPuSf8Rg6zeRyd7ffecTJ3HLaIrTcxNw5Az15JT0S24dCFsVwux4QJE6QOg3qoW+pWqJvaDdrs7cxuV+FCIiIi6o0cevFdUFAQjh8/LnUY1EOpm9qxef8Zg5/7C2UyjQuJiIioN3LoM8bHjx/Hrl274OLiAhcXFwghIJPJOMeY6CFxIREREfVGDl0Yb9++XeoQiOwWFxIREVFv49BTKYYMGYIzZ84gLy8P3t7eOHXqFIYMGSJ1WEREREQkAYcujHNycvDxxx9DpVKhtbUVGzZswAcffCB1WEREREQkAYcujP/0pz9hy5Yt6NevHwYMGIC8vDwUFRVJHRYRERERScCh5xg7OzvD1dVV/1gul8PZ2aGHhDph6uYcAC/jRkREZA8cugp89NFHcezYMchkMrS3t2Pr1q2cY0wPZOrmHID93aCDiIjIETl0Ybxs2TIsXLgQFy5cQHBwMIKCgpCZmSl1WEREREQkAYcujBUKBXbs2IGWlhbodDq4u7tLHRIRERERScShC+Nt27aZbP/Nb37TzZEQERERkdQcujC+ePGi/vf29naUlZVh7NixEkZERERERFJx6MJ49erVBo/r6+uxcOFCiaIhIiIiIik59HWM7+ft7Y2bN29KHQYRERERScChzxjfO8dYCIHy8nIMHDhQwoiIiIiISCoOXRjfO8cYuHNdY06lICIiInJMDl0Y3z/HmIiIiIgcl0MXxomJiZDJZGaf37lzZzdGQ0RERERScujC2N/fH9988w2mTZsGFxcXFBQUQKvVYuLEiRZt/9577+HWrVtYs2YNzp07h/T0dNy+fRu/+MUv8Pbbb8PZ2aGHl4iIiKhXceirUnz11VfYtGkTXnjhBURERCAzMxPfffcdoqKiEBUV9cBtT548if379+sfv/nmm1i2bBkOHz4MIQTy8vK6OnwiIiIisiGHLozr6+vR3t6uf9zU1ITW1tZOt/v++++RlZWFl19+GQBw8+ZNtLa2Ijg4GAAQHx8PlUrVJTETERERUddw6O/6Y2NjMW3aNERGRkIIgT//+c9ISkrqdLu33noLaWlpqKysBADU1NTAx8dH/7yPjw+qq6uNtlOr1VCr1QZtOp3uIY+CiIiIiGzBoQvj119/HaNHj8Y//vEPuLm5ISMjA88888wDt/n000/x6KOPIjQ0FPn5+QDuXAP5fqYW9e3YsQMbNmwwaBs0aBDWr1+P8vLyB75uWVlZZ4djV1zcfdHY2GjQ1qBuQNl3lx7Y9+5/zfV92Bi0Wq1Rm7nXM7W9uX3YIl5TzMUAmH9PjRkzxuZxEBER9QYOXRgDgEKhwJNPPon4+HicPXu20/6HDh1CbW0tJk+ejIaGBjQ3N0Mmk6Gurk7fp7a2Fr6+vkbbJicnIy4uzqBNp9Ohrq4O/v7+cHNzM/maZWVlDlesVFSq4eHhYdDmKfeE38hhZvs2Njbqtxng5YkOuadRX/kjrhgg7/ujY3B2djZqMxebqe3N7cPcsT0sczEALICJiIju59CF8b59+/Dhhx+ira0NkZGRmD9/PtLS0jBt2jSz29x7t7z8/Hx88cUXWL16NWJjY/UF7IEDBxAeHm60rVwuh1wuN2hra2szKKrJNppatNh56JxR+7y4AIsLYyKi7lRYWIhNmzZBo9Fg9uzZmDVrlsl+x44dQ0ZGBo4ePdrNERLZP4defLdr1y7s2bMH7u7uGDhwIPLz87Fjx44fta/MzEysXr0aL774IlpaWiyaq0xERAQA1dXVyMrKQm5uLgoKCrBnzx5cvnzZqF9dXR3ee+89CSIkcgwOfca4T58+cHd31z9+9NFH4eTkZPH28fHxiI+PBwCMGjUKe/futXmMRERk/0pKShASEgIvLy8AQFRUFFQqFRYsWGDQLz09HQsWLMAf//hHk/vhIm+ih+PQhbGXlxfOnTunXyh38OBBeHoaz0slksotdSvUTe0GbdbMk5YSvxYmstz9Vzfy9fXF6dOnDfrs3LkTo0ePRlBQkNn9cJF31+M4Wa43LvJ26MJ4yZIleP3113Ht2jU8++yzcHNzw8aNG6UOi0hP3dSOzfvPGLT1hnnSd78Wzs/Ph6urK6ZPn46xY8fiiSeeMOjHr4WJ7ujs6kYXL15EcXExtm/fjqqqKrP74SLvrsVxslxvHSuHLoxbW1tRUFCAq1evQqfT4bHHHoOLi4vUYRH1erb6WpjIUSgUCpSWluof19TUGFzdSKVSoba2FlOnToVGo0FNTQ1mzpyJ3Nxcg/1wkTfRw3HoxXdvvPEGnJycMHz4cIwYMYJFMZGNmPpa+P6b3ljytbBarcaNGzcMfh50toyotwoLC8PJkydRX1+PlpYWFBcXG1zdKDU1FYcPH0ZBQQFycnLg6+trVBQT0cNz6DPGI0eORGFhIcaMGYP+/fvr2++e5SKiH8dWXwv3hvmS1tzIxZobxNgiDkv2e3eczB1HV918pqeS6qY4CoUCaWlpSEpKgkajQUJCAgIDA5GSkoLU1FQEBAQ89GsQUeccujA+cuQIVCqVQZtMJsO5c8bXvyUiy9nqa+HeMF/Smhu5WHODGFvE0dl+7x0nc8fRVTef6amkvCmOUqmEUqk0aNuyZYtRv6FDh3KxKlEXcejC+MyZM513IiKrhYWFITs7G/X19ejXrx+Ki4uxcuVK/fOpqalITU0FANy4cQNJSUkmvxbmfEkiIupODjnHeNmyZfrf6+vrJYyEyD7d+7XwlClTEBsbq/9amH+QEhFRT+WQZ4zvnZs4Z84c7N+/X8JoiOwTvxYmIqLexiHPGN+7MMjUIiEiIiIicjwOWRjf696V8kRERETkuBxyKkVHRwcaGhoghIBOp9P/fhcv10ZERETkeByyML548SJCQkL0xfDYsWP1z/FybURERESOySEL4/Pnz0sdAhERERH1MA4/x5iIiIiICHDQM8ZE97qlboW6qd2oXaPtkCAaIiIikgoLY3J46qZ2bN5vfNOJpJifSRANERERSYWFMZEN9OkDVFSqDdp4xpmIiKh3YWFMZANNLVrsPGR4NROecSYiIupduPiOiIiIiAgsjImIiIiIALAwJiIiIiICwMKYiIiIiAgAC2MiIiIiIgAsjImIiIiIALAwJiIiIiICwMKYiIiIiAgAC2MiIiIiIgAsjImIiIiIALAw/lE2bNiAiRMnYuLEiVi7di0AoKSkBEqlEhMmTEBWVpbEERIRERGRtVgYW6mkpAQnTpzA/v37ceDAAZw9exZFRUVYsmQJNm7ciEOHDqG8vBzHjx+XOlQiIiIisoKz1AH0Nj4+Pli8eDFcXV0BAMOHD8fVq1fh5+eHYcOGAQCUSiVUKhUiIiIMtlWr1VCr1QZtOp2uewInIiIiogdiYWylJ598Uv/71atXcejQISQmJsLHx0ff7uvri+rqaqNtd+zYgQ0bNhi0DRo0COvXr0d5efkDX7esrOwhI7eMxwAFWjXCqL2viwyNt4yPqau4uPuisbHRoK1B3YCy7y49sO/d/2q1WqPtze3D1GuZ24e5/T5sX0uOrbO+ppg7NsD8e2rMmDEW7ZuIbKuwsBCbNm2CRqPB7NmzMWvWLIPn//KXvyA7OxtCCAwdOhSrV6+Gp6enRNES2ScWxj/SpUuXMG/ePCxatAjOzs749ttvDZ6XyWRG2yQnJyMuLs6gTafToa6uDv7+/nBzczP5WmVlZd1WrFRUqrFddcaofV5cAEY8PrRbYrgbh4eHh0Gbp9wTfiOHme3b2Nio38bZ2dloe3P7MPVa5vZhbr8P27ezY7Okrynmjg3o+gKYH/JElquurkZWVhby8/Ph6uqK6dOnY+zYsXjiiScAALdv38aKFSuwb98+KBQKrFu3DtnZ2UhPT5c4ciL7wjnGP0JZWRlmz56N//qv/0JcXBwUCgXq6ur0z9fU1MDX19doO7lcjqFDhxr8DB48uDtDJ+oWdz/kc3NzUVBQgD179uDy5cv65+9+yOfk5ODgwYMYOXIksrOzJYyYSFolJSUICQmBl5cX+vfvj6ioKKhUKv3zGo0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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T21:19:05.802369\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -40934,1754 +2690,7 @@ { "data": { "image/png": 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" ] @@ -43455,7 +3464,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.13" }, "vscode": { "interpreter": { diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index ea8f1d75..edc3a568 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -23,9 +23,9 @@ from sec_certs.dataset.cve import CVEDataset from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset, logger from sec_certs.dataset.protection_profile import ProtectionProfileDataset -from sec_certs.model.dependency_finder import DependencyFinder -from sec_certs.model.dependency_vulnerability_finder import DependencyVulnerabilityFinder +from sec_certs.model.reference_finder import ReferenceFinder from sec_certs.model.sar_transformer import SARTransformer +from sec_certs.model.transitive_vulnerability_finder import TransitiveVulnerabilityFinder from sec_certs.sample.cc_certificate_id import CertificateId from sec_certs.sample.cc_maintenance_update import CommonCriteriaMaintenanceUpdate from sec_certs.sample.common_criteria import CommonCriteriaCert @@ -664,14 +664,14 @@ def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: def _compute_transitive_vulnerabilities(self): logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") - cve_dependency_finder = DependencyVulnerabilityFinder() - cve_dependency_finder.fit(self.certs) + transitive_cve_finder = TransitiveVulnerabilityFinder() + transitive_cve_finder.fit(self.certs) for dgst in self.certs: - dependency_cve = cve_dependency_finder.predict_single_cert(dgst) + transitive_cve = transitive_cve_finder.predict_single_cert(dgst) - self.certs[dgst].heuristics.direct_dependency_cves = dependency_cve.direct_dependency_cves - self.certs[dgst].heuristics.indirect_dependency_cves = dependency_cve.indirect_dependency_cves + self.certs[dgst].heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves + self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves def _compute_heuristics(self, fresh: bool = True) -> None: logger.info("Computing various heuristics on CC certificates.") @@ -705,11 +705,11 @@ def func(cert): return func logger.info("Compute references between certificates.") - for dep_source in ("report", "st"): - kw_source = f"{dep_source}_keywords" - dep_attr = f"{dep_source}_references" + for ref_source in ("report", "st"): + kw_source = f"{ref_source}_keywords" + dep_attr = f"{ref_source}_references" - finder = DependencyFinder() + finder = ReferenceFinder() finder.fit(self.certs, lambda cert: cert.heuristics.cert_id, ref_lookup(kw_source)) # type: ignore for dgst in self.certs: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index c73f2775..1f4bab81 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -15,7 +15,7 @@ from sec_certs.dataset.cve import CVEDataset from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset -from sec_certs.model.dependency_finder import DependencyFinder +from sec_certs.model.reference_finder import ReferenceFinder from sec_certs.sample.fips import FIPSCertificate from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers @@ -366,13 +366,13 @@ def web_lookup(cert): filter(lambda x: x, map(lambda cid: "".join(filter(str.isdigit, cid)), cert.web_data.mentioned_certs)) ) - finder = DependencyFinder() + finder = ReferenceFinder() finder.fit(self.certs, lambda cert: str(cert.cert_id), pdf_lookup) # type: ignore for dgst in self.certs: setattr(self.certs[dgst].heuristics, "st_references", finder.predict_single_cert(dgst, keep_unknowns=False)) - finder = DependencyFinder() + finder = ReferenceFinder() finder.fit(self.certs, lambda cert: str(cert.cert_id), web_lookup) # type: ignore for dgst in self.certs: diff --git a/sec_certs/model/__init__.py b/sec_certs/model/__init__.py index d53c223a..fe1024f9 100644 --- a/sec_certs/model/__init__.py +++ b/sec_certs/model/__init__.py @@ -4,8 +4,8 @@ """ from sec_certs.model.cpe_matching import CPEClassifier -from sec_certs.model.dependency_finder import DependencyFinder -from sec_certs.model.dependency_vulnerability_finder import DependencyVulnerabilityFinder +from sec_certs.model.reference_finder import ReferenceFinder from sec_certs.model.sar_transformer import SARTransformer +from sec_certs.model.transitive_vulnerability_finder import TransitiveVulnerabilityFinder -__all__ = ["CPEClassifier", "DependencyFinder", "DependencyVulnerabilityFinder", "SARTransformer"] +__all__ = ["CPEClassifier", "ReferenceFinder", "TransitiveVulnerabilityFinder", "SARTransformer"] diff --git a/sec_certs/model/dependency_finder.py b/sec_certs/model/reference_finder.py similarity index 91% rename from sec_certs/model/dependency_finder.py rename to sec_certs/model/reference_finder.py index d717738e..8399614a 100644 --- a/sec_certs/model/dependency_finder.py +++ b/sec_certs/model/reference_finder.py @@ -5,7 +5,7 @@ Certificates = Dict[str, Certificate] ReferencedByDirect = Dict[str, Set[str]] ReferencedByIndirect = Dict[str, Set[str]] -Dependencies = Dict[str, Dict[str, Optional[Set[str]]]] +ReferencesType = Dict[str, Dict[str, Optional[Set[str]]]] IDMapping = Dict[str, List[str]] UnknownReferences = Dict[str, Set[str]] IDLookupFunc = Callable[[Certificate], str] @@ -13,7 +13,7 @@ # TODO: All of this can and should be rewritten on top of networkx or some other graph library. -class DependencyFinder: +class ReferenceFinder: """ The class assigns references of other certificate instances for each instance. Adheres to sklearn BaseEstimator interface. @@ -21,7 +21,7 @@ class DependencyFinder: """ def __init__(self): - self.dependencies: Dependencies = {} + self.references: ReferencesType = {} self.id_mapping: IDMapping = {} self._fitted: bool = False @@ -100,7 +100,7 @@ def _build_referenced_by( referenced_by_indirect = self._compute_indirect_references(referenced_by) return referenced_by, referenced_by_indirect - def _get_reverse_dependencies( + def _get_reverse_references( self, cert_id: str, references: Union[ReferencedByDirect, ReferencedByIndirect] ) -> Optional[Set[str]]: result = set() @@ -116,11 +116,11 @@ def _build_referencing( ) -> None: for cert_id, cert_digests in self.id_mapping.items(): cert_dgst = cert_digests[0] - self.dependencies[cert_dgst] = { + self.references[cert_dgst] = { "directly_referenced_by": referenced_by_direct.get(cert_id, None), "indirectly_referenced_by": referenced_by_indirect.get(cert_id, None), - "directly_referencing": self._get_reverse_dependencies(cert_id, referenced_by_direct), - "indirectly_referencing": self._get_reverse_dependencies(cert_id, referenced_by_indirect), + "directly_referencing": self._get_reverse_references(cert_id, referenced_by_direct), + "indirectly_referencing": self._get_reverse_references(cert_id, referenced_by_indirect), } def fit(self, certificates: Certificates, id_func: IDLookupFunc, ref_lookup_func: ReferenceLookupFunc) -> None: @@ -139,7 +139,7 @@ def fit(self, certificates: Certificates, id_func: IDLookupFunc, ref_lookup_func # Build the referenced_by first referenced_by_direct, referenced_by_indirect = self._build_referenced_by(certificates, ref_lookup_func) - # Build the referencing second (this actually writes into self.dependencies). + # Build the referencing second (this actually writes into self.references). self._build_referencing(referenced_by_direct, referenced_by_indirect) self._fitted = True @@ -153,7 +153,7 @@ def unknown_references(self) -> UnknownReferences: result = {} for cert_id, digests in self.id_mapping.items(): cert_digest = digests[0] - cert_references = self.dependencies[cert_digest] + cert_references = self.references[cert_digest] direct_refs = cert_references["directly_referencing"] if not direct_refs: continue @@ -192,14 +192,14 @@ def wrap(res): res = set(filter(lambda cert_id: cert_id in self.id_mapping, res)) return set(res) if res else None - if dgst not in self.dependencies: + if dgst not in self.references: return References() return References( - wrap(self.dependencies[dgst].get("directly_referenced_by", None)), - wrap(self.dependencies[dgst].get("indirectly_referenced_by", None)), - wrap(self.dependencies[dgst].get("directly_referencing", None)), - wrap(self.dependencies[dgst].get("indirectly_referencing", None)), + wrap(self.references[dgst].get("directly_referenced_by", None)), + wrap(self.references[dgst].get("indirectly_referenced_by", None)), + wrap(self.references[dgst].get("directly_referencing", None)), + wrap(self.references[dgst].get("indirectly_referencing", None)), ) def predict(self, dgst_list: List[str], keep_unknowns: bool = True) -> Dict[str, References]: diff --git a/sec_certs/model/dependency_vulnerability_finder.py b/sec_certs/model/transitive_vulnerability_finder.py similarity index 68% rename from sec_certs/model/dependency_vulnerability_finder.py rename to sec_certs/model/transitive_vulnerability_finder.py index dd25774d..adcf6173 100644 --- a/sec_certs/model/dependency_vulnerability_finder.py +++ b/sec_certs/model/transitive_vulnerability_finder.py @@ -7,24 +7,24 @@ from sec_certs.serialization.json import ComplexSerializableType -class DependencyType(Enum): +class ReferenceType(Enum): DIRECT = "direct" INDIRECT = "indirect" @dataclass -class DependencyCVE(ComplexSerializableType): - direct_dependency_cves: Optional[Set[str]] = field(default=None) - indirect_dependency_cves: Optional[Set[str]] = field(default=None) +class TransitiveCVE(ComplexSerializableType): + direct_transitive_cves: Optional[Set[str]] = field(default=None) + indirect_transitive_cves: Optional[Set[str]] = field(default=None) Certificates = Dict[str, Certificate] Vulnerabilities = Dict[str, Dict[str, Optional[Set[str]]]] -class DependencyVulnerabilityFinder: +class TransitiveVulnerabilityFinder: """ - The class assigns vulnerabilities to each certificate instance caused by dependencies among certificate instances. + The class assigns vulnerabilities to each certificate instance caused by references among certificate instances. Adheres to sklearn BaseEstimator interface. """ @@ -49,13 +49,13 @@ def _get_dataset_cert_ids_occurrences(self) -> Dict[str, int]: return cert_id_occurrences - def _get_cert_dependency_cves(self, dgst: str, dependency_type: DependencyType) -> Optional[Set[str]]: - dependency_type_dict = { - DependencyType.DIRECT: self.certificates[dgst].heuristics.report_references.directly_referenced_by, - DependencyType.INDIRECT: self.certificates[dgst].heuristics.report_references.indirectly_referenced_by, + def _get_cert_transitive_cves(self, dgst: str, reference_type: ReferenceType) -> Optional[Set[str]]: + reference_type_dict = { + ReferenceType.DIRECT: self.certificates[dgst].heuristics.report_references.directly_referenced_by, + ReferenceType.INDIRECT: self.certificates[dgst].heuristics.report_references.indirectly_referenced_by, } - references = dependency_type_dict[dependency_type] + references = reference_type_dict[reference_type] if not references: return None @@ -83,7 +83,7 @@ def _get_cert_dependency_cves(self, dgst: str, dependency_type: DependencyType) def fit(self, certificates: Certificates) -> Vulnerabilities: """ - Method assigns each certificate vulnerabilities caused by dependencies among certificates + Method assigns each certificate vulnerabilities caused by references among certificates :param Certificates certificates: Dictionary of certificates with digests :return Vulnerabilities: Dictionary of vulnerabilities of certificate instances @@ -104,11 +104,11 @@ def fit(self, certificates: Certificates) -> Vulnerabilities: continue self.vulnerabilities[dgst] = {} - self.vulnerabilities[dgst][DependencyType.DIRECT.value] = self._get_cert_dependency_cves( - dgst, DependencyType.DIRECT + self.vulnerabilities[dgst][ReferenceType.DIRECT.value] = self._get_cert_transitive_cves( + dgst, ReferenceType.DIRECT ) - self.vulnerabilities[dgst][DependencyType.INDIRECT.value] = self._get_cert_dependency_cves( - dgst, DependencyType.INDIRECT + self.vulnerabilities[dgst][ReferenceType.INDIRECT.value] = self._get_cert_transitive_cves( + dgst, ReferenceType.INDIRECT ) if thrown_away_cert_counter > 0: @@ -116,27 +116,27 @@ def fit(self, certificates: Certificates) -> Vulnerabilities: return self.vulnerabilities - def predict_single_cert(self, dgst: str) -> DependencyCVE: + def predict_single_cert(self, dgst: str) -> TransitiveCVE: """ Method returns vulnerabilities for certificate digest :param str dgst: Digest of certificate - :return DependencyCVE: DependencyCVE object of certificate + :return TransitiveCVE: TransitiveCVE object of certificate """ if not self.vulnerabilities.get(dgst): - return DependencyCVE(direct_dependency_cves=None, indirect_dependency_cves=None) + return TransitiveCVE(direct_transitive_cves=None, indirect_transitive_cves=None) - return DependencyCVE( - self.vulnerabilities[dgst][DependencyType.DIRECT.value], - self.vulnerabilities[dgst][DependencyType.INDIRECT.value], + return TransitiveCVE( + self.vulnerabilities[dgst][ReferenceType.DIRECT.value], + self.vulnerabilities[dgst][ReferenceType.INDIRECT.value], ) - def predict(self, dgst_list: List[str]) -> Dict[str, DependencyCVE]: + def predict(self, dgst_list: List[str]) -> Dict[str, TransitiveCVE]: """ Method returns vulnerabilities for a list of certificate digests :param List[str] dgst_list: list of certificate digests - :return Dict[str, DependencyCVE]: Dictionary of DependencyCVE objects for specified certificate digests + :return Dict[str, TransitiveCVE]: Dictionary of TransitiveCVE objects for specified certificate digests """ cert_vulnerabilities = {} diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py index 79488f03..056c6c89 100644 --- a/sec_certs/sample/common_criteria.py +++ b/sec_certs/sample/common_criteria.py @@ -40,7 +40,7 @@ } -class DependencyType(Enum): +class ReferenceType(Enum): DIRECT = "direct" INDIRECT = "indirect" @@ -396,8 +396,8 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): st_references: References = field(default_factory=References) report_references: References = field(default_factory=References) extracted_sars: Optional[Set[SAR]] = field(default=None) - direct_dependency_cves: Optional[Set[str]] = field(default=None) - indirect_dependency_cves: Optional[Set[str]] = field(default=None) + direct_transitive_cves: Optional[Set[str]] = field(default=None) + indirect_transitive_cves: Optional[Set[str]] = field(default=None) @property def serialized_attributes(self) -> List[str]: diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 0308084d..399859f5 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -94,13 +94,13 @@ def cc_dset(data_dir: Path, cve_dset: CVEDataset, tmp_path_factory) -> CCDataset @pytest.fixture -def dependency_dataset(data_dir) -> CCDataset: - return CCDataset.from_json(data_dir / "dependency_dataset.json") +def reference_dataset(data_dir) -> CCDataset: + return CCDataset.from_json(data_dir / "reference_dataset.json") @pytest.fixture -def dependency_vulnerability_dataset(data_dir) -> CCDataset: - return CCDataset.from_json(data_dir / "dependency_vulnerability_dataset.json") +def transitive_vulnerability_dataset(data_dir) -> CCDataset: + return CCDataset.from_json(data_dir / "transitive_vulnerability_dataset.json") @pytest.fixture @@ -193,7 +193,7 @@ def test_protection_profile_matching(cc_dset: CCDataset, random_certificate: Com assert random_certificate.protection_profiles == {expected_pp} -def test_single_record_dependency_heuristics(random_certificate: CommonCriteriaCert): +def test_single_record_references_heuristics(random_certificate: CommonCriteriaCert): # Single record in daset is not affecting nor affected by other records assert not random_certificate.heuristics.report_references.directly_referenced_by assert not random_certificate.heuristics.report_references.indirectly_referenced_by @@ -201,9 +201,9 @@ def test_single_record_dependency_heuristics(random_certificate: CommonCriteriaC assert not random_certificate.heuristics.report_references.indirectly_referencing -def test_dependency_dataset(dependency_dataset: CCDataset): - dependency_dataset._compute_references() - test_cert = dependency_dataset["692e91451741ef49"] +def test_reference_dataset(reference_dataset: CCDataset): + reference_dataset._compute_references() + test_cert = reference_dataset["692e91451741ef49"] assert test_cert.heuristics.report_references.directly_referenced_by == {"BSI-DSZ-CC-0370-2006"} assert test_cert.heuristics.report_references.indirectly_referenced_by == { @@ -214,14 +214,14 @@ def test_dependency_dataset(dependency_dataset: CCDataset): assert not test_cert.heuristics.report_references.indirectly_referencing -def test_direct_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): - dependency_vulnerability_dataset._compute_transitive_vulnerabilities() - assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.direct_dependency_cves == {"CVE-2013-5385"} +def test_direct_transitive_vulnerability_dataset(transitive_vulnerability_dataset: CCDataset): + transitive_vulnerability_dataset._compute_transitive_vulnerabilities() + assert transitive_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.direct_transitive_cves == {"CVE-2013-5385"} -def test_indirect_dependency_vulnerability_dataset(dependency_vulnerability_dataset: CCDataset): - dependency_vulnerability_dataset._compute_transitive_vulnerabilities() - assert dependency_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.indirect_dependency_cves == {"CVE-2013-5385"} +def test_indirect_transitive_vulnerability_dataset(transitive_vulnerability_dataset: CCDataset): + transitive_vulnerability_dataset._compute_transitive_vulnerabilities() + assert transitive_vulnerability_dataset["d0705c9e6fbaeba3"].heuristics.indirect_transitive_cves == {"CVE-2013-5385"} def test_sar_object(): diff --git a/tests/data/cc/analysis/cc_full_dataset.json b/tests/data/cc/analysis/cc_full_dataset.json index 3d63bf31..6b03af43 100644 --- a/tests/data/cc/analysis/cc_full_dataset.json +++ b/tests/data/cc/analysis/cc_full_dataset.json @@ -721,8 +721,8 @@ } ] }, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null } } ] diff --git a/tests/data/cc/analysis/dependency_dataset.json b/tests/data/cc/analysis/reference_dataset.json similarity index 99% rename from tests/data/cc/analysis/dependency_dataset.json rename to tests/data/cc/analysis/reference_dataset.json index a1dc28f7..b5465d87 100644 --- a/tests/data/cc/analysis/dependency_dataset.json +++ b/tests/data/cc/analysis/reference_dataset.json @@ -10,7 +10,7 @@ "timestamp": "2021-11-14 13:18:53.333058", "sha256_digest": "not implemented", "name": "test dataset", - "description": "test dataset for testing dependencies", + "description": "test dataset for testing references", "n_certs": 3, "certs": [ { @@ -568,8 +568,8 @@ } ] }, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null } }, { @@ -1205,8 +1205,8 @@ } ] }, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null } }, { @@ -1836,8 +1836,8 @@ } ] }, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null } } ] diff --git a/tests/data/cc/analysis/dependency_vulnerability_dataset.json b/tests/data/cc/analysis/transitive_vulnerability_dataset.json similarity index 99% rename from tests/data/cc/analysis/dependency_vulnerability_dataset.json rename to tests/data/cc/analysis/transitive_vulnerability_dataset.json index 7c2e82f9..a2f89a6c 100644 --- a/tests/data/cc/analysis/dependency_vulnerability_dataset.json +++ b/tests/data/cc/analysis/transitive_vulnerability_dataset.json @@ -10,7 +10,7 @@ "timestamp": "2022-04-09 14:40:30", "sha256_digest": "not implemented", "name": "test dataset", - "description": "test dataset for testing dependency vulnerabilities", + "description": "test dataset for testing transitive vulnerabilities", "n_certs": 3, "certs": [ { @@ -1314,13 +1314,13 @@ } ] }, - "direct_dependency_cves": { + "direct_transitive_cves": { "_type": "Set", "elements": [ "CVE-2013-5385" ] }, - "indirect_dependency_cves": { + "indirect_transitive_cves": { "_type": "Set", "elements": [ "CVE-2013-5385" @@ -2254,13 +2254,13 @@ } ] }, - "direct_dependency_cves": { + "direct_transitive_cves": { "_type": "Set", "elements": [ "CVE-2013-5385" ] }, - "indirect_dependency_cves": { + "indirect_transitive_cves": { "_type": "Set", "elements": [ "CVE-2013-5385" @@ -3606,8 +3606,8 @@ } ] }, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null } } ] diff --git a/tests/data/cc/certificate/fictional_cert.json b/tests/data/cc/certificate/fictional_cert.json index b1b225cb..da82d70c 100644 --- a/tests/data/cc/certificate/fictional_cert.json +++ b/tests/data/cc/certificate/fictional_cert.json @@ -74,8 +74,8 @@ "cert_lab": null, "cert_id": null, "extracted_sars": null, - "direct_dependency_cves": null, - "indirect_dependency_cves": null, + "direct_transitive_cves": null, + "indirect_transitive_cves": null, "report_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, diff --git a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json index 3532ff03..cab918a9 100644 --- a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json +++ b/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json @@ -69,8 +69,8 @@ "indirectly_referencing": null }, "extracted_sars": null, - "direct_dependency_cves": null, - "indirect_dependency_cves": null + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "related_cert_digest": "8a5e6bcda602920c", "maintenance_date": "2019-08-26" diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json index 517e1f9e..110d578a 100644 --- a/tests/data/cc/dataset/toy_dataset.json +++ b/tests/data/cc/dataset/toy_dataset.json @@ -78,8 +78,8 @@ "cert_lab": null, "cert_id": null, "extracted_sars": null, - "direct_dependency_cves": null, - "indirect_dependency_cves": null, + "direct_transitive_cves": null, + "indirect_transitive_cves": null, "report_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, @@ -165,8 +165,8 @@ "cert_lab": null, "cert_id": null, "extracted_sars": null, - "direct_dependency_cves": null, - "indirect_dependency_cves": null, + "direct_transitive_cves": null, + "indirect_transitive_cves": null, "report_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, @@ -260,8 +260,8 @@ "cert_lab": null, "cert_id": null, "extracted_sars": null, - "direct_dependency_cves": null, - "indirect_dependency_cves": null, + "direct_transitive_cves": null, + "indirect_transitive_cves": null, "report_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, From c6d826cee82cfc43f0a5368d0aebd5f0aeb621f2 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 30 Nov 2022 10:32:12 +0100 Subject: [PATCH 41/81] fips refactor reference computation --- sec_certs/config/settings-schema.json | 4 +- sec_certs/config/settings.yaml | 4 +- sec_certs/dataset/common_criteria.py | 10 +- sec_certs/dataset/dataset.py | 10 +- sec_certs/dataset/fips.py | 118 ++-- sec_certs/model/reference_finder.py | 9 +- sec_certs/sample/fips.py | 153 +++-- sec_certs/utils/helpers.py | 12 + tests/data/fips/certificate/crt.json | 76 --- .../data/fips/certificate/fictional_cert.json | 18 +- tests/data/fips/dataset/toy_dataset.json | 534 +++++++++++------- tests/fips/test_fips_dataset.py | 403 ++++--------- 12 files changed, 587 insertions(+), 764 deletions(-) delete mode 100644 tests/data/fips/certificate/crt.json diff --git a/sec_certs/config/settings-schema.json b/sec_certs/config/settings-schema.json index 7e40a0ac..a31f6b3f 100644 --- a/sec_certs/config/settings-schema.json +++ b/sec_certs/config/settings-schema.json @@ -71,7 +71,7 @@ "log_filepath": { "$ref": "#/definitions/settings_string_entry" }, - "smallest_certificate_id_to_connect": { + "always_false_positive_fips_cert_id_threshold": { "$ref": "#/definitions/settings_number_entry" }, "year_difference_between_validations": { @@ -166,7 +166,7 @@ }, "required": [ "log_filepath", - "smallest_certificate_id_to_connect", + "always_false_positive_fips_cert_id_threshold", "year_difference_between_validations", "n_threads", "cpe_matching_threshold", diff --git a/sec_certs/config/settings.yaml b/sec_certs/config/settings.yaml index c823181c..8d645758 100644 --- a/sec_certs/config/settings.yaml +++ b/sec_certs/config/settings.yaml @@ -2,10 +2,10 @@ log_filepath: description: Path to the file, relative to working directory, where the log will be stored value: ./cert_processing_log.txt -smallest_certificate_id_to_connect: +always_false_positive_fips_cert_id_threshold: description: During validation we don't connect certificates with number lower than - _this_ to connections + _this_ to connections due to these numbers being typically false positives value: 40 year_difference_between_validations: description: diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index edc3a568..920c6d32 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -715,10 +715,10 @@ def func(cert): for dgst in self.certs: setattr(self.certs[dgst].heuristics, dep_attr, finder.predict_single_cert(dgst, keep_unknowns=False)) - def process_auxillary_datasets(self) -> None: - self.auxillary_datasets.pp_dset = self.process_protection_profiles() - self.auxillary_datasets.mu_dset = self.process_maintenance_updates() - super().process_auxillary_datasets() + def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + self.auxillary_datasets.pp_dset = self.process_protection_profiles(to_download=download_fresh) + self.auxillary_datasets.mu_dset = self.process_maintenance_updates(to_download=download_fresh) + super().process_auxillary_datasets(download_fresh) @serialize def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None: @@ -812,7 +812,7 @@ def _compute_heuristics(self, download_fresh_cpes: bool = False) -> None: def compute_related_cves(self, download_fresh_cves: bool = False) -> None: raise NotImplementedError - def process_auxillary_datasets(self) -> None: + def process_auxillary_datasets(self, download_fresh: bool = False) -> None: raise NotImplementedError def analyze_certificates(self, fresh: bool = True) -> None: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index dbb871b1..9251c439 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -209,9 +209,9 @@ def get_certs_from_web(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def process_auxillary_datasets(self) -> None: - self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset() - self.auxillary_datasets.cve_dset = self._prepare_cve_dataset() + def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) + self.auxillary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh_cves=download_fresh) @serialize def download_all_artifacts(self, fresh: bool = True) -> None: @@ -292,10 +292,6 @@ def _compute_heuristics(self, fresh: bool = True) -> None: self._compute_references() self._compute_transitive_vulnerabilities() - @abstractmethod - def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: - raise NotImplementedError("Not meant to be implemented by the base class.") - @abstractmethod def _compute_references(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 1f4bab81..f2f0a79d 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -86,6 +86,12 @@ def algorithms_dir(self) -> Path: def algorithm_dataset_path(self) -> Path: return self.algorithms_dir / "algorithms.json" + def __getitem__(self, item: str) -> FIPSCertificate: + if len(item) < 5: + return super().__getitem__(fips_dgst(item)) + else: + return super().__getitem__(item) + def _extract_data_from_html_modules(self, fresh: bool = True) -> None: """ Extracts data from html module file @@ -188,14 +194,14 @@ def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: html = BeautifulSoup(handle.read(), "html5lib") table = [x for x in html.find(id="searchResultsTable").tbody.contents if x != "\n"] - cert_ids: Set[int] = set() + cert_ids: Set[str] = set() for entry in table: if isinstance(entry, NavigableString): continue cert_id = entry.find("a").text if cert_id not in cert_ids: - cert_ids.add(int(cert_id)) + cert_ids.add(cert_id) return {FIPSCertificate(cert_id) for cert_id in cert_ids} @@ -233,9 +239,9 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru self.state.meta_sources_parsed = True @serialize - def process_auxillary_datasets(self) -> None: - self.auxillary_datasets.algorithm_dset = self._prepare_algorithm_dataset() - super().process_auxillary_datasets() + def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + self.auxillary_datasets.algorithm_dset = self._prepare_algorithm_dataset(download_fresh) + super().process_auxillary_datasets(download_fresh) def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSAlgorithmDataset: logger.info("Preparing FIPSAlgorithm dataset.") @@ -259,11 +265,6 @@ def _extract_algorithms_from_policy_tables(self, fresh: bool = True): progress_bar_desc="Extracting Algorithms from policy tables", ) - def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: - # TODO: Refactor me - for cert in self.certs.values(): - self._clean_cert_ids(cert) - def _extract_policy_pdf_metadata(self, fresh: bool = True) -> None: certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] processed_certs = cert_processing.process_parallel( @@ -297,87 +298,40 @@ def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> or cert_first.year < conn_first.year ) - def _clean_cert_ids(self, current_cert: FIPSCertificate) -> None: - # TODO: Refactor me - current_cert.clean_cert_ids() - current_cert.heuristics.clean_cert_ids = { - cert_id: count - for cert_id, count in current_cert.pdf_data.clean_cert_ids.items() - if self._validate_id(current_cert, cert_id.replace("Cert.", "").replace("cert.", "").lstrip("#CA0 ")) - and cert_id != current_cert.cert_id - } - def _compute_transitive_vulnerabilities(self, fresh: bool = True) -> None: # TODO: Implement me pass - @staticmethod - def _match_with_algorithm(processed_cert: FIPSCertificate, cert_candidate_id: str) -> bool: - for algo in processed_cert.heuristics.algorithms: - curr_id = "".join(filter(str.isdigit, algo)) - if curr_id == cert_candidate_id: - return False - return True - - def _validate_id(self, processed_cert: FIPSCertificate, cert_candidate_id: str) -> bool: - # TODO: Refactor me - candidate_dgst = fips_dgst(cert_candidate_id) - if candidate_dgst not in self.certs or not cert_candidate_id.isdecimal(): - return False - - # "< number" still needs to be used, because of some old certs being revalidated - if int(cert_candidate_id) < config.smallest_certificate_id_to_connect or self._compare_certs( - processed_cert, cert_candidate_id - ): - return False - - if self.auxillary_datasets.algorithm_dset is None: - raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") - - if not FIPSDataset._match_with_algorithm(processed_cert, cert_candidate_id): - return False - - algs = self.auxillary_datasets.algorithm_dset.get_algorithms_by_id(cert_candidate_id) - for current_alg in algs: - if current_alg.vendor is None or processed_cert.web_data.vendor is None: - raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") - - if FIPSCertificate.get_compare(processed_cert.web_data.vendor) == FIPSCertificate.get_compare( - current_alg.vendor - ): - return False - return True - - def _compute_references(self, fresh: bool = True) -> None: - # TODO: Refactor me - def pdf_lookup(cert): - return set( - filter( - lambda x: x, - map( - lambda cid: "".join(filter(str.isdigit, cid)), - cert.heuristics.clean_cert_ids, - ), - ) - ) + def _prune_reference_candidates(self) -> None: + for cert in self: + cert.prune_referenced_cert_ids() - def web_lookup(cert): - return set( - filter(lambda x: x, map(lambda cid: "".join(filter(str.isdigit, cid)), cert.web_data.mentioned_certs)) - ) + # Previously, a following procedure was used to prune reference_candidates: + # - A set of algorithms was obtained via self.auxillary_datasets.algorithm_dset.get_algorithms_by_id(reference_candidate) + # - If any of these algorithms had the same vendor as the reference_candidate, the candidate was rejected + # - The rationale is that if an ID appears in a certificate s.t. an algorithm with the same ID was produced by the same vendor, the reference likely refers to alg. + # - Such reference should then be discarded. + # - We are uncertain of the effectivity of such measure, disabling it for now. - finder = ReferenceFinder() - finder.fit(self.certs, lambda cert: str(cert.cert_id), pdf_lookup) # type: ignore + def _compute_references(self, fresh: bool = True, keep_unknowns: bool = False) -> None: + self._prune_reference_candidates() - for dgst in self.certs: - setattr(self.certs[dgst].heuristics, "st_references", finder.predict_single_cert(dgst, keep_unknowns=False)) + policy_reference_finder = ReferenceFinder() + policy_reference_finder.fit( + self.certs, lambda cert: cert.cert_id, lambda cert: cert.heuristics.policy_prunned_references + ) - finder = ReferenceFinder() - finder.fit(self.certs, lambda cert: str(cert.cert_id), web_lookup) # type: ignore + module_reference_finder = ReferenceFinder() + module_reference_finder.fit( + self.certs, lambda cert: cert.cert_id, lambda cert: cert.heuristics.module_prunned_references + ) - for dgst in self.certs: - setattr( - self.certs[dgst].heuristics, "web_references", finder.predict_single_cert(dgst, keep_unknowns=False) + for cert in self: + cert.heuristics.policy_processed_references = policy_reference_finder.predict_single_cert( + cert.dgst, keep_unknowns + ) + cert.heuristics.module_processed_references = module_reference_finder.predict_single_cert( + cert.dgst, keep_unknowns ) def to_pandas(self) -> pd.DataFrame: diff --git a/sec_certs/model/reference_finder.py b/sec_certs/model/reference_finder.py index 8399614a..9a94b7e2 100644 --- a/sec_certs/model/reference_finder.py +++ b/sec_certs/model/reference_finder.py @@ -1,15 +1,16 @@ -from typing import Callable, Dict, List, Optional, Set, Tuple, Union +from typing import Callable, Dict, List, Optional, Set, Tuple, TypeVar, Union from sec_certs.sample.certificate import Certificate, References -Certificates = Dict[str, Certificate] +CertSubType = TypeVar("CertSubType", bound=Certificate) +Certificates = Dict[str, CertSubType] ReferencedByDirect = Dict[str, Set[str]] ReferencedByIndirect = Dict[str, Set[str]] ReferencesType = Dict[str, Dict[str, Optional[Set[str]]]] IDMapping = Dict[str, List[str]] UnknownReferences = Dict[str, Set[str]] -IDLookupFunc = Callable[[Certificate], str] -ReferenceLookupFunc = Callable[[Certificate], Set[str]] +IDLookupFunc = Callable[[CertSubType], str] +ReferenceLookupFunc = Callable[[CertSubType], Set[str]] # TODO: All of this can and should be rewritten on top of networkx or some other graph library. diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 5275f726..b6c275d4 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -1,12 +1,11 @@ from __future__ import annotations -import copy import itertools import re from dataclasses import dataclass, field from datetime import date from pathlib import Path -from typing import Any, Callable, ClassVar, Dict, Final, List, Literal, Optional, Pattern, Set, Tuple +from typing import Any, Callable, ClassVar, Dict, Final, List, Literal, Optional, Set, Tuple import dateutil import numpy as np @@ -21,6 +20,7 @@ import sec_certs.utils.pdf as pdf import sec_certs.utils.tables as tables from sec_certs.cert_rules import FIPS_ALGS_IN_TABLE, fips_rules +from sec_certs.config.configuration import config from sec_certs.sample.certificate import Certificate from sec_certs.sample.certificate import Heuristics as BaseHeuristics from sec_certs.sample.certificate import PdfData as BasePdfData @@ -208,14 +208,14 @@ class FIPSCertificate( "cpe_matches", "verified_cpe_matches", "related_cves", - "web_directly_referenced_by", - "web_indirectly_referenced_by", - "web_directly_referencing", - "web_indirectly_referencing", - "st_directly_referenced_by", - "st_indirectly_referenced_by", - "st_directly_referencing", - "st_indirectly_referencing", + "module_directly_referenced_by", + "module_indirectly_referenced_by", + "module_directly_referencing", + "module_indirectly_referencing", + "policy_directly_referenced_by", + "policy_indirectly_referenced_by", + "policy_directly_referencing", + "policy_indirectly_referencing", ] @dataclass(eq=True) @@ -371,8 +371,14 @@ class PdfData(BasePdfData, ComplexSerializableType): """ keywords: Dict = field(default_factory=dict) - clean_cert_ids: Dict[str, int] = field(default_factory=dict) - st_metadata: Dict[str, Any] = field(default_factory=dict) + policy_metadata: Dict[str, Any] = field(default_factory=dict) + + @property + def certlike_algorithm_numbers(self) -> Set[str]: + """Returns numbers of certificates from keywords["fips_certlike"]["Certlike"]""" + fips_certlike = self.keywords["fips_certlike"].get("Certlike", dict()) + matches = {re.search(r"#\s{0,1}\d{1,4}", x) for x in fips_certlike.keys()} + return {"".join([x for x in match.group() if x.isdigit()]) for match in matches if match} @dataclass(eq=True) class Heuristics(BaseHeuristics, ComplexSerializableType): @@ -381,13 +387,23 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): """ algorithms: Set[str] = field(default_factory=set) - clean_cert_ids: Optional[Dict[str, int]] = field(default=None) extracted_versions: Set[str] = field(default_factory=set) cpe_matches: Optional[Set[str]] = field(default=None) verified_cpe_matches: Optional[Set[CPE]] = field(default=None) related_cves: Optional[Set[str]] = field(default=None) - policy_references: References = field(default_factory=References) - module_references: References = field(default_factory=References) + policy_prunned_references: Set[str] = field(default_factory=set) + module_prunned_references: Set[str] = field(default_factory=set) + policy_processed_references: References = field(default_factory=References) + module_processed_references: References = field(default_factory=References) + + @property + def algorithm_numbers(self) -> Set[str]: + """Returns numbers of algorithms""" + + def alg_to_number(alg: str) -> str: + return "".join([x for x in alg.split("#")[1] if x.isdigit()]) + + return {alg_to_number(x) for x in self.algorithms} @property def dgst(self) -> str: @@ -430,7 +446,7 @@ def label_studio_title(self) -> str: def __init__( self, - cert_id: int, + cert_id: str, web_data: Optional[FIPSCertificate.WebData] = None, pdf_data: Optional[FIPSCertificate.PdfData] = None, heuristics: Optional[FIPSCertificate.Heuristics] = None, @@ -462,14 +478,14 @@ def pandas_tuple(self) -> Tuple: self.heuristics.cpe_matches, self.heuristics.verified_cpe_matches, self.heuristics.related_cves, - self.heuristics.module_references.directly_referenced_by, - self.heuristics.module_references.indirectly_referenced_by, - self.heuristics.module_references.directly_referencing, - self.heuristics.module_references.indirectly_referencing, - self.heuristics.policy_references.directly_referenced_by, - self.heuristics.policy_references.indirectly_referenced_by, - self.heuristics.policy_references.directly_referencing, - self.heuristics.policy_references.indirectly_referencing, + self.heuristics.module_processed_references.directly_referenced_by, + self.heuristics.module_processed_references.indirectly_referenced_by, + self.heuristics.module_processed_references.directly_referencing, + self.heuristics.module_processed_references.indirectly_referencing, + self.heuristics.policy_processed_references.directly_referenced_by, + self.heuristics.policy_processed_references.indirectly_referenced_by, + self.heuristics.policy_processed_references.directly_referencing, + self.heuristics.policy_processed_references.indirectly_referencing, ) @staticmethod @@ -528,9 +544,9 @@ def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) if metadata: - cert.pdf_data.st_metadata = metadata + cert.pdf_data.policy_metadata = metadata else: - cert.pdf_data.st_metadata = dict() + cert.pdf_data.policy_metadata = dict() cert.state.policy_extract_ok = False return cert @@ -557,69 +573,19 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): itertools.chain.from_iterable([tables.get_algs_from_table(df.to_string()) for df in tabular_data]) ) - def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: - pass - - # def _process_to_pop(self, reg_to_match: Pattern, cert: str, to_pop: Set[str]) -> None: - # for found in self.pdf_data.keywords["fips_certlike"]["Certlike"]: - # match_in_found = reg_to_match.search(found) - # match_in_cert = reg_to_match.search(cert) - # if ( - # match_in_found is not None - # and match_in_cert is not None - # and match_in_found.group("id") == match_in_cert.group("id") - # ): - # to_pop.add(cert) - - # this_id = int("".join(filter(str.isdigit, cert))) - # for algo in self.heuristics.algorithms: - # try: - # algo_id = int("".join(filter(str.isdigit, algo.cert_id))) - # if algo_id == this_id: - # to_pop.add(cert) - # except ValueError: - # continue - - def clean_cert_ids(self) -> None: - # TODO: Refactor me + def prune_referenced_cert_ids(self) -> None: """ - Removes algorithm mentions from the cert_id rule matches and stores them into clean_cert_id matches. + This method goes through all IDs (numbers) that correspond to FIPS Certificates and are stored in + pdf_data.keywords or web_data.mentioned_certs. It performs prunning of these attributes and fills attributes + heuristics.prunned_module_references and heuristics.prunned_policy_references. These variables are further + processed and Reference objects are created from them. """ - if "Cert" not in self.pdf_data.keywords["fips_cert_id"]: - self.pdf_data.clean_cert_ids = {} - return - - matches = copy.deepcopy(self.pdf_data.keywords["fips_cert_id"]["Cert"]) - - alg_set: Set[str] = set() - # if self.web_data.algorithms is None: - # raise RuntimeError(f"Algorithms were not found for cert {self.cert_id} - this should not be happening.") - # for algo in self.web_data.algorithms: - # alg_set.add(algo.cert_id) - - for cert_rule in fips_rules["fips_cert_id"]["Cert"]: - to_pop = set() - for cert in matches: - if cert in alg_set: - to_pop.add(cert) - continue - self._process_to_pop(cert_rule, cert, to_pop) - - for r in to_pop: - matches.pop(r, None) + html_module_ids = set(self.web_data.mentioned_certs.keys()) if self.web_data.mentioned_certs else set() + self.heuristics.module_prunned_references = self._prune_reference_ids_variable(html_module_ids) - matches.pop("#" + str(self.cert_id), None) - self.pdf_data.clean_cert_ids = matches - - @staticmethod - def get_compare(vendor: str) -> str: - """ - Tokenizes vendor name of the certificate. - """ - vendor_split = ( - vendor.replace(",", "").replace("-", " ").replace("+", " ").replace("®", "").replace("(R)", "").split() - ) - return vendor_split[0][:4] if len(vendor_split) > 0 else vendor + pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict().keys())) + pdf_policy_ids = {x.replace("#", "").strip() for x in pdf_policy_ids} + self.heuristics.policy_prunned_references = self._prune_reference_ids_variable(pdf_policy_ids) def compute_heuristics_version(self) -> None: """ @@ -633,3 +599,18 @@ def compute_heuristics_version(self) -> None: if self.web_data.fw_versions: versions_for_extraction += f" {self.web_data.fw_versions}" self.heuristics.extracted_versions = helpers.compute_heuristics_version(versions_for_extraction) + + def _prune_reference_ids_variable(self, attribute_to_prune: Set[str]) -> Set[str]: + """ + Prunnes cert_ids from variable "attribute_to_prune", return result. Steps: + 0. Consider only ids != self.cert_id + 1. Consider only ids > config.always_false_positive_fips_cert_id_threshold + 2. Consider only ids s.t. they don't appear in self.heuristics.algorithms + 3. Consider only ids s.t. they don't appear in self.pdf_data.keywords["fips_certlike"]["Certlike"] + """ + prunned = {x for x in attribute_to_prune if x != self.cert_id} + prunned = {x for x in prunned if int(x) > config.always_false_positive_fips_cert_id_threshold} + prunned = {x for x in prunned if x not in self.heuristics.algorithm_numbers} + prunned = {x for x in prunned if x not in self.pdf_data.certlike_algorithm_numbers} + + return prunned diff --git a/sec_certs/utils/helpers.py b/sec_certs/utils/helpers.py index 6052faaf..d4c668c5 100644 --- a/sec_certs/utils/helpers.py +++ b/sec_certs/utils/helpers.py @@ -149,6 +149,18 @@ def tokenize(string: str, keywords: Set[str]) -> str: return " ".join([x for x in string.split() if x.lower() in keywords]) +def normalize_fips_vendor(string: str) -> str: + """ + "Normalizes" FIPS vendor. Precisely: + - Removes some punctuation and non-alphanumerical symbols + - Returns only first 5 tokens + # TODO: The rationale of the steps outlined above should be investigatated + """ + return " ".join( + string.replace("(R)", "").replace(",", "").replace("®", "").replace("-", " ").replace("+", " ").split()[:4] + ) + + # Credit: https://stackoverflow.com/questions/18092354/ def split_unescape(s: str, delim: str, escape: str = "\\", unescape: bool = True) -> List[str]: """ diff --git a/tests/data/fips/certificate/crt.json b/tests/data/fips/certificate/crt.json deleted file mode 100644 index 36cdcef3..00000000 --- a/tests/data/fips/certificate/crt.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "184097a88a9b4ad9", - "cert_id": 3518, - "web_data": { - "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", - "module_name": null, - "validation_history": null, - "vendor_url": null, - "vendor": null, - "certificate_pdf_url": null, - "module_type": null, - "standard": null, - "status": null, - "level": null, - "caveat": null, - "exceptions": null, - "embodiment": null, - "description": null, - "tested_conf": null, - "hw_versions": null, - "fw_versions": null, - "sw_versions": null, - "mentioned_certs": null, - "historical_reason": null, - "date_sunset": null, - "revoked_reason": null, - "revoked_link": null - }, - "pdf_data": { - "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", - "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} - }, - "heuristics": { - "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", - "algorithms": { - "_type": "Set", - "elements": [] - }, - "clean_cert_ids": null, - "extracted_versions": { - "_type": "Set", - "elements": [] - }, - "cpe_matches": null, - "verified_cpe_matches": null, - "related_cves": null, - "policy_references": { - "_type": "sec_certs.sample.certificate.References", - "directly_referenced_by": null, - "indirectly_referenced_by": null, - "directly_referencing": null, - "indirectly_referencing": null - }, - "module_references": { - "_type": "sec_certs.sample.certificate.References", - "directly_referenced_by": null, - "indirectly_referenced_by": null, - "directly_referencing": null, - "indirectly_referencing": null - } - }, - "state": { - "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", - "module_download_ok": true, - "policy_download_ok": true, - "policy_convert_garbage": false, - "policy_convert_ok": true, - "module_extract_ok": false, - "policy_extract_ok": false, - "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", - "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" - } -} \ No newline at end of file diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json index 36cdcef3..50f2e7aa 100644 --- a/tests/data/fips/certificate/fictional_cert.json +++ b/tests/data/fips/certificate/fictional_cert.json @@ -1,7 +1,7 @@ { "_type": "sec_certs.sample.fips.FIPSCertificate", "dgst": "184097a88a9b4ad9", - "cert_id": 3518, + "cert_id": "3518", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -30,8 +30,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -39,7 +38,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -47,14 +45,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index c4a4994f..ea9d9621 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -5,19 +5,19 @@ "meta_sources_parsed": true, "artifacts_downloaded": true, "pdfs_converted": true, - "auxillary_datasets_processed": true, + "auxillary_datasets_processed": false, "certs_analyzed": false }, - "timestamp": "2022-11-23 16:23:44.899168", + "timestamp": "2022-11-29 16:42:34.785695", "sha256_digest": "not implemented", "name": "FIPSDataset dataset", - "description": "No description provided", + "description": "29/11/2022 16:42:34", "n_certs": 22, "certs": [ { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "184097a88a9b4ad9", - "cert_id": 3518, + "dgst": "0ab77c56eece2d24", + "cert_id": "3651", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -46,8 +46,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -55,7 +54,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -63,14 +61,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -86,14 +92,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", - "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", + "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "3b05cf67849835e3", - "cert_id": 2997, + "dgst": "0e8b7a7f79b3098b", + "cert_id": "2665", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -122,8 +128,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -131,7 +136,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -139,14 +143,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -162,14 +174,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", - "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" + "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", + "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ad1e075215267a28", - "cert_id": 2441, + "dgst": "5b00a7bb92c1cb19", + "cert_id": "2721", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -198,8 +210,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -207,7 +218,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -215,14 +225,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -238,14 +256,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", - "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" + "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", + "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "e1ff7d9997726782", - "cert_id": 3090, + "dgst": "2aec3694418f35d8", + "cert_id": "3488", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -274,8 +292,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -283,7 +300,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -291,14 +307,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -314,14 +338,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", - "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" + "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", + "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "9ca7b8be590113b8", - "cert_id": 2711, + "dgst": "ed16d272044ed81f", + "cert_id": "2860", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -350,8 +374,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -359,7 +382,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -367,14 +389,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -390,14 +420,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", - "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" + "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", + "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "24a05f4d1305d667", - "cert_id": 3493, + "dgst": "69d9200c309f5f97", + "cert_id": "2590", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -426,8 +456,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -435,7 +464,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -443,14 +471,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -466,14 +502,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", - "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" + "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", + "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "618102f275d5649a", - "cert_id": 3495, + "dgst": "ad1e075215267a28", + "cert_id": "2441", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -502,8 +538,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -511,7 +546,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -519,14 +553,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -542,14 +584,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", - "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" + "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", + "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0df59fd6c41cadd2", - "cert_id": 3711, + "dgst": "75ca28395bfc9ad1", + "cert_id": "2630", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -578,8 +620,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -587,7 +628,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -595,14 +635,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -618,14 +666,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", - "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" + "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", + "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0ab77c56eece2d24", - "cert_id": 3651, + "dgst": "51c94a510b50e68f", + "cert_id": "3141", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -654,8 +702,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -663,7 +710,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -671,14 +717,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -694,14 +748,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", - "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" + "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", + "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "cd0666cdd7ce0244", - "cert_id": 3095, + "dgst": "618102f275d5649a", + "cert_id": "3495", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -730,8 +784,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -739,7 +792,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -747,14 +799,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -770,14 +830,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", - "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" + "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", + "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ed16d272044ed81f", - "cert_id": 2860, + "dgst": "9ca7b8be590113b8", + "cert_id": "2711", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -806,8 +866,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -815,7 +874,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -823,14 +881,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -846,14 +912,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", - "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" + "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", + "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "69d9200c309f5f97", - "cert_id": 2590, + "dgst": "73e7f3f802b0e919", + "cert_id": "3176", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -882,8 +948,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -891,7 +956,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -899,14 +963,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -922,14 +994,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", - "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" + "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", + "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0e8b7a7f79b3098b", - "cert_id": 2665, + "dgst": "c333a75353784177", + "cert_id": "2779", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -958,8 +1030,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -967,7 +1038,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -975,14 +1045,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -998,14 +1076,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", - "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" + "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", + "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ceab64951a30f684", - "cert_id": 3197, + "dgst": "24a05f4d1305d667", + "cert_id": "3493", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1034,8 +1112,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1043,7 +1120,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1051,14 +1127,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1074,14 +1158,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", - "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" + "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", + "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "73e7f3f802b0e919", - "cert_id": 3176, + "dgst": "cd0666cdd7ce0244", + "cert_id": "3095", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1110,8 +1194,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1119,7 +1202,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1127,14 +1209,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1150,14 +1240,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", - "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" + "policy_pdf_hash": "3c12cc775fd0b1dfb896bf5bb5b42116f36576ac55e4c2009083ee6a5d0ac63b", + "policy_txt_hash": "7fbeb02d4ed3aa9f5c64c0957e4a8b6542ad7ab86ebe7f5ae5da4863303d5da3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "c333a75353784177", - "cert_id": 2779, + "dgst": "756a6524cdb2d4ea", + "cert_id": "3850", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1186,8 +1276,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1195,7 +1284,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1203,14 +1291,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1226,14 +1322,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", - "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" + "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", + "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "75ca28395bfc9ad1", - "cert_id": 2630, + "dgst": "ceab64951a30f684", + "cert_id": "3197", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1262,8 +1358,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1271,7 +1366,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1279,14 +1373,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1302,14 +1404,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", - "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" + "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", + "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "51c94a510b50e68f", - "cert_id": 3141, + "dgst": "184097a88a9b4ad9", + "cert_id": "3518", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1338,8 +1440,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1347,7 +1448,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1355,14 +1455,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1378,14 +1486,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", - "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "756a6524cdb2d4ea", - "cert_id": 3850, + "dgst": "0df59fd6c41cadd2", + "cert_id": "3711", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1414,8 +1522,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1423,7 +1530,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1431,14 +1537,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1454,14 +1568,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", - "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" + "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", + "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "2aec3694418f35d8", - "cert_id": 3488, + "dgst": "e1ff7d9997726782", + "cert_id": "3090", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1490,8 +1604,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1499,7 +1612,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1507,14 +1619,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1530,14 +1650,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", - "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" + "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", + "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "f7df34ce0a0ad316", - "cert_id": 3093, + "dgst": "3b05cf67849835e3", + "cert_id": "2997", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1566,8 +1686,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1575,7 +1694,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1583,14 +1701,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1606,14 +1732,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", - "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" + "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", + "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "5b00a7bb92c1cb19", - "cert_id": 2721, + "dgst": "f7df34ce0a0ad316", + "cert_id": "3093", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1642,8 +1768,7 @@ "pdf_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.PdfData", "keywords": {}, - "clean_cert_ids": {}, - "st_metadata": {} + "policy_metadata": {} }, "heuristics": { "_type": "sec_certs.sample.fips.FIPSCertificate.Heuristics", @@ -1651,7 +1776,6 @@ "_type": "Set", "elements": [] }, - "clean_cert_ids": null, "extracted_versions": { "_type": "Set", "elements": [] @@ -1659,14 +1783,22 @@ "cpe_matches": null, "verified_cpe_matches": null, "related_cves": null, - "policy_references": { + "policy_prunned_references": { + "_type": "Set", + "elements": [] + }, + "module_prunned_references": { + "_type": "Set", + "elements": [] + }, + "policy_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null }, - "module_references": { + "module_processed_references": { "_type": "sec_certs.sample.certificate.References", "directly_referenced_by": null, "indirectly_referenced_by": null, @@ -1682,8 +1814,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", - "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" + "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", + "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" } } ] diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index a7c9247b..58744e0c 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -2,16 +2,14 @@ import shutil from pathlib import Path from tempfile import TemporaryDirectory -from typing import Dict, List +from typing import Optional, Set import pytest import sec_certs.constants as constants import tests.data.fips.dataset from sec_certs.dataset.fips import FIPSDataset -from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset from sec_certs.sample.fips import FIPSCertificate -from sec_certs.utils.helpers import fips_dgst @pytest.fixture(scope="module") @@ -24,298 +22,118 @@ def toy_dataset(data_dir: Path) -> FIPSDataset: return FIPSDataset.from_json(data_dir / "toy_dataset.json") -def generate_html(ids: List[str], path: Path): - def generate_entry(certificate_id: str) -> str: - return f""" - - - {certificate_id} - - - """ - - html_head = """ - - - - - Cryptographic Module Validation Program | CSRC - - - - - - - - - """ - rows = "" - for cert_id in ids: - rows += f"\n{generate_entry(cert_id)}\n" - html_body = f""" - - - - - - - - - - - - - {rows} - -
Certificate NumberVendor NameModule NameModule TypeValidation Date
- - """ - with open(path, "w") as f: - f.write(f"{html_head}\n{html_body}\n") - - -def _set_up_dataset(td, certs): - dataset = FIPSDataset({}, Path(td) == "test_dataset", "fips_test_dataset") - generate_html(certs, td + "/test_search.html") - dataset.get_certs_from_web(test=td + "/test_search.html") - return dataset - - -def _set_up_dataset_for_full(td, certs, cpe_dset_path: Path, cve_dset_path: Path): - dataset = _set_up_dataset(td, certs) - - dataset.auxillary_datasets_dir.mkdir(exist_ok=True) - shutil.copyfile(cpe_dset_path, dataset.cpe_dataset_path) - shutil.copyfile(cve_dset_path, dataset.cve_dataset_path) - - dataset.web_scan(set(certs)) - dataset.download_all_artifacts(set(certs)) - dataset.convert_all_pdfs() - dataset._extract_data() - dataset._extract_algorithms_from_policy_tables() - dataset.algorithms = FIPSAlgorithmDataset.from_json(Path(__file__).parent / "data/test_fips_oop/algorithms.json") - dataset.analyze_certificates(use_nist_cpe_matching_dict=False, perform_cpe_heuristics=False) - return dataset +@pytest.fixture(scope="module") +def toy_static_dataset(data_dir: Path) -> FIPSDataset: + return FIPSDataset.from_json(data_dir / "toy_dataset.json") @pytest.fixture(scope="module") -def certs_to_process() -> Dict[str, List[str]]: - return { - "microsoft": [ - "3095", - "3651", +def processed_dataset(toy_static_dataset: FIPSDataset, tmp_path_factory) -> FIPSDataset: + tmp_dir = tmp_path_factory.mktemp("cc_dset") + toy_static_dataset.root_dir = tmp_dir + + tested_certs = {toy_static_dataset["3095"], toy_static_dataset["3093"], toy_static_dataset["3197"]} + toy_static_dataset.certs = {x.dgst: x for x in tested_certs} + + toy_static_dataset.download_all_artifacts() + toy_static_dataset.convert_all_pdfs() + toy_static_dataset._extract_data() + toy_static_dataset._compute_references(keep_unknowns=True) + return toy_static_dataset + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [ + ("3095", {"3093", "3094", "3096"}), + ("3093", {"3090", "3091"}), + ("3197", {"3195", "3096", "3196", "3644", "3651"}), + ], +) +def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.directly_referencing == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", {"3093", "3094", "3096"}), ("3093", {"3090", "3091"})]) +def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.directly_referencing == expected_refs + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [ + ( "3093", - "3090", - "3197", - "3196", - "3089", - "3195", - "3480", - "3615", - "3194", - "3091", - "3690", - "3644", - "3527", - "3094", - "3544", - "3096", - "3092", - ], - "redhat": [ - "2630", - "2721", - "2997", - "2441", - "2711", - "2633", - "2798", - "3613", - "3733", - "2908", - "2446", - "2742", - "2447", - ], - "docusign": ["3850", "2779", "2860", "2665", "1883", "3518", "3141", "2590"], - "referencing_openssl": [ - "3493", - "3495", - "3711", - "3176", - "3488", - "3126", - "3269", - "3524", - "3220", - "2398", - "3543", - "2676", - "3313", - "3363", - "3608", - "3158", - ], - } - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -def test_regress_125(): - with TemporaryDirectory() as tmp_dir: - dset = _set_up_dataset(tmp_dir, ["3493", "3495"]) - assert set(dset.certs) == {fips_dgst("3493") == fips_dgst("3495")} - assert isinstance(dset.certs[fips_dgst("3493")].cert_id, int) - assert dset.certs[fips_dgst("3493")].cert_id == 3493 - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -def test_size(certs_to_process): - for certs in certs_to_process.values(): - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset(tmp_dir, certs) - assert len(dataset.certs) == len(certs) - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -def test_metadata_extraction(): - with TemporaryDirectory() as tmp_dir: - dset = _set_up_dataset_for_full(tmp_dir, ["3493"], Path(".") == Path(".")) - assert dset.certs[fips_dgst("3493")].pdf_data.st_metadata - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -@pytest.mark.xfail -def test_connections_microsoft(certs_to_process): - certs = certs_to_process["microsoft"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, Path(".") == Path(".")) - - assert {"3093", "3094", "3096"} == set( - dataset.certs[fips_dgst("3095")].heuristics.st_references.directly_referencing - ) - - assert {"3093", "3096", "3094"} == set( - dataset.certs[fips_dgst("3095")].heuristics.web_references.directly_referencing - ) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.st_references.directly_referencing) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.web_references.directly_referencing) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3093")].heuristics.web_references.directly_referencing) - assert {"3089"} == set(dataset.certs[fips_dgst("3090")].heuristics.st_references.directly_referencing) - assert {"3195", "3096", "3196", "3644", "3651"} == set( - dataset.certs[fips_dgst("3197")].heuristics.web_references.directly_referencing - ) - assert {"3091", "3194"} == set(dataset.certs[fips_dgst("3196")].heuristics.st_references.directly_referencing) - assert {"3194", "3091", "3480", "3615"} == set( - dataset.certs[fips_dgst("3196")].heuristics.web_references.directly_referencing - ) - assert not dataset.certs[fips_dgst("3089")].heuristics.st_references.directly_referencing - assert {"3091", "3194"} == set(dataset.certs[fips_dgst("3195")].heuristics.st_references.directly_referencing) - assert {"3194", "3091", "3480"} == set( - dataset.certs[fips_dgst("3195")].heuristics.web_references.directly_referencing - ) - assert {"3089"} == set(dataset.certs[fips_dgst("3480")].heuristics.st_references.directly_referencing) - assert {"3089"} == set(dataset.certs[fips_dgst("3615")].heuristics.st_references.directly_referencing) - assert {"3089"} == set(dataset.certs[fips_dgst("3194")].heuristics.st_references.directly_referencing) - assert {"3089"} == set(dataset.certs[fips_dgst("3091")].heuristics.st_references.directly_referencing) - assert {"3644", "3196", "3651"} == set( - dataset.certs[fips_dgst("3690")].heuristics.st_references.directly_referencing - ) - assert {"3644", "3196", "3651"} == set( - dataset.certs[fips_dgst("3690")].heuristics.web_references.directly_referencing - ) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3527")].heuristics.st_references.directly_referencing) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3527")].heuristics.web_references.directly_referencing) - assert {"3090", "3091"} == set(dataset.certs[fips_dgst("3094")].heuristics.st_references.directly_referencing) - assert {"3093", "3096", "3527"} == set( - dataset.certs[fips_dgst("3544")].heuristics.st_references.directly_referencing - ) - assert {"3093", "3096", "3527"} == set( - dataset.certs[fips_dgst("3544")].heuristics.web_references.directly_referencing - ) - assert {"3194", "3091", "3090"} == set( - dataset.certs[fips_dgst("3096")].heuristics.st_references.directly_referencing - ) - assert {"3090", "3194", "3091", "3480"} == set( - dataset.certs[fips_dgst("3096")].heuristics.web_references.directly_referencing - ) - assert {"3093", "3195", "3096", "3644", "3651"} == set( - dataset.certs[fips_dgst("3092")].heuristics.web_references.directly_referencing - ) - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -@pytest.mark.xfail -def test_connections_redhat(certs_to_process): - certs = certs_to_process["redhat"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, Path(".") == Path(".")) - assert set(dataset.certs[fips_dgst("2630")].heuristics.st_references.directly_referencing) == {"2441"} - assert set(dataset.certs[fips_dgst("2633")].heuristics.st_references.directly_referencing) == {"2441"} - assert not dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("2446")].heuristics.st_references.directly_referencing) == {"2441"} - assert set(dataset.certs[fips_dgst("2447")].heuristics.st_references.directly_referencing) == {"2441"} - assert set(dataset.certs[fips_dgst("3733")].heuristics.st_references.directly_referencing) == {"2441"} - assert not dataset.certs[fips_dgst("2441")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("2908")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("3613")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing) == {"2441", "2711"} - assert set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing) == {"2441", "2711"} - assert set(dataset.certs[fips_dgst("2798")].heuristics.st_references.directly_referencing) == {"2711", "2721"} - assert set(dataset.certs[fips_dgst("2798")].heuristics.web_references.directly_referencing) == {"2711", "2721"} - assert not dataset.certs[fips_dgst("2711")].heuristics.st_references.directly_referencing - assert not dataset.certs[fips_dgst("2997")].heuristics.st_references.directly_referencing - assert set(dataset.certs[fips_dgst("2742")].heuristics.st_references.directly_referencing) == {"2711", "2721"} - assert set(dataset.certs[fips_dgst("2742")].heuristics.web_references.directly_referencing) == {"2721", "2711"} - assert set(dataset.certs[fips_dgst("2721")].heuristics.st_references.directly_referencing) == {"2441", "2711"} - assert set(dataset.certs[fips_dgst("2721")].heuristics.web_references.directly_referencing) == {"2441", "2711"} - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -@pytest.mark.xfail -def test_docusign_chunk(certs_to_process): - certs = certs_to_process["docusign"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) - assert set(dataset.certs[fips_dgst("3850")].heuristics.policy_references.directly_referencing) == { - "1883", - "3518", - } - assert set(dataset.certs[fips_dgst("3850")].heuristics.module_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2779")].heuristics.policy_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2860")].heuristics.policy_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2665")].heuristics.policy_references.directly_referencing) == {"1883"} - assert not dataset.certs[fips_dgst("1883")].heuristics.policy_references.directly_referencing - assert set(dataset.certs[fips_dgst("3518")].heuristics.policy_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("3141")].heuristics.policy_references.directly_referencing) == {"1883"} - assert set(dataset.certs[fips_dgst("2590")].heuristics.policy_references.directly_referencing) == {"1883"} - - -@pytest.mark.skip(reason="FIPS tests to-be-refactored") -@pytest.mark.xfail -def test_openssl_chunk(certs_to_process): - certs = certs_to_process["referencing_openssl"] - with TemporaryDirectory() as tmp_dir: - dataset = _set_up_dataset_for_full(tmp_dir, certs, Path("."), Path(".")) - assert set(dataset.certs[fips_dgst("3493")].heuristics.policy_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3495")].heuristics.policy_references.directly_referencing - assert not dataset.certs[fips_dgst("3711")].heuristics.policy_references.directly_referencing - assert not dataset.certs[fips_dgst("3176")].heuristics.policy_references.directly_referencing - assert not dataset.certs[fips_dgst("3488")].heuristics.policy_references.directly_referencing - assert not dataset.certs[fips_dgst("3126")].heuristics.policy_references.directly_referencing - assert set(dataset.certs[fips_dgst("3126")].heuristics.module_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3269")].heuristics.policy_references.directly_referencing - assert set(dataset.certs[fips_dgst("3524")].heuristics.module_references.directly_referencing) == {"3220"} - assert set(dataset.certs[fips_dgst("3220")].heuristics.policy_references.directly_referencing) == {"2398"} - assert not dataset.certs[fips_dgst("3220")].heuristics.module_references.directly_referencing - assert not dataset.certs[fips_dgst("2398")].heuristics.policy_references.directly_referencing - assert set(dataset.certs[fips_dgst("3543")].heuristics.module_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("2676")].heuristics.module_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("3313")].heuristics.module_references.directly_referencing) == {"3220"} - assert not dataset.certs[fips_dgst("3363")].heuristics.policy_references.directly_referencing - assert set(dataset.certs[fips_dgst("3608")].heuristics.policy_references.directly_referencing) == {"2398"} - assert set(dataset.certs[fips_dgst("3158")].heuristics.module_references.directly_referencing) == {"2398"} + { + "3090", + "3091", + }, + ), + ("3095", {"3090", "3091", "3093", "3094", "3096"}), + ], +) +def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referencing == expected_refs + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [("3095", {"3090", "3091", "3093", "3094", "3096"}), ("3093", {"3090", "3091"})], +) +def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.indirectly_referencing == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_html_modules_directly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.directly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_pdf_policies_directly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.directly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_html_modules_indirectly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_pdf_policies_indirectly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs def test_dataset_to_json(toy_dataset: FIPSDataset, data_dir: Path, tmp_path: Path): @@ -398,9 +216,8 @@ def test_download_and_convert_artifacts(toy_dataset: FIPSDataset, data_dir: Path assert abs(crt.state.policy_txt_path.stat().st_size - template_policy_txt_path.stat().st_size) < 1000 -@pytest.mark.skip(reason="FIPS tests to-be-refactored") def test_to_pandas(toy_dataset: FIPSDataset): df = toy_dataset.to_pandas() assert df.shape == (len(toy_dataset), len(FIPSCertificate.pandas_columns)) assert df.index.name == "dgst" - assert set(df.columns) == (set(FIPSCertificate.pandas_columns).union("year_from")) + assert set(df.columns) == set(FIPSCertificate.pandas_columns).union({"year_from"}) - {"dgst"} From fc49b6a96e97693f6d3fab8743768a101bf30098 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 30 Nov 2022 16:27:14 +0100 Subject: [PATCH 42/81] implement transitive vuln. search for FIPS --- sec_certs/dataset/common_criteria.py | 6 +- sec_certs/dataset/dataset.py | 26 +- sec_certs/dataset/fips.py | 33 +-- .../model/transitive_vulnerability_finder.py | 111 ++++----- sec_certs/sample/fips.py | 2 + tests/fips/test_fips_analysis.py | 224 ++++++++++++++++++ tests/fips/test_fips_dataset.py | 115 --------- 7 files changed, 304 insertions(+), 213 deletions(-) create mode 100644 tests/fips/test_fips_analysis.py diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 920c6d32..932cba9b 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -664,8 +664,8 @@ def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: def _compute_transitive_vulnerabilities(self): logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") - transitive_cve_finder = TransitiveVulnerabilityFinder() - transitive_cve_finder.fit(self.certs) + transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: cert.heuristics.cert_id) + transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.report_references) for dgst in self.certs: transitive_cve = transitive_cve_finder.predict_single_cert(dgst) @@ -815,7 +815,7 @@ def compute_related_cves(self, download_fresh_cves: bool = False) -> None: def process_auxillary_datasets(self, download_fresh: bool = False) -> None: raise NotImplementedError - def analyze_certificates(self, fresh: bool = True) -> None: + def analyze_certificates(self) -> None: raise NotImplementedError def get_certs_from_web( diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 9251c439..45182faf 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -212,6 +212,7 @@ def get_certs_from_web(self) -> None: def process_auxillary_datasets(self, download_fresh: bool = False) -> None: self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) self.auxillary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh_cves=download_fresh) + self.state.auxillary_datasets_processed = True @serialize def download_all_artifacts(self, fresh: bool = True) -> None: @@ -256,7 +257,7 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @serialize - def analyze_certificates(self, fresh: bool = True) -> None: + def analyze_certificates(self) -> None: if not self.state.pdfs_converted: logger.info( "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." @@ -267,26 +268,19 @@ def analyze_certificates(self, fresh: bool = True) -> None: "Attempting to run analysis of certifies while not having the auxillary datasets processed. Returning." ) - if fresh: - logger.info("Analyzing certificates.") - else: - logger.info("Re-analyzing certificates on which analysis failed.") - - self._analyze_certificates_body(fresh) - if fresh: - self.analyze_certificates(False) - + logger.info("Analyzing certificates.") + self._analyze_certificates_body() self.state.certs_analyzed = True - def _analyze_certificates_body(self, fresh: bool = True) -> None: - self._extract_data(fresh) - self._compute_heuristics(fresh) + def _analyze_certificates_body(self) -> None: + self._extract_data() + self._compute_heuristics() @abstractmethod - def _extract_data(self, fresh: bool = True) -> None: + def _extract_data(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") - def _compute_heuristics(self, fresh: bool = True) -> None: + def _compute_heuristics(self) -> None: self.compute_cpe_heuristics() self.compute_related_cves() self._compute_references() @@ -297,7 +291,7 @@ def _compute_references(self, fresh: bool = True) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod - def _compute_transitive_vulnerabilities(self, fresh: bool = True) -> None: + def _compute_transitive_vulnerabilities(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _prepare_cpe_dataset(self, download_fresh_cpes: bool = False) -> CPEDataset: diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index f2f0a79d..43ae8893 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -16,6 +16,7 @@ from sec_certs.dataset.dataset import AuxillaryDatasets, Dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset from sec_certs.model.reference_finder import ReferenceFinder +from sec_certs.model.transitive_vulnerability_finder import TransitiveVulnerabilityFinder from sec_certs.sample.fips import FIPSCertificate from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers @@ -276,31 +277,15 @@ def _extract_policy_pdf_metadata(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _compare_certs(self, current_certificate: FIPSCertificate, other_id: str) -> bool: - other_dgst = fips_dgst(other_id) - other_cert = self.certs[other_dgst] - - if ( - current_certificate.web_data.validation_history is None - or other_cert is None - or other_cert.web_data.validation_history is None - ): - raise RuntimeError("Building of the dataset probably failed - this should not be happening.") - - cert_first = current_certificate.web_data.validation_history[0].date - cert_last = current_certificate.web_data.validation_history[-1].date - conn_first = other_cert.web_data.validation_history[0].date - conn_last = other_cert.web_data.validation_history[-1].date - - return ( - cert_first.year - conn_first.year > config.year_difference_between_validations - and cert_last.year - conn_last.year > config.year_difference_between_validations - or cert_first.year < conn_first.year - ) + def _compute_transitive_vulnerabilities(self) -> None: + logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") + transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: cert.cert_id) + transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.policy_processed_references) - def _compute_transitive_vulnerabilities(self, fresh: bool = True) -> None: - # TODO: Implement me - pass + for dgst in self.certs: + transitive_cve = transitive_cve_finder.predict_single_cert(dgst) + self.certs[dgst].heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves + self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves def _prune_reference_candidates(self) -> None: for cert in self: diff --git a/sec_certs/model/transitive_vulnerability_finder.py b/sec_certs/model/transitive_vulnerability_finder.py index adcf6173..17fcad84 100644 --- a/sec_certs/model/transitive_vulnerability_finder.py +++ b/sec_certs/model/transitive_vulnerability_finder.py @@ -1,11 +1,16 @@ import logging +from collections import Counter from dataclasses import dataclass, field from enum import Enum -from typing import Dict, List, Optional, Set +from typing import Callable, Dict, List, Optional, Set, TypeVar -from sec_certs.sample.certificate import Certificate +from sec_certs.sample.certificate import Certificate, References from sec_certs.serialization.json import ComplexSerializableType +CertSubType = TypeVar("CertSubType", bound=Certificate) +IDLookupFunc = Callable[[CertSubType], str] +ReferenceLookupFunc = Callable[[CertSubType], References] + class ReferenceType(Enum): DIRECT = "direct" @@ -13,12 +18,12 @@ class ReferenceType(Enum): @dataclass -class TransitiveCVE(ComplexSerializableType): +class TransitiveCVEs(ComplexSerializableType): direct_transitive_cves: Optional[Set[str]] = field(default=None) indirect_transitive_cves: Optional[Set[str]] = field(default=None) -Certificates = Dict[str, Certificate] +Certificates = Dict[str, CertSubType] Vulnerabilities = Dict[str, Dict[str, Optional[Set[str]]]] @@ -28,116 +33,112 @@ class TransitiveVulnerabilityFinder: Adheres to sklearn BaseEstimator interface. """ - def __init__(self): + def __init__(self, id_func: IDLookupFunc): self.vulnerabilities: Vulnerabilities = {} self.certificates: Certificates = {} + self._cert_id_counter: Counter = Counter() + self._fitted = False + self._id_func: IDLookupFunc = id_func - def _overwrite_previous_state(self, certificates: Certificates) -> None: + def _clear_state(self) -> None: self.vulnerabilities = {} - self.certificates = certificates - - def _get_dataset_cert_ids_occurrences(self) -> Dict[str, int]: - cert_id_occurrences: Dict[str, int] = {} + self.certificates = {} + self._cert_id_counter = Counter() - for dgst in self.certificates: - cert_id = self.certificates[dgst].heuristics.cert_id - - if cert_id is None: - continue + def _fill_dataset_cert_ids_counter(self) -> None: + self._cert_id_counter = Counter([self._id_func(x) for x in self.certificates.values()]) - cert_id_occurrences[cert_id] = cert_id_occurrences.get(cert_id, 0) + 1 + def _get_cert_transitive_cves( + self, cert: CertSubType, reference_type: ReferenceType, ref_func: ReferenceLookupFunc + ) -> Optional[Set[str]]: - return cert_id_occurrences - - def _get_cert_transitive_cves(self, dgst: str, reference_type: ReferenceType) -> Optional[Set[str]]: - reference_type_dict = { - ReferenceType.DIRECT: self.certificates[dgst].heuristics.report_references.directly_referenced_by, - ReferenceType.INDIRECT: self.certificates[dgst].heuristics.report_references.indirectly_referenced_by, - } - - references = reference_type_dict[reference_type] + references = ( + ref_func(cert).directly_referenced_by + if reference_type == ReferenceType.DIRECT + else ref_func(cert).indirectly_referenced_by + ) if not references: return None vulnerabilities = set() - dataset_cert_id_occurrences = self._get_dataset_cert_ids_occurrences() for cert_id in references: - if cert_id is None: + if self._cert_id_counter[cert_id] != 1: continue - cert_id_occurrences = dataset_cert_id_occurrences.get(cert_id) - - if cert_id_occurrences is None or cert_id_occurrences >= 2: - continue - - for dgst in self.certificates: - cert_obj = self.certificates[dgst] - cert_obj_cves = cert_obj.heuristics.related_cves - - if cert_obj.heuristics.cert_id == cert_id and cert_obj_cves: - vulnerabilities.update(cert_obj_cves) + for cert in self.certificates.values(): + cves = cert.heuristics.related_cves + if self._id_func(cert) == cert_id and cves: + vulnerabilities.update(cves) return vulnerabilities if vulnerabilities else None - def fit(self, certificates: Certificates) -> Vulnerabilities: + def fit(self, certificates: Certificates, ref_func: ReferenceLookupFunc) -> Vulnerabilities: """ Method assigns each certificate vulnerabilities caused by references among certificates :param Certificates certificates: Dictionary of certificates with digests :return Vulnerabilities: Dictionary of vulnerabilities of certificate instances """ - self._overwrite_previous_state(certificates) + self._clear_state() + self.certificates = certificates + self._fill_dataset_cert_ids_counter() - cert_id_occurrences = self._get_dataset_cert_ids_occurrences() thrown_away_cert_counter = 0 - for dgst in self.certificates: - cert_id = self.certificates[dgst].heuristics.cert_id + for cert in self.certificates.values(): + cert_id = self._id_func(cert) - if cert_id is None: + if not cert_id: continue - - if cert_id_occurrences[cert_id] >= 2: + if self._cert_id_counter[cert_id] > 1: thrown_away_cert_counter += 1 continue - self.vulnerabilities[dgst] = {} - self.vulnerabilities[dgst][ReferenceType.DIRECT.value] = self._get_cert_transitive_cves( - dgst, ReferenceType.DIRECT + self.vulnerabilities[cert.dgst] = dict() + self.vulnerabilities[cert.dgst][ReferenceType.DIRECT.value] = self._get_cert_transitive_cves( + cert, ReferenceType.DIRECT, ref_func ) - self.vulnerabilities[dgst][ReferenceType.INDIRECT.value] = self._get_cert_transitive_cves( - dgst, ReferenceType.INDIRECT + self.vulnerabilities[cert.dgst][ReferenceType.INDIRECT.value] = self._get_cert_transitive_cves( + cert, ReferenceType.INDIRECT, ref_func ) if thrown_away_cert_counter > 0: logging.warning("There were total of %s certificates skipped due to duplicity", thrown_away_cert_counter) + self._fitted = True + return self.vulnerabilities - def predict_single_cert(self, dgst: str) -> TransitiveCVE: + def predict_single_cert(self, dgst: str) -> TransitiveCVEs: """ Method returns vulnerabilities for certificate digest :param str dgst: Digest of certificate :return TransitiveCVE: TransitiveCVE object of certificate """ + if not self._fitted: + raise ValueError("Finder not yet fitted") + if not self.vulnerabilities.get(dgst): - return TransitiveCVE(direct_transitive_cves=None, indirect_transitive_cves=None) + return TransitiveCVEs(direct_transitive_cves=None, indirect_transitive_cves=None) - return TransitiveCVE( + return TransitiveCVEs( self.vulnerabilities[dgst][ReferenceType.DIRECT.value], self.vulnerabilities[dgst][ReferenceType.INDIRECT.value], ) - def predict(self, dgst_list: List[str]) -> Dict[str, TransitiveCVE]: + def predict(self, dgst_list: List[str]) -> Dict[str, TransitiveCVEs]: """ Method returns vulnerabilities for a list of certificate digests :param List[str] dgst_list: list of certificate digests :return Dict[str, TransitiveCVE]: Dictionary of TransitiveCVE objects for specified certificate digests """ + if not self._fitted: + raise ValueError("Finder not yet fitted") + cert_vulnerabilities = {} for dgst in dgst_list: diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index b6c275d4..c38f2ba4 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -395,6 +395,8 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): module_prunned_references: Set[str] = field(default_factory=set) policy_processed_references: References = field(default_factory=References) module_processed_references: References = field(default_factory=References) + direct_transitive_cves: Optional[Set[str]] = field(default=None) + indirect_transitive_cves: Optional[Set[str]] = field(default=None) @property def algorithm_numbers(self) -> Set[str]: diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py new file mode 100644 index 00000000..b4d5106b --- /dev/null +++ b/tests/fips/test_fips_analysis.py @@ -0,0 +1,224 @@ +from pathlib import Path +from typing import Optional, Set + +import pytest + +import tests.data.fips.dataset +from sec_certs.dataset import CPEDataset, CVEDataset +from sec_certs.dataset.fips import FIPSDataset +from sec_certs.sample.cpe import CPE +from sec_certs.sample.cve import CVE + + +@pytest.fixture(scope="module") +def data_dir() -> Path: + return Path(tests.data.fips.dataset.__path__[0]) + + +@pytest.fixture(scope="module") +def vulnerable_cpe() -> CPE: + return CPE("cpe:2.3:o:redhat:enterprise_linux:7.1:*:*:*:*:*:*:*", "Red Hat Enterprise Linux 7.1") + + +@pytest.fixture(scope="module") +def some_random_cpe() -> CPE: + return CPE( + "cpe:2.3:a:ibm:security_key_lifecycle_manager:2.6.0.1:*:*:*:*:*:*:*", + "IBM Security Key Lifecycle Manager 2.6.0.1", + ) + + +@pytest.fixture(scope="module") +def cve(vulnerable_cpe: CPE) -> CVE: + return CVE( + "CVE-1234-123456", + [vulnerable_cpe], + CVE.Impact(10, "HIGH", 10, 10), + "2021-05-26T04:15Z", + {"CWE-200"}, + ) + + +@pytest.fixture(scope="module") +def some_other_cve(some_random_cpe: CPE) -> CVE: + return CVE( + "CVE-2019-4513", + [some_random_cpe], + CVE.Impact(8.2, "HIGH", 3.9, 4.2), + "2000-05-26T04:15Z", + {"CVE-611"}, + ) + + +@pytest.fixture(scope="module") +def cpe_dataset(vulnerable_cpe: CPE, some_random_cpe: CPE) -> CPEDataset: + cpes = { + vulnerable_cpe, + some_random_cpe, + CPE( + "cpe:2.3:a:semperplugins:all_in_one_seo_pack:1.3.6.4:*:*:*:*:wordpress:*:*", + "Semper Plugins All in One SEO Pack 1.3.6.4 for WordPress", + ), + CPE( + "cpe:2.3:a:tracker-software:pdf-xchange_lite_printer:6.0.320.0:*:*:*:*:*:*:*", + "Tracker Software PDF-XChange Lite Printer 6.0.320.0", + ), + } + return CPEDataset(False, {x.uri: x for x in cpes}) + + +@pytest.fixture(scope="module") +def cve_dataset(cve: CVE, some_other_cve: CVE) -> CVEDataset: + cves = {cve, some_other_cve} + cve_dset = CVEDataset({x.cve_id: x for x in cves}) + cve_dset.build_lookup_dict(use_nist_mapping=False) + return cve_dset + + +@pytest.fixture(scope="module") +def toy_static_dataset(data_dir: Path) -> FIPSDataset: + return FIPSDataset.from_json(data_dir / "toy_dataset.json") + + +@pytest.fixture(scope="module") +def processed_dataset( + toy_static_dataset: FIPSDataset, cpe_dataset: CPEDataset, cve_dataset: CVEDataset, tmp_path_factory +) -> FIPSDataset: + tmp_dir = tmp_path_factory.mktemp("cc_dset") + toy_static_dataset.root_dir = tmp_dir + + tested_certs = { + toy_static_dataset["3095"], + toy_static_dataset["3093"], + toy_static_dataset["3197"], + toy_static_dataset["2441"], + } + toy_static_dataset.certs = {x.dgst: x for x in tested_certs} + + toy_static_dataset.download_all_artifacts() + toy_static_dataset.convert_all_pdfs() + toy_static_dataset._extract_data() + toy_static_dataset._compute_references(keep_unknowns=True) + + toy_static_dataset.auxillary_datasets.cpe_dset = cpe_dataset + toy_static_dataset.auxillary_datasets.cve_dset = cve_dataset + toy_static_dataset.compute_cpe_heuristics() + toy_static_dataset.compute_related_cves() + toy_static_dataset._compute_transitive_vulnerabilities() + + return toy_static_dataset + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [ + ("3095", {"3093", "3094", "3096"}), + ("3093", {"3090", "3091"}), + ("3197", {"3195", "3096", "3196", "3644", "3651"}), + ], +) +def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.directly_referencing == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", {"3093", "3094", "3096"}), ("3093", {"3090", "3091"})]) +def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.directly_referencing == expected_refs + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [ + ( + "3093", + { + "3090", + "3091", + }, + ), + ("3095", {"3090", "3091", "3093", "3094", "3096"}), + ], +) +def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referencing == expected_refs + + +@pytest.mark.parametrize( + "input_dgst, expected_refs", + [("3095", {"3090", "3091", "3093", "3094", "3096"}), ("3093", {"3090", "3091"})], +) +def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.indirectly_referencing == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_html_modules_directly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.directly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_pdf_policies_directly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from policy not extracted") + assert crt.heuristics.policy_processed_references.directly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_html_modules_indirectly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.module_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs + + +@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) +def test_pdf_policies_indirectly_referenced_by( + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] +): + crt = processed_dataset[input_dgst] + if not crt.state.policy_extract_ok: + pytest.xfail(reason="Data from module not extracted") + assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs + + +def test_match_cpe(processed_dataset: FIPSDataset, vulnerable_cpe: CPE, some_random_cpe: CPE): + assert processed_dataset["2441"].heuristics.cpe_matches + assert vulnerable_cpe.uri in processed_dataset["2441"].heuristics.cpe_matches + assert some_random_cpe.uri not in processed_dataset["2441"].heuristics.cpe_matches + + +def test_find_related_cves(processed_dataset: FIPSDataset, cve: CVE, some_other_cve: CVE): + assert processed_dataset["2441"].heuristics.related_cves + assert cve.cve_id in processed_dataset["2441"].heuristics.related_cves + assert some_other_cve not in processed_dataset["2441"].heuristics.related_cves + + +def test_keywords_heuristics(processed_dataset: FIPSDataset): + keywords = processed_dataset["2441"].pdf_data.keywords + assert keywords + assert keywords["symmetric_crypto"]["AES_competition"]["AES"]["AES"] == 53 + assert not keywords["pq_crypto"] + assert keywords["crypto_library"]["OpenSSL"]["OpenSSL"] == 83 + assert keywords["side_channel_analysis"]["SCA"]["timing attacks"] == 1 diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 58744e0c..7e8a6e4d 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -2,7 +2,6 @@ import shutil from pathlib import Path from tempfile import TemporaryDirectory -from typing import Optional, Set import pytest @@ -22,120 +21,6 @@ def toy_dataset(data_dir: Path) -> FIPSDataset: return FIPSDataset.from_json(data_dir / "toy_dataset.json") -@pytest.fixture(scope="module") -def toy_static_dataset(data_dir: Path) -> FIPSDataset: - return FIPSDataset.from_json(data_dir / "toy_dataset.json") - - -@pytest.fixture(scope="module") -def processed_dataset(toy_static_dataset: FIPSDataset, tmp_path_factory) -> FIPSDataset: - tmp_dir = tmp_path_factory.mktemp("cc_dset") - toy_static_dataset.root_dir = tmp_dir - - tested_certs = {toy_static_dataset["3095"], toy_static_dataset["3093"], toy_static_dataset["3197"]} - toy_static_dataset.certs = {x.dgst: x for x in tested_certs} - - toy_static_dataset.download_all_artifacts() - toy_static_dataset.convert_all_pdfs() - toy_static_dataset._extract_data() - toy_static_dataset._compute_references(keep_unknowns=True) - return toy_static_dataset - - -@pytest.mark.parametrize( - "input_dgst, expected_refs", - [ - ("3095", {"3093", "3094", "3096"}), - ("3093", {"3090", "3091"}), - ("3197", {"3195", "3096", "3196", "3644", "3651"}), - ], -) -def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): - crt = processed_dataset[input_dgst] - if not crt.state.module_extract_ok: - pytest.xfail(reason="Data from module not extracted") - assert crt.heuristics.module_processed_references.directly_referencing == expected_refs - - -@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", {"3093", "3094", "3096"}), ("3093", {"3090", "3091"})]) -def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): - crt = processed_dataset[input_dgst] - if not crt.state.policy_extract_ok: - pytest.xfail(reason="Data from policy not extracted") - assert crt.heuristics.policy_processed_references.directly_referencing == expected_refs - - -@pytest.mark.parametrize( - "input_dgst, expected_refs", - [ - ( - "3093", - { - "3090", - "3091", - }, - ), - ("3095", {"3090", "3091", "3093", "3094", "3096"}), - ], -) -def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): - crt = processed_dataset[input_dgst] - if not crt.state.module_extract_ok: - pytest.xfail(reason="Data from module not extracted") - assert crt.heuristics.module_processed_references.indirectly_referencing == expected_refs - - -@pytest.mark.parametrize( - "input_dgst, expected_refs", - [("3095", {"3090", "3091", "3093", "3094", "3096"}), ("3093", {"3090", "3091"})], -) -def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): - crt = processed_dataset[input_dgst] - if not crt.state.policy_extract_ok: - pytest.xfail(reason="Data from policy not extracted") - assert crt.heuristics.policy_processed_references.indirectly_referencing == expected_refs - - -@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) -def test_html_modules_directly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] -): - crt = processed_dataset[input_dgst] - if not crt.state.module_extract_ok: - pytest.xfail(reason="Data from module not extracted") - assert crt.heuristics.module_processed_references.directly_referenced_by == expected_refs - - -@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) -def test_pdf_policies_directly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] -): - crt = processed_dataset[input_dgst] - if not crt.state.policy_extract_ok: - pytest.xfail(reason="Data from policy not extracted") - assert crt.heuristics.policy_processed_references.directly_referenced_by == expected_refs - - -@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) -def test_html_modules_indirectly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] -): - crt = processed_dataset[input_dgst] - if not crt.state.module_extract_ok: - pytest.xfail(reason="Data from module not extracted") - assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs - - -@pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) -def test_pdf_policies_indirectly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] -): - crt = processed_dataset[input_dgst] - if not crt.state.policy_extract_ok: - pytest.xfail(reason="Data from module not extracted") - assert crt.heuristics.module_processed_references.indirectly_referenced_by == expected_refs - - def test_dataset_to_json(toy_dataset: FIPSDataset, data_dir: Path, tmp_path: Path): toy_dataset.to_json(tmp_path / "dset.json") From cae2dc21ec1b7333566f73ba7354c49123386ab2 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 30 Nov 2022 16:47:53 +0100 Subject: [PATCH 43/81] restrict usage of fresh bool param --- sec_certs/dataset/common_criteria.py | 60 ++-- sec_certs/dataset/dataset.py | 2 +- sec_certs/dataset/fips.py | 35 ++- .../data/fips/certificate/fictional_cert.json | 4 +- tests/data/fips/dataset/toy_dataset.json | 260 ++++++++++-------- 5 files changed, 203 insertions(+), 158 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 932cba9b..f79da2fc 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -564,8 +564,8 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: self._convert_reports_to_txt(fresh) self._convert_targets_to_txt(fresh) - def _extract_report_metadata(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze(fresh)] + def _extract_report_metadata(self) -> None: + certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_metadata, certs_to_process, @@ -575,8 +575,8 @@ def _extract_report_metadata(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_targets_metadata(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze(fresh)] + def _extract_targets_metadata(self) -> None: + certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_metadata, certs_to_process, @@ -586,13 +586,13 @@ def _extract_targets_metadata(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_pdf_metadata(self, fresh: bool = True) -> None: + def _extract_pdf_metadata(self) -> None: logger.info("Extracting pdf metadata from CC dataset") - self._extract_report_metadata(fresh) - self._extract_targets_metadata(fresh) + self._extract_report_metadata() + self._extract_targets_metadata() - def _extract_report_frontpage(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze(fresh)] + def _extract_report_frontpage(self) -> None: + certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_frontpage, certs_to_process, @@ -602,8 +602,8 @@ def _extract_report_frontpage(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_targets_frontpage(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze(fresh)] + def _extract_targets_frontpage(self) -> None: + certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_frontpage, certs_to_process, @@ -613,13 +613,13 @@ def _extract_targets_frontpage(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_pdf_frontpage(self, fresh: bool = True) -> None: + def _extract_pdf_frontpage(self) -> None: logger.info("Extracting pdf frontpages from CC dataset.") - self._extract_report_frontpage(fresh) - self._extract_targets_frontpage(fresh) + self._extract_report_frontpage() + self._extract_targets_frontpage() - def _extract_report_keywords(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze(fresh)] + def _extract_report_keywords(self) -> None: + certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_keywords, certs_to_process, @@ -629,8 +629,8 @@ def _extract_report_keywords(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_targets_keywords(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze(fresh)] + def _extract_targets_keywords(self) -> None: + certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_keywords, certs_to_process, @@ -640,16 +640,16 @@ def _extract_targets_keywords(self, fresh: bool = True) -> None: ) self.update_with_certs(processed_certs) - def _extract_pdf_keywords(self, fresh: bool = True) -> None: + def _extract_pdf_keywords(self) -> None: logger.info("Extracting pdf keywords from CC dataset.") - self._extract_report_keywords(fresh) - self._extract_targets_keywords(fresh) + self._extract_report_keywords() + self._extract_targets_keywords() - def _extract_data(self, fresh: bool = True) -> None: + def _extract_data(self) -> None: logger.info("Extracting various stuff from converted txt files from CC dataset.") - self._extract_pdf_metadata(fresh) - self._extract_pdf_frontpage(fresh) - self._extract_pdf_keywords(fresh) + self._extract_pdf_metadata() + self._extract_pdf_frontpage() + self._extract_pdf_keywords() def _compute_cert_labs(self) -> None: logger.info("Deriving information about laboratories involved in certification.") @@ -657,7 +657,7 @@ def _compute_cert_labs(self) -> None: for cert in certs_to_process: cert.compute_heuristics_cert_lab() - def _compute_normalized_cert_ids(self, fresh: bool = True) -> None: + def _compute_normalized_cert_ids(self) -> None: logger.info("Deriving information about sample ids from pdf scan.") for cert in self: cert.compute_heuristics_cert_id() @@ -673,7 +673,7 @@ def _compute_transitive_vulnerabilities(self): self.certs[dgst].heuristics.direct_transitive_cves = transitive_cve.direct_transitive_cves self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves - def _compute_heuristics(self, fresh: bool = True) -> None: + def _compute_heuristics(self) -> None: logger.info("Computing various heuristics on CC certificates.") self._compute_normalized_cert_ids() super()._compute_heuristics() @@ -686,7 +686,7 @@ def _compute_sars(self) -> None: for cert in self: cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) - def _compute_references(self, fresh: bool = True) -> None: + def _compute_references(self) -> None: def ref_lookup(kw_attr): def func(cert): kws = getattr(cert.pdf_data, kw_attr) @@ -806,10 +806,10 @@ def certs_dir(self) -> Path: def __iter__(self) -> Iterator[CommonCriteriaMaintenanceUpdate]: yield from self.certs.values() # type: ignore - def _compute_heuristics(self, download_fresh_cpes: bool = False) -> None: + def _compute_heuristics(self) -> None: raise NotImplementedError - def compute_related_cves(self, download_fresh_cves: bool = False) -> None: + def compute_related_cves(self) -> None: raise NotImplementedError def process_auxillary_datasets(self, download_fresh: bool = False) -> None: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 45182faf..da6d7617 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -287,7 +287,7 @@ def _compute_heuristics(self) -> None: self._compute_transitive_vulnerabilities() @abstractmethod - def _compute_references(self, fresh: bool = True) -> None: + def _compute_references(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") @abstractmethod diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 43ae8893..5b5a5b44 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -93,12 +93,12 @@ def __getitem__(self, item: str) -> FIPSCertificate: else: return super().__getitem__(item) - def _extract_data_from_html_modules(self, fresh: bool = True) -> None: + def _extract_data_from_html_modules(self) -> None: """ Extracts data from html module file :param bool fresh: if all certs should be processed, or only the failed ones. Defaults to True """ - certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze(fresh)] + certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.parse_html_module, certs_to_process, @@ -109,19 +109,18 @@ def _extract_data_from_html_modules(self, fresh: bool = True) -> None: self.update_with_certs(processed_certs) @serialize - def _extract_data(self, fresh: bool = True) -> None: - if fresh: - for cert in self: - cert.state.policy_extract_ok = True - cert.state.module_extract_ok = True + def _extract_data(self) -> None: + for cert in self: + cert.state.policy_extract_ok = True + cert.state.module_extract_ok = True - self._extract_data_from_html_modules(fresh) - self._extract_policy_pdf_metadata(fresh) - self._extract_policy_pdf_keywords(fresh) - self._extract_algorithms_from_policy_tables(fresh) + self._extract_data_from_html_modules() + self._extract_policy_pdf_metadata() + self._extract_policy_pdf_keywords() + self._extract_algorithms_from_policy_tables() - def _extract_policy_pdf_keywords(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + def _extract_policy_pdf_keywords(self) -> None: + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.extract_policy_pdf_keywords, certs_to_process, @@ -256,8 +255,8 @@ def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSA return alg_dset - def _extract_algorithms_from_policy_tables(self, fresh: bool = True): - certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + def _extract_algorithms_from_policy_tables(self): + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] cert_processing.process_parallel( FIPSCertificate.get_algorithms_from_policy_tables, certs_to_process, @@ -266,8 +265,8 @@ def _extract_algorithms_from_policy_tables(self, fresh: bool = True): progress_bar_desc="Extracting Algorithms from policy tables", ) - def _extract_policy_pdf_metadata(self, fresh: bool = True) -> None: - certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze(fresh)] + def _extract_policy_pdf_metadata(self) -> None: + certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.extract_policy_pdf_metadata, certs_to_process, @@ -298,7 +297,7 @@ def _prune_reference_candidates(self) -> None: # - Such reference should then be discarded. # - We are uncertain of the effectivity of such measure, disabling it for now. - def _compute_references(self, fresh: bool = True, keep_unknowns: bool = False) -> None: + def _compute_references(self, keep_unknowns: bool = False) -> None: self._prune_reference_candidates() policy_reference_finder = ReferenceFinder() diff --git a/tests/data/fips/certificate/fictional_cert.json b/tests/data/fips/certificate/fictional_cert.json index 50f2e7aa..a1dbec54 100644 --- a/tests/data/fips/certificate/fictional_cert.json +++ b/tests/data/fips/certificate/fictional_cert.json @@ -66,7 +66,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", diff --git a/tests/data/fips/dataset/toy_dataset.json b/tests/data/fips/dataset/toy_dataset.json index ea9d9621..37853ff9 100644 --- a/tests/data/fips/dataset/toy_dataset.json +++ b/tests/data/fips/dataset/toy_dataset.json @@ -8,16 +8,16 @@ "auxillary_datasets_processed": false, "certs_analyzed": false }, - "timestamp": "2022-11-29 16:42:34.785695", + "timestamp": "2022-11-30 16:38:51.953055", "sha256_digest": "not implemented", "name": "FIPSDataset dataset", - "description": "29/11/2022 16:42:34", + "description": "30/11/2022 16:38:51", "n_certs": 22, "certs": [ { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0ab77c56eece2d24", - "cert_id": "3651", + "dgst": "69d9200c309f5f97", + "cert_id": "2590", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -82,7 +82,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -92,14 +94,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", - "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" + "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", + "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0e8b7a7f79b3098b", - "cert_id": "2665", + "dgst": "3b05cf67849835e3", + "cert_id": "2997", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -164,7 +166,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -174,14 +178,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", - "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" + "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", + "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "5b00a7bb92c1cb19", - "cert_id": "2721", + "dgst": "618102f275d5649a", + "cert_id": "3495", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -246,7 +250,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -256,14 +262,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", - "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" + "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", + "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "2aec3694418f35d8", - "cert_id": "3488", + "dgst": "5b00a7bb92c1cb19", + "cert_id": "2721", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -328,7 +334,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -338,14 +346,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", - "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" + "policy_pdf_hash": "51ef058076692a3dc7d051e70e9613598442d38ea9d4d709a7d3a2a7288e4b50", + "policy_txt_hash": "27581efa0595a0daa6a71f55903b725e8e8c3619ed767c51a7b924e0ddcc32d3" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ed16d272044ed81f", - "cert_id": "2860", + "dgst": "e1ff7d9997726782", + "cert_id": "3090", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -410,7 +418,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -420,14 +430,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", - "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" + "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", + "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "69d9200c309f5f97", - "cert_id": "2590", + "dgst": "ed16d272044ed81f", + "cert_id": "2860", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -492,7 +502,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -502,14 +514,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "cd61ac2e08ea43ddc6824d406daaf3f5bc1370bb8c1a16313b29e9b34cf630b9", - "policy_txt_hash": "fd3c1588a897a114e8603694f326b582294f6ee40c06b430dedf2b10c1fa5792" + "policy_pdf_hash": "d6c29154d6ac935216ce3b3067989436bd2ec34c5c6249a50858b988d8e5ee4b", + "policy_txt_hash": "9a3316575ea311f7a2945d79c64beb2ead789c13bf6f2d8af8d37fd09a613256" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ad1e075215267a28", - "cert_id": "2441", + "dgst": "73e7f3f802b0e919", + "cert_id": "3176", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -574,7 +586,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -584,14 +598,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", - "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" + "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", + "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "75ca28395bfc9ad1", - "cert_id": "2630", + "dgst": "756a6524cdb2d4ea", + "cert_id": "3850", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -656,7 +670,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -666,14 +682,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", - "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" + "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", + "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "51c94a510b50e68f", - "cert_id": "3141", + "dgst": "0e8b7a7f79b3098b", + "cert_id": "2665", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -738,7 +754,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -748,14 +766,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", - "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" + "policy_pdf_hash": "1a54d50de140adf39788168b445a8443863dd93e4e2d61c6833117734eeef303", + "policy_txt_hash": "21088d33845b139189b1054abb8d98c2bd994885cbc9f2ce47a15d06fc813f00" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "618102f275d5649a", - "cert_id": "3495", + "dgst": "f7df34ce0a0ad316", + "cert_id": "3093", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -820,7 +838,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -830,14 +850,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "90a0425ae63aa28b1a365efe6dffe1b091871efb32422cdeb4ab0fe056f51b3a", - "policy_txt_hash": "b741eba5464b2e5ad057da3907b52c0163f3483ace52d6f4fce0a13255d2475e" + "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", + "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "9ca7b8be590113b8", - "cert_id": "2711", + "dgst": "0df59fd6c41cadd2", + "cert_id": "3711", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -902,7 +922,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -912,14 +934,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", - "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" + "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", + "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "73e7f3f802b0e919", - "cert_id": "3176", + "dgst": "24a05f4d1305d667", + "cert_id": "3493", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -984,7 +1006,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -994,14 +1018,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3c4d11b0edd0c7d2535b2afc732be3cd3d9090a023e5421c120ea365745722fc", - "policy_txt_hash": "46152aef1b1c4c7fb4c98873840615973cdc39bfcf6ccf6581e4f63df4a57633" + "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", + "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "c333a75353784177", - "cert_id": "2779", + "dgst": "51c94a510b50e68f", + "cert_id": "3141", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1066,7 +1090,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1076,14 +1102,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", - "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" + "policy_pdf_hash": "102edd217f5526882d7293205e7eb7a08cf1200e5a7942411e0c3fd7f60fca79", + "policy_txt_hash": "14ce24aa060f55846826d46aec52b93cf96c9da40bf3ee361aeac0d682bbb2fa" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "24a05f4d1305d667", - "cert_id": "3493", + "dgst": "75ca28395bfc9ad1", + "cert_id": "2630", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1148,7 +1174,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1158,8 +1186,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "5a99b0c105ac337b83d15ee7e30a569dfa36d9c329741cf7207a4359180470ae", - "policy_txt_hash": "022f49e5061fa19dbc161d898beb8c02e5e314b700d15710424238255b8c298b" + "policy_pdf_hash": "57e27b3b224ac32cd565bb653ae9141a80b6bb2c4121f75b583c64fe55e4bd28", + "policy_txt_hash": "d784c6554261dba00314c9ec8fc1145b2d211e4e10b94757f914cfe2d9d4e111" } }, { @@ -1230,7 +1258,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1246,8 +1276,8 @@ }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "756a6524cdb2d4ea", - "cert_id": "3850", + "dgst": "ceab64951a30f684", + "cert_id": "3197", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1312,7 +1342,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1322,14 +1354,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "263a3374eac8840197cab762ff9d5ef6cb16f50cd0978a6b29c470a47e0612fe", - "policy_txt_hash": "2f08752b55cf1c148da9f307d9ba1b9147dbfcc4521a7ccc175b630cfc7fe136" + "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", + "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "ceab64951a30f684", - "cert_id": "3197", + "dgst": "0ab77c56eece2d24", + "cert_id": "3651", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1394,7 +1426,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1404,14 +1438,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "1f7c50818433e91649ae338c80a4b5da158b05812302e1d326d75b80693e26b8", - "policy_txt_hash": "719a71302456a5ae9f44064f9707d7b30c4003b378f015812242d353dfacecd5" + "policy_pdf_hash": "c3db1ac50cf492cb87f39e78e3f226564596ccf1f3eb518bd6a1796cec001629", + "policy_txt_hash": "2ed639ebae177a801d27142a5c4a8b7d1ac313143ff1ff3100297caed537ce3d" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "184097a88a9b4ad9", - "cert_id": "3518", + "dgst": "9ca7b8be590113b8", + "cert_id": "2711", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1476,7 +1510,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1486,14 +1522,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", - "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" + "policy_pdf_hash": "6d82b67546b3714a5e54b2385681679e63801727a128be27b08f916d66da702d", + "policy_txt_hash": "2aa26b31917696d4105e53a04f57e2105fdbcc0ebb2be34171e3b58c1e2bfcc8" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "0df59fd6c41cadd2", - "cert_id": "3711", + "dgst": "184097a88a9b4ad9", + "cert_id": "3518", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1558,7 +1594,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1568,14 +1606,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "3fb4374e11575cf1cddd5e852fe4acc4456c7fb5e03bef4d0e108d9c333f67e4", - "policy_txt_hash": "c8d31c56206143407662b4079e60e66299db0bc7be7fb7f72649fefcf0d5a5ad" + "policy_pdf_hash": "36b63890182f0aed29b305a0b4acc0d70b657262516f4be69138c70c2abdb1f1", + "policy_txt_hash": "cc1f8d0f7bb759894e81cbbfd1cfe02cdc443879fd47d3306f2a1b419fbf2872" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "e1ff7d9997726782", - "cert_id": "3090", + "dgst": "c333a75353784177", + "cert_id": "2779", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1640,7 +1678,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1650,14 +1690,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "19a4e72da9e25bb1776667ce0153bb14441037742d382e40e3388be321e8ab29", - "policy_txt_hash": "f68d9d0b85e348b54ae8569cebd6cd98a8157ebbd0aaea88440bd8271b1b49d5" + "policy_pdf_hash": "7d120cfc4073c1a120a86b16655c016fb36f4855d376baa811ca6187d1c801d3", + "policy_txt_hash": "49df600e899ed4d00a99dbd9727b9fa4ab5f684dbb681b7e6f49b1debf44a7b6" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "3b05cf67849835e3", - "cert_id": "2997", + "dgst": "ad1e075215267a28", + "cert_id": "2441", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1722,7 +1762,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1732,14 +1774,14 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "eab0fb2c68771543868254864f3b7b576fa42461fe895d57da5f5c7873d3ebf4", - "policy_txt_hash": "a5153056afaf3475697ea8ea024b0caaa1c56b9da7b15d5e0ce77a276a7cf953" + "policy_pdf_hash": "a7b8d26e4e2bf23b0995074ca3566fb43b557e727b6f82fb0ba174185a2ce51a", + "policy_txt_hash": "18e94120db72047c5ce2e3c59d27ce266f86c052b04b06e75b0b7cf8cb0c0dd4" } }, { "_type": "sec_certs.sample.fips.FIPSCertificate", - "dgst": "f7df34ce0a0ad316", - "cert_id": "3093", + "dgst": "2aec3694418f35d8", + "cert_id": "3488", "web_data": { "_type": "sec_certs.sample.fips.FIPSCertificate.WebData", "module_name": null, @@ -1804,7 +1846,9 @@ "indirectly_referenced_by": null, "directly_referencing": null, "indirectly_referencing": null - } + }, + "direct_transitive_cves": null, + "indirect_transitive_cves": null }, "state": { "_type": "sec_certs.sample.fips.FIPSCertificate.InternalState", @@ -1814,8 +1858,8 @@ "policy_convert_ok": true, "module_extract_ok": false, "policy_extract_ok": false, - "policy_pdf_hash": "140b89794386b5684a3a29a6ff41b206cacdd9dc91192257510795b5d577c66a", - "policy_txt_hash": "b875301d2b9331cb100016a7c380f93ec0352a79f6584addebb933eb161ae4c7" + "policy_pdf_hash": "c865c04955e36a4068bf5f331e5658d8d62dedeef963c5ac1b02700243143ad4", + "policy_txt_hash": "fa544520b43adcd5dffffe34513322f5a63f0d0b0a3dc1d2b8575a7a80f490e4" } } ] From e062f3ee1efe4b3cfb20dc2d8de90557a6be0b2d Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 30 Nov 2022 17:12:00 +0100 Subject: [PATCH 44/81] improve dataset processing logging --- sec_certs/dataset/common_criteria.py | 70 ++++++++++++++++++++-------- sec_certs/dataset/dataset.py | 16 ++----- sec_certs/dataset/fips.py | 41 +++++++++++----- 3 files changed, 85 insertions(+), 42 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index f79da2fc..7de0de32 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -517,47 +517,76 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_targets(fresh) def _download_reports(self, fresh: bool = True) -> None: - logger.info("Downloading PDFs of CC certification reports.") + self.reports_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh) and x.report_link] + + if fresh: + logger.info("Downloading PDFs of CC certification reports.") + if not fresh and certs_to_process: + logger.info( + f"Downloading {len(certs_to_process)} PDFs of CC certification reports for which previous download failed." + ) + cert_processing.process_parallel( CommonCriteriaCert.download_pdf_report, certs_to_process, config.n_threads, - progress_bar_desc="Downloading reports", + progress_bar_desc="Downloading PDFs of CC certification reports", ) def _download_targets(self, fresh: bool = True) -> None: - logger.info("Downloading PDFs of CC security targets.") self.targets_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh)] + + if fresh: + logger.info("Downloading PDFs of CC security targets.") + if not fresh and certs_to_process: + logger.info( + f"Downloading {len(certs_to_process)} PDFs of CC security targets for which previous download failed.." + ) + cert_processing.process_parallel( CommonCriteriaCert.download_pdf_st, certs_to_process, config.n_threads, - progress_bar_desc="Downloading targets", + progress_bar_desc="Downloading PDFs of CC security targets", ) def _convert_reports_to_txt(self, fresh: bool = True) -> None: - logger.info("Converting CC sample reports to .txt") self.reports_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_convert(fresh)] + + if fresh: + logger.info("Converting PDFs of certification reports to txt.") + if not fresh and certs_to_process: + logger.info( + f"Converting {len(certs_to_process)} PDFs of certification reports to txt for which previous conversion failed." + ) + cert_processing.process_parallel( CommonCriteriaCert.convert_report_pdf, certs_to_process, config.n_threads, - progress_bar_desc="Converting reports to txt", + progress_bar_desc="Converting PDFs of certification reports to txt", ) def _convert_targets_to_txt(self, fresh: bool = True) -> None: - logger.info("Converting CC security targets to .txt") self.targets_txt_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.st_is_ok_to_convert(fresh)] + + if fresh: + logger.info("Converting PDFs of security targets to txt.") + if not fresh and certs_to_process: + logger.info( + f"Converting {len(certs_to_process)} PDFs of security targets to txt for which previous conversion failed." + ) + cert_processing.process_parallel( CommonCriteriaCert.convert_st_pdf, certs_to_process, config.n_threads, - progress_bar_desc="Converting targets to txt", + progress_bar_desc="Converting PDFs of security targets to txt", ) def _convert_all_pdfs_body(self, fresh: bool = True) -> None: @@ -565,6 +594,7 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: self._convert_targets_to_txt(fresh) def _extract_report_metadata(self) -> None: + logger.info("Extracting report metadata") certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_metadata, @@ -576,6 +606,7 @@ def _extract_report_metadata(self) -> None: self.update_with_certs(processed_certs) def _extract_targets_metadata(self) -> None: + logger.info("Extracting target metadata") certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_metadata, @@ -587,11 +618,11 @@ def _extract_targets_metadata(self) -> None: self.update_with_certs(processed_certs) def _extract_pdf_metadata(self) -> None: - logger.info("Extracting pdf metadata from CC dataset") self._extract_report_metadata() self._extract_targets_metadata() def _extract_report_frontpage(self) -> None: + logger.info("Extracting report frontpages") certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_frontpage, @@ -603,6 +634,7 @@ def _extract_report_frontpage(self) -> None: self.update_with_certs(processed_certs) def _extract_targets_frontpage(self) -> None: + logger.info("Extracting target frontpages") certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_frontpage, @@ -614,11 +646,11 @@ def _extract_targets_frontpage(self) -> None: self.update_with_certs(processed_certs) def _extract_pdf_frontpage(self) -> None: - logger.info("Extracting pdf frontpages from CC dataset.") self._extract_report_frontpage() self._extract_targets_frontpage() def _extract_report_keywords(self) -> None: + logger.info("Extracting report keywords") certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_report_pdf_keywords, @@ -630,6 +662,7 @@ def _extract_report_keywords(self) -> None: self.update_with_certs(processed_certs) def _extract_targets_keywords(self) -> None: + logger.info("Extracting target keywords") certs_to_process = [x for x in self if x.state.st_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( CommonCriteriaCert.extract_st_pdf_keywords, @@ -641,29 +674,28 @@ def _extract_targets_keywords(self) -> None: self.update_with_certs(processed_certs) def _extract_pdf_keywords(self) -> None: - logger.info("Extracting pdf keywords from CC dataset.") self._extract_report_keywords() self._extract_targets_keywords() def _extract_data(self) -> None: - logger.info("Extracting various stuff from converted txt files from CC dataset.") + logger.info("Extracting various data from certification artifacts") self._extract_pdf_metadata() self._extract_pdf_frontpage() self._extract_pdf_keywords() def _compute_cert_labs(self) -> None: - logger.info("Deriving information about laboratories involved in certification.") + logger.info("Computing heuristics: Deriving information about laboratories involved in certification.") certs_to_process = [x for x in self if x.state.report_is_ok_to_analyze()] for cert in certs_to_process: cert.compute_heuristics_cert_lab() def _compute_normalized_cert_ids(self) -> None: - logger.info("Deriving information about sample ids from pdf scan.") + logger.info("omputing heuristics: Deriving information about sample ids from pdf scan.") for cert in self: cert.compute_heuristics_cert_id() def _compute_transitive_vulnerabilities(self): - logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") + logger.info("omputing heuristics: computing transitive vulnerabilities in referenc(ed/ing) certificates.") transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: cert.heuristics.cert_id) transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.report_references) @@ -674,14 +706,14 @@ def _compute_transitive_vulnerabilities(self): self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves def _compute_heuristics(self) -> None: - logger.info("Computing various heuristics on CC certificates.") + logger.info("Computing various heuristics on certificates.") self._compute_normalized_cert_ids() super()._compute_heuristics() self._compute_cert_labs() self._compute_sars() def _compute_sars(self) -> None: - logger.info("Computing SARs") + logger.info("omputing heuristics: Computing SARs") transformer = SARTransformer().fit(self.certs.values()) for cert in self: cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) @@ -704,7 +736,7 @@ def func(cert): return func - logger.info("Compute references between certificates.") + logger.info("omputing heuristics: references between certificates.") for ref_source in ("report", "st"): kw_source = f"{ref_source}_keywords" dep_attr = f"{ref_source}_references" @@ -716,9 +748,9 @@ def func(cert): setattr(self.certs[dgst].heuristics, dep_attr, finder.predict_single_cert(dgst, keep_unknowns=False)) def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + super().process_auxillary_datasets(download_fresh) self.auxillary_datasets.pp_dset = self.process_protection_profiles(to_download=download_fresh) self.auxillary_datasets.mu_dset = self.process_maintenance_updates(to_download=download_fresh) - super().process_auxillary_datasets(download_fresh) @serialize def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None: diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index da6d7617..2589d6aa 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -210,6 +210,7 @@ def get_certs_from_web(self) -> None: @abstractmethod def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + logger.info("Processing auxillary datasets.") self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) self.auxillary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh_cves=download_fresh) self.state.auxillary_datasets_processed = True @@ -220,11 +221,7 @@ def download_all_artifacts(self, fresh: bool = True) -> None: logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") return - if fresh: - logger.info("Attempting to download certification artifacts.") - else: - logger.info("Attempting to re-download failed certification artifacts.") - + logger.info("Attempting to download certification artifacts.") self._download_all_artifacts_body(fresh) if fresh: self._download_all_artifacts_body(False) @@ -241,11 +238,7 @@ def convert_all_pdfs(self, fresh: bool = True) -> None: logger.error("Attempting to convert pdfs while not having the artifacts downloaded. Returning.") return - if fresh: - logger.info("Converting all PDFs to txt") - else: - logger.info("Re-converting all failed PDFs to txt") - + logger.info("Converting all PDFs to txt") self._convert_all_pdfs_body(fresh) if fresh: self._convert_all_pdfs_body(False) @@ -281,6 +274,7 @@ def _extract_data(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _compute_heuristics(self) -> None: + logger.info("Computing various heuristics from the certificates.") self.compute_cpe_heuristics() self.compute_related_cves() self._compute_references() @@ -468,7 +462,7 @@ def compute_related_cves( download_fresh_cves, use_nist_cpe_matching_dict ) - logger.info("Computing CVEs in certificates.") + logger.info("Computing heuristics: CVEs in certificates.") self.enrich_automated_cpes_with_manual_labels() cpe_rich_certs = [x for x in cast(Iterator[Certificate], self) if x.heuristics.cpe_matches] diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 5b5a5b44..09866055 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -98,18 +98,20 @@ def _extract_data_from_html_modules(self) -> None: Extracts data from html module file :param bool fresh: if all certs should be processed, or only the failed ones. Defaults to True """ + logger.info("Extracting data from html modules.") certs_to_process = [x for x in self if x.state.module_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.parse_html_module, certs_to_process, config.n_threads, use_threading=False, - progress_bar_desc="Extracting data from module html", + progress_bar_desc="Extracting data from html modules", ) self.update_with_certs(processed_certs) @serialize def _extract_data(self) -> None: + logger.info("Extracting various data from certification artifacts.") for cert in self: cert.state.policy_extract_ok = True cert.state.module_extract_ok = True @@ -120,13 +122,14 @@ def _extract_data(self) -> None: self._extract_algorithms_from_policy_tables() def _extract_policy_pdf_keywords(self) -> None: + logger.info("Extracting keywords from policy pdfs.") certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.extract_policy_pdf_keywords, certs_to_process, config.n_threads, use_threading=False, - progress_bar_desc="Extracting keywords from policy", + progress_bar_desc="Extracting keywords from policy pdfs", ) self.update_with_certs(processed_certs) @@ -135,9 +138,14 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_policies(fresh) def _download_modules(self, fresh: bool = True) -> None: - logger.info("Downloading HTML cryptographic modules.") self.module_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.module_is_ok_to_download(fresh)] + + if fresh: + logger.info("Downloading HTML cryptographic modules.") + if not fresh and certs_to_process: + logger.info(f"Downloading {len(certs_to_process)} HTML modules for which download failed.") + cert_processing.process_parallel( FIPSCertificate.download_module, certs_to_process, @@ -146,9 +154,14 @@ def _download_modules(self, fresh: bool = True) -> None: ) def _download_policies(self, fresh: bool = True) -> None: - logger.info("Downloading PDF security policies.") self.policies_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.policy_is_ok_to_download(fresh)] + + if fresh: + logger.info("Downloading PDF security policies.") + if not fresh and certs_to_process: + logger.info(f"Downloading {len(certs_to_process)} PDF security policies for which download failed.") + cert_processing.process_parallel( FIPSCertificate.download_policy, certs_to_process, @@ -160,13 +173,16 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: self._convert_policies_to_txt(fresh) def _convert_policies_to_txt(self, fresh: bool = True) -> None: + self.policies_txt_dir.mkdir(parents=True, exist_ok=True) + certs_to_process = [x for x in self if x.state.policy_is_ok_to_convert(fresh)] + if fresh: logger.info("Converting FIPS security policies to .txt") - else: - logger.info("Attempting re-conversion of failed PDF security policies to .txt") + if not fresh and certs_to_process: + logger.info( + f"Converting {len(certs_to_process)} FIPS security polcies to .txt for which previous convert failed." + ) - self.policies_txt_dir.mkdir(parents=True, exist_ok=True) - certs_to_process = [x for x in self if x.state.policy_is_ok_to_convert(fresh)] cert_processing.process_parallel( FIPSCertificate.convert_policy_pdf, certs_to_process, @@ -188,8 +204,6 @@ def _get_all_certs_from_html_sources(self) -> Set[FIPSCertificate]: ) def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: - logger.debug(f"Getting certificate ids from {html_file}") - with open(html_file, "r", encoding="utf-8") as handle: html = BeautifulSoup(handle.read(), "html5lib") @@ -240,8 +254,8 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru @serialize def process_auxillary_datasets(self, download_fresh: bool = False) -> None: - self.auxillary_datasets.algorithm_dset = self._prepare_algorithm_dataset(download_fresh) super().process_auxillary_datasets(download_fresh) + self.auxillary_datasets.algorithm_dset = self._prepare_algorithm_dataset(download_fresh) def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSAlgorithmDataset: logger.info("Preparing FIPSAlgorithm dataset.") @@ -256,6 +270,7 @@ def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSA return alg_dset def _extract_algorithms_from_policy_tables(self): + logger.info("Extracting Algorithms from policy tables") certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] cert_processing.process_parallel( FIPSCertificate.get_algorithms_from_policy_tables, @@ -266,6 +281,7 @@ def _extract_algorithms_from_policy_tables(self): ) def _extract_policy_pdf_metadata(self) -> None: + logger.info("Extracting security policy metadata from the pdfs") certs_to_process = [x for x in self if x.state.policy_is_ok_to_analyze()] processed_certs = cert_processing.process_parallel( FIPSCertificate.extract_policy_pdf_metadata, @@ -277,7 +293,7 @@ def _extract_policy_pdf_metadata(self) -> None: self.update_with_certs(processed_certs) def _compute_transitive_vulnerabilities(self) -> None: - logger.info("Computing transitive vulnerabilities in referenc(ed/ing) certificates.") + logger.info("Computing heuristics: Computing transitive vulnerabilities in referenc(ed/ing) certificates.") transitive_cve_finder = TransitiveVulnerabilityFinder(lambda cert: cert.cert_id) transitive_cve_finder.fit(self.certs, lambda cert: cert.heuristics.policy_processed_references) @@ -298,6 +314,7 @@ def _prune_reference_candidates(self) -> None: # - We are uncertain of the effectivity of such measure, disabling it for now. def _compute_references(self, keep_unknowns: bool = False) -> None: + logger.info("Computing heuristics: Recovering references between certificates") self._prune_reference_candidates() policy_reference_finder = ReferenceFinder() From 5b0a7cb8950930a40f9025152753138bff73d160 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 08:24:21 +0100 Subject: [PATCH 45/81] fix table extraction from fips policies --- sec_certs/sample/fips.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index c38f2ba4..fe2c4cf6 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -79,9 +79,9 @@ def parse_single_detail_entry(key, entry): if "description" in entries: algs = FIPSHTMLParser.get_algs_from_description(entries["description"]) if "algorithms" in entries: - entries["algorithms"].update(algs) + entries["algorithms"].update({"UNKNOWN": x for x in algs}) else: - entries["algorithms"] = algs + entries["algorithms"] = {"UNKNOWN": x for x in algs} return entries @@ -498,6 +498,8 @@ def parse_html_module(cert: FIPSCertificate) -> FIPSCertificate: parser = FIPSHTMLParser(soup) algorithms, cert.web_data = parser.get_web_data_and_algorithms() cert.heuristics.algorithms |= algorithms + cert.state.module_extract_ok = True + return cert @staticmethod @@ -567,14 +569,13 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): pdf.repair_pdf(cert.state.policy_pdf_path) try: tabular_data = read_pdf(cert.state.policy_pdf_path, pages=list(table_rich_page_numbers), silent=True) + cert.heuristics.algorithms |= set( + itertools.chain.from_iterable([tables.get_algs_from_table(df.to_string()) for df in tabular_data]) + ) except Exception as e: logger.warning(f"Error when parsing tables from {cert.dgst}: {e}") cert.state.policy_extract_ok = False - cert.heuristics.algorithms |= set( - itertools.chain.from_iterable([tables.get_algs_from_table(df.to_string()) for df in tabular_data]) - ) - def prune_referenced_cert_ids(self) -> None: """ This method goes through all IDs (numbers) that correspond to FIPS Certificates and are stored in @@ -586,7 +587,7 @@ def prune_referenced_cert_ids(self) -> None: self.heuristics.module_prunned_references = self._prune_reference_ids_variable(html_module_ids) pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict().keys())) - pdf_policy_ids = {x.replace("#", "").strip() for x in pdf_policy_ids} + pdf_policy_ids = {"".join([y for y in x if y.isdigit()]) for x in pdf_policy_ids} self.heuristics.policy_prunned_references = self._prune_reference_ids_variable(pdf_policy_ids) def compute_heuristics_version(self) -> None: From f6812444caf3b89bccf80eadea05e64dee819656 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 08:58:39 +0100 Subject: [PATCH 46/81] fix reference computation fips --- sec_certs/sample/fips.py | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index fe2c4cf6..0e9e13dd 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -76,12 +76,13 @@ def parse_single_detail_entry(key, entry): if "caveat" in entries: entries["mentioned_certs"] = FIPSHTMLParser.get_mentioned_certs_from_caveat(entries["caveat"]) - if "description" in entries: - algs = FIPSHTMLParser.get_algs_from_description(entries["description"]) - if "algorithms" in entries: - entries["algorithms"].update({"UNKNOWN": x for x in algs}) - else: - entries["algorithms"] = {"UNKNOWN": x for x in algs} + # Temporarily disabled, as this isn't extracting anything useful. Only UNKNOWN#1-9 algs were extracted over whole dataset. + # if "description" in entries: + # algs = FIPSHTMLParser.get_algs_from_description(entries["description"]) + # if "algorithms" in entries: + # entries["algorithms"].update({"UNKNOWN": x for x in algs}) + # else: + # entries["algorithms"] = {"UNKNOWN": x for x in algs} return entries @@ -376,9 +377,12 @@ class PdfData(BasePdfData, ComplexSerializableType): @property def certlike_algorithm_numbers(self) -> Set[str]: """Returns numbers of certificates from keywords["fips_certlike"]["Certlike"]""" - fips_certlike = self.keywords["fips_certlike"].get("Certlike", dict()) - matches = {re.search(r"#\s{0,1}\d{1,4}", x) for x in fips_certlike.keys()} - return {"".join([x for x in match.group() if x.isdigit()]) for match in matches if match} + if self.keywords and "fips_certlike" in self.keywords: + fips_certlike = self.keywords["fips_certlike"].get("Certlike", dict()) + matches = {re.search(r"#\s{0,1}\d{1,4}", x) for x in fips_certlike.keys()} + return {"".join([x for x in match.group() if x.isdigit()]) for match in matches if match} + else: + return set() @dataclass(eq=True) class Heuristics(BaseHeuristics, ComplexSerializableType): @@ -586,8 +590,12 @@ def prune_referenced_cert_ids(self) -> None: html_module_ids = set(self.web_data.mentioned_certs.keys()) if self.web_data.mentioned_certs else set() self.heuristics.module_prunned_references = self._prune_reference_ids_variable(html_module_ids) - pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict().keys())) - pdf_policy_ids = {"".join([y for y in x if y.isdigit()]) for x in pdf_policy_ids} + if self.pdf_data.keywords: + pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict().keys())) + pdf_policy_ids = {"".join([y for y in x if y.isdigit()]) for x in pdf_policy_ids} + else: + pdf_policy_ids = set() + self.heuristics.policy_prunned_references = self._prune_reference_ids_variable(pdf_policy_ids) def compute_heuristics_version(self) -> None: From 08ff031712b92f59f3dd7cc482fe89562f03cfd2 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 08:59:54 +0100 Subject: [PATCH 47/81] update readme --- README.md | 199 +++++++++--------------------------------------------- 1 file changed, 32 insertions(+), 167 deletions(-) diff --git a/README.md b/README.md index c4b19802..39db402d 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,7 @@ ![](docs/_static/logo.svg) -Tool for analysis of security certificates and their security targets (Common Criteria, NIST FIPS140-2...). - -This project is developed by the [Centre for Research On Cryptography and Security](https://crocs.fi.muni.cz) at Faculty of Informatics, Masaryk University. +A tool for data scraping and analysis of security certificates from Common Criteria and FIPS 140-2/3 frameworks. This project is developed by the [Centre for Research On Cryptography and Security](https://crocs.fi.muni.cz) at Faculty of Informatics, Masaryk University. [![Website](https://img.shields.io/website?down_color=red&down_message=offline&style=flat-square&up_color=SpringGreen&up_message=online&url=https%3A%2F%2Fseccerts.org)](https://seccerts.org) [![PyPI](https://img.shields.io/pypi/v/sec-certs?style=flat-square)](https://pypi.org/project/sec-certs/) @@ -15,192 +13,59 @@ This project is developed by the [Centre for Research On Cryptography and Securi ## Installation -Use Docker with `docker pull seccerts/sec-certs` or just `pip install -U sec-certs`. For more elaborate description, see [docs](https://seccerts.org/docs/installation.html) +Use Docker with `docker pull seccerts/sec-certs` or just `pip install -U sec-certs`. For more elaborate description, see [docs](https://seccerts.org/docs/installation.html). -## Usage (CC) +## Usage -There are two main steps in exploring the world of Common Criteria certificates: +There are two main steps in exploring the world of security certificates: -1. Processing all the certificates -2. Data exploration +1. Data scraping and data processing all the certificates +2. Exploring and analysing the processed data For the first step, we currently provide CLI and our already processed fresh snapshot. For the second step, we provide simple API that can be used directly inside our Jupyter notebook or locally, at your machine. -### Explore data with MyBinder Jupyter notebook +More elaborate usage is described in [docs/quickstart](https://seccerts.org/docs/quickstart.html). Also, see [example notebooks](https://github.com/crocs-muni/sec-certs/tree/main/notebooks/examples) either at GitHub or at docs. From docs, you can also run our notebooks in Binder. -Most probably, you don't want to process fresh snapshot of Common Criteria certificates by yourself. Instead, you can use our results and explore them using [online Jupyter notebook](https://mybinder.org/v2/gh/crocs-muni/sec-certs/dev?filepath=notebooks%2Fcpe_cve.ipynb). +## Data scraping -### Explore the latest snapshot locally +Run `sec-certs cc all` for Common Criteria processing, `sec-certs fips all` for FIPS 140 processing. -In Python, run +## Data analysis -```python -from sec_certs.dataset.common_criteria import CCDataset -import pandas as pd - -dset = CCDataset.from_web_latest() # now you can inspect the object, certificates are held in dset.certs -df = dset.to_pandas() # Or you can transform the object into Pandas dataframe -dset.to_json( - './latest_cc_snapshot.json') # You may want to store the snapshot as json, so that you don't have to download it again -dset = CCDataset.from_json('./latest_cc_snapshot.json') # you can now load your stored dataset again -``` - -### Process CC data with Python - -If you wish to fully process the Common Criteria (CC) data by yourself, you can do that as follows. Running +Without needing to run the whole processing pipeline, you can fetch fresh snapshot of the dataset and explore it yourself. ```python -cc-certs all --output ./cc_dataset -``` +dset = CCDataset.from_web_latest() -will fully process the Common Criteria dataset, which can take up to 6 hours to finish. You can select only same tasks to run. Calling `cc-cli --help` yields +# Get certificates with some CVE +vulnerable_certs = [x for x in dset if x.heuristics.related_cves] +df_vulnerable = df.loc[~df.related_cves.isna()] -``` -Usage: cc_cli.py [OPTIONS] - [all|build|download|convert|analyze|maintenances]... +# Show CVE ids of some vulnerable certificate +print(f"{vulnerable_certs[0].heuristics.related_cves=}") - Specify actions, sequence of one or more strings from the following list: - [all, build, download, convert, analyze] If 'all' is specified, all - actions run against the dataset. Otherwise, only selected actions will run - in the correct order. +# Get certificates from 2015 and newer +df_2015_and_newer = df.loc[df.year_from > 2014] -Options: - -o, --output DIRECTORY Path where the output of the experiment will be - stored. May overwrite existing content. - - -c, --config FILE Path to your own config yaml file that will override - the default one. - - -i, --input FILE If set, the actions will be performed on a CC - dataset loaded from JSON from the input path. - - -s, --silent If set, will not print to stdout - --help Show this message and exit. +# Plot distribution of years of certification +df.year_from.value_counts().sort_index().plot.line() ``` -### Process CC data with Docker - - 1. pull the image from the DockerHub repository : `docker pull seccerts/sec-certs` - 2. run `docker run --volume ./processed_data:/home/user/sec-certs/examples/debug_dataset -it seccerts/sec-certs` - 3. All processed data will be in the `~/processed_data` directory - -## Usage (FIPS) - -Currently, the main goal of the FIPS module is to find dependencies between the certified products. - -### MyBinder Jupyter Notebook - -Without the need of processing the data locally, you can use the online MyBinder Jupyter notebook: - -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crocs-muni/sec-certs/fips?filepath=.%2Fnotebooks%2Ffips_data.ipynb) +### Explore data with MyBinder Jupyter notebook +Most probably, you don't want to process fresh snapshot of Common Criteria certificates by yourself. Instead, you can use our results and explore them using [online Jupyter notebook](https://mybinder.org/v2/gh/crocs-muni/sec-certs/dev?filepath=notebooks%2Fcpe_cve.ipynb). ### Explore the latest snapshot locally -You can also explore the latest snapshot locally using Python: -```py -from sec_certs.dataset.fips import FIPSDataset - -dset: FIPSDataset = FIPSDataset.from_web_latest() # to get the latest snapshot -dset.to_json('./fips_dataset.json') # to save the dataset -new_dset = FIPSDataset.from_json('./fips_dataset.json') # to load it from disk - -``` - -### Process FIPS data manually with Python - -You can also process FIPS data manually using `fips-certs` in terminal after installation. -You can also use the `fips_cli.py` script. - -Calling `fips-certs --help` outputs following: -``` -Usage: fips-certs [OPTIONS] [new-run|all|build|convert|update|web-scan|pdf- - scan|table-search|analysis|graphs]... - - Specify actions, sequence of one or more strings from the following list: - - ["new-run", "all", "build", "convert", "update", "pdf-scan", - "table-search", "analysis", "graphs"] - - If 'new-run' is specified, a new dataset will be created and all the - actions will be run. If 'all' is specified, dataset will be updated and - all actions run against the dataset. Otherwise, only selected actions will - run in the correct order. - - Dataset loading: - - 'build' Create a skeleton of a new dataset from NIST pages. - - 'update' Load a previously used dataset (created by 'build') - and update it with nonprocessed entries from NIST pages. - - Both options download the files needed for analysis. - - Analysis preparation: - - 'convert' Convert all downloaded PDFs. - - 'pdf-scan' Perform a scan of downloaded CMVP security policy - documents - Keyword extraction. - - 'table-search' Analyze algorithm implementation entries in tables in - security policy documents. - - Analysis preparation actions are by default done only for - certificates, where each corresponding action failed. This - behaviour can be changed using '--redo-*' options. These actions - are also independent of each other. - - Analysis: - - 'analysis' Merge results from analysis preparation and find - dependencies between certificates. - - 'graphs' Plot dependency graphs. - -Options: - -o, --output DIRECTORY Path where the output of the experiment will be - stored. May overwrite existing content. - - -c, --config FILE Path to your own config yaml file that will - override the default one. - - -i, --input FILE If set, the actions will be performed on a CC - dataset loaded from JSON from the input path. - - -n, --name TEXT Name of the json object to be created in the - <> directory. Defaults to - timestamp.json. - - --no-download-algs Don't fetch new algorithm implementations - --redo-web-scan Redo HTML webpage scan from scratch - --redo-keyword-scan Redo PDF keyword scan from scratch - --higher-precision-results Redo table search for certificates with high - error rate. Behaviour undefined if used on a - newly instantiated dataset. - - -s, --silent If set, will not print to stdout - --help Show this message and exit. -``` - -The *Analysis* part is designed to find dependecies between certificates. - -#### First run -The first time you are using the FIPS module, use the following command: -``` -fips-certs new-run --output --name -``` -where `` is the name of the working directory of the FIPS module -(e.g. where all the metadata will be stored), and `` is the name of the resulting dataset. - -This will download a large amount of data (4-5 GB) and can take up to 4 hours to finish. +In Python, run -#### Next runs +```python +from sec_certs.dataset.common_criteria import CCDataset +import pandas as pd -When a dataset is successfully created using `new-run`, you can use the command `all` to update the dataset -(download latest files, redo scans for failed certificates, etc.). It is also **strongly advised** to use the `--higher-precision-results` -switch on the **second run**. The following command should be used to update the dataset: -``` -fips-certs all --input +dset = CCDataset.from_web_latest() # now you can inspect the object, certificates are held in dset.certs +df = dset.to_pandas() # Or you can transform the object into Pandas dataframe +dset.to_json( + './latest_cc_snapshot.json') # You may want to store the snapshot as json, so that you don't have to download it again +dset = CCDataset.from_json('./latest_cc_snapshot.json') # you can now load your stored dataset again ``` -where `` is the **path to the dataset file**, i.e. `/.json` from the first run. From 3fbf5f0992b53727db3cbd1b361f20885c5cd34c Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 14:58:58 +0100 Subject: [PATCH 48/81] random fixes for cc pipeline --- sec_certs/dataset/common_criteria.py | 21 ++++++++++----------- sec_certs/model/cpe_matching.py | 4 ---- 2 files changed, 10 insertions(+), 15 deletions(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 7de0de32..4269e68e 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -8,7 +8,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Callable, ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union +from typing import ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union import numpy as np import pandas as pd @@ -763,13 +763,13 @@ def process_protection_profiles(self, to_download: bool = True, keep_metadata: b :raises RuntimeError: When building of PPDataset fails """ logger.info("Processing protection profiles.") - constructor: Dict[bool, Callable[..., ProtectionProfileDataset]] = { - True: ProtectionProfileDataset.from_web, - False: ProtectionProfileDataset.from_json, - } - if to_download is True and not self.auxillary_datasets_dir.exists(): - self.auxillary_datasets_dir.mkdir() - pp_dataset = constructor[to_download](self.pp_dataset_path) + + self.auxillary_datasets_dir.mkdir(parents=True, exist_ok=True) + + if to_download or not self.pp_dataset_path.exists(): + pp_dataset = ProtectionProfileDataset.from_web(self.pp_dataset_path) + else: + pp_dataset = ProtectionProfileDataset.from_json(self.pp_dataset_path) for cert in self: if cert.protection_profiles is None: @@ -786,10 +786,9 @@ def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMain """ logger.info("Processing maintenace updates") - if to_download and not self.mu_dataset_dir.exists(): - self.mu_dataset_dir.mkdir(parents=True) + self.mu_dataset_dir.mkdir(parents=True, exist_ok=True) - if to_download: + if to_download or not self.mu_dataset_path.exists(): maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] updates = list( itertools.chain.from_iterable( diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index 23a683ee..40a7fa66 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -107,10 +107,6 @@ def predict_single_cert( :param bool relax_title: See step 7 above, defaults to False :return Optional[Set[str]]: Set of matching CPE uris, None if no matches found """ - - if "Active Directory Federation Services 2.0" in product_name: - print("geee") - lemmatized_product_name = self._lemmatize_product_name(product_name) candidate_vendors = self._get_candidate_list_of_vendors( CPEClassifier._discard_trademark_symbols(vendor).lower() if vendor else vendor From 6953dfbe463fbac37e39b00ccebd7453d362a4c4 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 15:28:58 +0100 Subject: [PATCH 49/81] fix CC notebooks --- notebooks/cc/cert_id_eval.ipynb | 15 +- notebooks/cc/temporal_trends.ipynb | 323 +-- notebooks/cc/vulnerabilities.ipynb | 3202 +++------------------------- 3 files changed, 332 insertions(+), 3208 deletions(-) diff --git a/notebooks/cc/cert_id_eval.ipynb b/notebooks/cc/cert_id_eval.ipynb index ad1abde2..4965a3f7 100644 --- a/notebooks/cc/cert_id_eval.ipynb +++ b/notebooks/cc/cert_id_eval.ipynb @@ -124,7 +124,7 @@ }, "outputs": [], "source": [ - "missing_manual = pd.read_csv(\"../data/cert_id_eval/missing_ids.csv\")\n", + "missing_manual = pd.read_csv(\"../../data/cert_id_eval/missing_ids.csv\")\n", "print(set(missing_manual.id) == missing_id_dgsts)\n", "print(set(missing_manual.id).difference(missing_id_dgsts))\n", "print(set(missing_id_dgsts).difference(missing_manual.id))" @@ -299,7 +299,7 @@ }, "outputs": [], "source": [ - "duplicate_manual = pd.read_csv(\"../data/cert_id_eval/duplicate_ids.csv\")\n", + "duplicate_manual = pd.read_csv(\"../../data/cert_id_eval/duplicate_ids.csv\")\n", "set(duplicate_manual.id) == duplicate_id_dgsts\n", "print(set(duplicate_manual.id).difference(duplicate_id_dgsts))\n", "print(set(duplicate_id_dgsts).difference(duplicate_manual.id))" @@ -358,10 +358,13 @@ "correct = set()\n", "possible = set()\n", "impossible = set()\n", - "with open(\"../data/cert_id_eval/truth.csv\", \"r\") as f:\n", + "with open(\"../../data/cert_id_eval/truth.csv\", \"r\") as f:\n", " reader = csv.DictReader(f)\n", " for line in reader:\n", - " cert = dset[line[\"id\"]] # and (line[\"cert_id\"] or cert.heuristics.cert_id is not None)\n", + " if line[\"id\"] not in dset.certs:\n", + " continue\n", + " else:\n", + " cert = dset[line[\"id\"]] # and (line[\"cert_id\"] or cert.heuristics.cert_id is not None)\n", " if line[\"cert_id\"] != cert.heuristics.cert_id:\n", " print(line[\"id\"], line[\"cert_id\"], cert.heuristics.cert_id, line[\"source\"], line[\"possible\"])\n", " if line[\"possible\"] == \"y\":\n", @@ -385,7 +388,7 @@ "source": [ "correct = set()\n", "incorrect = set()\n", - "with open(\"../data/cert_id_eval/random.csv\", \"r\") as f:\n", + "with open(\"../../data/cert_id_eval/random.csv\", \"r\") as f:\n", " reader = csv.DictReader(f)\n", " for line in reader:\n", " cert = dset[line[\"id\"]]\n", @@ -425,7 +428,7 @@ "metadata": {}, "outputs": [], "source": [ - "manual_references = pd.read_csv(\"../data/cert_id_eval/random_references.csv\")" + "manual_references = pd.read_csv(\"../../data/cert_id_eval/random_references.csv\")" ] }, { diff --git a/notebooks/cc/temporal_trends.ipynb b/notebooks/cc/temporal_trends.ipynb index 83786e5c..d7d69ca9 100644 --- a/notebooks/cc/temporal_trends.ipynb +++ b/notebooks/cc/temporal_trends.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,17 +30,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading CC Dataset: 100%|██████████| 135M/135M [00:49<00:00, 2.87MB/s] \n" - ] - } - ], + "outputs": [], "source": [ "dset = CCDataset.from_web_latest()\n", "df = dset.to_pandas()" @@ -48,21 +40,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "long_categories = {'Access Control Devices and Systems': 'Access control',\n", " 'Biometric Systems and Devices': 'Biometrics',\n", @@ -93,28 +73,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N certs in popular categories: 3792 (73.77%)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "popular_categories = {\"ICs, Smart Cards and Smart Card-Related Devices and Systems\", \"Multi-Function Devices\", \"Network and Network-Related Devices and Systems\", \"Other Devices and Systems\"}\n", "df[\"popular_categories\"] = df.category.map(lambda x: x if x in popular_categories else \"One of 11 other categories\")\n", @@ -152,31 +113,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sec_levels = df.loc[df.year_from < 2022].groupby([\"year_from\", \"eal\"], as_index=False).size()\n", "line = sns.lineplot(data=sec_levels, x=\"year_from\", y=\"size\", hue=\"eal\")\n", @@ -200,43 +139,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "eal_to_num_mapping = {eal: index for index, eal in enumerate(df['eal'].cat.categories)}\n", "df[\"eal_number\"] = df.eal.map(eal_to_num_mapping)\n", @@ -300,28 +205,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 145/145 [00:06<00:00, 21.28it/s]\n" - ] - }, - { - "data": { - "image/png": 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T8fTTTyMgIAAAoFarIRQK8fzzz2P8+PFIS0vDqFGjcOrUqWpvfxozZgwWLVqE4cOH19rnzJkzkZGRAWtra63lhw4dgkKhwPr163Hu3DlwHAfGGCIiIjB//nxwHIelS5fiwoULcHBwAMdxUCqV8PLywpo1a+Do6NjEZ4c86vFc+f777/H7778DAFJTU2Fvb6/5mX755Zf4+uuvNT+rRz3zzDN4/vnn6+xr6NCh2LBhA7p06YKhQ4eiW7du+PTTTzXrb9++jVdeeQWnT5/GlStXMG/ePLRr1w5AVb5aWFjgP//5DwYNGtSUp4DU4vHcWLZsGYRCId5//32t7f73v//hq6++wuHDhwEAixYtQlRUFCIjIyEWi+vsgzGGp59+Gq+++qrWK50BYPXq1eA4DsuXL6/xmgZUXdeeeuoprFmzBpMnT9Ys37JlC+Li4vDxxx8/6WkgtTDktaNDhw4ICAjQenNg586d8cEHH2DmzJmYMWMGRo4cqVlXWFiIPn36IDY2lnLESAyZHyqVCtu3b8eRI0egUqk0fb/yyiuaWXsKCwvx+eef48qVKxCLxeDxeBgzZgxmz54NPp+PmTNnon///pg/f75W2z/++COioqLw7bffNtWp0R9GtPz+++9szpw5rFevXiw+Pp4xxlhaWhoLDQ3V2i49PZ0NGzaMnThxgjHG2EsvvcS+/vprrW2uXbvGBg0axJRKZZ19Pvfcc+z48eM1rtu8eTNbtGgRUygUjDHGSktL2YQJE9ivv/7KGGNsyZIl7IcfftDa56OPPmKLFi2q5xGTxqopVx6q6Wda08+qvoYMGcJu3bql+f/OnTuzgwcPatbfunWLDRkyhDHG2OXLl1lERITW/jExMaxv377sxo0bjeqfNMzjuXHr1i3WrVs3VllZqbXdnDlzNP+Ws7OzWa9evdhLL73Efvnll3r188MPP7DZs2drLausrGQ9evRgCQkJNcbyUFpaGgsMDGRhYWEsMTFRq80lS5Y06rhJ/Rjy2hEQEMAKCgpqXFdTXwUFBSwgIIAxRjliLIbMj+XLl7NFixax0tJSxhhjFRUV7OWXX2ZvvvkmY4yxkpISNnz4cLZ582ZNHVJcXMwWL17MXnvtNcYYY8ePH2fDhw+v1vaIESPYuXPnGhWXodEwicfs2rULw4YNQ3h4OLZt21brdm3atMHixYs1d1qmT5+O/fv3gz0yU92ePXswZcoU8Pn8RseTl5cHhUIBuVwOALC2tsa6devQtWvXWvfp06cPEhMTG90nqZ/65oo+vPbaa1izZg3S0tLqtX1gYCBmzpyJrVu36jcwAqB6bnTp0gXt2rXDiRMnNNukp6fjzp07mldC79mzB3369MGECROwfft2rWtJbZ555hlcv34dGRkZmmXHjx9HUFAQ2rdvX2MsjzI3N8cLL7yAN954Q3ONIfpnzGtHQ1GOGJ6h8iMtLQ1HjhzBhx9+qLnTbGFhgffeew9PP/20JpaOHTvixRdfhEBQNZjA1tYW69atw+XLl3Hr1i0MGzYMEokEV69e1bQdFRUFxhj69eunt/ibEhXDj4iPj8eNGzcwatQojB8/HocOHUJRUVGt2wcGBuLBgwcAgAEDBoAxhqioKABAWVkZTp06hWeffbZefa9btw7jxo3TfJ09exYA8MILLyAnJwe9e/fGzJkzsX79esjlcs2QjcdJpVIcPHgQvXr1asihkwZqaK48tHXrVq2f87hx4xAbG9vg/nv06IHp06fjzTffhFKprNc+j+Yr0Z/acmPGjBnYt2+fZru9e/di3LhxsLCwgFKpxJ49ezB27FgMHToUBQUF+Ouvv3T2ZWdnh1GjRmH//v2aZbt378aMGTPqjOVRL7/8MsRiMdavX99EZ4DUxRjXjlmzZmntV1BQ0KCYKUcMx5D5ce/ePfj5+cHKykprubOzs2Zo5/Xr19GjR49q+5qZmSEsLAzXrl2DQCDAlClT8Ntvv2nW7969G9OnTwfHcfU5bKOjMcOP2LVrFwYPHgw7OzvY2dnB09MTu3fvrjYe7yGO42Bubg4A4PF4mDp1Kvbt24devXrh8OHDGDhwYL3H7b799tta47YecnNzw/79+xEfH48rV67gypUrmDJlCpYuXar5hbd161bNmEOVSoUePXrg9ddfb8wpIPVUW64sWLCgzv1mz56NuXPnNkkMixYtwqVLl/Dll19i2LBhOrd/NF+J/tSWGy+88ALWrl2L1NRUeHh4YP/+/dixYwcA4NSpU1Cr1RgwYAAEAoHmjlB9xnhPmzYNr776Kv773/8iPj4eubm5GDJkSJ2xPJqnPB4Pn3zyCSZMmID+/fvr56QQDWNcO7Zt21ZtPCmAGgsVxpjW+GKAcsSQDJkfPB4ParVa53YKhaLG5Y9+UvDss88iIiIC5eXlUCqVOH/+PFatWtWgeIyJiuF/SCQSHDx4EGZmZhg6dCgAoLy8HDt37qyxSAWqHlp69A7tM888g5EjR6K8vBx79uzBe++998RxrVu3DpMnT4afnx/8/PwwY8YMHDp0CJs3b9YUw01ZYBHd6sqVuXPnQigUGiQOgUCAzz77DBMnToSdnZ3O7R/PV9L0dOXGxIkTsW/fPnTp0gUBAQFo27YtgKpfgFKpVHM3Ri6XIy8vD3FxcfD396+zz+DgYDg4OODixYuIjIzE1KlTwefzdcbyKA8PD6xatQpLlizB+PHjm/akEI3mcu14yN7eHsXFxVrL8vPza7yeUI7on6HzIzg4GImJiSgvL9e6O5yTk4N3330XGzduRLdu3RAVFYXZs2dr7VtRUYHbt29j4cKFAAAXFxf07dsXx44dg0QiwYgRI6pNCtCc0TCJfxw5cgT29vY4d+4cTp8+jdOnT+PkyZOQSCQ4fvx4te2TkpLw9ddfY86cOZpl9vb2GDJkCDZu3Ag+n4/Q0NAnjquwsBAbNmxAZWUlgKq/2pOSktCpU6cnbps0TkNzRZ+8vLzwf//3f/j888/r3O7WrVvYtWsXZs2aZaDIWidduTFt2jQcO3YM+/fv1/wxm5SUhKioKBw4cECzz/nz59G9e/d6jxecPn06Dhw4gD/++AOTJk2qVyyPGzVqFAYOHNjsx7CasuZ07QCAgQMHYv/+/SgrKwMAKJVK7Ny5s9ZPJChH9MvQ+eHq6ooxY8bgnXfeQXl5OYCq4nvVqlWws7ODubk5pk+fjoSEBHz//fdQqVQAgJKSEixduhTdu3dHcHCwpr3p06fjyJEjOHjwoOb6ZirozvA/du3ahRdeeEHrYTcbGxvMnDkT27Ztg1Qqxbhx4wBUfbRgZmaG119/HYMHD9ZqZ/r06Xj22WfxwQcfNElcK1euxPr16zF27FiIRCIolUr07t0bK1asaJL2ScPpypWxY8fWuu+jQ1oeCgkJqTblVkOMHz8e58+fx7Vr1zTLUlNTtfLVysoKn376KQIDAxvdD9GtPrnRrl07PHjwQHPtePiwjLe3t1Zb//3vfzF//ny8/vrrNX7E/aiIiAisW7cOgwYN0myrK5Zu3bpVa2f58uWIjo5u7OETHZrbtWPixInIzc3F9OnTwePxIJVK0atXLyxfvrzWfShH9McY+bFy5Up8/fXXmk+U5HI5hg0bhkWLFgEArKyssHv3bmzYsAHh4eEQCoXgOA5jxozRuhkIAL169cKaNWtga2uLDh06NPTwjYpj9XlkmRBCCCGEkBaI7gwbwA8//IAjR47UuG7u3Ll1/rVHWodXX30VSUlJNa5bv369Zpos0vpMnz4dFRUVNa7buXNntSfBSetC1w5SF8qP+qE7w4QQQgghpNWiB+gIIYQQQkirZdRiuCkH4d+9e7fJ2jIEU4sXME7MTZUjdL4Nw9Axt+ZrCGB6MZvyNQQwvfMNmF7MlCOGZWrxAvqJucXcGZZKpcYOoUFMLV7ANGN+yBRjp5gNyxRjN7WYTS3ex5li/KYWs6nF+zhTi9/U4gX0E3OLKYYJIYQQQghpKCqGTUh8ejEKS03vrzhCCCGEkOaKimETUVQqxZIvz+HHw6Y3vocQ0jwoVAoUVZYYOwxCCGlWqBg2Efsj4yFXqnEzPg80Gx4hpDF23TqE10+8D4VKYexQCCGk2aBi2AQUl8lw/FIybCxFKC6TIT233NghEUJMjFKtwl8pV1AhlyA2P9HY4RBCSLNBxbAJOHg2HnKFCq9O7QoAuJ2Qb+SICCGm5k7OfZTKqv6Qvp1z38jREEJI80HFcDNXUi7D7xeSMCCkDbp3dIWTrTluxVMxTAhpmHMpUbAUiuHr4INbOTHGDocQQpoNKoabucPnEiGVq/Ds0wHgOA5d/JxwJyGfxg0TQupNppQjKuMmenl1Qzf3zkgsTEW5rMLYYRFCSLNAxXAzIJEq8OaGv/D2l+cQn16sWV4ukePo+UT0C/aAj5sNACDYzwkl5XKk5pQZKVpCiKExxlBUWQKpUtao/a9m3oRMKcMAn54IdusIBoY7ubFNHCUhhJgmgbEDaO1UaobPdl5DXHoxrMRCvPHFWYT3bYcZozriyLlESKRKTHk6QLN9Z18nAMDt+HxNgUwIadkupF7Fxss/AgDMBGawNbOCrbkNOgt9EYYwnfufT/kbDmI7dHT2g5oxiAXmuJUdg95e3fQdOiGENHtUDBvZtt/vIepeNhZM6IJBYV74+XgMfr+YhPO3MiFXqNC7sxvaedhqtndztISLvRi3E/Ixun97I0ZOCDGUm9n3YC2yxJjAp1EqLUOJrAzxhck4UngG/Uv6wMvWo9Z9y2TluJF1F+EBQ8HjeOBxQJBLAI0bJoSQf9AwCSP640oKDkTGI6JfO0T0bw8rsRALJgbj81cGwdlODKlchSlPd6i2X2dfJ9yOL4BaTeOGCWkN7ucnoKOzP8Z3HIHnu07Cot4v4L0hr0PEE2L9xR/qHD5xOe06VEyN/j49NcuC3Toit6IA2eV5hgifEEKaNSqGjSQpR4qvf7uJrgHOmDeus9Y6Py87fLJ4IH56dzj8PO2q7Rvs54QyCY0bJqQ1KKosQU55HgKd/bSW24ltMcZ1CDJKs/Hjtd217n8+NQptbNzQ1s5TsyzYNRAAcDubplgjhBAqho2goKQSe84Vwt3JEm8/3wN8fvUfA5/HwcHGvMb9u/wzbvhWPN3VIaSli81PAAAEOvlWW9fWog0mdBqJyKRL+Cv5SrX1+RWFiMmLR3/vHuA4TrPc3doVjmJ7GipBCCGgYtgoLt/OQqVcjaXP94CVWNjg/V0cLODqYIHbNN8wIS1eTF48zPgitLX3qnH95KAIdHT2x+boXcgozdZadz71bwBAf58eWss5jkMXt0DcyY2FWq3WT+CEEGIi6AE6I4hJLoK1mAdvN+tGtxHs54RLt7OgVjPweJzuHQghJul+fjz8HdtBwOPXuJ7P4+OV3nPw1h8fYHXkBnjbekDEF0HEF+JeXhz8HdvB1cq52n7Brh0RmXQJiUWp8HNsq+ejIISQ5ovuDBvB/ZRCeDmZaX1s2VCdfZ1QXqlAclZpE0ZGCGlOKhVSJBenI9C5+hCJRzlY2OGNvi+hjY0ryuUSZJXnIq4wGRw4RAQ8VeM+XVyrHs6loRKEkNaO7gwbWGGpFDmFEoT42OreuA7Bfv/MN5yQj/ZtnqwtQkjzFFeQBMYYAp38dG7bycUfnVxerXfbtuY2aGvniVvZMZjYadQTREkIIaatXneGs7OzcfbsWahUKmRlZdW78Q0bNiA8PBwRERH46aefGh1kS3I/uRAA4OUseqJ2nOzEcHeypHHDhLRg9/PjwXEc/B3b6aX9Lq6BiC1IrHNqNqVahT/iz6JUSrPXEEJaJp3FcGRkJKZOnYr33nsPBQUFCA8Px8mTJ3U2HBUVhcuXL+Pw4cPYt28fduzYgcTExCYJ2pTFJBdCKODB3f7JimGgalaJOwn5UKnoARhCWqL7eQloa+cJsbDmmWWeVLBbR6jUKtzLjat1m8iki/gh+ld8HbUdjD3Z3OYqRtcqQkjzo7MY3rRpE/bs2QMbGxu4uLjgl19+wcaNG3U23LNnT2zfvh0CgQAFBQVQqVSwsLBokqBNWWxKEfw87SDgP/lDb706u6FCqsTZ6xlNEJlpkMRfQ2n0CWOHQYjeKdUqxBUk1WuIRGN1dPKDtZkVDsacqLHQlasU2Hf3OMRCc1zLuoML/8xO0RhX0q9jQ+J2nE+JepKQCSGkyekcM6xWq+Hi4qL5vmPHjvV+8EsoFGLjxo348ccfMXLkSLi6ulbbJjo6ugHh1q0p29IHpYrhQWohenWwAmDxxPHyGIOrnRDbf78Fa5aj11klVGoGlZo1OuawsLBG9/1onxZ3jkGY+wBxqP50fEPaMRWtJeamyo8n1ZzOd6Y0FzKVHKIyrs64njTmvjah+F/eefx8di86WWs/qPd38R0UVBZhisconCuIxuaoX8By5LDgixvUR4WyEltS90HJVNh0eSsyUzLha1l9qri4ihTcK4tHhMsgCHjav54oRxrP1GI29u+ZJ9VazrcxNfXvGZ3FsFgsRmZmpqYAvnr1KszMzOrd+eLFizFv3jwsWLAAe/bswZQpU+odXENER0c3WVv6cj+5ECp1Bgb36gjIs5ok3tnCDKzdfhVSgTsGdG3TBFHW7MOtUbibkItvlg6HjeWTD/FoiEfPU7EsBYXpN9C1cyfwzOr/C9kU8uNxFHP9tNRrSFbsSSAdCO85HPbimh+SbYqYu6q74sGfKbhYegOT+4+DuaDq+i5VSPHN77+is0sHPDNgLHqV9MDbf3yIaFUsXu05V6sNxhhyyvPgauVc482Szy9uhgJKzGgzGhckN3A49zTeHfwKOvzzIhE1U+O3u79jf/yf8Hdshx7de4DHNd1kRy01R+rD1GI2VrytNUdMLV5APzHrvNq88cYbmDNnDlJTUzFlyhQsXLgQb775ps6GExISEBNTNWWPWCzG8OHDERsb++QRm7CYfx6eC/RxaLI2+3bxgJerFXafjIVa/WTj+WoTm1KIS7ezUCpRYdPeG088bvBJCOzdAQCKomwdWxJi2u7nJcDVyrnWQrip8Hg8vNDtWRRUFuFQzB+a5cfizqBUVo6pXcYCADxt3TGx0yhcTL2Kqxk3NdslF6Xh/cgvsPjYSnwVtQ0KlUKr/Utp0bicdg2TgyLQRuyKdwb+F45ie3z811dILc5AhVyCdee/xW93j2Fw2z5YOeS1Ji2ECSFEF51XnG7dumHPnj34/PPPsWDBAhw/fhw9e/bU2XB6ejqWL18OuVwOuVyOU6dOmdxfH03tfkoh3BwtYF/La5Ybg8fj8OxTAUjJLsOVu/Wf6aMhdp64DxtLEQZ1tsal21k4GZWql37qQ2hfNdRGUUzFMGm5GGO4nx9f4yuY9SHQ2Q/9vLvj8P0/kFuejwq5BEfu/4luHl0Q4NRes934wOHwtm2DzdG7kFmajW+jdmDJHx8htTgDA3164a/kK1hz9kuUyyoAACXSUvwQ/St87X0wNvBpAFVTuv3f4MUQCUT44OyXeOfPtbiZdRdzu03Fyz1nQsRv+Fs5CSHkSegcJnH37l0AgJNT1by2WVlZyMrKQlBQUJ37DRo0CDdv3sT48ePB5/MxfPhwRERENEHIpokxhpikQoT4N3ysqy4DQtvglz9i8eufD9C7s7vWx5R5RZW48SAXQgEPZiIBzEV8iM0EsLIQwtbKDJbmwjrHGt9NLMD1B3l4YXQQvKyLUSQ1w/cHbyOovSM8nK002yVnlWL9rmvw87TDomdDm/wYHxLauwEAlIVUDJOWK6s8F6WycoMVwwDwXMhEXM24he0398HTxg0VikpM7TxGaxsBX4CXe87EOyfX4tXj74HP4yMiYCgmBo2ClcgSoe5B+DpqO/7v5DosG7gQv9w+hEqFFP/p9Tz4j7xBz8XSEcsHLcaK059BrVBjxZBX0dHZ32DHSgghj9JZDC9atEjz/wqFAnl5eejcuTN+++03nY0vXrwYixcvfrIIW4jcokoUlckQ2Lbphkg8xOfz8OxT/tiw+wauxuSgRyc3qNQMv59PxI7jMZDKVbXuy+NxsLUUIcDbHq9N6wZLsfZdmV/+dx921mYI79cWd2/fxGvTumHRp2fw6c5orFs0AHwehyPnE7H16D1YioUYEubZ5MenFa+ZBXgWNjRMgrRo9/MSAFTdsTUURwt7TOg0Er/ePoxrPAH6eIWhrX31h9x8HXwwM+QZJBamYHLn0XC3/vcB6/4+PeBk4YBPLnyLt/74EDKlDNO6jIOXrUe1drxsPfD5yBUQ8gSwMrPU67ERQkhddBbDp0+f1vr+xo0b9SqEibaH44U76qEYBoDBYV7Y9ecD7P7zAZztLfDlnut4kFqMsEAXzIroBKGAB6lcBZlchUqZEmUSOUrK5SitkKGoVIYz0WlY+f0lvPdSH01BfCs+D7fi8/HiuM4wF1WlipOdGAsnh2Dt9qvYcvgOsvIrEH0/F907uuKVKV1hZ13/hysbS2jvBkVxjt77IcRY7ufHw1pkCQ/r6jPw6NPoDsNwOvEC8iSFeLbz6Dq2q/kVzwAQ6OyLD4a9jbXnvoaV0EIzPKIm+h4PTQgh9dHg1zGHhobivffe00csLVpsciHEZnz4uFnrpX0Bn4dJQ/3x9W838cpnZ2BlIcIbM8IwqGubek2F1zPIDWu3/40V31/Eey/1haW5AL/8LxYONuYY2aet1rb9Q9rgao8cHD2fBJGAhwUTuiC8X7t6T7n3pIT2bpCm3jNIX4QYQ2x+Ajo4+Rrs39RDIr4QSwb8B9nleWhj49bodtysnPHZyHehZkxreAQhhDRH9R4zDFSNe71z5w6kUqleg2qJYlIK4e9lDz5ff09JD+vhhTNX0+DuZIk5Y4Jga1X/u7R9urhj6aweVQXxdxcxcYgf7iYWYMGELjATVv9l9tL4LnCwMcegrp7wcbdpysPQSWDvBuWdc2BKBTgBPWxDWhapUobssjwM8NH9oLI+eNl61DisoaF4HA96nPqcEEKaTIPGDHMcBwcHB6xatUqfMbU4lTIlkjJLMXmofh8QEQr4WLdoQKP3793ZHctm9cRH26KwdvtVONmJMby3T43bWpgL8Xx4p0b39SSqZpRgUJTkQuSov7mVCTGGtJJMMDB421JuE0KIITR4zDBpuLi0IqjVTC8PzzW1nkFuWDa7Jz79+SpmjuoIoaD5fcT56IwSVAyTlialuOr16m3t9PswKiGEkCq1FsNr1qypc8fly5c3eTAt1f3kIgBAoI+9kSOpn56d3LBrdbheh3Q8CeHDF2/QXMOkBUotzoBYYA4ny+b/xzMhhLQEtRbDdnZ2BgyjZbufUggvVytYWRj2NcZPorkWwgDAs7ABJzKn6dVIi5RSkgFvWw96CxshhBhIrcXwf//731p3kkgkegmmpUrOKkWnto7GDqPF4DgOQjs3KOjFG6SFYYwhpTgd/by7GzsUQghpNXSOGT558iQ2btwIiUQCxhjUajWKi4tx/fp1Q8Rn8iRSBfKKKuHdWz9TqrVWQgc3yPOM91poQvShQFIEiaISPjRemBBCDEZnMbxu3Tq8+uqr2LVrF+bNm4eTJ0/C0pLeFlRf6bnlAAAvVyqGm5LA3g0VD66CqVXgaB5T0kKklFQ9POdjRw+GEkKIoegclCYWixEeHo7Q0FCYmZlh1apVuHz5siFiaxHScsoAAN56etlGayW0cwXUSijLCowdCiFNJqU4HQCaZJ5fQggh9aOzGBaJRJDL5fD29kZMTAx4PB7kcrkhYmsR0nLKIODz4OZgYexQWhShQ9WMEsoiei0zaTlSijPgYukIC6HY2KEQQkirobMYfuqpp/DSSy9hwIAB2Lp1KxYtWkQzTTRAak4ZPF2smvXsDKZIYO8KAFAUZhk5EkKaTmpxBrxpvDAhhBiUzjHDCxYswNixY+Hm5oavvvoKV69exejRow0RW4uQllMGfy/TmF/YlAisHQGeAIpiujNMWga5Uo7M8hz08e5m7FAIIaRV0Xm7csqUKbh06RIqKysRFBSEWbNmwdGRpgmrD6lciZxCCT08pwccjw+hvQuUNNcwaSHSS7PAGL2GmRBCDE1nMbxw4UKcP38eQ4cOxYoVK3D79m1DxNUiZOSWgzHAm4phvRDQXMOkBXn4GmaaVo0QQgxL5zCJgQMHYuDAgSgtLcWRI0ewYsUKMMZw8OBBA4Rn2tL+mVbN09XKyJG0TEJ7N0jT7oExBo7jjB0OIU8kpTgdZnwRXC2djB0KIYS0KvV6qkupVOLy5cs4f/48CgoK0Lt3b33H1SKk5ZSBx+Pg4UTFsD4IHdzA5FKoJaXGDoWQJ5ZSkgEvWw/wePSwLSGEGJLOO8OrV6/G8ePHERgYiEmTJmHDhg0QiUSGiM3kpeWUwcPJEkIB/XLTB6GdGwBAUZQFvqWtkaMhpPEYY0gtzkBPz67GDoUQQlodncWwlZUVdu/eDS8vL0PE06KkZpfRyzb0SDO9WlE2zD0DjRwNIY1XJC1BmbyC3jxHCCFGoLMYfu211wwRR4ujUKqQVVCB/iH0Jil9Edq5AuCgoBdvEBP38M1zNJMEIYQYHn1+ryeZeRVQqxlNq6ZHnEAIgY0jTa9GTN7DmSS87eiPZ0IIMTQqhvUkNacMAGiYhJ4J7N2goGKYmLjU4gw4WTjASmRp7FAIIaTV0WsxvGnTJkRERCAiIgLr1q3TZ1fNTlpOGXgc4OFMM0nok7CVFMOMqSHNeAC1TGLsUIgepJRkwJvGCxNCiFE0qhh+9913dW5z8eJFnD9/HgcOHMDBgwdx9+5d/Pnnn43pziSl5pTB1dESZkK+sUNp0YT2blBLSlt0kSjNeIDMn5Yhc+sypGx8Cfl/bIGiMEtrG6ZSQJoei7Kbp6FWyo0UKWkMhUqBzNJs+NB4YUIIMQqdD9DVJCgoSOc2zs7OWLp0qWYaNl9fX2RmZjamO5OUllNGb54zAIH9w+nVsmHm1l6vfSnLiyCJj4ZaKoFaXgkmr4RaLgXH44MTmYMnEoNnJgZPJAYnEIETCKv+KxQBKhWYUgG1Ug6mlEEtk0JdWQqVpAyqylKoKysgdHCHuVcgzD0DIbBxBCcrR+6RTSi/dQZ8K3s4Dp8LWWYcSqP/QOnfx2Hh1w0i17aQpt2HLDMO7J8iWJ6fBsenZun1XJCmk1GaDRVT00wShDQT5aVSSCrkcHG3MXYoxEA4xhjTdyfJycmYOnUqfv31V7Rt21azPDo6Wt9dG4VKzfDBngz0DbTGsFCa/xYAwsLCGrWfrhzhl+bA5uIWlIeMg8Jd9x9pjcFJy2CedAlmaTfAqZWa5YwvBOOLAKYGp5SBY+oGt60WmIOJxGACM/ArCsCpFAAAldgWPHkloFZC2rYXpL59AYHZP/GUwyztGszSroGTV0Jl4wqlvReU9l4Q5sZBlHkHZX1mQ2Xr3jQnwAD0lR+mIEuah10Zv2Ou9zOwFdIf0LVpzTlC6qepciTmeikykisxbIJrU4RFmom68kPnneFly5Zpfc9xHMRiMfz9/TF58mTw+XUPA4iLi8P8+fOxZMkSrUK4PsE1RHR0dJO19aTSc8ugVmegR4gfwsK8a9ymOcVbX8aKua4+mVKBpEs/wdOSD4c6tmto7IwxKIuyUHLlKEpvngIYg3WXQbDtOQYCWydwQjNwPO3cZ0oF1P/cLWZK+b9fCjnA54MTmIH38I6xyBx8sTU4/r//BJlKCVlOMmTp9yFNu4+ikhL4jn8ZQoeaZhgYBKZSgqmU4InMNUtV0gqkf/cKnBIj0eaFj7XaNwRj5EhLuIaMVD8NAa/hQ6pM7TrSHK8hDWFq5xswvZibQ45IS+KRGHMfnYOCYWYubFA7dL71Tx8x1+s35b179zBhwgTweDwcPXoUjo6OyMnJQVxcXJ3jh6Ojo7F48WK88847iIiIaLKgm7u0f2aSoGnV9I8TCCF09IA8L/WJ25LlJEOaeg/S9PuQpsVAVVYI8ASwDhkKu74TILRz0RkLXyAE36JxH61xfAHMPfxg7uEH256jkR4dXUsh/O/2jxe7fHNLOI2Yh5x961ASdRR2fcY3KhZiWI0phAkh+mHvaAEAKMyXwN2z5X26q1Kp8eBuDq5eTEF+XjEELB1durUBj8cZOzSj0VkMJyQkYOfOnbCyqpoVYfLkyZgzZw5++eUXjB49utb9srKysHDhQqxfvx59+vRpuohNwMNp1TxdqBg2BJGLD2QZcU/URmn0CeSf2AwA4Ns4wdy7E8w9A2EZ0AMCG6emCNNgLAN7waJDLxT9tRuWHXpB6GA6wyUIIcTY7B2rpjgsKqiodzFcUSbDvp+voaiwFNfPndMs927viKHhHSAQGP8P3pKiSly7nIrrV1JRXiaDrb0YHIBDu27gwql4DBoegE4h7uDqKIorJXJkZ5bC2dUaVtZmhgu+FgV55UhPLoJMqoRMpoRMqoRKqUb3fj5wbMBsXjqL4ZKSEk0hDADm5uYoLy8Hx3EQCmv/+GDLli2QyWT4+OOPNcumTp2KadOm1Ts4U5WWXQ4XezHEZob9iLq1Ern4oOLeBahlEvDMLKqtl+enwyZyEyqd3oTYp/q4YkVRNgpObYe4XQicI16GwNbZEGHrldOIF5H23SvIO/4d3KevBMe13r/4CSGkIRyc/r0zXF83r6YjOb4Azu5msLCsmjhAqVTj8tlEpKcUYfKsMFjbmOtoRX+SEwrwy+YrUCrV8A90QVhfH/gFuuDatWhYitog8n8PsO/nazh3yhptfR1hZWMOa1tzWNuYQy5TICWhEMkJBcjJKgX+edLMzcMG7Ts4o32AM7zb2xuk4GeMobRYgbP/e4CY21nIzSrTWs/n82AuFsCvo0vTFsMhISF48803MWnSJDDGsH//fgQHB+Ps2bMQi8W17rd8+XIsX7683oG0JGk5ZTREwoBEzlXjsuV5qTD3DKy2XpJwDXxpKXL2fwrPOeu0il3G1Mg7+jXA48N59H9M7i5wbQTWDnAcOhP5x79DSdRR2PYcTQUxIYTUg5m5EBZWIhQVVNR7n9vXMtDG2w6h/cVa41nv3czEoV9v4of15zB5dnd4+tjrI+Q6pacU4dctUbBzsMC0uT00d76BqufAOga7o0NnN9y9kYnLZxNx82o6ZFKlVhsCIQ9ebR0weEQA3D1tkZNZhoTYPFz+KxEXzyTAxc0as//bF+biho2xrgljDCVFlchILUZOZinKy2QoL5OhokyGshIpystkAJcP73YOGDGuE3w7uMDCUgSROb/RBbnOYvj999/HV199hY8++gh8Ph9DhgzBSy+9hFOnTuH9999vVKctmUrNkJ5bhmD/llFUmQKRqw8AQJ6TUmMxLMuIg1ooBqdSInvvWnjM+gA8YdXHO6XR/4M09S6cIl5uMYXwQ9Zdh6Hi/iUUntyK8ltnYNfvGVgG9q724B8hhBBt9o4WKCqo353h3Owy5GSWYuT4IACFWus6hXjA0dkKu3+6im1fXcKoiZ3RtadXnUMRmlJ2Rgl+2RwFSyszzJzfG9a2Nd+d5vE4dOnWBl26VU3xKJcpUf5P8cnj8+DhaQu+4N9XU/h3dEX/p/wglykReycbh3bfxJ6tVzFjXi+t7epLpVTjxt9piI/JRUZqcVXBC4DjcbCyEsHKxhyWViK4uFtDhVIMD+8Jqya8066zGD548CDmz5+PN954Q2t5eHh4kwWhb4wx7Dn1AI42YvQP9YC5SH/DF7ILKiBXqmmOYQMS2DiDM7Oo9SE6WWYclI4+8Bg4Edl7PkL+sW/hPHYxlMU5KDz9M8TtQ2Ed8pSBo9Y/juPBbepylN89h+IL+5B74HMIHT1g2yMCnEAElbQc6soKqKXl4ETmENq7Q+jgBqG9O/jWDnQnmRDSajk4WSIloaBe296+lgGOx6FTqAdiHxRWW+/qYYMXX+2PfTuu4ejeWzj7xwN0DHZDx2B3eLd1eOLCmDGGijIZhCIBzMz/rW/yssvw83dXIDLjY+aC2gvhmojMBHAwE8DBqe5XxIvMBOgS5gkG4OAvN3Bkz02MmxZa798fTM1w50YmIk/EoqhAAntHC7QLcIKntz3a+NjB1cMGfL52cR0dHd2khTBQj2I4KioKX3zxBYYMGYIpU6YgNDS0SQMwhL+uZ+Dn4/cBAD8cuo3BYV4Y0dsH7Tya9ilRtZrhh0N3IOBzCGrv2KRtk9pxHAeRszfkuSnV1inLi6EsyYPSLRgW/mGwHzwNRZG/QOTaFpL4aIDj4Bzxcost/DgeH9ZdBsMqaAAqYq+g+PxvmgcF/9kAPHMLqOVSQPXvx2I8Cxu4jH8VFu1CjBA1IYQYl72jBW5fy4BSqarzo3emZrhzLQPt/Z3qfKDMwlKEGfN64u7NLNy7mYnoS6mIOpcMKxsz+AY4w8PbDh5ednD1sNb5UX+lRI6E2DxkZ5RWfWWWQFJe9dIlaxszOLpYwdHZCrF3s8HjcZi5oDfsHKo/T9OUgsM8UVxYicgTsbB1sMCQkR3q3J4xhoTYPJz+/T6yM0vh6mGD6fN6wreDs1F+H+sshj///HOUlJTg6NGjWLNmDWQyGSZNmoRZs0zjDVdSuRJbf7+H9m1s8eK4zvjjcgr+uJKC3y8kIdTfGUtm9YBVE4xxAYDdJx/gakwOXn4mGB4NGLhNnpzIxRsVd8+DMab1D0mWWTXLhNKuaooyu74TIc9OQuGp7QAAp/CWNzyiJhyPD6uOfWEZ2AeK/HRwQhF45lbgmYnBcTwwtQrK0gIoCrOgLMpC6bX/IfvXD+AcvgDWIUONHT4hhBiUg6MlwIDigko4udb++zwtuQglRZU6iz8A4PF5mqEIMqkScTE5iLmVhbj7ubh5NR1A1QNgbm1s0D7AGX6BzmjjbQcenwfGGFKTCnH9ciru3cyCUqkGj8/Bxc0aAZ1c4epuDYVCjYLccuTnlePujUyIzPiYPq9Xgx4kexIDhvmhpFCCc3/Gwc5ejK69an7PQnpKEU79fh8pCQWwc7DAhBld0TnUw2BDR2pSr/ECtra2mDJlClxcXLB582Zs3rzZZIrhA5EJyC+uxJszwhDU3hFdfJ0wb3wXnIxKwY7jMVj1/SW8P78PLBo4sfbjou/nYNcf9zEkzBOj+rRtmuBJvZm5+KDs2h9QlRVoFbeyjAcAx4PKpuq1zRzHwXnMQihL8sC3tod1aMsbHlGXqrvoXtWX8/gQ2rn8M5dyCKw6D0TOvk+Rd/QrKIpzYT9wSou9e04IIY+z/2d4QGFBRZ3F8O1rGRAIeejQ2a1B7ZuZC9C5axt07tpG88BYZloxMlKLkZZUhPOn4nDuZBzMxUK09XNEfk458nPLITITIKSHF0J6eMK9jW2t43MfvlzYkNdtjuMQPqkLSoqlOPrbbdy7mYX2AU5oH+AMF3drFORW4PTx+7h/OxuWViKMnBCEsN4+jRpj3NR0FsN3797F/v37ceLECXTq1Anz5s3D0KGmcacov7gS+87EoV+Ih9awBRtLESYO8YeHsxU+3vY3Vn5/Ce+91PiCOKdQgs92RsPHzQb/mRRCRYMRiFz+fYhOqxjOjKtax//3Z8sTieHxwkcAOPpZ1YJnZgG3Ke8g79h3KD6/F8qSPDhHLADHr/nfiLK0AOV3z0GUkw95W3cIHdzp3BJCTJbDPy/eKMqvfUYJlVKNezcz0SHITWusbkNxHAc7BwvYOVigU0jVp5iVEjmS4vIRfz8PSXH5sLYxx9gpIegU4g5RPaZtNdb1l8/nYfKsbjj7Rxzi7+fizyMxAGJgaSWCRKKAUMjDoBEB6DOofb2Ow1B0RrJw4UJMmjQJe/fuhYuLC44fP46pU6di7969hoivXiplSqjUrNrybcfuQa1mmB3Rqcb9end2x9szu2PtjqtYtfkyVs3rDQtzIQpLpTh9NQ0no1JRIVVgRC8fjOrbFo621aeSkytU+HhbFNRqhmWze+j14TxSO6FmerUUWPhXTWvDmBrSrARYBfWvtj3HGf8v0eaO4wvgPPo/ENq7oujsLlQmXoeFfw9YBPSAuG0XcAIRpKl3UXr1BCpirwBMDUsA6XePg2/lALFPEMw8/MCJxOCJzMEJROCZW8LcK5DOPyGkWbOwEkFkxq9zRon42DxUShToEtamyfsXW4jQKcRDUxybEjNzIYaP7YThYzuhtLgSiQ/ykRSfDytrM/Qd4gtLK+O/rONxOiu3M2fOoLS0FLt378bOnTshkUjw3HPPGSK2elGpGf6z9hSUSgVe4NIwqJsn+DwOsSmFiIxOx+Sn/OHmWPvTkH2DPfD2c92x7uerWPn9JVhbihB9PxdqNUNQe0e4O1liz6kH+O101R3mkX3aQqFUIzW7FClZZYhNLUJaThmWv9ATHk40TthY+OaWENg4QfbIQ3SK/AwwmQTmHv6Aso6dSa04joN9/0kwc2uPsltnUH7vAspunAQnNAPf0g7K4hzwxFaw7TUaNt1G4M7tW/C1AipT7qAy+TbK756r1qbL+Ndq/AOFEEKaC47j4OBoicI6iuE71zIgthDCt4Ppv6hJX2zsxAjt6YXQntWH5zUndRbDiYmJ2LZtGw4fPow2bdpAKpXi9OnTsLZuPtOGpWaXIr9ECgszHtbvuoZ9Z+Lw3MiO2HcmDvbWZpg01F9nG/1CPPAmC8OnO6NhZ2WGZ4b44ake3mjzz6DzrPwKHL2QiJNRqfjreoZmP3trM3i7WWP8IF/06kyvvDU2kYsP5Ln/Tq/28OE5szYBQEqOscJqESz8usHCrxuYUoHKlDuoeBAFZVEO7PtPgmWnfpp5m9WWGbDpFgabbsPBGINaUgq1UgYml4EpZGBMDTMPPyMfDSGE6GbvZFHtDWcPyaRKxN7NRmgPr2pTfxHTU2sxPG/ePNy9exfh4eHYvn07unTpgqFDhzarQhgA7iVWzQP44ggXiO088fPx+/hwaxQA4JUpofUeBzwgtA06t3eEjaWoWmK7O1li3rgumDEiENdic2FrZQZvV2vYNsNb/a2ZyMUHksQbYCoFOL4Q0owH4JlZQOjoQcVwE+EEQlj4doWFb1fd23Ic+Ja2oFd8EEJMkb2jJWLv5kCtZuA9NtNB7J1sKBVqdO7a9EMkiOHVWgzHxMSgU6dO8Pf3R9u2bQEYb0B2Xe4lF8LBxhz2lnx0D2mDPp3dcfpqGjLyyjG0e83TetTGXsckzhbmQvQPocRvrkQu3oBaBXl+Bsxc20KWEQczD38an0oIIaTBHJwsoFYxlBZXVpun9+7NTNjai+HV1vCvVyZNr9ZiODIyEn/++Sd++eUXfPjhhxg0aBBkMpkhY6uXe0mF6NTu37dl8fk8PN3Lx8hREWPQzCiRlwqhvRvkeamw859o5KgIIYSYIvt/njcqzJdoFcNKpQrJ8QUI6e5p1LlxSdOp9ZaZQCDAqFGjsGPHDuzbtw8uLi6QSqUYPnw4du3aZcgYa5VbJEF+cSU6taO3vRFA6OAB8ASQ56ZAlp0AMDXM2ugeM04IIYQ87uGriIsKtKdXS0sqgkKuogfnWpB6fX7s5+eH5cuX49y5c5g7dy727Nmj77jq5V5S1TvAO7VzMHIkpDng+AKInDyriuGMqofnzD2oGCaEENJw1rbm4PN51aZXS4jNA4/Hoa1fy397aWvRoMGUYrEYU6ZMwYEDB/QVT4PcSyqA2EyAtu42xg6FNBMiF++qYjgzDgI7F/AtbY0dEiGEEBPE43GwcxCj8LEXbyTG5sGzrf0TvWiDNC8m/WRRTFIhAn3saVoToiFy8YGqrBCVKXdgRneFCSGEPAEHJ0utO8PlZTJkZ5bSEIkWxmSryPJKBVKyS9GpPY0XJv96+BCdurIc5m0CjBwNIYQQU2bvZIGiggowVvWW28TYPACgYriFMdli+H5yIRgDOral8cLkXw+LYQB0Z5gQQsgTsXe0hFymQkW5HACQ8CAPFpYiuLehIXgtickWw/eSCsDjcejgTXP8kX/xrezBE1sBPAFEbu2MHQ4hhBAT5uBUNaVaUX4FmJohMTYP7QOcaEq1FsZkR3/fSyqEbxtbmJuZ7CEQPeA4Dmbu/mAKKXgCkbHDIYQQYsIezjVcVCCBQMhHRbmchki0QCZZSSqUasSlFmFUX7rzR6pzGfcKwNTGDoMQQkgt1HI5lOUVENrZguNV/5BarVAg7+xfyD11BkI7O1j5todl+3aw8vOF0MZwM0jZOYgBDijMr0BZqRQA0J6K4RbHJIvhhIxiyJVqml+Y1IhvYW3sEAghhPxDkpqG3DORKL0bA0VJCRQlJVBVVgIAhPZ2cOjZAw49e8AuuAuYTIaMg4eRefgI5AWFEHt6Ql5YhIKLlzTtuY0aAd8FLxkkdoGAD1s7MYoKJCgrlcLF3RrWNuYG6ZsYjkkWw/cSq1620ZGKYUIIIUQvVFIppFnZkBcXQ2hrA5G9PYQ2NuD4/Fr3YWo1VFIZlOVlKLzyN3LPnEVFQgLA48GmYyCsAvwhtLWB0NYWfLEYpfdikHf2HHL+9yd45uZQA0iWSmEb3AV+ixbCLjQEHMdBWV6O8sQkVCQkwqKtT63964O9owVyMkuRn1eOXgPoE+mWSK/FcHl5OaZOnYpvv/0Wnp6eTdbuvaQCuDtZwt6a/jojhBBCGoupVJDm5KAyIxOV6RlV/83KgjQzC/LCwuo78HgQ2tqCJxICjIGp1P/8VwmVVAa1TKa1uaVve7R78QU4DRgAkV31GRg8RodDrVCg5PYdFFyOQl5WJjrPfA7WAdqzAQmsrGAX3AV2wV2a9Pjrw8HJEsnxBQBoSrWWSm/F8M2bN7F8+XIkJyc3abuMMcQkF6JHJ9cmbZcQQggxRYwxVGZUFbKKklIoS0uhKC2ForQM8owM3N5/CKoKCVSVEqhkMjCVGkylBFOqoFYoAPW/z1gIbGwg9nCHXWgwzN3dIfZwh9DeHsrSMsiLiqq+CovAlMqqGRU4HjgeDxyfB565Ofj/fPHMzWHTqSMsfbx1xs8TCmHfrSvsu3VFSXR0tULY2Owdq2aUEAh58KZPpFskvRXDe/bswcqVK/H22283aD+5QgWVmtW6Piu/AqUVcnRqRy/bIIQQ0nBquRxMpWrwfkwu14x1bSzGALVcBlVlpeZLLVcAqJoN55//qXqojPdPofnP/2sfgwzlcQkovXcPpffuQ1lWprWeZ2YGoY01GI8HODpC5OQAgYUXeGYicHx+1ZdAAJ5QCHN3N4jbtIG4jQeE1vTMxeMcnKpmlPDxdYRAWPsQEWK69FYMf/DBBw3eJymzBK9/8ReUKt0zAdDDc4QQQhqqMjMTN155A2q5vFH7X27ieJ6UubsbHHp0h01QR1j4+Pw7HtfMDAAQHR2NLmFhRo7StDk4VxXDNESi5eLYw3cM6snQoUOxffv2GscMR0dHa30vV6pxM0kCubLukKzN+QhuZ9GkcRL9CmvkxfjxHCEtE+UH0aWpcoQplVDdug1IZbXsYQBCITgzESCq+uIEj9yX+udXMlOr/7mNzKqmimQMwCMveuBx4Lm6grO2MmzszZi+riOMMWSnSeHiYQ6+gF62Yarqyg+jzybxeHB9ejWunejo6Eb/QzAGU4sXMF7MTdEnnW/DMEbMTdUfnW/9azbXkF6N+0VjaucbML2Ym02OPK57/dqh861/+ojZZF/HTAghhBBCyJOiYpgQQgghhLRaeh8zXBca79e6NOZjDcqR1oPyg+hCOUJ0oRwhdaktP4xaDBNCCCGEEGJMNEyCEEIIIYS0WlQME0IIIYSQVouKYUIIIYQQ0mpRMUwIIYQQQlotKoYJIYQQQkirRcUwIYQQQghptagYJoQQQgghrRYVw4QQQgghpNWiYpgQQgghhLRaVAwTQgghhJBWi4phQgghhBDSalExTAghhBBCWi2BsQNoLhQKBYYMGYIOHTpgy5YtWuv27t2LPXv2oLy8HAqFAl5eXnj11VcREhICAJg5cyYyMjJgbW2taatHjx546623YGVlpbPvwsJCfP7557hy5QrEYjF4PB7GjBmD2bNng8/nIz09HU8//TQCAgI0+zDG8Pzzz2PSpEk1rn80dpFI9CSnhvzDWDny5ZdfYufOnXB1dQXHcVCpVHB0dMTKlSvRrl07rfVAVW6Ul5fj6aefxtKlS8FxXLX+H3r55ZcxcuTIpjpFrV5NOZKeno4xY8bg+vXrNe6Tk5OD9evX4+7du+A4DmZmZpg/fz6GDRumsz+ZTIZvvvkGkZGRYIxBrVZjzJgxmDdvHjiOw9KlS3HgwAFs3boVffr00eyXnp6OYcOGYfr06VixYgUUCgXWr1+Pc+fOgeM4MMYQERGB+fPng+O4pjk5BIDp5ggAJCQk4IsvvkBycjI4joONjQ1effVVdO/evQnODHlUbb9vbty4gc8++wzFxcVgjMHNzQ1LliyBv78/AKBDhw4ICAgAj6d9r/Orr76Cp6dnjX2VlpZi5syZAACJRIKcnBy0a9cOANC3b18sWbIEkZGR+Oabb1BZWQmVSgU/Pz8sW7YMbm5uAKBzfbPHCGOMsd9//53NmTOH9erVi8XHx2uWf/bZZ2zq1KksPT1ds+zixYusV69eLCMjgzHG2HPPPceOHz+uWS+Xy9mKFSvY/PnzdfZbUlLChg8fzjZv3swUCgVjjLHi4mK2ePFi9tprrzHGGEtLS2OhoaFa+2VnZ7Pu3buzmJiYGteTpmesHNm4cSN77733tJZt376dTZgwodb1xcXFbODAgeyvv/6qsX+iHzXlSF3/PgsKCtjgwYPZgQMHmFqtZowxFhMTw3r37s3Onz9fZ19qtZrNmTOHrV69mkmlUsYYY4WFhWzy5Mls/fr1jDHGlixZwgYPHsyWLl2qte+XX37J+vbtq8mbzZs3s0WLFmmuQaWlpWzChAns119/bdyJILUy1RxJSEhg/fr101xTGKu6zoWFhbEHDx40/ESQOtWUJzKZjPXs2ZPduXNHs93BgwfZoEGDmFKpZIwxFhAQwAoKChrd7+XLl1lERITWsuzsbNazZ0+t33Fff/01mzJlSr3WmwIaJvGPXbt2YdiwYQgPD8e2bdsAAPn5+di2bRs2bNiANm3aaLbt06cPli5disrKyhrbEgqFWLZsGf7++28kJCTo7Ldjx4548cUXIRBU3ai3tbXFunXrcPnyZdy6davG/VxdXeHj44Pk5ORGHC1pDGPlSE369OmDpKSkWtfn5+dDKpXC1ta2wW2TxqspR+ryyy+/oFu3bhg/frzmDmxgYCC+/PJLODk51bnv33//jcTERCxbtgxmZmYAAHt7e6xbtw49evTQbBceHo7Tp09DJpNplh0/fhyjRo3SfJ+XlweFQgG5XA4AsLa2xrp169C1a9f6HzypF1PNkc2bN+OZZ57BgAEDNMv69OmDzz77DObm5vU7eFJvNeVJZWUlysrKIJFINNuNHTsW7777LlQqld5iKSoqgkKh0Op31qxZWLBgQb3WmwIaJgEgPj4eN27cwJdffomgoCDMnDkTr732Gm7cuAFfX1+4uLhU22f8+PF1tmlubo62bdviwYMH8PX1rXW769eva11cHjIzM0NYWBiuXbtW40dh169fR2pqKkJCQqBSqSCVSjFu3Ditbbp164aVK1fWGSepH2PmyOOUSiV+++039OrVS7Ps2LFjiI6OhlQqRXFxMTp16oT33nsPwcHBmm3WrVuHb775RqutrVu3wt7evt59k9rVliN1uXPnTo3//uvzsfOdO3cQHBwMPp+vtbxt27Zo27at5nsHBwd07doVp06dQnh4OK5evQpfX1/Y2tqiqKgIAPDCCy/gP//5D3r37o2QkBB069YNI0aMQKdOnepx5KS+TDlH7ty5gzfffLNaH4MGDdIZB2mY2vLE3t4eb731Fl588UU4OTmhW7du6NWrFyIiIrSGQ86aNUtrmISnpye++uqrRscTGBiIZ599FhMmTIC3tze6deuGPn36YMSIEfVabwqoGEbVX2CDBw+GnZ0d7Ozs4Onpid27d1crUMrLyzFjxgwAVeNqRo0ahddff73WdjmOg1gs1tm/QqGocfnDuzQAtIpdlUoFe3t7fPLJJ3B3d0d6ejrMzc1x6NAhnX2RxjF2jjwsdoGqfAkKCsLq1as168PDw7FixQrI5XKsXr0acXFxGDhwoFYbb7/9No0P1qPacmT06NG17vNwfG5j8Hi8eu87btw4HDp0COHh4Th48CAmTJiAO3fuaNa7ublh//79iI+Px5UrV3DlyhVMmTIFS5cu1eQzeXKmnCMcx0GtVjcqDtIwteXJggUL8MILL2Dy5Mn4+++/8ffff2Pz5s3YvHkzfvvtN80zIdu2bYODg0OTxrR06VLMnz8fUVFR+Pvvv7Fu3Trs2LEDO3fuBJ/P17m+uWv1wyQkEgkOHjyI6OhoDB06FEOHDkVeXh527tyJTp06ISkpSfOXsZWVFQ4dOoRDhw5h7NixKC8vr7XdyspKJCQkaAa116Zbt26IioqqtryiogK3b99Gt27dAEBT7B46dAhHjx7Fjh076C9yAzF2jgBVxe7Ddo8dO4ZPPvkEzs7O1bYTiUR49913UVFRgU8++aTxB00apK4cUSqVte4XGhqKGzduVFv+66+/4qeffqqzz5CQENy+fbvax6O3bt3CW2+9pbXsqaeews2bN5GVlYW///672p3GdevWISkpCX5+fpgxYwY2btyINWvWYNeuXTqOnNSXqedIbXFs2rQJhw8frjMOUn915UlUVBR++OEHWFlZYciQIXj77bfx+++/g8fj4cKFC3qL6dSpU9i3bx/s7e0xYsQILF++HMeOHUNCQgLu3bunc70paPXF8JEjR2Bvb49z587h9OnTOH36NE6ePAmJRILo6Gg8//zzeOWVV5CZmanZJzMzE9euXav2tOZDUqkUH374IQYOHKg1jrQm06dPR0JCAr7//nvNBaukpARLly5F9+7dtT7mJsZh7BxpKJFIhJUrV2L37t24e/duk7ZNalZXjhw/frzW/aZMmYKoqCgcPnxYcwfvzp072LhxY42zwzyqa9euaN++PT766CPNWM/8/HysWbOm2lPjIpEITz/9NN5++20MHTpU83zCQ4WFhdiwYYNmjDtjDElJSTRMogmZeo7MnTsXe/fuxfnz5zXL/vrrL+zYsQOBgYENOhekdnXlSVRUFL755htcvXpVs31eXh4qKyt15sKTsLS0xOeff474+HjNsvT0dJiZmcHb21vnelPQ6odJ7Nq1Cy+88ILWbXwbGxvMnDkT27Ztw759+3D48GG8+eabkEgkUCqVEIlECA8P1/r48OF4TB6PB6VSib59++L//u//dPZvZWWF3bt3Y8OGDQgPD4dQKATHcRgzZgzmzJlT7+OoacwwAHz88cfo2LFjvdsh1Rk7Rxqje/fuGDNmDFavXq25u1fTmOGnn34a//3vf/USQ2uiK0ckEkm1h9F+/fVXdOjQATt27MAnn3yC7777DjweD2KxGB988AH69euns9+NGzdi/fr1mDhxIvh8PtRqNcaPH4+5c+dW23bcuHGYPn063n333WrrVq5cifXr12Ps2LEQiURQKpXo3bu3Zkot8uRMPUd8fHzw7bff4osvvsDatWuhVqvh4OCAb775Rq+FWGtTV56cOXMGX331FdavX4/s7GyYmZnB2toa77//Ptq3b6/Z/vExwwDw+uuvN/rT5N69e+Pdd9/FkiVLUFZWBj6fD2dnZ3z99dewtbXVud4UcKyxg5EIIYQQQggxca3+zrC+JSYm1vq0cLt27fDFF18YNiDS7FCOkNpMnz4dFRUVNa7buXNnvV7qQ1o2yhFSH/R7pm50Z5gQQgghhLRarf4BOkIIIYQQ0npRMUwIIYQQQlotoxbDD18i0BRMbQopU4sXME7MTZUjdL4Nw9Axt+ZrCGB6MZvyNQQwvfMNmF7MlCOGZWrxAvqJucXcGZZKpcYOoUFMLV7ANGN+yBRjp5gNyxRjN7WYTS3ex5li/KYWs6nF+zhTi9/U4gX0E3OLKYYJIYQQQghpKCqGCSGklSgtLUVSUpKxwyCEkGaFimFCCGkFFAoF9u/fjwMHDmhezUsIIYSKYUIIMUnFxcWoqKiASqWq1/anT59GQUEBGGPIzMzUc3SEEGI66A10hBBiQhQKBS5evIjo6GgwxhAdHQ0HBwc4OTnB3d0dISEh4PP5Wvvcu3cPd+7cQVhYGK5fv460tDS0a9fOSEdACCHNCxXDhBBiIjIzM3HixAkUFRWhS5cuUCgUsLa2RkFBATIyMnD//n3cuXMH4eHhcHJyAgAUFhbi5MmTaNOmDQYOHIjs7GykpaUZ+UgIIaT5oGKYEEKaOaVSifPnzyM6OhrW1taYNGkSfHx8EB0djbCwMM128fHx+OOPP/Dzzz+jX79+CA0NxdGjR8Hn8xEREQEejwcvLy9cuXIFMpkMZmZmRjwqQghpHqgYJoSQZu7GjRuIjo5GSEgIBg4cCJFIVON2fn5+8PDwwJ9//om//voLV69ehUQiwYQJE2BtbQ0A8PT0xOXLl5GZmUlDJVoQlVIKtUoBoZm1sUMhxORQMUwIIc1cTEwM3NzcMGzYMJ3bWlhYYOzYsbh37x7OnDmDnj17on379pr1Hh4e4PF4NG64hUm69QtK8mNgYeMFO5fOsHMJgtjKtUFtSErTkZV4CrZOgXDy7KWnSAlpfqgYJoSQZqywsBC5ubkYPHhwvffhOA5BQUHo2LEjeDztSYOEQiHc3d1p3HALolLKUFrwAFZ27aBWK5EZfxyZ8cdhZuEEKC2QlVAIcys3iK1cYSZ2BMfTfsBSVlmEzPgTKMy6BgCQSvKpGCatChXDhBDSjMXGxgIAAgICGrzv44XwQzRuuGUpK4wDYyp4+I2AtYMv5NJiFOfeQ2lBLGQFKchM+OPfjTkezMSOMLd0hrmFE9RqFfLTLwMcB7d2QwBwyE46DYWsjIZckFaDimFCCGnGYmNj4enpqRnz2xRo3HDLUpJ/Hzy+Gazs2gIAROZ2cPHuCxfvvoiOjkZoSGdIK3IhLc+BVJIHaUUeZJI8lBY8AFOr4OgRBg+/ERCZ26GiJA3ZSadRVpgAB/dQox4XIYZCxTAhhDRT+fn5KCgowFNPPdWk7dK44ZaDMYaSvPuwcfSvNvzhIb7ADJa2XrC09XpsXzXUKjn4AnPNMgubNuALzFFWGE/FMGk16A10hBDSTN2/fx8cxzVqiERdHo4bTk9Pb9J2ieFJy3OgkJXA1qljg/flOJ5WIfxwmZW9L0oL45oqREKaPSqGCSGkGWKMITY2Ft7e3rCwsGjy9r28vJCdnQ25XN7kbRPDKcmPAQDYOHVosjZtHPwgryyErLKwydokpDmjYpgQQpqhnJwcFBcXo0OHpityHuXp6QnGGDIyMvTSPjGMkvz7EFu7Q2Ru22RtWjv6AQDKCuObrE1CmjMqhgkhpBm6f/8+eDwe/P399dL+o+OGiWlSKSpRXpzcqCESdTG3dIVAZEXFMGk16lUMZ2dn4+zZs1CpVMjKyqp34xs2bEB4eDgiIiLw008/NTpIQghpTR4OkWjbti3Mzc1179AING7Y9JUWxgFMDVunwCZtl+M4WDv4obQgHoyxJm2bkOZIZzEcGRmJqVOn4r333kNBQQHCw8Nx8uRJnQ1HRUXh8uXLOHz4MPbt24cdO3YgMTGxSYImhJCWLCMjA+Xl5QgMbNoi53E0bti0leTfB18ghqWtd5O3bePgB6W8DNKK3CZvm5DmRmcxvGnTJuzZswc2NjZwcXHBL7/8go0bN+psuGfPnti+fTsEAgEKCgqgUqn08hAIIYS0NLGxsRAIBPD19dVrPzRu2HQxxlCaHwsbx4Bap1R7EtYOVcNzaKgEaQ04puMzkIkTJ2L//v0YP348Dh48CAAYN24cDh06VK8ONm7ciB9//BEjR47ERx99BI7jNOuio6MbHzkxKWFhYY3aj3KkdaD80Hb58mVYWVmhc+fOeu1HpVLh4sWLsLe313tfT4py5DHKIqDkf4BlL8BcT3NFFx0BBPaAdX/9tN/EKEdIXerKD50v3RCLxcjMzNQUsVevXm3Q6zsXL16MefPmYcGCBdizZw+mTJlS7+AaIjo6usnaMgRTixcwXsxN0Sedb8MwRswt7RqiVCoRGRmJrl276oynKWKWy+W4cuUKfHx84OTk9ERt6WLK1xCg+eQIAGQlnkJmCRDcfUSdr01+kpiT7yaiOOc2Qrp1BccZ5nl7yhHDMrV4Af3ErDO733jjDcyZMwepqamYMmUKFi5ciDfffFNnwwkJCYiJqZr/UCwWY/jw4YiNjX3yiAkhpAUrKSkBANjZ2Rmkv7CwMAiFQly+fNkg/ZGmUZJ/HxY2nnUWwk/KxsEPKmUlJGWZeuuDkOZA553hbt26Yc+ePbh+/TrUajVCQkLg4OCgs+H09HRs3LgRu3btAgCcOnUKzzzzzJNHTAghLdjDYtjWtunmja2LWCxGaGgo/v77b/Tt27de13diXEqFBBXFKXBv37Sv6X6ctUPVmPWygjhY2njqtS9CjElnMXz37l0A0Hx8lpWVhaysLAQFBdW536BBg3Dz5k2MHz8efD4fw4cPR0RERBOETAghLVdxcTEAwN7e3mB9du/eHdevX8eVK1cwatSoauslEglKSkpQUVGh+XJycmrQa6Lz8/Nx8+ZNtG3bFo6Ojk0ZfqsjKc0EwGBlr6exwv8QmtnA3NIVZYXxcGs3RK99EWJMOovhRYsWaf5foVAgLy8PnTt3xm+//aaz8cWLF2Px4sVPFiEhhLQixcXFEAqFEIvFBuvTwsICISEhuHbtGvr06aMZosEYw7Vr13D27Nka55sdMGAAevbsqbN9tVqN48ePo6ioCIcOHcKMGTMa9OwJ0fZwujNzS1e992Xt4IeCjCioVQrw+EK990eIMegshk+fPq31/Y0bN+pVCBNCCGm44uJi2NnZac28Ywjdu3fHjRs3cOXKFYwYMQJKpRKnTp3CnTt34Ovri+DgYFhaWsLS0hLm5ub43//+h3PnzkGpVKJPnz51xnv16lXk5ubCy8sLGRkZOHbsGMaPH2/wY2wppJJc8PhmEJrZ6L0vO5fOyEu7gPz0K3DxMY1ZJQhpqAY/HhoaGqoZOkEIIaRpPSyGDc3KygpdunTBvXv3kJ2djb179+LOnTvo3bs3xo0bh/bt28PV1RVWVlYQCAQYNWoUgoKCcOnSJZw/f77WN5UVFhbi4sWL8Pf3h6+vLwYNGoTExERcunRJazvGGJKTk+tsi1SRVeTB3NLZIH9MWDv4wtrBD5mJf0KlqNR7f4QYQ73HDANVF6s7d+5AKpXqNShCCGmN1Go1SkpK4OfnZ5T+e/TogVu3bmHnzp0QCAQYPXo0OnToUOO2PB4PI0aMAJ/PR1RUFFQqFQYNGqRVoDHG8Mcff0AgEGDo0KGIjY1F165dkZubi0uXLsHFxQV+fn7Izs7GuXPnkJqaCgcHB/Tu3RsCgc5fT62WtCIXVvbtDdIXx3FoExCB+5c3IDs5Em38q48pJ8TUNWjMMMdxcHBwwKpVq/QZEyGEtErl5eVQq9VGuTMMADY2NujWrRvi4uIwZswYuLrWPSaV4zgMGzYMfD4f0dHRSElJQZ8+feDv7w+O43Dz5k1kZGRgxIgRsLKy0uzz1FNPIT8/H8ePH4ePjw/i4uIgFosxZMgQBAcHUyFcB5VSDrm0GOaWLgbr09LGE/ZuochJOQdnr74QmetnppPKsixAng7AtOa9JaavwWOGCSGE6MfDmSSMVQwDwMCBAzFw4MB6fwTPcRyGDBkCDw8PXLp0CUeOHIGzszO6du2Kv/76C97e3tVmHxIKhRg7dix+/vlnJCUloXfv3ujevTs9VFcPMkkeAMDcwtmg/bbxG4XinNvISvgDPkGTm7z9kvxYJN7cDqjkyElxgKvPgCbvg5Da1FoMr1mzps4dly9f3uTBEEJIa9YciuHGjEPlOA6BgYEICAhAbGwsLl26pBkeMXz48BrbtLGxwfPPPw8ejwcLC4umCL1V+HcmCcPdGQYAMwsHOHv1QW7qBbj4DITYqvqnBkytgkJeDoW8DEp5OSysPer1kF9B1jUk39kNsZUrKqU8pMceBo8ngLNXH30cCiHV1FoMG/NiTAghrVFxcTF4PJ5mSIGp4fF46NixIzp06IAHDx5ALBbX+fIQUz1OY5JW5AHgYGZh+Lma3dsPQ37mVWTEHYNf1xegUkpRkheDopzbKC9KhFIhAfDIw48cD7aOHeDYpgdsnTuCx6tecuSknEN67GFY2fvCL3QWbty8DVveHaTG7AfHE8CpTQ/DHSBptWothv/73//WupNEItFLMIQQ0poVFxfD1tYWPF6DJ/ppVng8HgIDA40dRoskleTCTOxglDl/BSJLuLUdjMz4E3hw9XuUFyeBqZUQmtnA1rkTRGI7CEXWEJpZgy8Qo7QgFgWZ0Si5GQOB0BK2zoHgCyzA4wvB4wshqyxCQUYU7Fy6oF2XaVXHxPHRPmQm4m/8hJS7e8HjCWDvGgyFrBRyaXHVeGkrV1hYexj8+EnLpXPM8MmTJ7Fx40ZIJBIwxqBWq1FcXIzr168bIj5CCGk1jDWtGjEd0opcmFkadrzwo1x9BiA/IwpSSR6cPfvA3rULLO18wHHV/4CzdvCFh+8IlBY8QH7G3yjJfwC1WgG1Sg4wNQDA2asPvALHa+3P4wvhFzobcdd+RNLtXUi6vQuP3nG2dw1B+5Dn9H6spPXQWQyvW7cOr776Knbt2oV58+bh5MmTsLS0NERshBDSajDGUFxcDE9PT2OHQpopxtSQVuTB2sHfaDHw+CJ07r8EAFev8eUcjw9b546wde6otZypVVCrVeALRLX249f1BWQnnQEAiMR2EJnbQWRub5QhIqRl01kMi8VihIeHIyYmBmZmZli1ahWeeeYZQ8RGCCGtRmVlJRQKBd0ZJrWSS4vB1EqDPzz3uJruAje4DR4ffB6/zm34AjO08R/5xH0RoovOjBaJRJDL5fD29kZMTAx4PB7kcrkhYiOEkFajqKgIAD28TGpX9fAcYG7EYRKEtEQ67ww/9dRTeOmll/Dxxx9j6tSpiI6Opos1IYQ0sZKSEgCoc/YF0rppplWzMO6dYUJaGp3F8IIFCzB27Fi4ubnhq6++wtWrVzF69GhDxEYIIa3GwzvDVAyT2kgrcsEXiCEQ0XM7hDQlncMkpkyZgkuXLqGyshJBQUGYNWsWHB1p8DohhDSlkpISWFtb06uISa2kFXkwt3Rp1ItRCCG101kML1y4EOfPn8fQoUOxYsUK3L592xBxEUJIq0LTqhFdpBW5Rn94jpCWSOctiIfvqS8tLcWRI0ewYsUKMMZw8OBBA4RHCCGtQ3FxMfz8/IwdBmmmVIpKKOVl9PAcIXpQr/lRlEolLl++jPPnz6OgoAC9e/fWd1yEENJqyGQyVFZW0p1hUiuphGaSIERfdN4ZXr16NY4fP47AwEBMmjQJGzZsgEhU8yTZhBBCGq64uBgATatGaqeZSYKGSRDS5HQWw1ZWVti9eze8vLwMEQ8hhLQ6VAwTXaQVeQDHg5mYHmAnpKnpLIZfe+01Q8RBCCGtFhXDRBdpRS7MxI7gdLy1jRDScE/+TkVCCCFPpKSkBGKxmIagkVo9nFaNENL0qBgmhBAjo2nVSF2YWgWZJJ+KYUL0RK+zu2/atAnHjx8HAAwaNAhvv/22PrsjhBCTVFxcDE9PT2OHQZopWWUhGFPRTBKE6Emj7gy/++67Ore5ePEizp8/jwMHDuDgwYO4e/cu/vzzz8Z0RwghLZZSqURZWRndGSa1klY8nFaN7gwTog+NujMcFBSkcxtnZ2csXbpUMwbO19cXmZmZjemOEEJarJKSEgD08Byp3b/TqtGdYUL0oVHF8NSpU3Vu4+/vr/n/5ORkHDt2DL/++mtjuiOEkBarrKwMABXDpHZyaSEEIisIhBbGDoWQFoljjLG6Nli2bJn2DhwHsVgMf39/TJ48GXx+3dO8xMXFYf78+Vi0aBEmTJigtS46OrqRYRNTExYW1qj9KEdah9acH0qlErm5uXB3dwfHccYOp9lqzTkCZTGgrgRE7saOpFlr1TlCdKorP+p1Z/jevXuYMGECeDwejh49CkdHR+Tk5CAuLq7O8cPR0dFYvHgx3nnnHURERDQ4uIaIjo5usrYMwdTiBYwXc1P0SefbMIwRc2u9hgCmF7MpX0MA0zvfgOnFTDliWKYWL6CfmHUWwwkJCdi5cyesrKwAAJMnT8acOXPwyy+/YPTo0bXul5WVhYULF2L9+vXo06dP00VMCCGEEEJIE9FZDJeUlGgKYQAwNzdHeXk5OI6DUCisdb8tW7ZAJpPh448/1iybOnUqpk2b9oQhE0IIIYQQ0jR0FsMhISF48803MWnSJDDGsH//fgQHB+Ps2bMQi8W17rd8+XIsX768SYMlhBBCCCGkKemcZ/j999+Hu7s7PvroI3zyySfw9vbGihUrUFFRgffff98QMRJCCCGEEKIXOu8MHzx4EPPnz8cbb7yhtTw8PFxvQRFCCCGEEGIIOu8MR0VFYdiwYVi2bBlu3LhhgJAIIYQQQggxDJ13hj///HOUlJTg6NGjWLNmDWQyGSZNmoRZs2YZIj5CCCGEEEL0RuedYQCwtbXFlClTMH/+fIjFYmzevFnfcRFCCCGEEKJ3Ou8M3717F/v378eJEyfQqVMnzJs3D0OHDjVEbIQQQgghhOiVzjvDCxcuhL29Pfbu3YtvvvkGUqkUU6dONURshBBCCCGE6JXOO8NnzpxBaWkpdu/ejZ07d0IikeC5554zRGyEEEIIIYToVZ3FcGJiIrZt24bDhw+jTZs2kEqlOH36NKytrQ0VHyGEEEIIIXpT6zCJefPm4bnnnoNQKMT27dtx9OhRWFpaUiFMCCGEEEJajFqL4ZiYGHTq1An+/v5o27YtAIDjOEPFRQghhBBCiN7VWgxHRkbimWeewdGjR9G/f38sXrwYMpnMkLERQgghhBCiV7UWwwKBAKNGjcKOHTuwb98+uLi4QCqVYvjw4di1a5chYySEEEIIIUQv6vXSDT8/Pyxfvhznzp3D3LlzsWfPHn3HRQghhBBCiN7Vqxh+SCwWY8qUKThw4IC+4iGEEEIIIcRgGlQME0IIIYQQ0pJQMUwIIYQQQlotKoYJIYQQQkirpfN1zIQQQgghhEgUSpxOyUOwiy28bSx0bl9QKcPt3FLczS+Fm6U5xgW4Q8BrfvdhqRgmhBBCCGnBymQKfHAxFvbmQgzwckKYuz3M+A0rSnMrZPjyajyyK2Q49CALPdztMdbfHW5W5pptlGqGhKJy3M4rwe3cUmSWSwEA9uZC3MgpQXxRORZ0aw9bM2GTHt+TomKYEEIIIaQF2xebiWKpHHyOw0+3UvDrvTT08nCAhQywLCgDh6q3DPM5Dp424mqF8oPCMnwTnQgG4L9hvkgqqcDJpFxEZxehn6cj2tlZ4k5eKe7ll0KqVIPPcQhwsEI/T0cEu9jC1dIMV7OKsPVWCtZcuI//dGuPdnaWDToGxhjyJDKUqar+vynfikzFMCGEEEJICxVfWI4L6QUY0d4Vz3TwQFxROc6lFeBCegEUag7HrsRpbS/icejkbIOurnYIcbHFjdwS7LidCmcLERZ194WLpTlCXG0x1McZxxKycTY1H+fSCmBnLkQPd3t0cbZFRydrmAv4Wu328HCAm5U5vopOxLrLDzCtkxf8HSwh4vMg4vNhxudByOO0ilw1Y0gsrsCNnGLcyClBToUMAIcDf96Cp40YntZi+NhaoIuLLaxFjS9pqRgmhBBCCGmBVGqGnXfTYG8uxGg/N3AchwAHawQ4WGNaJ0+cvnoT/gEBYKi62ypTqXEvvwzX/yk+eRygZkBHR2vM79YOlsJ/y0YbMyGmdvLCyPauqFCo4GFlrvNurZeNBf6vXyC+u56IHXdSq63ngH+K46ovmUqNcrkSfA7o4GiNoT7OSE9LA2dnj4yySlxIL8DplDxwAAIcrNDVzQ5dXe3gIBY16DxRMUwIIYQQ0gJFpuYhvawSC7q2q3an1kIogLuoqsh8VKirHaZ28kRKiQTXc4phxudjRHtXCHg1F7p25iLYmde4qkbWIgFe6+GP+wVlqFAoIVepIVOpIX/k6+H3HIBOzjbo4mwDi38K8eiCNIR18QZQdec4rbQS13OKcT27GL/eS8ev99KxMKw9Ql3t6h2TXovh8vJyTJ06Fd9++y08PT312RUhhBBCCPlHsVSBQw8yEeRkg25udg3al8dxaGdn2eBxvfXF53EIcrZ54nZ4HAcfWwv42FpgfIAHssuliCkoq9dMF4/SWzF88+ZNLF++HMnJyfrqghBCCCGE1GDf/Qwo1AzTgjyb9GGz5szNylxrdov60lsxvGfPHqxcuRJvv/22vrpoFu5fu4B7V881eL/ysjKkRP+ph4j0pz4xd+o+AIHd+hkoItNQejcHpbdzGryfRbkM6XG39BCR/tQnZpsurrAJcjVQRIQQ0jDJxRWITM1v1L75JcDtWylNHFHDKdRqRGUWIcLPDa6WDS8OWxuOMcb02cHQoUOxffv2GodJREdH67Nrg8hLuY/cpHvGDqPZcGnXCc4+gdWWh4WFNaq9lpAjwmwlhFkqY4fRbCjc+VC4af8d3przg9QP5QjRpalyJF4KXC5rioiMy04AjLADhK3jprBOdeWH0R+ga2zyPi46OrrJ2mqQJ/jHZ5R4n4CxYm6KPul8G4YxYjb5a8gTMLWYTfkaApje+QZML+bmkCNhAKY0sh063/qnj5ib3zvxCCGEEEIIMRAqhgkhhBBCSKtFY4aJwTTmYw3KkdaD8oPoQjlCdKEcIXWpLT/0XgwTQgghhBDSXNEwCUIIIYQQ0mpRMUwIIYQQQlqtZl0Mb9q0CREREYiIiMC6desAABcvXsSYMWMwfPhwrF+/XrNtTEwMnnnmGYwYMQL/93//B6VSCQDIzMzEjBkzMHLkSLz88suoqKhoNjE/tGTJEuzfv1/zvSFjbki8J0+exLhx4zB27Fj85z//QUlJicHjfZL4m0OOmFp+NDTm5pYjppYfDY35IcoRw8ROOaL/eJtbfjQ0/uaQI6aWHw2NWS85wpqpCxcusClTpjCZTMbkcjl7/vnn2ZEjR9igQYNYamoqUygUbM6cOSwyMpIxxlhERAS7fv06Y4yxZcuWsZ07dzLGGHvppZfY0aNHGWOMbdq0ia1bt67ZxJydnc3mz5/PgoOD2b59+zTtGCrmhsRbVlbG+vXrx7KzsxljjH3xxRds9erVBo33SeJnzPg5Ymr50dCYm1uOmFp+NCZmyhHDxc4Y5Yi+421u+dHQ+Bkzfo6YWn40NGZ95UizvTPs7OyMpUuXQiQSQSgUwtfXF8nJyfDx8YGXlxcEAgHGjBmDEydOICMjA1KpFKGhoQCAiRMn4sSJE1AoFPj7778xYsQIreXNIWYAOHLkCJ566imMGjVK04YhY25IvAqFAqtWrYKra9VrdDt06ICsrCyDn+PGxt8ccsTU8qOhMTe3HDG1/GhozADliCFjpxzRf7zNLT8aGn9zyBFTy4+GxqyvHGm2xbC/v78moZKTk3Hs2DFwHAdnZ2fNNi4uLsjJyUFubq7WcmdnZ+Tk5KCoqAhWVlYQCARay5tDzADw4osvYvLkyVptGDLmhsRrb2+PYcOGAQCkUim+//57DBs2zODnuLHxN4ccMbX8aGjMzS1HTC0/GhozQDliyNgpR/Qfb3PLj4bG3xxyxNTyo6Ex6ytHmm0x/FBcXBzmzJmDJUuWwNvbu9p6juPAapgdrq7l+lafmGtjjJgbEm9ZWRnmzZuHwMBATJgwwWjn+FGmliOmlh+AaeeIqeUHQDny+Pb6RjnSvK57zS0/ANPLEVPLD8C4OdKsi+Ho6GjMnj0bb7zxBiZMmABXV1fk5+dr1ufm5sLFxaXa8ry8PLi4uMDBwQHl5eVQqVRay5tDzLUxdMwNiTc3NxfTp09HYGAgPvjgA6PE29j4m0uOmFp+NDTm5pYjppYfDYm5Ns09ZsoRw8Vcm+Z83Wtu+dGQ+JtLjphafjQ0Zn3kSLMthrOysrBw4UJ8+umniIiIAACEhIQgKSkJKSkpUKlUOHr0KAYOHIg2bdrAzMxM8xaZgwcPYuDAgRAKhejevTuOHTumtbw5xFwbQ8bckHhVKhUWLFiAUaNG4f/+7/80f3EZ+hw3Nv7mkCOmlh8Njbm55Yip5UdDY65Nc46ZcsSwMdemuV73mlt+NDT+5pAjppYfDY1ZXznSbN9At2bNGuzbt0/rVvnUqVPRtm1bfPTRR5DJZBg0aBCWLVsGjuNw//59LF++HBUVFejUqRM++ugjiEQiZGRkYOnSpSgoKIC7uzs+//xz2NraNouYH1q6dCl69uyJiRMnAoDBYm5IvCdPnsSiRYvQoUMHzbadO3fGBx98YNBz3Nj4m0OOmFp+NDTm5pYjppYfjYn5IcoR/cdOOaL/eJtbfjQ0/uaQI6aWHw2NWV850myLYUIIIYQQQvSt2Q6TIIQQQgghRN+oGCaEEEIIIa0WFcOEEEIIIaTVomKYEEIIIYS0WlQME0IIIYSQVqvVFcPvv/8+nn32Wc3EzACgUqkwdepUrF+/3qCx7N+/H4MHD8bcuXObvO20tDQsWrSoydttDShHSF0oP4gulCNEF8qR5qXVFcNLliyBRCLBd999p1n23Xffgc/nY/HixQaN5eDBg3jttdewZcuWJm87MzMTSUlJTd5ua0A5QupC+UF0oRwhulCONC8CYwdgaGZmZvjss88wbdo0DBkyBIwx/PLLL9i3bx/4fD727t2LXbt2Qa1Ww87ODu+++y58fX2RlJSE999/HxKJBLm5uQgMDMQXX3wBMzMzdO7cGU899RTu37+PTz/9FF26dNH0V1ZWhvfeew/3798Hx3EYMGAAXn/9daxbtw63b99Geno6ioqKMHv2bK04f/vtN/z000/g8Xiwt7fH2rVr4e7ujtOnT+Obb76BQqGAubk5lixZgq5du+LLL7/EjRs3kJubC39/f9y+fRs5OTmYO3euXhK8JaMcIXWh/CC6UI4QXShHmhnWSm3fvp1NmDCBjRs3jkVGRjLGGLty5QqbPn06k0gkjDHGzp07x0a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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sec_certs.utils.pandas import discover_sar_families, get_sar_level_from_set\n", "\n", @@ -359,53 +245,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MHz5c/Pj169fRv39/6OnpAQBGjhyJHTt2ICsrS2qc0dHRqFGjBpo0aQIAqFu3Lpo3b467d+/C0dFRarnIyEhxq4qGhoZ4jNXatWuRkpIiHsfC5/NhZmYmLqfs++f379/x/PlzDBo0CABgbW0tHp+7Y8cOhIeHIzg4GLGxsWAYJt/nWXp6Ou7du4cfP36Ix5Klp6fj+fPn6NGjh8SxgwYNwqlTp9C9e3ecPHlSXKe092kTE5N88b58+RIcDkf8PtyrVy+ZY6Xq16+PatWqITw8HLVq1UJiYqK4VSOvYcOGgcViwcrKCu3bt0dERAS0tbWRmJgo0dOAxWLh7du3AICmTZuCw5H8KlrY5+ePHz/QtWtXGBgYAAAGDBiAgwcPAlDuvbt9+/bQ0tICANjZ2cnVBbWgz6hbt26hV69e0NLSgpaWFtzd3fHixQuJctWrV4e9vT3CwsLg5OSEW7duwdfXF1wuV3zMrVu3sGjRIrBYLFSqVElqK1NuVatWxerVq3Hu3DkkJCSIe2IUpn///ti6dSuSk5MREhKCjh07wsjIqNDvYYXdaxwOR/wdyM7ODhoaGhg0aBDatWuHbt26oXHjxjKfR1lHCVUFlZ6ejtOnT0NbWxsuLi4AgNTUVAQGBmLcuHH45ZdfEB4ejpMnT4q/oIpEIowfP17cjJ+VlSXx5pPzgQkAnp6esLe3R/v27eHm5oZHjx6BYRhoaGiAYRjxcbn7wMs6vyJEIhE2btwoHhP28+dPsFgsmeVyd1XJ220FyB4QbWRkJPGl5Nu3b+Km/dz158UwDAQCQaH15/1g+fbtG0QikULXXlEcDkfi2rx79w6mpqYYM2ZMga9hXtKuNZvNxqFDh/D48WPcunULfn5+cHR0hI+PT6Hx5CRPOTEVVOeQIUMwaNAgtGrVCnXr1kW1atXw4sULqa9NeHg4fH19MWbMGHTu3Bm1a9fG2bNnxcflvn4eHh7o1KkTIiIicOPGDWzZsgVnz57N9xoTUtY1b94ctWrVwuPHjwv8f5b7Pev169eoXLkyHj9+LHFM7vdwhmHAZrPznSM3kUgk832wKO+fud/LPn36BB0dHYhEIixatAgdOnQAkN01kMfjiY9T9v0z5/06d51xcXGwsrJCv3790KVLF7Ro0QIDBgzAlStXCrwWDMPg6NGj0NXVBQAkJydL/ICUo3v37vD390dsbCzu3buHVatWAZD+WVsQFouV77G8nzkF8fT0xF9//YWaNWti8ODBUj9Lc59LJBKBzWZDJBLByclJ/OMgkP26WFhY4PLlywVe+8Je/7zPIff9p8x7d86Pl0DB16cg8nxGSRvfN2jQIJw+fRpJSUno2rUr9PX1JRKqvOfLe57cj+X8KPH06VNMnToVo0ePRtu2bdGyZUssW7as0OdgZGSE7t274+zZszh37hyWLFkCoPDvYYXda1paWuLXP+dz+MGDB7h9+zZmz55dYPf98oZm+augzp07B1NTU9y4cQNhYWEICwvDlStXkJ6ejr///huDBw/Grl27kJmZKf71vl27djhx4gRSU1MBZPdjnjdvXr5z//jxA0+ePIGXlxdcXV3x5csXvH37FiKRCO3atcOtW7fEA16DgoLE5eQ9vzzatWuHffv2gWEYZGVlYcqUKeJfKzU0NGR+MEtTq1YtaGtri7+0f/r0Cb169co3412tWrWgqakpHuz95csXXLx4EW3atCn0/E5OTggODkZWVhZEIhGWLl2K8+fPK3RtFH1+Tk5O+OuvvwAAKSkpGDVqFOLj46W+hnnrkHatnz9/jl69esHW1haTJk3C6NGj8/1ip6wqVaqgadOm8PPzEw9kLuy1iYiIQKdOnTBs2DA0atQIV65cgVAoLPDcHh4eiImJQf/+/bFixQr8/PlT6cSekNLszZs3iI+PR/369dGuXTtcuHBBPPvcX3/9JR6jEh0djQMHDuCvv/7Cz58/sX//fvE5/v77b2RlZYHH4+HUqVPo1KmTRB3t2rXDyZMnxa3bBw8eRMuWLaGlpSX1vapJkyZ48+YNoqOjAWSPR7137x5atWpV6PNxcnLCqVOnIBKJkJWVhZkzZ+LevXto164dAgMDxe+rixcvxh9//FHgORR5/zQwMECDBg3EM8B9+vQJQ4cOxcuXL5GamorZs2fDxcUFd+/eFdeduw4DAwM0bdoUe/fuBZD9Y9TQoUMRGhqary5tbW307NkTCxYsgKurK3R1dQv9rC2InZ0dGIbBtWvXAGRPVFTQe1vea9CtWzfExMTg0qVL4paLguRch48fPyIiIgLOzs5o3bo1IiIiEBsbCwC4du0aevfuLZHQ5lXY52f79u0REhKCnz9/QiQSSfyAps737vbt2+P06dPg8Xjg8Xi4cOFCgcd17doVT58+xfHjxzF48OACz3PixAmIRCL8+PFD4l6oVKmS+LvGP//8g69fvwIA7t27h4YNG2LMmDFo1aoVQkNDpX6+5ebp6YkDBw6AYRhxC5K07xqK3GtXr17F6NGj0axZM8yYMQN9+/bF8+fPZcZT1lELVQV15MgRjBkzRuLXDyMjI4wYMQL79+/H0aNHsWzZMkyYMEH8+KBBg/DlyxfxL1TW1tbiX8lyMzY2xsSJE9GvXz+YmJjA1NQUzZs3R0JCApycnLBw4UKMGzcOWlpaqF+/vviXOXnPLw9vb2/4+vrC3d0dfD4fbdq0wfjx4wEAnTp1wurVq8Hn8xU+r5aWFrZt2wZfX1/873//g0AgwKxZsyS6jAHZrVvbtm3DypUrsXnzZgiFQkybNg2tW7cu9PweHh748OED+vfvD4Zh0KpVK4wYMQJsNlvua+Pk5IQZM2ZAU1MTDRo0kPmcfv/9dyxduhTu7u5gGAaTJk1Cw4YNC30NcwZ7r1y5Uuq11tTUhJubGwYMGAA9PT3o6OjIbJ1SRM6HZs6vzoW9NiYmJvDy8oK7uzs0NDTQokUL8aDbvLy8vODn54cNGzaAzWZj+vTpqFatmsriJkRdMjMz0adPH/G2SCTC8uXLUatWLdSqVQujR4/GqFGjIBKJUKlSJfz5559IT0/Hr7/+Ch8fH1haWmLVqlUYNGiQuNufjo4Ohg0bhp8/f6Jbt275vnAPHDgQnz59wqBBgyASiWBjY4OAgAAAku9VixcvFpepVKkSNm7ciBUrViAzMxMsFgv+/v6oVasW3r9/L/X5TZ8+Hb6+vujTpw+EQiF69OgBV1dXODs7Y/Xq1ejXrx+EQiHq168vntI6L2dnZ6xYsULua7pu3TosW7YMBw8eBIvFgq+vLxo3boyOHTvCzc0NRkZGqFGjBurUqYOEhATUqFEDXbt2xbBhw7Bt2zYEBARgxYoVcHd3R1ZWFnr16oXevXsXWNegQYNw6NAhLF26FEDhn7XVq1fPV15TUxNbt27F0qVL8ccff6B+/foSXR9zNGvWDBs2bMC0adOwdetWaGlpoVu3bvj27Zu4S2hB3r9/j/79+yMzMxM+Pj6oXbs2gOypv3/99VcwDAMOh4Pt27cX2ioo6/PzxYsXGDBgAIyMjGBvby9u4VHne7eHhwfevn2LXr165ZssIzctLS306NEDkZGRBXaDmzFjBpYsWQI3NzdUqlQJdnZ24se8vLywdOlSHDt2DA0aNBB/vvfq1QuXLl1Cjx49oKmpCScnJ/z48UOcFEljb28PY2NjiSnbpX0PU+Rec3Z2xvXr19GrVy/o6enB2NhYof9TZRWLkad9kxAVeffuHc6cOYOpU6eCzWbj0qVL2LVrl0RLFSGy5HwRrFKlCiZOnKjucAipkHLPfkbKr/T0dAwfPhxLliwRj2vLK+8MdKT0e/v2LUaMGIGQkBDxD9tEedRCRUqUlZUVEhMTxS0FhoaG+aZ4JaQwqamp6NSpExo3bqx0l1BCCCGy3bhxA7/99hsGDBggNZkiZc/GjRtx/PhxeHt7UzKlInK1UGVlZeHDhw/Q0NCAtbV1gYP1CSGEEEIIIaSiKbSF6vnz59iyZQtu3rwJAwMDCIVC8Hg8dOrUCZMmTZLo20kIIYQQQgghFY3UFqqtW7ciKioKAwcORIcOHcTrTKSmpuLmzZs4cuQIWrZsienTp5dowIQQQgghhBBSWkhNqEJDQ8WLl0lz5coVdOnSpVgCI4QQQgghhJDSTqFZ/lJTU/Hz509UqVKlOGNCVFRUsZ6fEEJI6ZJ36YHSij6fCCGkYpHn80nmLH+XL1/G7du3MWfOHPTu3RspKSmYPn06Ro0apZIgpSkrH66qEhUVVeGesyLo+hSOrk/h6PrIps5rVNaSFLqXCCGkYpD384kt64A///wTgwcPxqVLl9C0aVNcvXoVZ8+eLXKAhBBCCCGEEFLWyUyoGIZBvXr1EBkZCWdnZxgYGIDWAiaEEEIIIYQQORIqNpuNCxcu4ObNm2jbti2uXbtWEnERQgghhBBCSKknM6FasGABjh8/jl9//RXm5ubYvn07fHx8SiI2QgghhBBCCCnVZE5KER4ejn379om3jx49WpzxFJlIxOBHGg98gQiaHDaM9bXBZrPUHRYhhBBCCCGkHJLZQhUeHl4CYaiGSMQg4fNPeG28jnErL8Nr43UkfP4Jkah4x3yJRAy4KZlI5KaDm5JZ7PURQgghhBBCSgeZLVTVqlXD2LFj0bx5c+jr64v3jxkzRubJU1NT4eHhgR07dqBatWq4efMm1qxZA5FIhF9++QUrV66ElpZW0Z5BLj/SeFi55w4SuRkAgERuBlbuuYOAWc4wNdRRWT255SRxOfVamOrCZ6wjbKyMqGWMEEKIymzfvh2xsbEAAC6Xi+Tk5AKPq1SpEkxNTQEAtra2mDJlSonFSAghFZHMhMrExAQA8OHDB4VO/OjRI/j4+CA+Pl68z9vbG3v27IGtrS1mzpyJM2fOYNCgQQqdtzB8gUicTOVI5GaALxCprI681JHEEUIIqXhiY2Px6tlj1DDmgM8TQcQr+LONz80EL/0z3v4QlHCEhBBSMclMqPz9/ZU68fHjx7FkyRLMmzdPvE8oFCI1NRVCoRA8Hg/a2tpKnVsaTQ4bFqa6EkmVhakuNDkyezYqTR1JHCGEkIqphjEH89qYyHXsmsjvxRoLIYSQbDITqocPH2Lnzp1IT08HwzAQiUR4//69zLFVvr6++fYtXboUI0aMgIGBAapVq4bu3btLLS/vysS56erpYcFIB6w6ECXufvfrsOb49P4N4tLTFT6fPCyq1CwwieNlZiAqKkahcynznCsSuj6Fo+tTOLo+stE1IoQQQhQnM6Hy8fFBnz59cPHiRXh4eCA0NBSurq4KV/T161cEBAQgODgY1apVg7+/P/z9/bFkyZICj3dwcFC4DiB7TFPALOfslqPkdASGxGDBKEcY6aturFbe+n7zdMC6wP+SuN88HVDVshKqW5vJfZ6oqCiln3NFQNencHR9CkfXRzZ1XiN1JHJbtmzB33//DQDo0KED5s2bh8jISPj7+4PH48HNzQ1z5swp8bgIIYSUPTL7wrFYLEycOBGtWrVC7dq1sXHjRty/f1/hiu7fvw87OzvUqFEDbDYbgwcPxt27d5UKujBsNgumhjqwMNWDoZ4WnsQl4/S11yqvJ8fnpDTsPfcUc4e3wG6frlgwsiX2nH2K208+FVudhBBClBcZGYmbN2/i1KlTOH36NJ4+fYrg4GAsWrQI27Ztw4ULF/DkyRNayJ4QQohcZCZUOTP71ahRA69evYK2tjaEQqHCFdnZ2SE6Ohrfvn0DAISGhqJRo0YKn0cRNtZGaNu4CoJvxuFnWlax1BF48TnefPoJy0p6sDDVQ62qxhCIRNj21yP8SOUVS52EEEKUZ25ujgULFkBLSwuampqwtbVFfHw8bGxsUL16dXA4HLi7uyMkJETdoRJCCCkDZHb5a9y4MWbPno1Zs2Zh0qRJiI+Ph4aGhsIV2draYtasWRg5ciQ0NDRgY2OD5cuXKxW0Ijy61sPNRx9x5nosRrjVV+m533z8gesPP2BQ57owNcqe0Y+jwcYcj+aYvf4atv31CAtGtgSLRdOnE0JIaVG3bl3xv+Pj43HhwgWMGDEC5ubm4v0WFhb48uVLgeXVNdYsJSUFinZeT0lJobFxhBBSzGQmVIsWLcKjR49Qq1YtLFq0CJGRkQgICJC7grCwMPG/+/Xrh379+ikXqZJsrI3QtkkVnLsRhz7OtiodS3Xw7xjo62qif8c6+eoc1q0eDlyIwfWHH9CheTWV1UkIIUQ1Xr16hUmTJmH+/PngcDh48+aNxOPSfgxT11gzQ0ND8H4qXobGDxJCiHLk/UFKZkLFYrHAZrNx9OhR9O/fH8bGxqhdu3aRAyxJQ7vWQ4SKW6li3iTj3rMvGNmjPgz08idp/TvWwZ0nn7HjZDQa1amMSka0JhUhhJQWUVFRmDlzJhYtWoSePXvi7t274i7pAJCYmAgLCws1Rqha8i4KDNDCwIQQoiiZY6j++usvLFy4EP/73/+QkpKCqVOn4vjx4yURm8rkbqVSxVgqhmFw4O9nMDHQhnu7gpNLDQ02Zg9thiy+EFuC/gHDMEWulxBCiKQPHz7g5s2buHXrFj5//ixXmU+fPmHatGkICAhAz549AQBNmjTBmzdvkJCQAKFQiODgYDg7Oxdn6CUqNjYWL2OikfLxMTK47yDkpUn9k8F9h5SPj/EyJlqchBFCCJFOZgvVoUOHcOzYMQwfPhxmZmY4efIkxo8fj8GDB5dEfCqjylaqhy+/4klsEib2bQQdbemXsJqFIUb0+AW7zz5B6L136NKqRpHqJYQQki08PBwbN25EYmIiqlWrBqFQiA8fPsDGxgZTpkxBhw4dpJbdvXs3eDweVq1aJd7n4eGBVatWYcaMGeDxeOjQoUOhayWWRdbGLExylr/b+5/Xi2cyJ0IIKW9kJlRsNhsGBgbibWtra6UmpVC3nBn/zt2IQ98OtjAsoJuePBiGwcELz2BhqovuTjYyj+/dvjZuP/mEXWceo0ldc5ib6ipVLyGEkGy///47hEIhli5diiZNmkg8Fh0djSNHjuDSpUsFLjAPZK+v6OPjU+BjZ8+eVXm8pQGXy8XX74xCSdKn7wwEutxijIoQQsoHmV3+TExMEBMTIx6ce/bsWRgbGxd7YMXBw7UeMngCnLmmfBeGW48/4fX7Hxjqag9NjuzEks1mYdaQZhCJGGw+/pC6/hFCSBENGTIEvr6++ZIpIHtmWn9/fwwdOlQNkRFCCKmI5Jrlb9asWXj79i3atWsHbW1tbNu2rSRiU7ma/7ZSnb0Rhz5KtFIJRQwOhcSgmoUBOjnIP3OfdWV9jHFvgO1/RSPkdgLcnGoqGDkhhJAcDRo0yLfv3bt3+Pz5M1q2bAkAaNiwYUmHVaqZmpqCk/Fe4S5/hv9OTkEIIUQ6mQmVjY0Nzpw5g/j4eAiFQtSqVQuampolEVux8HCth4jojzhzLRbDFRxLFR71Du++pGLBqJbQ0JDZuCfBzakmbkV/wp6zT9DMzhxWZvoKlSeEECLp8OHDiIqKgre3Nzw8PGBgYABXV1f89ttv6g6NEEJIBSIzK+jUqRM2bdoEXV1d2NnZlelkCpBspUpJl78vOV8gxOGLz1GnmjHaNLJWuF4Wi4UZQ5qCzWZh47GHEImo6x8hhBTFiRMnsHDhQoSEhKBz5844f/48IiIi1B0WIYSQCkZmQnX8+HFoaGjA09MTkyZNQnh4eJkfB6TMWKpLtxOQyM3ACLdfpC72KIuFqR7G926IJ7FJCI6IU+ochBBCsrFYLFSuXBm3bt1C69atweFwIBKJ1B0WIYSQCkZmlz9ra2vMnDkT06dPR1hYGFasWIHly5dj2LBhGD58OHR0yt6CtTWtjdCmsTXO3ZRvLFUmT4CjV16iQW0zNKtnXqS6u7SqgcjHn7D/fAwc7C1R1dxAdiFCCCH5aGlpYdeuXbh79y5WrlyJw4cPQ1eXZlItDgsXLsTz588BAAKBAHw+v8DjNDU1weFkf7Wwt7eHv79/icVICCHqItdAoNjYWKxZswZLly5F/fr14ePjgw8fPmDGjBnFHV+x8ehaD+mZApy5LruV6tzNOHxP4WFkj/pKt07lYLFYmD6oCbQ4bGw48gBC6vpHCCFK8fX1RXx8PFavXg1jY2NERUVJnSqdFE1iYiLS09MgyEqDUMADw4gK/CMU8CDISkN6ehoSExPVHTYhhJQImS1UHh4eeP/+PQYOHIigoCBYW2ePH+rYsSNat25d7AEWl1pVjLNbqW7EoY+z9Faq1Aw+/rr6Gi3qW+KXWmYqqdvMWBeT+jXCusMPcDr8NQa41FXJeQkhpCLZsWMH1qxZI95et26dGqMp30xNTYHMdxjaSebXBgDAkauC7DKEEFIByGyh8vT0xNWrVzF79mxxMgVkL/h79erVYg2uuMnTSnXy6iukZfAxQsEZAWXp0LwanBpZ487Tz/iclAbLqrbgpmTKPVmFSMSAm5KJRG66QuUIIaS8eP78eZkf00sIIaTsk/lTk7u7u9TH9PXL9tTfuVup+jrbwiBPKxU3JRNnb8TBuWlV1K6q2sWMWSwWpg1sgoRPP+G9PQKJ3AxYmOrCZ6wjbKyMwGZL71ooEjFI+PwTK/fcUagcIYSUJ+bm5ujZsyeaNGki8Xnk4+OjxqiKD5fLxbcfAqyJ/C7X8W9/CFBZj1u8QRFCCJGdUJV3Hl3rITL6E85cj4Nnd3uJx4JCX4EvEGFYnv2qImIYbDz2EIncDABAIjcDK/fcwfRBTXHk0gvoaGlAW0sDOloc8d86Whpo3dAa/vvv5isXMMsZpoZlb5IQQghRRrNmzdCsWbMSr/fy5cviBe55PB4EAkGBx3E4HGhrawMApk6diq5du5ZYjIQQQkpOhU+oalUxhlMja5y9EYs+zrXFrVSJyen4OzIeXVrWKLaZ+PgCkTgpypHIzYC+ria0NNlI5wnATeEhM0uAzCwheP/+3dTOvMByfAFNF0wIqTimT5+eb196eroaIikZpqam0Ev/jHltTOQ6fk3kd2jTOCZCCCl2ciVUWVlZyMjIkOirbmJiUlwxlbihrvVw67FkK9WRSy/AYmW3YBUXTQ4bFqa6EsmRhakuzE11sXJy2wLLMAwD7s/MAstpcuSatJEQQsqFK1euYNOmTUhPTwfDMBCJRPj+/TsePnxYrPV27dqVWpsIIYSIyfwGvn//frRo0QKtW7eGk5OT+O/yJHcrVWp6Ft59SUHY/bfo0aYWzE2Lb00TY31t+Ix1hMW/deSMhTLW15ZahsViwcRQJ1+5haNaFVqOEELKmzVr1mDy5MmwtrbGkiVL0L59e3h4eKg7LEIIIRWMzBaqgwcP4siRI2jQoIFSFaSmpsLDwwM7duxAtWrV8PDhQ/j7+yMtLQ316tXDqlWroKVV+MK6JWGoaz0k/8jEp29pAAtYNLoV6teqVKx1stks2FgZIWCWM9LSMqGvrwNjfW2ZE0vkLpfFF+Lt5xRciIzDjMElP5aAEELURVdXFz169EBMTAy0tbWxdOlSDBgwQN1hkUJcvnwZFy9eBJA9yQYA8fTq3bp1o5Y/QkiZJDOhMjc3VzqZevToEXx8fBAfHw8gO7maMWMG/ve//8He3h6//vorTpw4gWHDhil1flWysTLCGPcGWHXgnsTMeQa6WsU6cx6bzYKpoQ7iXj6Fg4ODwuUA4J+X33D57js41LdC28ZViitUQggpVbS0tJCVlYUaNWogJiYGjo6OyMrKUndYcqsoycX27dsRG5u9PAmXy0VycjIAICMju9t6zvbRo0fF18PW1hZTpkxRQ7SEEKI4mV3+2rZti8OHD+PLly/4/v27+I88jh8/jiVLlsDCwgIAEBERgaZNm8LePnucko+PT6n5wPiRxsMfh6PyzZz3I42n5shk69KyOqpbGmL/+WcQCGliCkJIxdC5c2dMnDgR7du3x759+zBjxowyO743OTlZnFiUN7GxsXgeE42vn6Mh4L2DkX4ajPTTYKgvgqG+SLwt4L3D18/ReB4TLU7ACCGkLJDZQrVz505kZWVh+fLl4n0sFgsxMTEyT+7r6yuxnZCQAD09PUybNg1v375FixYtsGDBggLLRkVFyTy/KllWtS1w5ry0tEzEvXxaIjEU5Tm3t9fC4WtJ2HX8BlrZFc+shOpW0vdEWUPXp3B0fWQra9do8uTJ6N27N6ysrLBt2zbcu3cPvXr1UndYcss9uYWXlxcAICAgQJ0hFRszU8C9s3wTJ50LpR8GCSFli8yEKjo6WmWVCYVC3Lx5E8eOHUOVKlXg7e2NnTt3YsaMGfmOVaT7mypwUwqeOU9fX6dEYomKiipSPc2bM3j8PhIRz39iVL820NPRVGF06lfU61Pe0fUpHF0f2dR5jYqSyEVHR+PYsWOYPHky4uLiYGZmpsLICCGEENmk/lx05swZAMDevXsL/KOMypUro0mTJqhevTo0NDTg5uam0oStKJSZca80YbFYGN3rF/xIzcLJq6/VHQ4hhBS7nTt34siRIwgJCUFmZia2bNmCrVu3qjssQgghFYzUFqqEhAQAwMuXL1VWWbt27bB582Z8+vQJ1tbWuHr1qtITXqha7pnz+AIRNDlsuWbcK03sapjCuWlVnLoWC7c2NWFmXHxTvhNCiLqdP38eQUFBGDx4MExNTXH8+HEMGTIE06ZNU3doJBcul4skrvxd+ZK4AEebW8xREUKI6khNqCZPngwA8Pf3l1o4KytLoSnPra2tsXz5ckyePBk8Hg/169fH/PnzFQi3eOWeOa+sGtGjPiIff0RgyHPMHELTqBNCyi8OhyPxGWRkZAQOR6716gkhhBCVkfrJM2XKFAwZMgRdunQBmy3ZM5BhGISEhCAoKAh79uyRWUlYWJj43x07dkTHjh2Vj5gUyspMHz3b1sa5G7Ho42wLG2sjdYdECCHFwtraGuHh4WCxWMjKysLu3btRtWpVdYdVqNxTiOeWsy9ncorcyvoU4qamphDw3ik0KUXO9PGEEFIWSE2oNm3ahICAAKxatQqOjo6wsbGBUCjE+/fvcefOHbRr1w4bN24syViJnAZ3scOVuwnYd/4Zloxvre5wCCGkWCxevBjz5s3Dixcv0LRpUzRp0gTr1q1Td1iFio2NxeuYZ6hhbCix34jFAACyPr6T2P/2R0qJxVbaVZR1uwghZY/UhEpfXx9LlizB58+fERoairi4OLBYLDRs2BCzZ8+GpaVlScZJFGCkr4VBne2w7/wzRL/+isZ1zNUdEiGEqJympib279+PjIwMCIVCGBiUjSUjahgbwrt9K7mO9b1xt5ijKZty1uyilixCSGkgs7O5lZUVPD09SyIWokLu7WvjfOQb7D33FOtmdShTk2sQQog8evXqBScnJwwbNoymxa8AKtK6XYSQsoVG75ZTWpoaGN69PtYfeYAb/3xAh+bV1B0SIYSoVFhYGIKDg7F69WpkZGTAw8MDffr0KTMtVcp4+0OANZHfJfb94GXPnmeszc53bN3SPaSMEELKBUqoyrGOzavh9LXXOPB3DNo0toYmR0PdIRFCiMro6Ohg4MCBGDhwIO7cuYNFixYhICAAffv2xfTp08vdIr+2trYF7k/5d0ILi6qSj9etKr0MIYQQ1aGEqhxjs1kY06sBft95C+cj4tG3A32wEkLKl+vXryMoKAhRUVFwd3dH//79ce3aNUyZMgXHjx9Xd3gqJW2mP+r+Rggh6iUzofr27RsePXqEzp07w9fXFy9evMCiRYtgb29fEvGRImpWzwLN61ng2OUX6NKyOgz05F83jBBCSrOOHTvC1NQUw4YNw9q1a6Gjk72OYL169XDs2DE1R1cwLpeLb99T5J5sIuF7CirrVtxFbiviNPOEkLJHZkK1YMECtGvXDrdu3cKdO3cwevRorFy5EocOHSqJ+IgKjO71C2b9EY6g0FcY495A3eEQQohK/PHHH2jevHmBj4WGhpZwNKQ4xMbGIiYmGiZ5JvPLWR7z0+doif3fK27uSQhRI5kJ1ffv3zF69GisXr0avXr1Qv/+/REYGFgSsREVqVXFGJ0cquPczTj0bFsLFpX01B0SIYQUmbRkqjQzNTWFfkaqQtOma1XwqcFNTAEXV/lmqg27xBRzNIQQkp/MhIrP54PP5+PGjRtYtWoVMjIykJ6eXhKxERUa3r0+bv7zAYdCYvDrMJpemBBCSMWRe1FggBYGJoSoFlvWAZ07d4aTkxNMTU3RsGFDDBo0CL169SqJ2IgKmZvqorezLcIfvEfs++/qDocQQghRm+TkZPHiwIQQUlQyW6hmzpyJwYMHw8rKCkD2LEI0IUXZNNClLi7eTsC+4GdYPskJLBYt9ksIKds+fPiAHz9+gGH+6+rVoAGNFSWSci8KDNDMiIQQ1ZKZUIlEIpw/fx6vXr2Cj48Prl27hrp160JDg9Y0Kmv0dTXh0dUO1x9+wIfEVGhpaUCTw4axvjbYbEquCCFly9q1a3Ho0CGJ9aZYLJbcE1KkpqbCw8MDO3bsQLVq1bBw4UJERUVBV1cXADB9+nTqBkYIIUQmmQnVmjVrkJycjMePHwMAbty4ga9fv8LHx6fYgyOq192pJmpVMcaSXbeQyM2AhakufMY6wsbKiJIqQkiZ8vfff+PSpUuwtLRUuOyjR4/g4+OD+Ph48b4nT57g0KFDsLCwUGGU+b39kX/a9B+ZPACAsY52vmPrVCnWcAghhBSRzDFUt27dwqpVq6CtrQ0DAwPs2bMHERERJREbKQZpmXxsOPoAidwMAEAiNwMr99zBjzSemiMjhBDFWFtbK5VMAcDx48exZMkScfKUnp6Ojx8/YvHixXB3d8emTZsgEolUGS6A7DWS6tT/BVpVqkv8+cmw8JNh5dtfp/4vsLWlRdkJIaQ0k9lCxeFwwGb/l3dpaWmBw5FZjJRSfIFInEzlSORmgC9Q/RcHQggpTk5OTlizZg06d+4sXtQXkG8Mla+vr8R2UlISWrdujeXLl0NPTw+TJk3CiRMnMHjwYJXGLG3BWRrTo3rSFgUGaGHg4pZ7VkWaUZFUBDIzIzs7OwQGBkIoFCIuLg779u1DvXr1SiI2Ugw0OWxYmOpKJFUWprrQ5MhsrCSEkFLl5MmTAICQkBDxPkXGUOVWvXp1bN26Vbw9YsQInD59usCEKioqSoloC5eSkqL0ueUpm3OMMueOiopSqry6y/7zzz949+ENDCvlP4b599vPu0TJhYFTkv8rT5QXHx8vfu2+fv0KAOIf4+Pj4+n6knJHZkLl7e0NPz8/JCUlYejQoWjfvr3c46fyDvjNERgYiJCQEBw8eFD5yIlSjPW14TPWESv33BGPofIe4whjfW3ZhQkhpBQJCwtT2blevHiB+Ph4dOvWDQDAMIzU3hgODqpfy8/Q0FDpc8tT1tDQEMrkVIaGhnBwcIChoSG4qcqXzUxTvmxqEcoaVgJausk/Pvje34y4PFGeg4MDJkyYAIBaX0nZJm/yLzOhiouLg5+fn8S+yMhItGnTptByBQ34BYDXr1/jzz//hI2NjVwBEtVis1mwsTJCwCxnZPIESPiUgocvvqB2VWN1h0YIIXLZtWsXJkyYgJUrVxb4uDKTJjEMAz8/P7Ru3Rp6eno4duwY+vXrV9RQSTlBXdgIIYWRmlA9e/YMDMNg/vz5WLdunXiND4FAAB8fH5m/DOYM+J03b554X1ZWFn7//XfMmjULp0+fVs0zIApjs1kwNdQBDIEz1+Pw9614tGpgjeqWhuoOjRBCZMppkTExMVHZOe3t7TFx4kQMHToUAoEArq6uxb6Ife4v6XnH9NCX9NIrZ0HgnISKEEKkJlRHjhxBREQEEhMTMX369P8KcDjiLhGFyTvgFwDWrVuHAQMGSHT/I+o11LUewu6/w/7zz+Az1lHd4RBCiEyurq4AIPHZlFdSUpLE+lTS5P5x0NPTE56enkUPUAmVKhUw0IeUGrkXBqYubISQvKQmVCtWrAAArF+/HnPmzClyRREREfj06RMWLlyIO3fuyDy+Ig5YVNdzbmOvh9BHn/HX35GoaVF6x1JVxHtCEXR9CkfXR7ayco0WLVoER0dH9O/fH8bGkt2VU1NTcfToUdy5cwe7du1SU4Tyyf0lnRBCSNklcwzVnDlz8OzZM6Snp4NhGAiFQrx9+1bhqWSDg4Px6tUr9OnTB+np6fj27Rtmz56NDRs2FHh8RRsQGhUVpbbn3LCxEI/iryDiBR/9ujmVygV+1Xl9ygK6PoWj6yObOq+Roonctm3bsGfPHvTq1Qu1atWCjY0NhEIh3r17hzdv3mDkyJHYtm1bMUVLCCGESJKZUPn4+CA0NBSZmZmwtLTE27dv4eDgoHBC5e/vL/73nTt3sGXLFqnJFClZ2poaGNGjPtYfeYgb/3xAh+bUJZMQUnqx2WyMHz8ew4cPx+3btxEXFwc2mw1XV1e0adMGWlpa6g6REEJIBSIzoYqMjERoaCiWLVuGadOm4fPnz6W+GwVRXMfm1XHmWhwOXHgGp0bW0NLUUHdIhBBSKB0dHXTs2BEdO3ZUdygljia0IISQ0kNmQmVubg49PT3Url0bL1++RJcuXbBmzRq5KyhoNkBHR0c4OtIECKUJm83CWPcG8PkzEsE336B/pzrqDokQQogcaEKL4rF9+3Zxsppb3gQ2N1tbW0yZMqXYYyOElC4yEypNTU3cu3cPtra2uH79OhwdHcVrMJDypYmdORzsLXA89CW6tKoBI33qNkMIIaURTWhR/GJjY/HkeTR08kwWKfj3m9Prr9ES+zOTSigwQkipIzOh8vLywsGDB7Fq1Sr8+eefaN26NSZOnFgSsRE1GNOrAWauu4pjV15gQp9G6g6HEEIIURsdM6Bmb7Zcx8afFRW4nxYFJqT8k5lQNW3aFGZmZtDU1MTevXuRkJCABg0alERsRA1srI3QpZUNLkS8Qa+2tWFdWV/dIRFCSIFiY2Px4MEDDBw4ENOmTcOLFy/g6+uL1q1bqzs0QgpEiwITUj7J/Nnl4MGDmDp1KoDsX1ZmzpyJoKCgYg+MqI9nd3toaLBx4MIzdYdCCCFSLVmyBNra2rh69Sq4XC78/Pywfv16dYdFiISuXbsiICAAAQEBsLW1ha2trXibWqcIKR9kJlTHjh3DkSNHAADVq1fH6dOnceDAgWIPjKhPJSMd9O9YBzcffcTzhGR1h0MIIQXi8Xjo3bs3IiIi4ObmBkdHR/D5fHWHRQghpIKRmVAJhUIYGBiItw0NDcFilb6FX4lq9etYB6aG2thz9ikYhlF3OIQQkk9WVha+ffuG8PBwtGnTBt++fQOPx1N3WIQQQioYmQlV7dq1ERAQgHfv3uHdu3fYuHEjatasWQKhEXXS1ebAs7s9YuKTcevxJ3WHQwgh+QwZMgSdOnWCg4MD6tSpg4EDB2LUqFHqDosQQkgFI3NSimXLlmHp0qXo27cvOBwO2rRpg6VLl5ZAaETdurSsgTPX47Dv/DO0/MUKmhz5ZjoihJCSMGzYMHh4eIDNzn5vOnXqFA32L8SnHwz+vJ6Vb39KZnYvBEMdVr7jDauUSGiEEFKmyUyoKleujC1btpRELKSU0dBgY6x7Ayz7322E3IqHe/va6g6JEELE0tLSsG7dOsTGxmLjxo1Yv3495s+fD319mp00L1tbW6mPJf67UG2VKpLHGFYpvBwhhJBsUhMqX19feHt7Y/LkyQU+vmPHjmILipQeDvYWaFynMo5cegGXFtWhr6up7pAIIQQAsHLlSlhYWCApKQna2tpITU3F77//jnXr1qk7tFJnypQpUh/z8vICAAQEBJRUOOXe9u3bEftvoppbzr6ca56bra1toa8TIaT0kppQ2djYAMhedI5UXCwWC2PdG2DOhmsICn2J0b1oDTJCSOkQExMDf39/XLt2Dbq6uggICECvXr3UHRYhiI2NRfTzx0BlLckHNIUAgOhvLyT3f8vfFROgRYEJKSukJlQnT57E8OHDERkZibVr15ZkTKSUsa1mgo7Nq+HsjTj0aFsLFqZ66g6JEELEY6dyCIXCfPsIUZvKWtDoZyHXocJTiTKPoUWBCSm9pCZUKSkpWLx4MSIiIrBy5cp8j/v4+BRrYKR0Ge5WH4nJ6fjKzQAAaHLYMNbXBptNU+gTQtSjZcuWWLt2LTIzM3Hjxg0EBgaiVatW6g6LEJXp2rWruBWKumYSUnpJTag2b96M0NBQsNlsmJiYlGBIpDSqbKyLUb0aIODQfSRyM2BhqgufsY6wsTKipIoQohZeXl7YuXMnDA0NsX79erRv3x7Tpk1Td1jlVuJ34MhVgcS+tMzsv/V18h9ral0iYRFCiNpJTajs7e1hb2+PGjVqwN3dvSRjIqXQjzSeOJkCgERuBlbuuYOAWc4wNdSRUZoQQlTv2rVrmDZtmkQSdfr0afTt21d9QZVT0mb7S/53koVq1pKPm1rTDIHKogktCCl7ZM7yd/78eZw/fz7f4zTLX8XCF4jEyVSORG4G+AKRmiIihFRUYWFhEAgEWLNmDRiGAcNkr6MkEAiwfv16SqiKgbQv69QNTfWyJ7R4ClQ2kHxAM/vzNvpbguT+b6klFBkhRBqpCZWTkxMAmuWPZNPksGFhqiuRVFmY6oLHE6oxKkJIRRQTE4Pbt28jKSkJBw4cEO/ncDgYN26cGiMjREUqG4DTp4lchwrOPCrmYAghskhNqFxcXAAACQkJmD17tsRjK1euRL9+/Yo1MFK6GOtrw2esI1buuSMeQzVnaHNsPPYQLRtYYnBnO7BYNJaKEFL8crr5BQYGwtPTU93hEEIIqeCkJlSbNm3Cz58/ceHCBaSm/teczOfzERYWJvcsf6mpqfDw8MCOHTtQrVo1HDt2DAcPHgSLxULDhg2xbNkyaGlpyT4RUSs2mwUbKyMEzHIGXyCCJocNXW0OrMz0cejv50j4lIKZQ5pCR0vqLUUIISo1aNAgXL58GWlpaQCyp01/+/Yt5syZo+bICCEV2evXr+Hl5YU//vgDtWvXVnc4pARI/fbbpEkTPH78ON8sfxoaGti8ebNcJ3/06BF8fHwQHx8PAHjz5g12796NkydPQl9fHwsWLMDhw4cxevToojwHUkLYbFa+CSh+82yOWlWMsP/CM3z4mgrvMa1onSpCSImYM2cO3r17h69fv+KXX37Bo0ePaNp0Qv6Ve1FggBYGLkmrV69Geno6/P39sWvXLnWHQ0qA1ISqQ4cO6NChAwBg+vTpSp38+PHjWLJkCebNmwcA0NLSwtKlS2FgkD3Q0s7ODh8/flTq3KR0YLFYGOBSFzWsDBEQGIXfNlzHglEt0aC2mbpDI4SUczExMbh06RKWLl2KMWPGgGEYLFu2TN1hkVKCy+UiJQm49zcjd5mUJICryS3GqNSHFgYuGa9fv0ZCQvbEIQkJCYiLi6NWqgpAZv+skJAQpRMqX19fie2qVauiatWqALL/YwcGBsLf31+pc5PSpeUvVgiY6YwVe+7AZ0cEJvdvgm6tbdQdFiGkHLOwsACHw0HNmjXx8uVLuLm5ISMjQ3ZBQiqA3IsCAzQjY0lZvXq1xDa1UlUMMhOqWrVqwcfHBy1atICe3n9duVxdXZWu9MuXLxg/fjwGDBgAR0fHAo+JiopS+vxlVXl4ziM7GuPETQG2BP2Du49eoVtzE2ioaOHf8nB9ihNdn8LR9ZGtrF0jPT09nDt3Dvb29jh+/Dhq166N79+/qzssUkqYmpoilf8OLd3k/wy69zdDLTikSHJap6Rtk/JJZkL1/ft3fP/+XeKGYLFYSidUsbGxmDBhAoYPH46xY8dKPc7BwUGp85dVUVFR5eY5O7USYd/5Zzh9LRaZIl3MH9kSRvpFm3ikPF2f4kDXp3B0fWRT5zVSNpH7/fffERQUBC8vL5w4cQIjRoygCSlIhSZtUWCAFgYuKTY2NhLfmW1sFOutk5SUBD8/P3h7e6NSpUqqDo8UE5kJ1cGDB1VWWWpqKsaNG4c5c+agT58+KjsvKV00NNgY17shalobYUvQI/y64RqWT3SCrg5HPEOgsb422CpquSKEVEw1a9ZEx44dwWKxsHTpUty7d48G2ZMKLXtR4GdgmRnne4zhZH/mPv76QXJ/0o8Sia2imD9/PqZOnSreXrhwoULlAwMD8eTJEwQGBmLGjBmqDo8UE5kJ1devX+Ht7Y2EhAQcPnwY8+bNw6pVq2Bubq5wZSdOnMC3b9+wZ88e7NmzB0D2elezZs1SPHJS6nVuWQNVLQxw9NILfP2egU3HHorXsPIZ6wgbKyNKqgghSlu/fj0ePHiAgwcPIjMzE7t27cKrV68kvswQ1cs9e1zeVg+aOU79WGbG4PRpK/fxgjMRxRhNxVOnTh1xK5WNjY1CE1IkJSXh0qVLYBgGFy9ehKenJ7VSlRFsWQcsW7YMXbp0gba2NoyMjGBvbw9vb2+FKgkLC0O1atUwevRoPHnyBGfOnBH/oWSqfLO3qYRpA5uIkykASORmYOWeO/iRxlNzdISQsiw0NFT845yVlRUOHTqECxcuqDmqiqVSpUr0hY+QPObPnw89PT2lWqdEIhEAQCQSITAwsDjCkyopKQm//fabeEZIIj+ZLVQfPnzA4MGDcfjwYWhqamLu3Llwd3cvidhIOcEA4mQqRyI3A3yBSD0BEULKBT6fD01NTfG2pqYmWCxq9S5ueWePI6UHl8sFk/RDoVYnJukHuBxaP1KV6tSpg9OnTytcLiwsDAKBAAAgEAgQGhpaot3+qLuh8mQmVCwWS5wtA9njoHJvEyKLJocNC1NdiaTKwlQXmhyZDaSEECJV8+bN8dtvv2HgwIFgsVg4ffo0mjRpou6wCCkXcnftLMlFgdVVb2ng4uKCkJAQCAQCcDgcdO7cucTqpu6GRSMzoXJ1dYWXlxdSUlJw9OhRBAUFwc3NrSRiI+WEsb42fMY6YuWeO+IxVHOHt4Cxvra6QyOElGGLFy/Gxo0b4e/vDw6HAycnJ6XXTSSkPDA1NcV7QbrCY6hkTRWvrkWBy+pixMrO1Ofp6YlLly4BANhsNjw9PYsrxHwK6m5IrVTyk5lQTZ48GadPn4ZIJEJkZCQ8PDwwcODAkoiNlBNsNgs2VkYImOUMvkCEr9wM7D77BON6N4R9Tfr1gxCiHGXGKBD1SOIC50Ile7ek/9tpQU83/7HmViUUGJEqd9fOklwUWF31qpKyXefMzMzg6uqK8+fPo1u3biXaQqTu7oZlncw+VwKBAGw2Gxs2bMDixYuRlpYGhmFKIjZSjrDZLJga6sDCVA81rY2QnMLD2sAopGXw1R0aIaSMyZnMyN3dvcA/8kpNTUWvXr3w/v17AEBkZCTc3d3h6uqK9evXF0vsFZGtrS3s6zeGuZXkH4FIHwKRfr799vUbw9bWVt1hkwouKioK3bt3x8OHDxUql7frnKITPLRt2xYsFgvt2rVTqFzu+pWZWMLFxQUcTnY7izLdDSv6hBYyW6iWLl2K9PR09O7dG2w2G1FRUXj//j18fHxKIj5SDunramKupwPmb72JHSej8ZsnLbhKCJHfyJEjAWR3+VPWo0eP4OPjg/j4eABAZmYmFi1ahIMHD8La2hqTJk3CtWvX0KFDB1WEXKFJWzC2rLY+kIrB19cXIpEIK1aswMmTJ+UuV9Suczt27IBIJMK2bduwa9cuheNWtnXM09NTPHZNme6GFX1CC5kJ1T///IPg4GAA2U2RGzdupEV5SZHZ16yEoa71EBjyHM3qWcClRXV1h0QIKSNWrFiB06dPY+vWrdi/f79S5zh+/DiWLFmCefPmAQCio6NhY2OD6tWz34vc3d0REhJCCZWacblcfOcCYZfk6xnznQvoaHOLOarya/v27eK1xXLLu95Ybra2tlKT5rIqKioKqampALJbsh8+fIhmzZrJVbYoXedev36NhIQEAEBCQgLi4uJKbB0rMzMzVKlSBQkJCbC2tlaouyFNaCFHQsXn85GVlQUtLS0AEN8khBTVoM52+OflV+w4+Qj1a1aCdWV9dYdECCkDhEIhxo4di2fPnmHy5Mn5Ht+xY4fMc/j6+kpsJyYmSixYb2FhgS9fvhQ9WELKkNjYWEQ/jwHLTHISCObfWXkff/0suT+pdCWvuWcIBJSfJTDv+4MirVRFmalv9erVEtv+/v4KtVIVpXUsKSkJHz9+BAB8/PgRycnJcidFNKGFHAlVx44dMW7cOPTp0wcsFgvBwcH0ix1RCQ02C78Oa46Z68IREHgfq6e3B0eDplInhBRu165duH37Nt68eYNu3bqp5JwFjQ2WtqZVVFSUSuosLVJSUgCU/POSp14OhwMTU8DFVb71xcIuMeBwOIiKihKfX5m4lC1fFsvmLc8yM4Vm7y5yleOfvSIuW9i5AeXuL0XLxsfHS1yDr1+/AoB4bFB8fLxc58ppncq9LW8MjRo1QkhIiMS2vGVzWqdybyty3S5fvizROnbp0iW0adNGrrKnT58Gn589rp3P52P9+vXo27dvsddbXshMqObNm4fAwECEhoaCw+Gga9eu8PDwKInYSAVgYaqHGYOaYtWBezh88TlG9vhF3SERQko5Kysr9O3bF1WqVEGrVq1Uck5LS0t8+/ZNvJ2YmAgLC4sCj3VwKF/jPg0NDQGUzPPK3YKQmJgIADhy5AiAglsPDA0NkZqmWB2GhoZwcHCAoaEhvmfIPr6w8l8ylS8LXlHKKjawX6Js5k/FKs5VXiAQgEnign/2ilzlmCQuBJbahd47Rbm/FC3r4OCACRMmiLeVHadnYGAgkVQZGBgoFP/jx49x/vx5uLm5oWPHjnKXq1q1Kj58+CDerlatmkL1du3aVaJ1zNXVVe7yK1askNh+/Phxvn3FUW9pJ29CKzOh0tDQwMiRI8WDgAlRtbZNqsDV0QYnwl6hqZ05Gtcxl12IEFJhzZo1Cxs3bpT6YX/u3DmFz9mkSRO8efMGCQkJqFatGoKDgzFgwICihkoKUdHGWJCyw9vbW2JJBkUnwPH09ERCQoLCEzvUrl1bIqFSZPxUTr3KrmNVr149iRkN7e3tS6Te8kJmQkVISZjQpyGexiXhj8MPsOm3TjDS11J3SISQUirnF+iizPKXl7a2NlatWoUZM2aAx+OhQ4cO6N69u8rOT7LlXmOIlD7ZCwPzFOryV9YW3ZWHg4ODuJXKwMBA7gkpcpiZmWHdunUK15u3NeT+/fsK16vsOlaPHz+W2I6Oji6RessLSqhIqaCjzcHc4Q7w2nQdm48/xKLRraSOXyCEVGwNGzYEkN3n38/PT+KxGTNmKNQNMCwsTPxvJycnnD17VjVBEpUpaJa/zH+78uno5j/WmhYFJirg7e0Nb29vlf5wI4uLiwsuXLgAkUgENput8FpQgPKtY3knnVN0Ejpl6y0vpCZUGzZswOzZsxEVFVVu+kGS0s22mglG9fwFu88+RcjtBLg51VR3SISQUmjJkiX48uULoqKiJBaRFAgEiIuLU2NkRNWkLfCbM423tZXk49ZW0suQ4lXeplx3cHCQmFyiJOR0ncvKygKHw1EqOVG2dYzFYklMzqPoj9rK1lteSE2ogoODMXToUCxbtgwHDx7MNwOSiYlJccdGKqDe7W3x8MVX/O/MEzSoVQk1rIzUHRIhpJQZOHAgXr16hRcvXkjM8qehoaFw1xxSutGiwGVH9pTrz8Eyk+zuxfw7w97jr4mS+5MUm3ijIlBF17mkpCT4+fnB29tbofJ5v+cXNPNpcdRbXkhNqNq2bSuemcTR0VHiMRaLhZiYmGINjFRMbDYLsz2aYea6cKw9FIV1s5yhpamh7rAIIaVIo0aN0KhRIzx48AD9+vVTdziEkH+xzCpB072XXMfyzwUXczRlU9u2bXHhwgW0a9dOqfKBgYF48uSJwmtBsdls8VpSOdslUW95ITWhWrZsGZYtWwZPT08EBgaWZEykgjM10sEsj2ZY9r/b2H/+GSb0baTukAghpdCjR4/UHQIh5F9cLhdMUpLciRKTlAQuR7OYoyp7duzYAZFIhG3btim0qC+Q3Up06dIlMAyDixcvwtPTU+7WIl1dXaSlpUlsl0S95YXMSSkCAwPx6NEj3LhxA3w+H+3atUPLli1LIjZSgbWob4ne7WvjRQIX776kwLKqLbgpmTDW1wabTZNVEEKy12gZO3YsmjdvDn19ffH+MWPGqDEqQghRzuvXr8WL+yYkJCAuLk6hqdMDAwPFrUwikUih1qLcyVRB28VVb3khM6E6c+YM/vjjD7i6uoJhGPz666+YMWMGBg8eLFcFqamp8PDwwI4dO1CtWjVERkbC398fPB4Pbm5umDNnTpGfBCmfRvWsjxdvv2PprltI5GbAwlQXPmMdYWNlREkVIUQ8ljf3ui2EEPXInnKdr1CXv/I45XpRrF69WmLb399foVaqsLAw8ex8AoEAoaGhcic2NjY24mQuZ7sk6i0vZCZUe/fuRVBQkHjF+AkTJmDcuHFyJVSPHj2Cj48P4uPjAQCZmZlYtGgRDh48CGtra0yaNAnXrl1Dhw4divYsSLmUlinAhiMPkMjNnh83kZuBlXvuIGCWM0wNddQcHSFE3fz9/QEAP3/+hJERTWBDCCnbcic0BW3L4uLigpCQEAgEAnA4HIWmXZ8/fz6mTp0q3s69sHFx1lteyBxxJhKJxMkUAFhaWso9UO348eNYsmSJuHx0dDRsbGxQvXp1cDgcuLu7l/iUlKTs4AtE4mQqRyI3A3yBSEoJQkhF8ubNG/Ts2RM9e/bEly9f4ObmVuC0zYQQUhbkbRVSpJUIyJ52Pec7OpvNVmja9Tp16ojrs7GxUairYe56WCxWhVyLSmYLlYmJCa5cuYIuXbJXzb5y5QqMjY3lOrmvr6/EdmJiIszNzcXbFhYW+PLlS4Fl864WXRFUxOdcGIsqNWFhqiuRVFmY6oKXmYGoKJplMi+6fwpH10e2snaNVqxYgUWLFmHt2rWwtLTE8OHD8fvvv9NESoSQMqkorURA9rTrzs7OuHLlCjp06KDwxBDz58+Hl5eXUvXmXreqok1IAciRUC1evBhTp07FihUrAACamprYunWrUpUVNKe9tIXDKtpiwrSAcn4iEQOfsY5YueeOeAzVzCHN8OJdKrq0omuVG90/haPrI5s6r5Gyidz379/Rtm1brF27FkD2r6THjx9XZWikjEtJBu79nf+7B+/f3+m0dfMfj3875XC5XGQmAfFn5esVkZkEcDncIkRLKrqcVqKEhASFW4ly8Hg8ib8Vrf/06dMKl3v9+jX4fD4AgM/nKzyZRlHXsCpKeT8/P4SHh6Nz586YP3++wnXnkJlQ1a1bFyEhIYiPj4dIJEKtWrXA4cgsViBLS0t8+/ZNvJ2YmCjRnZCQ3NhsFmysjBAwyxlpaZnQ09PBhYg3OHblJQAWurSqoe4QCSFqxuPxxD/Mff36VWIdFVKx2draSn0sp2todYs8x1gUXo6Q4qZsKxGQnVhEREQAAG7evInk5OQSaS1atGiRxPaCBQsU+nGrqGtYFaV8eHg4ACA0NLR4Eyoge/V5VbzBNGnSBG/evEFCQgKqVauG4OBgDBgwoMjnJeUXm82CqaEO4l4+hYODA4Z0rYcXb7nYHPQPjA200PIXK3WHSAhRk2HDhmHcuHFISkrCunXrcP78eYwfP17dYZFSYsqUKVIf8/LyAgAEBARIPcbU1BRJgneo2Vu+cePxZ0U0a52Stm/fXuD4x5x9Oa9Xbra2toW+xmWVsq1EALB7926J6ct3796NuXPnqjC6gn3//r3Q7cIUdQ2ropT38/OT2F69erXSSZViyyAXkba2NlatWoUZM2agR48eqF27Nrp3716SIZAyTpPDxsJRLVG7ihFWHbiP5wnJ6g6JEKImAwcOxKxZs+Du7g6BQIAVK1Zg2LBh6g6LEKKg2NhYRD9/jidfkyT+pHO0kM7Ryrc/+vlzmoCmAFevXpXYDgsLU1Mk8itoDauSKp/TOpUjNDRUobpzU67vnoJyv6BOTk44e/ZsSVRLyik9HU0sGe+EeZtvYPn/bmP19Paobmmo7rAIISUsNTUVDx48wNy5c/Hhwwfs27cPzZo1g56enrpDI0RpXC4X+JYKwZlH8hX4lgquRtkfu8U2M4e2+yC5juWdCyrmaEhJKeoaVqVlDSy5Wqiio6Nx9OhRZGVl4eHDh8UdEyEymRhqY9lEJ2iw2Viy6xaSfmTILkQIKVcWLlwo7lpiZGQEFouFxYsXqzcoQghREysryWEQ1tbWJVLv2LFjJbYnTJggd1kXFxdoaGgAyB5ipOgaVi4uLuK5HdS5BpbMFqqTJ09i9+7d4PF46Nq1K6ZOnYo5c+bItbAvIcXJurI+lkxojUXbbmLJzltYNb09DHQ11R0WIaSExMfHY/PmzQAAQ0NDLFq0CL1791ZzVIQUjampKd4Jf4LTp4lcxwvOPKKxW2Xc5cuXcfHiRQD/tlAC4te0W7du6Nq1q1znSU6WHAaRlJRUrHXnLpvbnTt3YGJiIlfcnp6eCA4OBgAIhUKF17Dy9PQUx6Do2lsdO3aU6PZXlGRMZgvVwYMHcezYMRgYGMDMzAwnT57E/v37la6QEFWqU80Ei0a3woevqVi55w6y+EJ1h0QIKSECgQCpqani7bS0tAKX5yCSLl++DC8vL/Gf2NhYxMbGircvX76s7hAJqbCSk5PzJUby6ty5s3jWUxaLJV5DtiTqzmklsrS0VKhcfHy8xHZCQoJC5c3MzFClShUA2S1yikxokXd2wmKd5Y/NZsPAwEC8bW1tLW6aI6Q0aGpngTlDm2PtoSgEBEZh/siW0GAXvL4ZIaT86Nu3LwYNGoTu3buDxWLh8uXL6N+/v7rDKnMq4iKchJQmXbt2FbfmyDMDpTQ5rTV8Ph8cDkeu1pqi1K2KuH19fSW2V6xYgZMnT8pdPikpCR8/fgQAfPz4UeGp4nNaqYraVVBmQmViYoKYmBhxxnv27FkYGxsXqVJCVM25WTVwU3j435kn+PNkNKYMaCx10WhCSPkwadIk1KlTB7du3QKHw4GXlxc6dOig7rBKvdxfgggh5YeZmRm6deuG8+fPo3v37mXix5LcvQwK2pYlMDBQYlIKRdeiWrRoUb6WKmXITKgWLVqEWbNm4e3bt2jXrh20tbWxbdu2IldMiKr1cbYF92cm/rr6GpWMdeDRtZ66QyKEFLPOnTurbRAyKf8yk7LXl8pNkJ79N0cv/7EwL5m4yH+krWEFVMx1rDw9PZGQkKDwWCR1MTAwkEiicveKk0dYWJi4qzfDMGqb5U9mQmVra4szZ84gPj4eQqEQtWrVgqYmDfwnpdOonr+Am8LD/Wdf4NjACvq6mtDksGGsrw02dQMkhJAyK/cA+LxflBUZuC8vW1vbAvfn1G1rnudx8//KZE99ngXhqUT5KvuWVS6mPleH7DWsXoBtZpXvMYajAwB48vWHxH5R0ucSiU0dzMzMsG7dOnWHITdvb28sXLhQvK3oTK2NGjXCnTt3xNuNGzdWWWyKkJlQZWZmIjQ0VDw17b179wCgzGS+pGJhsViYPqgJXr7lYuWeO0jkZsDCVBc+Yx1hY2VESRUhhJQDJdGVSVrrRVHGuJDiwTazgm7vUXIfn3GWJlcrLWrWrCmxbWNjo1D5x48fS2xHR0cXNSSlyEyoJk+ejJ8/f6JatWrifSwWixIqUmqlZvDxx+EHSORmr02VyM3Ayj13EDDLGaaGOmqOjhBSVIsWLYKfnx/+/vtvuLm5qTscUkLK0tiv7KnPE6HRz0Ku44WnEmnqc1IhBQYGQkNDA0KhEBoaGgqPgUpPTy90u6TITKi+fPmCCxcu0AB/UmbwBSJxMpUjkZuBL8npuPfsC5ybVoWOtsxbnxBSSt26dQsPHjzApk2bYGNjk2+q9AYNGqgpMkJISeNyuRAlJSrU6iRK+gwuRyT7QFLswsLCIBRmL3kjFArVNgaqqGR+q7Szs8O3b99gbk4jLUnZoMlhw8JUVyKpsjDVRXoGH5uP/4PdZ5/AxaE6ujvVhI21kRojJYQoY/DgwZg3bx4+f/6M6dOnSzzGYrEQGhqqpsgIIWWZqhbYJfJzcXHB+fPnwTAMWCxWmZ1kSGZC1b17d7i5ucHOzk68aBcAHDhwoFgDI0RZxvra8BnrmG8MVQ1LQ6ye3g5/34rHxTsJCI54g19qVYKbU020aVwFWpq0vhohZcGUKVMwZcoUzJkzB+vXr1d3OIQQNTI1NcUHAVvhMVSmpoUvAZSzwC11xSxePXr0QHBwMIDsWfp69uypUPkuXbrgypUr4m11Jb0yE6q1a9di0qRJqFGjRknEQ0iRsdks2FgZIWCWM/gCkcQsf7/UMsMvtcwwvndDhN1/h5Bb8Vh3+AF2nn6Czi2rw82pJqzM9PEjjZevLCGkdFm/fj0uXLiAGzdugM/no127dujbt6+6wyKElFGqWmCXyC9nWFFOC9X58+cV6vLXuXNniYSqS5cuxRGmTDITKn19fUyYMKEkYiFEZdhsVqETUBgbaKNfxzro42yLx6+/4e/b8Th3Iw4xb5Ix2v0XrP93UguaIZCQ0mvPnj04c+YM+vXrB4ZhsHfvXnz69Klcri1DiiZ3Vy6gZKZdJ2WDtHWsKuIaVupQ1HWk1q5dK7G9Zs0aHDlyRKUxykNmQtWpUycEBgaia9eu0NLSEu83MTEpzrgIKRFsNgtN7MzRxM4c3J+ZSPqRCf/9d/PNEOgz1hFCEQMbKyNocthqjpoQAgCnTp3CkSNHxAtBDhw4EIMHD6YvOkSmkph2ncgve2KJr+CdC5LreFHSV3BV9FmcvY7VS2iYVZWsg6MPAHj6NU1ivzDpg0rqJdlcXFwQEhICgUAADoej8BiqnK6ZOZKSklQZntxkJlT79u1DVlYWVqxYId7HYrEQExNTrIERUtJMjXTAFxY8Q2BaBh8Lt0WAo8FGrSpGqFPNBHWqm6BudRNUtzQER4MNkYihroKElLCcZAoADA0NJcb6EpKjLE25TkqehllVGPaWr1Uk5ezmYo6mYvH09MSlS5cAAGw2u8SXZUpKSoKfnx+8vb2L9EOLzE8edS2QRYg6SJsh0MJUD/NGtMDrd9/x+v13XHv4Hn/figcAaHHYcG5eFa6tbBAQGEVdBQkpIVWrVsX+/fsxbNgwANnrmVSpUkXNURHyr29ZEJ5KlNyXnj09NPQ08h2LyiUTVmmUPbGECNrug+Q6nncuSGWTRXC5XAiTvsqdKAmTPoDLoZmvVcXMzAyurq44f/48unXrVuKtx4GBgXjy5InC61/lJTWhOnPmDPr06YO9e/cW+PiYMWOUrpSQ0kraDIGVTXRhUUkP7ZtmdwkQiRh8TkrDq38TLIf6luJkCqDFhAkpCcuWLYOXlxfWrFkDAGjSpAkNICelgq2tbYH7c8bl2FbO83hl6WUIKe88PT2RkJCgVOtUvXr18OLFC/G2vb293GWTkpJw6dIlMAyDixcvwtPTU+mETmpClZCQAAB4+fKlUicuzJkzZ7Bz504AgLOzM+bPn6/yOghRRmEzBOY9roq5AaqYG6BD82pI5KYX2FUwPVMAU8OSfAaEVByWlpY4ePAgMjIyIBKJoK+vr+6QCAEAqeP4aOa40sfU1BQfBVoKdfkzNaX3GlUyMzPDunXrlCqbmppa6HZhAgMDJRYVLkorldSEaubMmQCypyPMOwXh6dOnlaoMADIyMuDr64uQkBAYGRlh6NChiIyMRJs2bZQ+JyGqJGuGwIJI6yr49nMKrtx9i2Hd6kGTQ+tcEVIcdHV11R0CIaSCo0WB1ePDB8lJQt6/fy932bCwMImEStEZBnOTmlCFhYVBIBBgzZo1YBhGPKWhQCDA+vXrlV7rQygUQiQSISMjA3p6ehAIBNDW1lbqXISUFgV1FfQe44i7Tz/hRNgr3Hv2GbOHNkedaibqDpUQQoiCcn9ZpinXiSzqXBRYVZMslBU2NjbiXnU52/Jq1KgR7ty5I95u3Lix0nFITahiYmJw+/ZtJCUl4cCBA/8V4HAwbtw4pSs0MDDArFmz4ObmBh0dHbRq1QrNmzfPd1xUVJTSdZRVFfE5K6K0Xx9dPT0sHecAFksDDCNEyvePqGuWjqEdzHDuLhe/bbiG9g0M4dzQCBrFMFFFab8+6kbXRza6RoTIViJfUr+lQnDmkeS+9Kzsv/W08h1bkSe0KC1Ky6LAu3fvxuPHj7F7927MnTu3xOsvafPnz8fUqVPF2wsXLpS7bN6J9x49eiTlSNmkJlTTpk3DtGnTEBgYqNIpDJ8/f46//voLV69ehaGhIby8vLB7926MHz9e4jgHBweV1VkWREVFVbjnrIgyeX2szQAADg6Ae5cs7Dz1GOEP3uMdl405Q5ujprWRyqoqk9enBNH1kU2d16goiVxISAhiYmIwefJkhIaGolevXkWKZeTIkUhKShJPv758+XI0adKkSOckZV9JTrsue0KLPL/A04QWalPaFgVOSkrClStXAGS3qo4bN67ct1LVqVNH3EplY2OD2rVry102IyOj0G1FyJw23cPDA7t378arV6/g4+ODwMBAjB8/Hhoayo0HuXnzJpycnGBmlv1ls3///jh8+HC+hIqQ8sRQTwu/eTqgTWNrbD3xCHPWh2NYN3v071gHGhrKL06Ys/aVZVVbcFMyae0rUqHs3LkTERER+Pz5M0aPHo0tW7YgISEB06ZNU+p8DMMgLi4O4eHhtJ4VUZuyPKEFk5QM/rlgyX3p2V9SWXq6+Y6FuUWJxVYcYmNj8fT5K+hXriGxX6iZPRtV/DeexP60b2+LNZ7du3fn2y6trVSqHHM2efJkeHt7S7RUlTSZnxhr165FcnIyHj9+DIZhcOPGDXz9+hU+Pj5KVWhvb4+1a9ciPT0durq6CAsLQ6NGjZQ6FyFljVOjKvillhm2/xWNAxdicPvJJ8z2aI6q5gYKLwosEjFI+Pwz3xTvtPYVqSjOnz+PoKAgDB48GKampjh+/DiGDBmidEIVFxcHFouFCRMmICkpCYMHD8bw4cNVHDUh5ZPMlrW8yZO5RalpWRMmfci3DpUoPQUAwNYzzHcszO0A/JcI5KWlZyy1LmllVCGndSrH5cuXS21ClVtRx5xFRESAYRjcvHkTzZo1U2VocpOZUN26dQunTp1C//79YWhoiD179qBPnz5KV9iuXTs8e/YM/fv3h6amJho1aoSJEycqfT5CyhpjA23MH9kCN/75gB0no7El6B+MdW+ANQfvF5oYCYQipKRl4Wd6FlLSsmCkryVOpgBa+4pUPBwOB1pa/40nMTIyKlLL0s+fP+Hk5ISlS5ciMzMTI0eORK1atdC2bVtVhEtIuVbUljVR0lfwzgVJ7GPS0wEALD29fMfC3CzX9mdknN2f75xMeuq/5Q3ylP8MmGcnPdITwc/Zj5tbST5gbqeSRHDhwoV4/vx5vv053c769euX7zF7e3v4+/vj8uXL2LZtGwCAx+NBIBBIradHjx7iyd+mTp2Krl27Fqnu4iybk3xv27ZN/PwKes7Snve5c+dw7tw5cDicfM9Z3muW93rJ22VS5icPh8MBm/1flyQtLa0id4WYOHEiJVGkQmOxWHBuVg2NbCvjfWKqOJkC/kuMZg1phr3BT/EznY+UtCxk8CT/8/tPbVvg2ld8gajEngch6mRtbY3w8HCwWCxkZWVh9+7dqFq1qtLna9asmfjXTT09PQwcOBDXrl3Ll1DR5B1EWSkp2a0eytxD8pTNOUaZc0dFRSlVPqesrJgKO8bAwAC1Cvi/+4mbBACwrmQi+UDVqjAwMEBUVNS/ZasUeN5P3LR/y+cZs1y1irh8q1at0KpVq3xlc9ZLHTp0aIHnjoqKAofDgX7lGmjYR76JEJ6c8QeHk4WoqCgkJCQgPT0DHE3JH0BZrOzv2Fl8RmK/gJ+JhIQEREVFIT4+Xjzdt0hU+Ge+SCQSHxsfHy9RtzZHcpZtjX/rFmZJnpMn4InrTkhIQEZaAWX/TSlEPFll0/OV5UDj37JCqWVzP2dZz7ug5yzvNctbVmUJlZ2dnXjhq7i4OOzbt0+hVYgJIdKZGumALxQVmBhpaWnA2EAb1SwNYaSnBUN9LRjqaf37b02YGesUuPaVJkf5MVmElCWLFy/GvHnz8OLFCzRt2hRNmjQp0viS+/fvg8/nw8nJCUD2mKqCfkCkCU6IsgwNs7uPKXMPyVPW0NAQyPyp1LkdHBxgaGgI5sN78M9Kdh2TPg6KC0N7K9kxyYhb2mPytG4Vdt6ijDuT93on8XhSH5dWxsHBAVWqVIGGpjm695ZvCE3I2ZWwNNeEg4MDHBwcMGHChHzHuLq65i8XEpJvX5UqVWDEqoxJneSr+8+rK2FgrSWO20xYGfNaLZKr7Jq7ftCu8l/ZykJjLHKUb60nvzuboVVFv9Dn3KdPH4nJJHR1dXHmzJl8xxVUvnv37hLJFZvNzne95P3xQ2ZC5e3tDT8/PyQlJWHo0KFo3749vL295To5IUQ2aYsCW1bSw9IJTlLLiURMvrWvZnk0Aw2fIhWFpaUl9u/fj4yMDAiFQhgYGMguVIiUlBRs2rQJR48eBZ/Px6lTp7Bs2TIVRUtIyWCSfkBwJiL//vTsL/4sPe18x8M8u3VI9jiovN3frErNOChSMVlYWEisQ2VhIf9EJy4uLhLjzjp37qx0HFITqkWLFsHPzw83btyAn5+f0hUQQgpX0KLAPmMdYaxf+ILXbDYLNlZGCJjljLS0THA0NbHh6AOIRIDvlLbUUkXKva9fv+LUqVP4/v27xP558+Ypdb5OnTrh0aNH6Nu3L0QiEYYNG6a2Ac6EKKOw5Oa/pChP1zrzquJyZXmGQZLdVTn933FnOdulzduUD/C7IzkByA9edrdQY23DfMfWgV2h50tMTCx0uzDjxo2TSKiKss6u1ITq1q1bePDgATZt2gQbGxswjGRfzgYNGihdKSHkP7kTI0Vm+cspa2qog7iXT+Hg4ICebWtjzcH72Hn6MaYNpLVzSPk2ZcoUWFlZoXr16io75+zZszF79myVnY+QklTY+kaUFJV/bdq0kUgQStuEOrkTfi6XK57dLyMzu4fOT1b2mLdKlSrB1NQUdSB7ApDOnTvj/PnzYBgGLBYLXbp0kTseMzMzdOnSBVeuXEHXrl2LtGaX1IRq8ODBmDdvHj5//ozp06dLPMZisRAaGqp0pYQQSTmJUVG1b1oVcR9+4ETYK9Suagw3p5pFD46QUorP52PLli3qDoMQQkoFXp7xXFlZWWqKpGC5E35VrUPl6emJixcvgs/ng8PhwNPTU6GYWrZsiStXrsDR0VGhcnlJTaimTJmCKVOmYM6cOVi/fn2RKiGElJzhbvUR9/EHdp6KRg1LQzSobSa7ECFlUIMGDfDy5UvY2RXeJYQQQiqCW7duSWxHROQfS1dadO3aVaHFe6UxMzNDlSpVkJCQgCpVqijcyrR5c3b3ww0bNsDZ2VnpOArt8ufk5ITu3bvj0qVL+R4vaCYRQoj6abBZmOvpgF83XseqA/ewfnYHVDbRlV2QkDKmefPm6Nu3L8zNzSVm46MeFISQiijv8Jy82+VRUlISPn78CAD4+PEjkpOT5U6qoqKikJqavVZZamoqHj58qPS4WakJ1fnz5+Hk5IRDhw7le4zFYlFCRUgpZqCnBZ8xreC16Tr89t3FqmntoKWpoe6wCFGpLVu2ICAgADVq1FB3KIRIlbtrU87EEDnjmRTp2kSILJ06dZIYQ+Xi4qLGaOSXlJQEPz8/eHt7K9zCFBgYKJ76XCQSITAwEDNmyDctu6+vr8T2ihUrcPLkSYXqzyE1oVq5ciUA4ODBg0qdmBCiXjWsjDBnqAP89t3F1hOPMNujGVgsmlOdlB/Gxsbo0aOHusMgRG5FGfROiCzjxo1DWFgYRCIR2Gx2kWatK0mBgYF48uSJQslQjrCwMPFCvEKhEKGhoXKfI6d1Stq2IqQmVJMnTy604I4dO5SulBBSMpwaWWOoaz0cufQCttWM0bs9rRdCyo+OHTti9erVcHV1hZaWlng/zUJLShNVjRUhRBYzMzPx2kqdO3cuEwl8UlISLl26BIZhcPHiRXh6eioUd1FmNmSxWBLdIovyo7PUhKpbt25Kn5QQUnp4dK2HuA8/sPvsU9S0NkLjOubqDokQlTh37hwAiLtTATQLLSGkYhs3bhy+fPlSplqnlO2yBxRtZkNVjjmTmlD169dP6ZMSQkoPNpuFX4c1x28br2PV/vtYP6cDLCuVvsX+CFFUWFiYukMghFRwad/e4skZf4l9Wek/AABaesb5jkXlusUaj5mZGdatW1esdahSWFgYBAIBAEAgECjUZQ8o2syGNjY2SEhIkNhWltSEihBSfujpaMJnrCN+23ANfnvvYvWMdtDRov/+pGzatWsXJkyYIB7rm5ePj08JR0QIKW/S09MRFxeHuLg41K5du8BjpC06GxubAgCoWdlC8oHKdWUuVFvRuLi4ICQkBAKBABwOB507d1aofFFamebPn4+pU6eKtxcuXKhQ3bnRNypCKoiq5gbwGt4Cy3ffxuZj/8BruANNUkHKJENDQwCAiYmJegMhhJRb7969g0gkgr+/P3bt2lXgMbkXqs0tZxbHgICAYotPmnXr1uHixYvo0aMHZs+eXeL1K8rT0xPBwcEAsluoFF2YtygzG9apUwcGBgZITU2FgYGB1MRZHpRQEVKBtKhviRFu9XHgQgxa1LdA03oW4AtE0OSwYayvDTabEixS+nl4eADInjFt2LBhEo/t3LlTHSERQsqBnCnu09PTxWNzEhISMG3aNPTt27dMTC6SM6b0woULZSKhMjMzk9hWdCKNcePGSSRUio4dy70OVVHITKi+fv0Kb29vJCQkIDAwEPPnz4e/vz8sLCxkFSWElEIDXeoiPZOPSsa68Np4HYncDFiY6sJnrCNsrIwoqSKl3pEjR5CZmYl9+/ZJDEjm8/k4ePAgJk6cqMboCCFl3bt37yS23759q6ZIFJN37NSGDRuKJal6m/IWa+76Sez7wcseN2asbZzv2LqoI/Vce/bskdjev38/Ro0aJXcsmzZtktjesmULfv/9d7nKbt26VWJ7x44dMmc5l0ZmQrVs2TJ06dIFhw4dgrGxMezt7eHj40O/AhJSRrFYLPRqVxvzNt9AIjcDAJDIzcDKPXcQMMsZpoY6ao6QkMJxOBy8fPkSmZmZePnypXi/hoYGFi9erMbICCFlWc4U966urhL7eTxesbROJSclIOSs5FjQjPTvAABdPZN8x1qaS09MAMkZT4HCW6k+fn+LP69K1p2SmZ0UGeoY5zvWzjq7bmljwFJifwIALKpLziRcF3UKHTd29OhRie3AwECFEqq8k1LcvHlT7rJnzpyR2D558mTxJVQfPnzA4MGDcfjwYWhqamLu3Llwd3dXqrIcYWFh2LJlC9LT09GuXTsaQExICROKGHEylSORmwG+QKSmiAiR36BBgzBo0CBcuXIFXbp0UXc4hJByJmdcTe5tVZM+oUV2YmJpLpmYWJoXnpioou4v/9ZtbS1Zt531f3WXxnFjpYHMhIrFYonnhwey+xjm3lbUu3fvsGTJEgQFBcHMzAyjRo3CtWvX0KFDB6XPSQhRjCaHDQtTXYmkysJUF9+4GRCJGFiZ6asxOkLk07p1ayxbtgxxcXHYuHEj/vjjD8yfPx/6+nT/EkKUlzONt7RtVVBnYkJJkeqxZR3g6uoKLy8vpKSk4OjRoxg1ahTc3NyUrvDy5cvo0aMHrKysoKmpifXr16NJkyZKn48QojhjfW34jHWEhakugOxkymt4Cxy48AxTVofiz1PR4KZkqjlKQgrn6+sLIyMjJCUlQVtbG6mpqXL3nSeEEGnyfi9t2rSpegJRULdu3SS2e/TooaZISo6Wllah24Xp06ePxHb//v2VjkNmQjV58mQ4OzujUaNGiIyMxJAhQzBt2jSlK0xISIBQKMS4cePQu3dvHD58GMbGxrILEkJUhs1mwcbKCAGznLHbpysCZjmjXg1TzB3RAl1a2eBCZDwm+l3BoZAYpGXw1R0uIQWKiYnBnDlzwOFwoKuri4CAAMTExKg7LEJIGRcdHS2x/ejRIzVFopjffvtNYrsszPKXd+0nRYcBZWVlFbpdmLz5jLLjpwA5p03v27cv+vbtq3QluQmFQty/fx8HDx6Enp4epk6dilOnTuXLCqOiolRSX1lSEZ+zIuj6FE5V16d1LaCOmQWuRv/Escsvcfb6a7T/xRAt7QygqVF2ZwCk+0e2snaN2GzJ3wSFQmG+fYQQoqiMjIxCt0uzbt26idehKgs6deoEf39/8bazs7NC5VkslsRivoqur9mxY0eEh4crvKBwXjITqgYNGuQbM6WjowM7Ozv4+fkpPECucuXKcHJyEs8z37lzZ0RHR+dLqBwcHBQ6b1kXFRVV4Z6zIuj6FK44rk+3TsDr999x8EIMLj1MxIM4HoZ1s0cnh2pIyeCXqfWr6P6RTZ3XSNlErmXLlli7di0yMzNx48YNBAYGwtHRUcXREUJI2VHW1qEqqnbt2uHGjRvibUUTsvDwcABAaGgo5s+fr3QcMn/KGzlyJKZNm4bbt2/j7t27mDt3Lnr27Inx48dj6dKlClfYqVMn3Lx5Ez9//oRQKMSNGzfQoEEDZWInhBSzOtVMsGyiE3yntIGZsS4u3k5ATDwXXhuvY9zKy/DaeB0Jn39CJGJkn4wQFfPy8oKenh4MDQ2xfv161KtXD/PmzVN3WISQMs7IyKjQbVJ6TJ06VWJb2oQbBXn9+rXEdlxcnNJxyEyo7ty5g+nTp8PY2BiGhoYYO3Ysnj9/jq5du+Lnz58KV9ikSROMHz8ew4YNQ48ePVClShUMGDBAqeAJISWjcR1zrJ3ZHjOHNMOGow/yrV9FE1iQkjR+/HgAwOHDhzFt2jQEBQXh5MmTmDNnDrS1tdUcHSGkrMv7/VaZ77vqkHf9rLzbpVFRYx46dKjEtoeHh9xl8yZjxTqGKiMjA4mJibCwsAAAJCYmilemFwqFSlU6cOBADBw4UKmyhBD1YLFY0NHWKHD9qk/f0rBq/z00sTNHk7rmsLcxhSZHQ02RkvIuNjYW586dw8GDB1GlShWJ/vNA2fgSQYg8Ll++LO7CFRsbC+C/qa27detW6GKzucsqU54QIj+ZCdWECRPQr18/tGvXDiKRCLdu3cLChQuxZcsWNG/evCRiJISUEtLWr9LS1AADIOjKSxy7/BLaWhpoUMsMTeqao6mdOWpaG4HNZkEkYvAjjVemxl+R0mfGjBk4ceIEkpKScODAAYnHWCwWJVSkXMoZe66u8oQQ6WQmVP3790eTJk1w48YNcDgcTJs2DTVr1sT79+9hZWVVEjESQkqJnPWrVu65g0RuBixMdeEz1jF7CvaZzkjL4ONJ7Df88+orHr36hr3BTwEARvpa6OZYA44Nq2DNwXv5ylJSRRTRv39/9O/fH/7+/vmm3CWkPOnatavSrUhFKVtWUascURe5pk3n8Xho2LAhGIbB58+fcffuXQwePLi4YyOElDK5168qqJVJX1cTjg2t4djQGgCQ9CMDj159w6NXX1G/lpk4mQL+G38VMMsZpoY6antOpOw5c+YM+vTpA0tLS+zduzff42PGjFFDVISQ0kbZVrn69etLrGlHk6cRWWQmVN7e3ggLC0NmZiYsLS3x9u1bODg4UEJFSAXFZrPkToDMjHXh0qI6XFpURyI3vcDxV3yBSEppQgqWkJAAAHj16pWaIyGElCaqapWrWrWqREJVpUqVIp+zJNjY2IjfH3O2S7tLly5JdNO+dOmSQuV79eqF4OBg8ba7u7vcZY8cOSIxqcXRo0cVqjs3mQnVrVu3EBoaimXLlmHatGn4/Pkzdu3apXSFhJCKSdr4Kw519yMKmjlzJgBILAaZg5IsQlSjKBNilHVXr16V2A4LC8PcuXMLLVMarlfuZKqg7fKoR48eEglVz5495S4bHx8vsZ2QkKB0q6bMadPNzc2hp6eH2rVr4+XLl2jVqhW4XK5SlRFCKq6c8VcWproAspOpmUOaIfDic6SkZ6k5OlJeDBkyRN0hEFLuVKpUqUJNasFisQrdlkVd1ytvi1RZaKECgC5dugCAUknnhQsXxK8Pi8XC+fPn5S7r6+srsb1ixQqF688hs4VKU1MT9+7dg62tLa5fvw5HR0dKqAghCito/FXCp58Iu/8eMfHJWDrBCRameuoOk5RxeadQJ4QopzxMapGUlAQ/Pz94e3srlOA4OTnhxo0b4u22bdvKLFMartf8+fMl1lYqC5P2JCUlISwsDAAQGhqKcePGKfRahYWFid/3GYZBaGgoZsyYIVfZ1NTUQrcVIbOFau7cuTh69Cg6dOiAmJgYtG7dGr1791a6QkJIxZUz/srCVA+mhjpoameB5ROdkPQjE/M230D8p7KxeCIpvRT9JZkQUn4FBgbiyZMnCAwMVKhc3gXCtbS0VBlWsalTpw4MDAwAAAYGBqhdu3ax1nf58mV4eXnBy8sLsbGxiI2NFW9fvnxZrnPs3r0bIlH2WGqRSITdu3crFIOLiws4nOz2IQ6Hg86dO8tdNudaSdtWhMyE6smTJ1i3bh00NTURFBSEW7duYdasWUpXSAghuTWqUxmrprUDwwALttzA49hv6g6JEEKImqjiSzqQ3fJx6dIlMAyDixcvIjk5We6ykZGREtsRERFyl1WnpKQk8Hg8ANkzdCvynItK2W6OBY1XU4SnpyfY7Ox0hs1mw9PTU+6y3t7eEtuLFy9WqO7cZHb5O3LkiERwRkZGSldGCCEFqVXFGGtntsfSXbfw+5+34OXpgLZNysasSqTkNWvWrMCWKIZhkJmZqYaICCG5qWqChqKMQwoMDJRo+QgMDJS7K1ibNm1w5coV8bY8Xf5Kg8DAQInub4o8Z2WoopujUCgsdFsWMzMzuLq64vz58+jWrZtC94yDgwMMDAyQmpoKAwMDNGvWTKG6c5OZUNWqVQs+Pj5o0aIF9PT+G99AK9ETQlTJwlQPq6e3x4rdd7D64D1MTGmEXu2Kt7sCKZtyz+hECCndFE2KVDUWKSwsDAKBAAAgEAgUGltTVinznEvD7IRF5enpiYSEBIVap3J4e3vD29u7SK1TgBwJ1ffv3/H9+3eJqRdZLBYlVIQQlTPU08KKyW2w9uB9/HnqMZJ/ZmKEW30aF0MkVK1aVd0hEEIKURomaHBxcUFISAgEAoHCY2sK6vIna9r00qCoLWvqmJnQysoKnz9/Fm9bW1srfA4zMzOsW7dOqfodHBwQEhKiVNncZCZUBw8eLHIlhBAiL21NDSwc1RI7Tj1GUOgrJP3IxIzBTcHRkDnkkyhBJGLwI40Hy6q24KZkwlhfG2xaG4wQUsZ5enqKF4lVdGyNi4sL/v77bwiFQmhoaCiUjAGKzy6ozlYidSe/devWlUio7Ozs1BZLUcj8hvL161dMnDgR3bp1Q1JSEsaNG4evX7+WRGyEkApKQ4ONqQMaw7O7PcLuv8Oec0+Q9CMDidx0cFMyIRKVz6mxRSIG3JTMEnueIhGDhM8/4bXxOqasvQavjdeR8PmnXPWWdKyEEKKInLE1LBZL4bE1np6e0NDQAABoaGgo3JVM2dkFgaKtYVXUyTSOHj0KV1dXBAUFKVx3VFQUunfvjocPHypcLrd79+4pXPfr16/Rt29fxMXFKVxWVWS2UC1btgxdunTBoUOHYGRkBHt7e3h7e2Pnzp0lER8hpIJisVjw6FoPNlZG0NPhYN7mG0jkZsDCVBc+Yx1hY2UksyWlKK0vOWVz1sySt2xRyiV8/omVe+4o/TwVqZNhGCT9yBTXBwCJ3Ays3HMHKye3gVDEQFebA11tDnS0OBLnK0qspd25c+ewfft28Pl8jB49Wqk++YSQ0kHZsTVFmegg7+yCnp6eMsurqpXIxcVFYoypoi1re/bsAQDs2rULgwYNUqisr68vRCIRVqxYgZMnT8pdrqgxA8Dq1auRnp4Of39/7Nq1S+HyqiAzofrw4QMGDx6Mw4cPQ1NTE3PnzoW7u3tJxEYIIbCvaQqvjdfzfemfMqAJjl1+AWMD7X//aMHk33+bGGijsqkueFkC+O69q1SCokzCIKscwzDIEoiQlsHP/pPJR3qGAGkZfFSzNJCa3Giw2TA21Ia2pobCdaZl8PHxWyo+fE3Dx6+p+Pg1DR++peLT11T4jHUU15cjkZuBpB+ZWLhN8pdNHS0N6PybYE3p3xhbgv7JF2vALGeYGurIflFLqS9fvmD9+vU4efIktLS04OHhAUdHR9SpU0fdoRFClFCUsTXKJmNFmV2wqHJa1XJoamrKXfbo0aMS20FBQXInVVFRUeJFcVNTU/Hw4UO5Z8yzsLCQ2LayspKrXI7Xr1+L53lISEhAXFxcsa+/VRCZCRWLxRLfGED2hcq9TQghxYkvEBX4pd/YQBs6Whx8SU7Hy7dc/EjLkuh2tmh0K/zvzON8X/qnDWyCA3/HQIujAU0OG1qa//7N0YCWJhuaHDZcWlTHmoP385WdP7IlIqM/QihiIPr3j5D5799dWtXAusCofOWmDmiC9UcfIC1DAIGw4PdP/6ltZSY3utocmBhow8Tw3wTSUAddW9XA6gP38tU5Y3BTrAt8gO+pPPH5WCzA3FQPVSvro55DdejqcGBhqitRr4WpLowNtPGbpwMyeAJk8gTIyPNHX1ezwFj5grL92RAZGYnWrVvDxMQEQPbYhZCQEEyfPl29gRFCSpyyyZg6Zxc8c+aMxPbJkycxefJkucrmtE7lUKSVytfXV2JbkVaqvXv3Smz/73//U6h1bPXq1RLb6mqlkplQubq6wsvLCykpKTh69CiCgoLg5uZWErERQgg0OewCv/RXNtHBisltxPtEIgZpmXx8T+HhRyoPxgbaBX7pN9TXQiUjHfD5ImQJhEjnCcDnC5ElEIn/dm5WtcCyWXwhzt2IA5vNyv7DYkn8u0urGgWWMzLQRptGVaCnw4G+rmb2H53cf2e3/EhLbmYMbip+Xt9TePieysPHb2mIiU9GBymx6mpz0KqBFaqa68O6sgGqmuvDykwfWrlauUQiBj5jHfO1blU1N0B1S0Oprwk3JbPAWDU5ZXvikMTERJibm4u3LSwsEB0dne+4vH3+CSEkR6NGjXD//n3xhBaNGjVS63tGUeqWt2xO61TubXnL5qyblXtbkZhzz0Kes62O6y0zoZo8eTJOnz4NkUiEyMhIDBkyROF+ldKsXr0aXC4Xq1atUsn5CCHlj7G+doFf+o31tSWOY7NZMNTTgqGeFqpbGkr90l/ZRBe/j2tdaJ3Syla1MMBfq6V3eZZepw6mDmxSaJ3KJjfJPwuu06KSHmYMblponWw2CzZWRgiY5Yy0tEzo6+vINf5K3tekrMn7wQ6gwCn7HRwcSiIcQkgZVLNmTYwaNUqcUM2ZM0ct05HnKMr7lbxlcxbHzb0tb1kWiyXx3stisRSK2cbGRiKpsrGxUel7tLzJmcyfE48ePYouXbpgw4YN2LRpEwYPHqySNWFu3bqFU6dOFfk8hJDyLfeX/t0+XREwy1mucVA5X/otTHUBQKEv/cqWLUqdyj5PEwPl68yp19RQB18+xMLUUEeuSSWUjbW0s7S0xLdv38TbiYmJ+fr3E0JIYYoyu2BReXh4SGwrMv5r7NixEtsTJkyQu6y3t7fEtiKL5ObtUj1r1iy5ywLA/PnzJbYXLlyoUHlVkdlCdffuXWzYsAGdOnXCkCFD0LRp0yJX+v37d6xfvx6TJ0/G8+fPi3w+Qkj5lvOlX9EyyrS+5C2ryMx5ypbLXb4oz1OZOpWlTKylXZs2bbB582YkJydDV1cXly5dwooVK9QdFiGkjFF2QouiGjt2rMTkEqNGjZK7rIeHh8Q4KkV6ozk4OIhbqQwMDOSekAIA3N3dsWXLFjAMAxaLhR49eshdFgDq1KkjbqWysbFRy4QUgBwtVH/88QcuXryIhg0bYuXKlXB3d8f+/fuLVOnvv/+OOXPmwMjIqEjnIYSQwijT+pK3rIWpnkJllS1XFOqoszyytLTEnDlzMHLkSPTt2xe9evVC48aN1R0WIaSMyZnQQh1d/XJaqZRJ5nJaqRRpncrh7e0NNputUOtUjpxWKkVbp3LMnz8fenp6amudAgAWU1Cn8QIIBAJcvXoVu3btwsePH3Hz5k2lKgwKCsLr16+xcOFCnDx5Enfv3s03hooG/BJCSMVSVsYlRUVFlZlYCSGEFI287/kyu/w9ffoUJ0+eREhICH755RdMmDABLi4uSgd24cIFfP36FX369MGPHz+Qnp4OPz8/LFq0SOK4ivaBRR/ShaPrUzi6PoWj6yObOq8R/YhGCCGkLJOZUE2bNg0DBw5EUFAQLCws8Pfff8PDwwNBQUFKVZh7vvmcFqq8yRQhhBBCCCGElAUyE6qrV6/i58+fOHbsGAIDA5Geno7hw4eXRGyEEEIIIYQQUqoVmlDFxcVh//79OHv2LKpWrYrMzEyEhYXB0FD6miiK6N+/P/r376+ScxFCCCGEEEJISZM6KcWECRPw9OlT9OjRA3369EGjRo3g4uKCsLCwYg+K+tMTQkjFUlbGuNHnEyGEVCxFmpQiJiYGv/zyC+rWrYuaNWsCKHjF+OJQVj5YCSGEVCz0+UQIISQvqS1UAoEAly9fxuHDhxEdHY0OHTrgwYMHSk+XTgghhBBCCCHljVzrUL1+/RpHjx7F6dOnUalSJYwZMwZDhw4tifgIIYQQQgghpNRiy3NQnTp14OPjgxs3bmDcuHE4fvx4ccdVLmzZsgU9e/ZEz549sWbNGgBAZGQk3N3d4erqivXr14uPjYmJwYABA9CtWzd4e3tDIBAAAD5+/AhPT090794dU6ZMQVpamlqeS3FQxfU5ffo02rVrhz59+qBPnz4SZco6Ra5Pjvnz5+PkyZPi7fJ8/wCquUZ0D2W7cuUK+vTpg969e2Pq1Kn48eMHgPJ/D5HSQY7fdssdZZ5zThmRSFSi9aqyvDqUxZiLStnnrO57rMy+VgwpFhEREcyQIUMYHo/HZGVlMSNHjmTOnTvHdOjQgXn79i3D5/OZsWPHMuHh4QzDMEzPnj2Zhw8fMgzDMAsXLmQCAwMZhmGYiRMnMsHBwQzDMMyWLVuYNWvWqOX5qJqqrs/y5cuZc+fOqetpFBtFr8/nz5+ZSZMmMY0bN2b++usv8XnK6/3DMKq7RnQPhTMpKSlM27Ztmc+fPzMMwzAbNmxgVqxYwTBM+b6HioNQKJT4W1kCgaDIMZR0eZFIVKR6VXUORfD5fKXKcblcie2SivvNmzdFujdUqSjPuaj3qLJK+v5iGOXuMXXdXwxTeu4xddxfRalTrhYqojhzc3MsWLAAWlpa0NTUhK2tLeLj42FjY4Pq1auDw+HA3d0dISEh+PDhAzIzM9G0aVMA2dPJh4SEgM/n4969e+jWrZvE/vJAFdcHAB4/fozTp0+jd+/e8PLyEv+qXtYpcn0A4Ny5c+jcuTPc3NzE5yjP9w+gmmsE0D2U816zdOlSWFpaAgDq1auHT58+lft7qDjs2bMHWVlZYLOV+3jN+VVYQ0ND6RgUrfvp06e4evUqQkNDxeUZhpH7l+L4+HgAyk1cdevWLezduxfnzp1T+BzR0dEICQlBdHS0eJ+8cefUx+FwwDCMuNeDvFavXo2YmBjxNovFkrvuyMhIbNmyBc+ePVM47rFjx8LX11ehWHPXe+jQIYSHh0vUK6+oqCgcOHAAx44dA/DfayXPOZS9x6KjoxEXF6dUvEDR7q+c+tVxj6nr/gLUd4+p4/7KUZT3sByUUBWTunXrihOA+Ph4XLhwASwWC+bm5uJjLCws8OXLFyQmJkrsNzc3x5cvX8DlcmFgYAAOhyOxvzxQxfXJ+feMGTNw5swZWFtbY/ny5SX6PIqLItcHAMaPH49BgwZJnKM83z+Aaq4RQPfQly9fYGpqii5dugAAMjMzsXPnTnTp0qXc30OqFhQUhICAAJw/fx6A4l/+rly5gjVr1sDd3R3//POPQmWvXbuG9evXY8CAAdi7dy+ePHkiV/fMY8eOYeXKlbh//z7+/PNPuLq6IiIiAiwWS64vF8+ePcOGDRtw4cIFiXtDnuceFBSELVu2gM/n49ixYzh8+LDMMrnLrlmzBjdv3sTZs2fBMAzevXsnjruw+j9//oy5c+di0KBBuHXrFlgslvgel8eRI0cgFApRv359PH36FHv37sX9+/flumbHjh3DunXrYGBgAGNjY4XiPnHiBLS1tfH27Vu8fv0agPz32PHjx7FlyxYkJibi7Nmz4PF4SEhIEMcrq3tXUFAQtm7dChaLhT179sDFxQVXrlwBIPtLaFHusWvXrsHX1xdBQUFISUlR6Et2Ue6vnPLquMfUdX8B6rvH1HV/AUV7D8tN/ncQopRXr15h0qRJmD9/PjgcDt68eSPxuLQbvLD95UlRrg8AbN26Vbxv/Pjx4i+F5YU810eainD/AEW7RgDdQ7mvT0pKCqZOnQp7e3v069evwOSpPN5DqsDj8XDy5ElMnz4dN27cQNeuXWFgYKBQ+bVr12LJkiUwMDDAxYsX8ePHD7BYLFStWhW2trZgGKbA68/j8bB69WosX74c9vb22LJlCy5fvowhQ4agV69eUlu7+Hw+Dh48iICAANjb2wMADhw4gN9++w0uLi5YtGiRzOdgZGSEW7dugc1mIyYmBk2bNkWbNm2gq6sLAFJjzsrKwr59+7Bp0ybY2trCwsICFy5cwMCBA6GlpVVonVlZWfjf//6HrVu3wtLSEoMGDcKiRYsQGRkJe3t7rF69GiYmJlLLW1lZYfDgwXjx4gWmTp2Kpk2bYtiwYXj+/DlmzJgBPp8PTU1NqXVv374dR48exR9//IGXL1+iUqVK+PPPP9GmTRssX75c6jUTCAQ4deoUli9fjgYNGiAwMBDPnz/H7du3UbNmTaxevRqVKlUqsCyPx8Px48exe/duhIaGIjg4GLNnz5br/yOfz8fhw4excuVKNGzYEMOGDcOCBQtw79492NnZYfXq1RI/tBT0nPfu3YsNGzbAzs4Obm5umDt3Ln7//XecOXMG/v7+Up+zsvdYzn2jo6ODt2/fIj4+Hn5+fnBxcUHXrl1lPu+i3F855dVxj6nr/gLUd4+p4/7KTdn3sLyohaoYRUVFYfTo0fjtt9/Qr18/WFpa4tu3b+LHExMTYWFhkW//169fYWFhgUqVKiE1NRVCoVBif3lR1OuTkpKCffv2ifczDKPQr4ylnbzXR5ryfv8ARb9GdA/9d30SExMxbNgw2Nvbi7t7VIR7SFU2bdqEpk2bYvr06QCA4OBg8WPy/NIZEhICW1tbtGnTBn369EFgYCDOnTuH/fv3Y9euXUhPT5f6oX7y5EnY2tqiRYsWcHNzw7p161C5cmUcP34cq1atkhqDhoYGWrduLfHL8ciRI3Hp0iVkZWXhzp07MuM2MjJC69at4eDgAC0tLVy7dg27d+/GgwcPAEhPwLOyslC3bl1YW1sDANzd3fH161dx966jR4/i1q1bhZatWbMmNDQ0kJKSgj59+uDs2bPiZFSW4cOHo3fv3nj48CFq1aqFGTNm4O7duwAgNZkCgNjYWDRt2hQRERF4/vw5duzYAT8/P0RERIBhmEKvGYfDwS+//CL+/3T8+HEMGDAAQUFBMDY2xpkzZ6SW3bhxI2xtbVGlShU0btwYZ8+eRWBgoMznCWS/Bi1atMD79+9x/fp1fPjwAZMnT0ZYWBisrKywZ8+eQsvz+XzUq1cPpqamALLfF+zs7HDy5EmwWCzcu3dPalll77Gc+6ZevXpo1aoVOnXqhLp16+LKlSvw8/PDw4cPC425KPdXTnk7O7sSv8eUvb9yPrsaNGig1P0FFP0ea9mypVL3GJ/Ph729fYneX7kZGhoq9R6WFyVUxeTTp0+YNm0aAgIC0LNnTwBAkyZN8ObNGyQkJEAoFCI4OBjOzs6oWrUq/t/encdFVe9/HH+NA7IkrhWhKCLgiiGKAiKYmpoLS2SJCxcVLRWXH16X0kpFKxdcUtRSVEgRU9YUC+v6cAGXNLumF1EvqaEkXEUT2Zn5/v7wwTxSZ4bVauz7/I+B93y/n3O+wPnOOed7TExM+OGHH4CHq455eXlhbGyMi4sLBw4ceOT1Z0F9bB9zc3MiIyM5d+4cADt37mTgwIF/Wk31qSbbR5dnefxA/WwjOYYebh+VSsXkyZMZMmQICxYs0PwDedbHUH3Jz8/np59+4u233wbAz8+PHTt2cPDgQaB6/5Dd3d1p2LAhCxcuZM6cOQQHBxMeHs7atWu5e/eu3oOK7t2706BBA3Jzc1GpVBw+fJhu3bqxfv16MjMzuXXrltY+NGjQgDZt2jBp0iSSkpI0rzdu3Ji+ffvy9ddf673vQwhB48aN8fDwoF+/foSEhNCnTx+Ki4tJSkoiKipKc3D3OFNTUywsLNi2bRsFBQUolUpat25NZmYmJSUl7Nq1CxsbG53ZJk2acOnSJUxMTNi5cydubm40adIEX19fzpw5Q3l5uc5+A1hbW3P27FlycnLw9fWlV69eVFRU4OTkpPdSSTs7OwYOHMiXX36JhYUFQgjKyspQKpV4enry3Xff6d1mrVu3Ztq0aSxfvpxhw4bRrVs3mjZtysiRIzl58iRlZWVac35+fixduhR4+Hu8YsUKLly4oPm/qG/SbmRkRKdOndixYwdxcXF0796dDh060LBhQyZOnEhWVhalpaU686ampjRt2pQ33niDiIgIFixYQG5uLi+99BK9e/fm8OHDOvdzgwYNsLS05J133uGrr77SvF7VGKvsj5OTE6+++iouLi6MGDECX19fGjduzIYNG/QeLJuZmdGwYUOioqIoLCys0fiqrNnU1JQrV65gbm5e4zFWUVGBjY0N586dq9EYs7Ozw8vLi7i4OJo2bVrt8VX5+/3CCy8wY8YMVq5cWaPxBfD666/z4YcfArUbY7a2tsTExJCYmFijMWZmZoapqSlvvvkmGzdurPH4srW1rfXfMIAmTZrg4eFB//79a/Q37IltUK2fkmps69atlJaWaj4dBAgICGDZsmVMnz6d0tJS+vbty2uvvQZAeHg477//PoWFhXTu3Jl//OMfACxcuJB3332XTZs2YWVlxerVq/+UeupbfWwfpVLJ2rVrWbRoESUlJbRt21azNLShq+n20eVZHT9QP9tIjqGH2+e7774jIyMDlUql+eTV0dGRjz766JkeQ/VBCEHz5s3ZvHkzZmZmVFRU4OXlRVFREfv27cPIyAgPDw9MTEz0vs+LL76Ir68v169fp6ysTPPJuoWFBc2bN+fmzZs6s7a2tpibmzN9+nTatGnDrVu3CA8Pp3nz5jRq1Igff/zxicVYioqKKC0tZfTo0XTt2pWVK1fy5ZdfMnfuXNq3b09aWppm8RJtioqKKCgooEGDBrz11lua1wcNGoSTkxOpqam0bt1a6+WGRUVFqFQqFixYgKmpqeb1Vq1akZ2dTXh4OP369aNly5Zas2q1mgULFmBubg48nKQ8ePCARo0a8e2332JjY6P1DEBRUZHmjKuVlRVjxoxh4cKF5OTk8Omnn2Jvb09WVhbPPfec1poLCwspLy/XHOAfOHAAIQSFhYU0bNiQ06dP07JlS63brHJ7jxkzho4dO7Jz504SEhJwcXGhe/fuJCQk0LFjR62XoxUWFtKiRQtu376tWTimY8eOtG3bls2bNzNq1CheeeUVrX0uKiqipKSE4cOH4+/vT0VFBYGBgfz44484OzsTGRlJ586ddY7Pyn01d+5chg4dSkJCAn369MHNzQ2As2fP0qZNG637uaSkBFNTUyZNmoSNjQ0bNmxg7969zJ49G3t7e51j7L///S9HjhzhlVdeoV27dvTr1w94eJDcu3dv2rZtS5cuXXB1ddXa58p2V6xYQX5+vmZ/WltbVzm+fp9fuXKl5jVbW1uKioowNzfXO8Yqs0ZGRpqFf8LCwrh58yZr1qzRO8aKi4sxMzNjxIgRtG/fnsOHD6NQKCguLq5yfFW2O3nyZKysrEhMTOTy5cv06tWLbt266R1flW07ODgADyeDCoUCR0dHzp49W+UYq2x79OjRDBgwABMTE6ZNm8a5c+dwcnLSO8Yqsx9//DEHDhzgm2++YejQofTq1QvQP77y8/PJzc3Fz88PBwcHli1bRnx8fJXj6/f57OxsWrRoUeO/YdpU68G+kiRJkiQ96ebNm+Tm5nL69Gn8/f019wmUlZWRkJDADz/8wNChQzUHhY/LyckhNzeXc+fO4ePjQ/PmzTl9+jRbt25l1KhRZGVlsX//fuLi4p5Yva8ye/HiRXx8fLh48SLm5uY0a9YMlUrFv/71L7766qsnsidOnCAqKopGjRpx584dnJycNDfPb9q0CRcXFyoqKggPD9e6YmBlvnHjxty+fRs7OztmzJhB48aNq9xev287Pz+fdu3a8X//939YWFiQmZnJuHHjsLa2Zs+ePU+0/fvs3bt3sbGxYdasWVhYWBAWFsbly5cxMTFhy5YtOrNNmjTR9HnmzJls3rwZhUJBaGgoKpVK58HT49usR48eTJgwgeeee44pU6Zw//59TE1N9bZdWXObNm0YOnQop0+fJiYmBicnJ0xNTVm9evUT2ePHjxMdHa3pt62tLTNnztRs6+joaHbu3MnBgwefOAv5eJ/t7OyYN28eX3/9NUuXLuXll1/GyMiITZs26d3Plf3u0aMHwcHBmJmZERUVRVpaGkIIrTVXToq8vLxo27YtxsbGPHjwgKioKOLi4nBzc6OsrEzrGDt58iTTp09n8ODBuLq60q1bN1q3bq11vzzu9+3a2to+cjD9n//8h4kTJ9KqVSut40tfvqCggPDwcH7++WcaNmyot+Z+/fphY2OjGUtz587F0tKSf/7znzrHWGW2b9++j0zW8vPzee+99ygqKqqy3d9v60uXLrFz505SU1Pp0aMHJiYmWseXvn5X2rFjB1988YXWMaar35s3b2bTpk306NEDpVKpdYzp21fbtm3j5MmTqFQqrTWnp6fz2WefYWlpyW+//cbs2bNp1aqVZny5urpSVlbGqlWrtNZcmW/ZsiV3795lypQpODs76/0bUBU5oZIkSZKkWgoKCuLll1/m1q1b7Nu3j/HjxzNz5kxMTU2pqKggOjqaTp060bt3b735nJwcUlJSGD9+PCEhISQnJ3P48GG6dOlCz5498fDw0Jrt2rUrN2/e5JtvviEkJIRJkyZhYmLCJ598glKpxNXVlb59+z6SGz58OKGhodjZ2VFSUsK2bdvIzMzk3Xffxc3NjXv37tGsWTOdlyo+nt++fTsZGRkEBwfj5+eHWq3WuXS7ruz48ePx9/dn6tSpDBgwgDfeeKPa2XfeeYdBgwZx5coVHBwctH4K/3h269atZGZm4uXlxcyZM2nYsGGN+l2Zf/vttxk0aBBZWVnY2dlp/RT+8Wx0dDQXLlxgypQpvPrqq9y+fRsrKyut27sya29vT3FxsabmcePGabaRrn7r2s8zZszAy8uL7OxsWrdurfOsxeP5LVu2cPnyZc3lwf/9739p27at1nzlpOi1117TnCWpnBSVl5dz//59mjdvrrXm/Px8goODcXd3Jy8vDysrK1xcXOjataveRRW0tevs7Iy1tbXm+5MnT2bgwIFax1dV+aKiIrKysjSXslWVdXJyok2bNmRnZ/P8889jZmamc1/pysLDM5RXr17FwcFB6/h6POvi4qI5w11SUsLt27dp1aqVzt/nyvyQIUPo2bPnE9sMdI8xffu5qKiInJwc2rRpU63t9fLLL2suwywrK+Pnn3+mXbt2WrP+/v7MmDGDLl26sGPHDu7fv8+iRYuAh+NLrVbTsGFDnTU/ni8oKGDhwoXVXoBCG3nJnyRJkiTVwo4dO2jUqBH//Oc/gYeXnu3bt49vvvmGsLAwPD09CQ4OrnbexsaG5ORkDh06xPr163nrrbd03rxemZ09e7Ymm5SURHx8PKtXr2b27Nlas/fu3aNDhw4MGDBA89onn3zC0aNHiYmJoUWLFnTo0EFnn/XlExMTad++PZ07d65xNiEhAUdHRyIiIrQeuOnLxsfH07FjR7p06VLtdpctW6ZZDjsrK4tOnTrpnExpyy9fvlzTdqdOnWpU85IlS0hLSyM+Ph4HBwfNpVY1qTkxMZEOHTrg6OhY7e21bNkyjhw5QkJCAm3btsXe3l5ru7ryK1eu1NTcoUMH2rdvrzPfvn17rK2tsbCw4MiRI1y6dAkXFxc6d+7Miy++SIsWLbTmKioqaN68OQMHDuT111+nsLCQhIQEUlJSuHTpEt27d6dHjx46D3ofb/fy5cu4uLjg6OhIixYtWLdund5V/nTlu3TpwvPPP0/Xrl1rlK2cZFSuGKdrjFXVrqOjY7XbvXTpEj179tRkH58c6co3atTokbZ/P4Gtbb/1jbGqspWr9j3u5s2bWFpaai5BDAgIICQkhBs3bmBtbc2lS5d47rnnsLW1rXZ+2rRp5OTk0LJlS/7973/TrFkzvffYaSMXpZAkSZKkWrCwsHjkn76NjQ3z5s1jypQpJCUlVXkzs7b8e++9x7hx49i4caPe1eYez9ra2vLuu+8yefJkvvjiC51ZCwsLFAoFgYGB/PLLL8DDewk9PDywt7fXLKShr93H8w0aNNDc23Lo0KFaZW1tbTl48KDOAzd92Xbt2vH111/XqN3Ks3fW1taah4HWtN/u7u61atvIyEiT1feg7Kq29e8fnlqTbFV9rmv+95OiwMBAJk+ejFqt5sCBAyQlJXHmzBmdCxxUXvbl5+eHlZUV9vb2zJ07F29vbzIyMrh69arOyZS2dlUqFSkpKcTHx3PmzBm9v1P68gkJCZw+fVpnv3Vlk5OTiY2N1VtzfberVqvZv39/ldmq2o6Li6tVv6vTdl1qbtq0KXfu3CEqKori4mIsLS0xMzPj1q1bACxdulTvIiva8qampuTk5AAPP3goLi7WmddFTqgkSZIkqQYq/9Hb29uzY8cO5s2bR0pKChEREZrnz9y/f5+MjIxa5UeNGkVhYSHnz5+vcXbkyJE8ePBAaxYeTiTCw8Px8vJi/vz5REREUFZWprk85v79+3prryr/22+//SlZff3WlTU1Na2yXX15ExOTWrddl2xdtld12q1O2/rytZkUZWdns3btWpYvX86qVavIzMx85Puenp4sX74cX1/fem23Jvlr167pzOvK+vr6Vtn202jXx8enymxdt1ld2q5Lzc899xzbt2/Hz88PMzMzlEolTZs25caNG2zbtg1bW1udZ7fqI6+LvIdKkiRJkmqgvLwcpVJJaWkp9+/fZ/PmzZiamtK9e3cGDBhAbm4uEyZMYOfOnZpnq9RXvrbZ7Oxs4uPjKSoqwsTEBHt7e1566SVSU1M5dOgQffr04cKFC2zZskXrAzjrkjfErKH2+69Qc2lpKUZGRjg7O9O/f/9Hfqa0tBSFQvHEZXcBAQH06dNHM2aPHTtGs2bNmDBhAg4ODnrvb6tLu3XNG2LWUPtdmS0uLsbY2BhHR0fNKr7fffcdn332GfBwQQxt99rVNV8VOaGSDF5YWBgXLlwgNjZWszqLSqVizJgxuLq6Ehoa+of1JSEhQfNk9q1bt9bre2dnZ7NixQrWr19Pbm4uM2fOZPfu3Tx48ICJEydSUFDAO++8w65du9i9e3et2ti7dy9lZWWMGTOG2NhYCgoKNM/WkSTpoVWrVvG///2PoqIijIyMCA0NpXXr1hQUFDB79myUSiWOjo5MnTq13vO1zf7+gFWhUJCeno6FhQXjx49HoVBw+/Zt2rdvz/PPP6+1z3XJG2LWUPv9V6kZqj8pys7O5oMPPtA8ZP3BgwfcuHGDQ4cOkZ+fz9SpU/Ue4NZlMlbXvCFmDbXfj4/No0eP0qxZM4KCgmjVqhWDBg1i7NixhISEPJV8lYQkGbiSkhIxbNgwsWHDBs1rGzZsEKNHjxYVFRV/aF8CAwNFUlLSU3nvkydPimHDhj3x+vfffy9effXVemlj3rx5IjIysl7eS5KeRUlJSWLs2LHi559/FufOnROvv/66GDBggAgLCxNlZWXi9OnTIj8//6nka5v95ZdfRFBQkObr+/fvi4yMDLFx40axePFiUVpaqrfmuuQNMWuo/f4r1VxQUCAuXrw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- "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:20.988806\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df[\"validity_period\"] = (df.not_valid_after - df.not_valid_before).dt.days / 365\n", "\n", @@ -441,32 +283,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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VXZL333+ftm3b8sILLzBhwgRycnL47rvvcHJyKtX9lVHehAkTWLJkCXfffTeJiYl89913ODo6FnmtVqvliy++wMXFhcmTJzNp0iTc3d355ptvCgxPGzNmDFlZWZbV+gA8PT2ZM2cOK1asYPjw4fznP//hwQcfJCAgoECv2DWTJ0/m8ccf58MPP2TkyJFs3bqVzz77jM6dO5fqtXj88ceZMGECM2fO5O677yYpKYmvvvoKa2trBg8ezKxZs/juu+8YMWIE3333HXPmzLH0nlUmV1dX7r33XmbOnMlnn31WobJK+/oLIURtoVEVGTgthBBCVJGBAwfy+OOP8/DDD9d0KEIIIUQh0iMlhBBCCCGEEGUkiZQQQgghhBBClJEM7RNCCCGEEEKIMpIeKSGEEEIIIYQoI0mkhBBCCCGEEKKMJJESQgghhBBCiDKSREoIIYQQQgghykgSKSGEEEIIIYQoI0mkhBBCCCGEEKKMJJGqgEOHDjFq1KiaDqPMpk6dSvfu3cnOzi5wfNKkSWzevLnAsaSkJAICAqozvFrhdmwbubm5/Pe//2XcuHGMHTuW0aNH8/XXX3PjDgk//PADAQEBHD9+vGYCrQVut/Zx6NAh2rdvz9ixYy1t4/777+fPP/+0XLNw4UJ69Ohhuebafx999FENRn57ut3aR2RkJK1atSr0bz927Fjy8vIA2LVrF/fffz9jxoxh5MiRvPTSS8TGxtZw5Len2tI+Pv30UwAGDhzI0KFDGTt2LOPGjWPEiBGMGjWK3bt313Dkt7fbrZ0ABAQEkJSUZPl7wYIFBc5v3ryZSZMm1URoN6Wv6QBE9YqLi+Pw4cN07NiR3377jYkTJ9Z0SOIWoJTi+eefp1mzZixfvhwbGxuSk5N55plnyMrK4uWXX7Zcu2zZMkaPHs3SpUvp2LFjjcUsqlfjxo1Zu3at5fH58+d54oknWLx4MR06dABgxIgR/Pvf/66pEEUNsrW1LdA+rhcXF8frr7/O6tWr8fb2BuCLL77g5ZdfZtmyZdUZpqghxbUPgI8++oh27dpZHm/evJm33nqLvXv3Vkd44hb13Xff0adPH+64446aDuWmJJG6TmZmJm+++SZhYWFotVratGnDnDlz0Gq1rFq1iiVLlqDVanF1deX9998HICsri2nTphEaGkpubi7/+c9/6Nq1K3l5eXz00UccPnwYk8lE69atmTlzJo6OjgwcOJBRo0axa9cuUlJSmDp1KkePHuXMmTPo9Xq++OILPD09iYuLY86cOcTExGAwGBg5ciTPPvtsobhffPFFwsLCChzz8fHh888/L3TtihUr6NmzJ0OHDuXTTz/lgQceQKPRVM0LWovU9rZx+PBhQkND+frrr9HpdAC4urrywQcfEBUVZbnu0KFDpKam8uqrrzJ48GBiYmJo2LBhZb/ct53a3j6KEhgYyKRJk/juu+8K/WooCqqL7eN6ycnJGAwGsrKyLMceeeQRWrVqVY5Xs/ap6+3jRkopIiMjcXZ2rlA5tU1dbCfTpk3j1VdfZe3atbdue1DCYs2aNerxxx9XSillNBrVjBkz1JUrV9S5c+dU9+7dVXR0tFJKqSVLlqhZs2apgwcPqlatWqnjx49bjk+ePFkppdTChQvV/PnzldlsVkop9fHHH6u3335bKaXUgAED1Ny5c5VSSm3YsEEFBgaqc+fOKaWUev7559UXX3yhlFJq0qRJavv27UoppXJyctSkSZPUhg0byv38DAaD6tOnj9qxY4fKzc1Vd9xxh9q1a5fl/MMPP6w2bdpU4J6rV68qf3//ctdZW9T2tvHtt9+qF198scTrXnrpJTV//nyllFJPPfWU+uCDD8pdZ21S29vHwYMH1ciRIwsd37lzpxoxYoRSSqnPPvtMde/eXY0ZM6bAf7t37y53vbVFbW8fERERKjAwsNC//ezZsy3XzJs3T7Vp00YNHz5czZgxQ61fv14ZDIZy11mb1MX2MX78eMv5AQMGqCFDhqgxY8aovn37qjvvvFO9+eabKjw8vNx11ka1vZ0opZS/v7+6evVqgb9feeUVNXXqVKWUUps2bVIPP/xwheqobNIjdZ0uXbqwYMECJk2aRK9evXjkkUdo0qQJS5YsoU+fPpZf3h999FEg/9d5X19fy7CWwMBAfv31VyB/PHh6ejr79+8HwGAw0KBBA0tdQ4YMAcDX1xc3NzcCAwOB/OEzqampZGVlcfjwYVJTUy3jiLOysjh//jwjRowoEHdps/3t27djNpu588470ev1jBgxgqVLl9KvXz+AInumlFJotTKVrra3Da1WW2gu1I0SEhL4448/LM9j3LhxzJ49mxdeeAF7e/tSvIq1V21vHzej0WiwtbW1PJahfUWrC+2jpKFbb7zxBs888wx//fUXhw8f5oMPPuCHH37gp59+svSC11XSPv4Z2hcREcFjjz1G8+bN8fX1Ld0LWEfUhXZSlHfeeYexY8eycuVKnJycSv16VRdJpK7j6+vLtm3bOHToEAcPHuSxxx5j5syZ6HS6AklGTk6OZbiTlZWV5bhGo7F8GTWbzbz11luWJCUzM5Pc3FzLtdbW1pa/ry/jGrPZjFKKZcuWYWdnB+Qv/GBjY1Po2s8++6xUz++XX34hJyfH8j9IXl4eCQkJXLx4kZYtW+Lq6kpKSkqBexITE3FxcSlV+bVZbW8bHTp0YOnSpZhMpgJfak6ePMkPP/zAhx9+yMqVKwF47rnnLHFkZGSwZs0aHnrooVLVU1vV9vZxM6dOncLf379CZdQFdbV9XLN9+3ZSUlK45557GDp0KEOHDmXatGn079+fs2fPFpgbUxfV9fZxPV9fXz744AMmTZpE165dLUmAqLvtxNHRkY8++oinnnqKJ598skJlVQXparjOzz//zJtvvkmfPn149dVX6dOnDxcvXqR79+4cOHCA+Ph4IH+y/YcfflhsWX369OGnn34iLy8Ps9nMrFmz+OSTT0odi6OjIx07dmTJkiUApKWlMXHiRLZv316u53b58mX++usv1qxZw44dO9ixYwd79+6la9euLF26FIC+ffuyevVq0tPTATAajfz000+W/9HqstrcNgA6deqEn58f8+bNs7yZJiYm8p///AcfHx9MJhMrVqzgnXfesbSfXbt28cwzz/D999+X2JtV29X29lGUkydP8ssvv/DII49Uarm1UV1sH9dzcHDgk08+ISQkxHIsMjISGxsbGjduXGX13i7qevu4UefOnRk/fjzvvPMOZrO52uq91dXldtKpUycee+yxCs+/qwrSI3WdcePG8ddffzFixAjs7Oxo1KgRkydPxtnZmVdffdWSCbu7uzN37lyuXLly07Kef/553n//fcaPH4/JZKJVq1a88cYbZYrno48+4t1332X06NHk5eUxatQoxowZU67n9ssvv3DXXXcV+tCaMmUKzzzzDK+88gp333038fHxPPjgg2i1WnJycujevTszZ84sV521SW1uG9d89tlnLFiwgLvvvhudTofZbGbcuHE88cQTlmGho0ePLnDPo48+yvfff8+ff/5J//79K1T/7awutI/w8HDGjh0L5A8FvfYr4bUhHwAbN24kKCiowH0NGzbkyy+/rFDdt7u60D5ycnIs7eN68+fPp0ePHsyaNYvXX3+d9PR0dDod7u7uLF68+NadQF6N6kL7KKtXXnmF4cOHs3z5clld+G91vZ0899xzHDhwoMrKLy+Nqus/JQshhBBCCCFEGcnQPiGEEEIIIYQooypNpDIyMhg1ahSRkZEA7N+/n9GjRzNkyJAC+46cO3fOMgl1xowZGI3GqgxLCCGEEEIIISqkyhKpEydOMHHiRMsYzZycHN566y0WL17Mxo0bOX36NH/++ScAr776KrNmzWLLli0opVixYkVVhSWEEEIIIYQQFVZlidSKFSt4++238fDwAPJXeGrSpAm+vr7o9XpGjx7N5s2biYqKIicnh44dOwJw9913s3nz5qoKSwghhBBCCCEqrMpW7XvvvfcKPI6Pj8fd3d3y2MPDg7i4uELH3d3diYuLK7LMG1eDErVHly5dKlyGtI/aS9qHKI60D1EcaR/iZqRtiOKUpn1U2/LnRS0OeP3mYDcev5nKaPSQ3/Arq6zqIjGXTNqHxFwcaR8Sc3GkfUjMxamsL93yOleP6oxb3jvqbszVtmqfp6cniYmJlsfx8fF4eHgUOp6QkGAZDiiEEEIIIYQQt6JqS6Q6dOjA5cuXCQsLw2QysX79evr27Yu3tzc2NjaWrtHffvuNvn37VldYQgghhBBCCFFm1Ta0z8bGhvnz5zN16lRyc3Pp168fw4YNA/J3R545cyaZmZm0bt2ayZMnV1dYQgghhBBCCFFmVZ5I7dixw/J3z549+f333wtdExgYyKpVq6o6FCGEEEIIIYSoFNU2tE8IIYQQQgghagtJpIQQQgghhBCijCSREkIIIYQQQogykkRKCCGEEEIIIcpIEikhhBBCCCGEKCNJpIQQQgghhBCijCSREkIIIYQQQogykkRKCCGEEEIIIcpIEikhhBBCCCGEKCNJpIQQQgghRI0ym42cC16ByZRX06EIUWqSSAkhhBBCiBoVE3uEg3/NJyx8R02HIkSpSSIlhBBCCCFqVEZGFADxCSdrOBIhSk8SKSGEEEIIUaMyMqMBiE84UcORCFF6+uqucOXKlfz444+Wx5GRkYwdO5bs7GyCgoKws7MDYMqUKQwePLi6wxNCCCGEENUsIyMGgKTkCxgM2VhZ2dVwREKUrNoTqXvvvZd7770XgIsXL/LCCy8wZcoUHnnkEX788Uc8PDyqOyQhhBBCCFGD0jOj0WmtMZnzSLx6hoZeXWs6JCFKVKND+2bPns20adOwtbUlOjqaWbNmMXr0aD777DPMZnNNhiaEEEIIIapJRkY03t69ARneJ24fNZZI7d+/n5ycHIYPH87Vq1fp0aMHc+fOZcWKFRw5coRVq1bVVGhCCCGEEKKaGE25ZGcn0qB+IC7OfpJIiduGRimlaqLiF198kSFDhjBq1KhC57Zt28Zvv/3G559/XuB4UFBQdYUnqlmXLl0qXIa0j9pL2ocojrQPURxpH7e+3NxYgi/NwrfR42RmXSQ1LYjWAQvQaKr2935pG6I4pWkf1T5HCiAvL4/Dhw8zf/58AIKDg7ly5QpDhw4FQCmFXl90aJXR6CG/4VdWWdVFYi6ZtA+JuTjSPiTm4kj7kJiLU1lfuuV1Liwq+gDBl6Bt216kp/uy98AeWrRsgIuzX5nLMpuNhFxaR2qKF3fc0bMKoi1M3jvqbszlSvWfeeaZClUaHBxM06ZNsbe3B/ITp7lz55KamorBYGD58uWyYp8QQggLQ0YCiceX13QYQogqkJGRv/S5o2MjPNw7ABAfX779pCKj9rHv4LtcTd5VWeEJcVPlSqTuv//+ClUaERGBl5eX5XFgYCBPP/00EydOZOTIkbRq1arIIX9CCCHqpsSjPxG7+78os6mmQxFCVLKMzGg0Gj32du7Uq9cEG2vncs+Tio45BMDVpF0oJQuXiapV5qF9BoOBgQMHVqjSESNGMGLEiALHHnroIR566KEKlSuEEKJ2yowMws6rDRqtrqZDEUJUsoyMGBwdvND+/f+3h3v7cidSMbF/YWXlQJ4hgeiYg3g36lWZoQpRQIk9UkeOHGHx4sXk5eUxfvx4unbtysaNG6sjNiGEEAJTbjrZ8edx9Lm9xuELIUonPTMaR8eGlsce7h1ITbtCTm5KmcrJykogJTWUdm0eQa9z4lzwikqOVIiCSkykPvzwQzp27Mgff/yBm5sbGzZs4H//+191xCaEEKIEZpOBGlp8tdpkRp8AZcZBEikhaqWMjGgcHRpZHnt45M+TSkg4VaZyYmL/AsC7UW/qu95JROQe0v+efyVEVSgxkTKZTPTq1Yv9+/dz11134ePjI5vlCiFEDVBmE5mRx0g8+hMRm2ZyYek9nPmsJ4lHvq/p0KpUZkQQGp0V9g3b1XQoQohKdm0PKUfHfxIptwat0Wh0ZR7eFx37FzbWzjSoH0B9175oNBqCL/xa2SELYVFiImU2mzl58iS7du2id+/eXLhwAYPBUB2xCSGEuE5i0A+ErnySmD8/ISMyCGuXJtg08CPx2C8oU+19X86fH9UWrZVdTYcihKhkmZlxAAWG9un1djSoH0B8QulX7lNKERNzGC+vrmg0WqytGuDr048LIWswmfIqPW4hoBSJ1LPPPsv06dOZMGECPj4+PPvss7z88svVEJoQQojrZcWcxNrZh8Cnt9DqqU00HfsJXr2nYMxMIC10d02HVyVMuRlkx5/DwadzTYcihKgClqXPrxvaB+Du3oHExNOYzaX7kSgtPYLMrFgaNexuORbofy+5uSlcCfuj8gIW4jolJlJeXl5s27bNkjxt27YNOzv5VVAIIapbdsJF7DxbY+XgZjnm1Kw3VvUacvVE7ZxUnRl9HJQZR5+uNR2KEKIKZGRe20OqYYHjHu7tMZpySEq+WKpyrs2PaujVzXKsUcNu1HNqzLkLtfP9UdS8myZSZ8+e5cyZM7z++uuWv8+cOcPp06eZOXNmdcYohBB1niknHUNaNLbuLQsc12h11G93D5kRR8i5ermGoqs6mRFBaLR6mR8lxG0gJzeFqOgDnDy9hMiofaW6JyPjnz2krmfZmDfhJGazieSUS1y8tI5jJ74kLy+9UDnRMYdwsPeinpOv5ZhGoyUw4F4SEk5yNel8BZ6ZEEW76T5Sv/zyC/v27SM+Pp4pU6b8c4Nez9ChQ6slOCGEEPlyEvN/lbV19y90rn7bscQf/IqkkytpNOC16g6tSmVGyfwoIW5lubmpHDg0j4TEU2Rkxlx3RkOfXm/TsvmYYu/PyIjBwcETrbbgV1JHBy/s7T05fuIrgo5+htGUYzmXlZ1I7x7//KivlJnY2CP4+uYvMHG9Fn6jCTr2OecvrCpwjxCV4aaJ1LvvvgvAjBkzeO+996otICGEEIVlJ1wAwM49oNA5vX19nFveRfLZ9Xj2noLO2r66w6saxmyy487j3u3Rmo5ECHETl8O2cTlsK00aDyIw4D4a1G+Fi3Mz9ux/m7373wE0tGw++qb3Z2RG4+jQsMhzrQLuJzJqN/Xrt8KtQSvc6rfmQsgazpz7iRZ+o/D06AjA1aRgcvNSaXTdsL5rbGzq0bnj8yhlqoynK0QBN02krgkKCqqOOIQQQhQjJ/ECOjsX9NfNj7pe/Q73kXJ+EynnN9Gg/T3VHF3V0KSFgDLh6C37Rwlxq4qM3IOjozcD+n5QoDdoUP9P2L7rFfbunw1w02QqIyMa70Y9izzXvu2jtG/7aIFjnTo8x5Xw7ew/NJexI39Cq7Uqcn7U9dq2friMz0qI0ilxsQlvb2+OHj0qe0cJIUQNykm4iK2bf6FhK9fYN2yHrXsASSdWlmqD3tthE19tyoX8+VGN2td0KELUSUlJFwgOmUVaWniR543GHKJjD+Pr3afQe5Neb8ug/p/QyKsbe/fP5uKldYXuN5nyyMpOKLCHVEmsrOzpccfrpKSEcPrsj0D+QhMuzn7Y27uXcLcQlavEROrSpUs8+OCDtG/fns6dO9OpUyc6d5ZlaIWoqwz/+xfG9QtrOow6RZmN5CSGYHfDQhPX02g0NOgwgZzEi2RFHy+2vKRTqwn+31jy0mOLvc5syC5PuCUy5WWhVMk/zmlSg7HzaiPzo4SoIUePf05uXiwXQtYWeT42LgiTKQcf7z5FntfrbRk04J9kKvHq2QLnMzLz34NuXPq8JI19+9Gk8UCOn/yalNTLxMYdu2lvlBBVqcRE6qeffmL79u1s2bKFdevWsX79etatK/yrghCi9lNJMZiDD2K+dKymQ6lTcpPDUaY8bIuYH3U9l8DhaG0cuXpi5U2vMZsMxB34GkNaFJGb/40yF543oJQiavtczn0zjLyUyArHX6B+Yx4XvhtP5JbZxV5nystEkx6Ogyx7LkSNSLx6loioPWg0ekKvbCryx4+IqD3odbZ4ed38/1O93o4B/T5Ep7Ph4g0JmWUPKcei50gVp/sdr6HV6tm2fSomUw4NG0oiJapfqYb2nTp1ihUrVlC/fn2OHTuGt7d3dcQmhLjFmE7tBEAlRtwWQ8Nqi5y/F5ooasW+62mt7HBtPYbUi3+QmxJR5DWpwVswZibgEjiCzMggEoN+KHTN1WO/kHTyV8y5mUTtmF+p/9bpV/ZhzEwk5dwGks9tvOl1WdEn0GDG0UfmRwlRE46f/Bpr63o08ryfzMxY4uKPFzivlCIycg8NG3ZDr7Mptixra0ca+/Tlcti2Ahvs/rOHVNl6pAAc7D3o3OF5MjKj0Wi0NPSU9wpR/UpMpL7++mt++eUXNm/eTE5ODosWLeLzzz+vUKWTJ09m5MiRjB07lrFjx3LixAnWrVvHiBEjGDx4MD/99FOFyhdCVA3zyfxEirxsSL9as8HUIdkJ+XOFbOo3LfFa966T0WitiNtX+H1aKUVi0A/YNGiOz7A51Gs5iNj9i8mOO2e5Jv3yPmJ2L6BeiwE07PcKGWEHSL2wrdKeS8r5TejsXLFv1IHoHfPJS40q8rrMiCMojU7mRwlRAxKvniUicjdtWz+Mq0tP9Ho7Ll0u+MNHSmooGZkx+HrfWaoy/ZoNJzc3hajog5ZjN9tDqrQCA+7D3a0tnh6dsbZ2KlcZQlREiYnUhg0b+Oabb7Czs8PV1ZUVK1awfv36cleolCI0NJS1a9da/vPy8mLBggX8/PPPrF27luXLlxMSElLuOoQQlU8lxaAiz6Fpkf+rn0osusdDVL6chAvYNPBDq7Mq8VorR3fcu04m9cI2MqNPFjiXEX6QnMQQ3Ls8jEajwfuuGVjZuxG+aQZmQzY5Vy8TvvFNbN1a4DN0Dg063o+dRytidn2EKafwBphlZcpNJz10Dy4BQ/Ad9h8AIjbPQpmNBa4zmwykhx9EOTWV+VFC1IDjJ7/B2roerQIeQKu1obFvf66E/YHJ9E9vUmTUHoCbzo+6kXejXthYOxdIyG62h1RpabU6hg3+mrsGfFqu+4WoqBITKb1ej7W1teVxvXr10OvL1+ABQkND0Wg0PPXUU4wZM4Yff/yR/fv306NHD1xcXLC3t2fo0KFs3ry53HUIISrftWF9+oGPAKASJJGqLjkJF0oc1nc9ty6T0Ns3IHb3fwsMy0s88gN6BzecA4YBoLd1xmfYHPKSw4n84z+ErX0Zrd6WJmM+QWdtj0arw/uuGRizk4ndt6jCzyM1ZCfKlIdL4HCsnRvhPehNsqJPEP/X/yzXZIT/RciPD5ATH4zZXeZHCVHdEq+eIyLyT9q0ehhra0cAmjcbQV5eGpHR+yzXRUTupb6rPw4OnqUqV6ezomnTwYRH/InBkAkUv4dUaen1tljJDy6ihpSYETVs2JBdu3ah0WjIy8vj22+/rdAcqbS0NHr27Mns2bPJyclh8uTJDB8+HHf3f7p1PTw8OHnyZJH3V+a+VrfjHlm1NeYuXSpnbLO0j6qL2e/gBjSuvpxOMtFaqyPmzBHi9BWfLyntowR5aVhnXSUxx474MtSp9R6B8eIPHNvyDcq9C5qMCDLCD2FsOp5jJ05dd6UGne9QUs9vRmn0GDtM59TFaCDacoWu0QCunvyVOG0LVD2/cj8V/cnlaGzdOReVC9FBgAc6j+7EHfiGqBTQJgahSziCsnXD2GYKqkF7aR/VqLbGfKu1j1v9db4S8Tk6rT15OQGWWGNjrNDpnAg6+hOJ8U4YTZnExR/Hw21YmZ6PMa85JlMOf+5egqtLT5KSw3BybFNlr0lJ5d5qbaOyy6outTXm0rSPEhOpWbNm8dprrxEcHEzHjh3p0KEDH330UemiLEKnTp3o1KkTAPb29kyYMIF58+bx7LPPFrjuZnulVGajr6yyqovEXDJpH1UTs0qOJS8pDN3wZ+lyxx3k7fbFU2fAp4L1SfsoWXrYAa4ALTsNxLFx6etU5o5c/HE/1tEbaDn4MU4u+x9aKzvaD38RnW29AteaO7YneocNTs364NxiYKGyTO0CubD0FHaRv9LiwR/QlGKI4Y0MGfGc3x2MR4+n8Oz6T0+Tqa0/F398EM25r9DobHDv+QzuXR9Bq7eR9lGNJOaSVUZdt/rrfDXpPCfPHqdTh+fo2D5/7lNQUBBdu3bDaB7BhZDfaNfOn8jo/RBspmuXe/H06FDq8pXqTFzCD5g5R8eOT3PybApNm7SjY/vKf02q87WW9466G3OJiZSnpyeLFy9Gq9ViMpnIzc2lQYMG5a7wyJEjGAwGevbM38VaKYW3tzeJiYmWa+Lj4/Hw8Ch3HUKIynVtWJ+u3QAANG6+MkeqmuQkXARKXrHvRhqtjoZ3vsSV314kbt9itAl/4drx/kJJFIBWZ4XP4H/ftCydtQONBrxK+LpXSTz2C+5dJ5ftSQApwVsAhUvg8IJl2zjRZPSHJJ1ag3uXSVi7+JS5bCFE5Th56n9YWzvROvCBQuf8mg3nXPBywsJ3EBN3BBsbF9zd2papfI1Gg1+zYZw68x2JV88A4FDBoX1C1KQS50ht3LiR8ePHY2dnR0JCAqNGjWLHjh3lrjA9PZ0PPviA3NxcMjIyWLNmDR9++CEHDhwgKSmJ7Oxstm7dSt++fctdhxCiMMP3b2Hc8nW57jWf3InG2x9Ng/yhfBp3X9TVqCL3IBKVKyfhAlaOnujtXMp8r2PTXjg27k5i0PegFG6dHix3HPWaD8DJrx9xB74iLzW65BtukHJ+E3aebbBxbVzonJ1HIN6D3pQkSogaZDBkERG5mxZ+o4pcAc/drR1OTj6EhG4gMmof3o16otXqylxP82YjUMrMqdPfAeBUjqXPhbhVlJhIffnll3z//fcANGvWjNWrV7Nw4cJyVzhgwAD69evHuHHjuOeee7jnnnvo0qUL06ZNY/LkyYwbN45Ro0bRvr0seStEZVHZ6ZjP7sV8Zk/Z702ORUWcRft3bxTk90hhMkBKfGWGKYqQnXABW/eW5bpXo9HgdedLgAazexesncv/hUWj0dBowKtoNBqid75fpr2lcq6GkhMfjEvgsHLXL4SoWlHRBzCZ82jsO6DI8xqNhubNhhMbd4Tc3JRSL3t+IxcXP+rXDyTi71X/KrrYhBA1qcShfWazGS8vL8vjhg0bYjYX3t26LF5++WVefvnlAsdGjx7N6NGjK1SuEKJo5isnQZlR8WGovGw01qVf4ch0ahcAuvb/zJ3RuOX3HKjECDT15UOwqpiNueQmXaFe837lLsPOI4Bm935FcGTFly+3rtcQj57PErt7AWkhO3BuOahU96Wc3wQaLc4BQyocgxCiaoRH7MTG2hlPj443vcav2QiOn/wGjUaHd6Oe5a6rebPhJCWdR6PRYW8vUznE7avEHqn69euzbNkyjEYjJpOJVatW4ebmVh2xCSEqibp07O8/zKjosu3RZj65o8CwPvi7RwrZS6qq5V4NBWXC1q18PVLXOPp0AavK2azSrdMD2LoHEL3zQ0y5GSVer5Qi5fxmHBt3x8pBPjuEuBWZzQYiovbi69O32D2dnOs1wcO9Iw09u2Jj41zu+po1HQpocLAv/x5SQtwKSkyk5syZw4oVK+jQoQPt27dnxYoVzJ49uxpCE0JUFnPosX+Sn6jgUt9X1LA+AJwagLWdJFJVLDsxf6EJO4+AGo7kHxqtHu+73sKYmUjc/sUlXp8VfRxDWnShRSaEELeO2Lhj5OWl0di3f4nXDh74GQP7l3/1ZgAHew8a+/SjQYNWFSpHiJpW4s8ATZs2ZfXq1aSmpqLT6XB0dKyOuIQQlURlp6OiL6Ib+AimQ2sxRwVT2unBpuPbgILD+iB/rHz+yn2RlRytuF5OwgU0elusnW+tRRjsvdrSoON9XD2+ApdWI7H3alPkdabcDKK2z0NnU496LfpXb5BCiFILj9iFTmeLd6MeJV57bZPeihrQ7wM0mhJ/zxfillbqFuzs7CxJlBC3oWvzo7TNO6H1CUBFni/VfUopzEc2oGnWscCwvms0bj6YpUeqSuUkXMDWrQWacqyMVdU8ez2H3qEBUdvexZSTVui8MpuI2DSD3KQrNB45H521Qw1EKYQoiVKK8IhdNGrYHb2+9PNnK0qr1UsiJW570oKFqOXUpWOgt0bTuA0a7wDLghMl3nflJCoxEt0dI4s8r3HzheRYlNFQ2SELwJSTTnZCMHZl3D+quuhsnPC+aya5SZcJ+XkSOYkF597F7l1E+uW9NBrwLxybdK+hKIUQJUlKDiYzK5YmpRjWJ4QoSBIpIWo58+XjaBq3RmNlg8Y7oNQLTpiObABrO7Tt+hd5XuPmA2YTKjmmkiMWppx0Lq95AWXIuaXnFtXzu5Nm936N2ZDNpWWPknpxOwDJZ34nMeh76ne4lwYd7qvhKIUQxQmP2IlGo8XXR/bvFKKsJJESohZT2RmoqAtom3UCQOudv2hBSQtOqNwszCd3ou1w102XSpeV+6qGKTc/icqJD6bxqA9w8Olc0yEVy6FRB1o89BM2bi0IX/8akVvnELV9Lg6+3WjUb3pNhyeEKEFYxC483Dtia+ta06EIcdspVyI1fvz4yo5DCFEFrp8fBYCzOzi6Yi4hkTKf2AF52Tcd1gfXJVIJkkhVFlNuOpdXX0ui3q/Q/lHVycrRHb8JX+PadhzJZ9Zi5dSQJqPeR6OzqunQhBDFSE+PJDn5ogzrE6KcyrV4/7vvvlvZcQghqoAKPQY6KzSN81dV02g0aL0DSuyRMh3ZgMajqeW+omgcnMG+HuqqrNxXGfKTqClkx5+nyagPqNe8f02HVCZavTXed83EucVAbN1aoLOtV9MhCSFKEB6xC6BUy54LIQorV49U27ZtKzsOIUQVMIcey19kwsrGckzjHYCKu4LKyyn6nvgwVNgptF1HoNFoii1f4+YrPVKVIDc5nEvLHic7/hyNR75/2yVR12g0Gpya9cbKybOmQxFClEJ45J+4urTAyenW2mJBiNvFTXuk3nzzzZvepNFomDt3bpUEJISoHNfmR+kGPlLguMbn7wUnYkLQNCn8o4j5yAbQ6tB1HlZiHRo3H8yXjlVazHVR2uW9RGyagUajo9n4RTg27lbTIQkh6oCcnGTi4o/Rvu0TNR2KELetmyZSLVu2LHQsOTmZpUuX4u1deE8ZIcStxTI/yq9jgeNa70Dg7wUnbkiklMmIKWgz2sBeaJzql1iHxs0Xjm5B5eWgsba96XUqOwPzsS35vVw3WbyirlFKkfDX/4jb/wW27v40Gf0R1s6NajosIUQdERaxE6XMMqxPiAq4aSL1+OOPF3i8f/9+Xn/9dUaPHs3MmTOrPDAhRMWoy8cLzI+yuLbgRGQwN27zag4+CBlJaItZZOJ6lgUnrkaiadjiptcZt3yN+cBqtKHH0D84B422bi8YqpQiYtMMUoO34BwwFJ/Bs9BaSYIphKg+oZc34VyvKQ3qB9Z0KELctkpcbMJoNPLxxx+zZs0aZs+ezbBhJQ/3EULUPPOlY/n7R93QU1TcghPmQ2vBqQHagB6lquOfJdAj4SaJlDkhHPOhtWjcG2M+tQvTH0vQD6nbQ0lyEoJJDd6Ce9dH8OwztcS5aEIIUZkyMmOIjQuiU4fn5P1HiAooNpEKCwtj2rRp2Nvbs2bNGho2bFgplS5atIhNmzYB0K9fP1577TXefPNNgoKCsLPL/1V2ypQpDB48uFLqE6KuUTmZqKhgdAMnF3le4x2A+cJfBYbkmY5txXz+ALphz6DRlW5BT41b/gTl4vaSMm38AqxssHr2c4ybvsC0fQkaz6boOgwq47OqPVIv/AEaHW5dJ8mXGCFEtQu9vAWA5s1u3Q2/hbgd3PTb0qpVq/jggw947LHHeO655yqtwv3797N3717WrFmDRqPhySefZNu2bZw+fZoff/wRDw+PSqtLiLpGKYW6chLT3hVFzo+65sYFJ1RSDMY1H6Np2g5d34mlrk9jYw9ODW6aSJlDj2M+uyc/OXN0RT/+XxgSIzGueA9Ng7o511IpRerFP3D07YreTjbAFEJUv9DLG3F3by+r9QlRQTdNpGbOnIlWq+Xrr7/mm2++sRxXSqHRaDh69Gi5KnR3d+eNN97A2toagObNmxMdHU10dDSzZs0iOjqawYMHM2XKFLR1fB6FEKWlstNpcGEXhl0LUPFXwMYB3Z0PoPHrVOT1Wu+A/PuiglE+gRiWzwHA6v5/l7o36hqNm2+RiZQymzFu+BycPdD1uS//Wr01VpPeI2/RUxiWvoG+79Qy1VUb5CRcIC8lAvcuRfcWCiFEVUpKvkhySgg9ur1e06EIcdvTKKVUUSeioqKKvbEyVu67cuUKDzzwAD///DMff/wxc+bMwd7enmeeeYZRo0Zx3333Fbg+KCiownWKW1OXLl0qXEZdbR8ak5EWW9/HJj2erPpNSPLrRWrjTii9zc1vUorA32eS3rA1eQ5ueJ7ZSET3SaQ26Vrm+hsdXka96FOcH/tegePO4UH4HvyeyG4Pk9L0jgLnbFKi8dvxX4x2zoT1fpK8esXvO1Sb2ofu8hq0EVsw9PgQrJ1qOpxaoTa1D1H5pH0UFBP3KwlXt9La/yP0+rr9HiRtQxSnVO1D1ZALFy6oAQMGqNWrVxc6t3XrVvX8888XOn7kyJFKq78yy6ouEnP11XU7vdaG3ctUzmu9VfBv35bpvtz/e0XlvDNS5bzRV+X9PLv89e/8UeW81luZYkOV2WxWSillzstROfPuUbn/fUyZTaYi7zOFHlcZs4aqnFmDlfHMnnLXX1q3Qvswm83q/P/GqdBVz1VaLKV1O7Xpa+T9o/pIzNVTV02/zmazSS1fNVxt3T611PfUdMzlVV1xy3tH3Y65RsbOBQUF8eijjzJ9+nTGjx9PcHAwW7ZsuT65Q68v2/AiIeoilZWGaft3aPy7k+7ToUz3an0CIDMFnN3Rj5te7hi0jfL3nDN8Mom8OSPJ+79pGH+aBcmx6Ee+cNOlzrXNOnBp8L/QuDfGuPQNjNu+RZnN5Y7jdpCTeJG8lHCcW95V06EIIWqRxKvnSEkJLfG6uPhjZGbF4ieLTAhRKao9kYqJieGFF17go48+YuTI/L1qlFLMnTuX1NRUDAYDy5cvlxX7hCgF046lkJOJfsTzZb5X26Jr/mp6989CY+dY7hi0/t2wemkJ+vH/QtumH2SmYg4+hLZNX7Qtiu8WN9i7YvXs52i7DMf0xxKMP86o1cnUtdX66rUYUNOhCCFqiYjI3WzY/CgbtjxGalpYsddeCt2IXm9HY5/+1ROcELVctXf7fPvtt+Tm5jJ//nzLsQceeICnn36aiRMnYjQaGTJkCKNGjaru0IS4rairUZj2/4q26wi0DZtDdNnGaWubd8b6nS1lXlyiyLIatYRGLS0b/CqjAbQ3bvdbNI2VDfp738LsHYBp/6+QkwH29Soc061G/b1an4NPF/T2slqfuH2YzUZCj/0P74DR2DlWzjYoonJERO5mx5+v4urSnIzMWP7Y+TKjhi3Fxqbwe6jJlMeV8D9o7NsfK9kAXIhKUe2J1MyZM5k5c2aR5x566KFqjkaI25dx81eg1aMf8mS5y6iMJKrIcvVWZbteo0HXewK63hOqJJ5bQU5iCHnJYbh1lvc5cXtJCN9DSNBXpCaco8uwBTUdjvhbROQedvz5KvVdWzJk0GJSUi+xedsz7NrzBoMHfoZWW/D9PTJqH3l56TRvNqKGIhai9pH1xYW4DZnDTmM+uQNdv4lo6rnVdDiiFFIvbAONFmcZ1iduM5HnfwcgIXw3V6OPVGldSTHHCNr8MsGHPiMrrfC2Csa8TCLOrebwhufJSztTpbHcyvKTqH/h6tKCIYMWY2NTD0+PTvTs/hbRMQf568gnlmuVMhMVvZ/jJ7/G1rY+jRp2r8HIhahdZEUHIW4h5tBjGH/9AI27LxrvQDQ+AWi9AwokS0qp/P2ZnBqUafNcUXMKDuurX9PhCFFqOVkJJEbso0nbicRd3kHwwf/Sc/z3aDSV+ztsblYiwYc+I/riBqxtXUmM2M/lE0tp4N0d31Z3Y+fYkIjzvxFzaTMmQxZanQ1KnSC9Q2+c6jev1Fiqi9GYzZ59b2Nj40yPbq8X6kG6mbj445YkauhdXxQYxuffYhwpKaGcOfcjDg4emM0mLlxcQ0ZmNDY2LnTvOr3U9QghSib/Nwlxi1BmE8a1C1DZ6XA1GvP5A1D0Nm8A6O9+DY2NfTVGKErLbMjGlJtheZyXEiHD+sRtKfrCRpQy0bj1vTi7t+bkzlnEhGymUcvKGR5mNhsJP7uSkMNfYDLl4dfpCZp3egxDbjqRwWuJPP8bx//I3zhWq7OhYfMh+LS6G1sHD/asmMjRLdPoOX4p1ra317xDozGbP3ZOIyb2LwAMhizu7D0HbQlzS3Pz0tm9dwYO9p6FkqhrunZ+idS0Kxw5+hkAXp5d6dJ5Kk18B6DTWVf+kxGiDpNE6halMrPROMhk0LrEfGQTKjYU/UNz0LUfiMrNQkVfxBwVDNnpBa7V1HNHe8fIGopUFMeUm07wt2Mw5aYVPKHRybA+cVtRShEVvBZXr444uDTB3tmXK6d+4sLhz/FsNhCd3rZC5SfHHufs3vmkJ12kgU8PWvd6DQeXJgDo9Ha06PwUzTs+TmLkAXKzEvFsNggrm382kK3X4nnSLn7CsW2vcceIxWh1ZZubWVOMxmy273yFmNjD3NlrDllZ8QQdXwQauLNX8cnUwUPzycyKZ+TQ/xWZRAFotTr69ZlLSOh6vBv1xLlek6p6KkLUeZJI3YKMB05gXLkF/QPD0XdrV9PhiGqg8rIxbv0GTeM2aNvlf9nW2NijadYBbbOy7Q8lalZK8FZMuWl49noenZ2L5biNs48M6xO3lZS4k2SmhtGs46MAaDRaAnq8zOH1zxJ2ehl+fx8vq9zsJIIPfUr0hfXYOnjS8a738Ww2CI1GU+hajVaHe+M+RZZj5eBH237/5uSOmZzdN582d84ssoxbidmcx/adrxAd+xd39nqHFs3/WaE46Pgi4ObJ1KXQjYRe2USnDs/j7l78dwNra0daBz5QucELIQqRROoWYw4Jx/jrNtBqMa7dia6VHxonh5oOS1QClRKPSghD07xLoU1qTbuXQfpV9A//55b/IiCKl3xmLbZuLXDv9rj8W4rbWmTwWnR6O7z8/tlAukGjO3BvfCehx/6HT8BYrO1KP6ROKUXE2ZVcOPw5JmMOzTo+SvNOT6KvwFLcjVoMJyMplNDj/8PRtTlN2z1Y7rKqmtGUy5WIRWRknqdPr9kFkqj27R5HoTh6/HMAenR7Axvrf3rf0tOjOPDXPDw9OtK+7WPVHrsQomiyat8txHw1hbyla9G4uWA9ZSLk5WFYt6umwxIVoMwmTOf2Y1j6BnnzJ2D4v2kYv38Tlf3P/BmVlojpz5/RtuuPtqn0QN7OchJDyI49g2ubsZJEidua0ZBF7KWteDUfgt6q4FzMgO4vYTLmcPHIF6hi5nHeWN7xP17n7L73cXZvQ+8JywjoNrVCSdQ1Le94Di+/IcSG/lHhsqrS4SOfkJF5jj69ZtOy+ehC5zu0e4LOHV8g9PImlq8ayp79bxOfcAKz2cjufTMBDXf2/k+J86iEENVHeqRuESonF8P//QpmhdUTd6N1r49uYHdM2w5g6toWnX/1jHFWSmE+dg6VnYuuR3s0OnnDLi/Twd8w7vwBUuLAsT66/g+BrSOmLV9j+Pxp9JPnofVognHbt2AyoB/2bE2HLCoo6czvaLR6XFoNr+lQhKiQ2NA/MBmz8QkYU+ico2szGre5l7DTy1DKROver6MtZhGDzNQIjm2dTkbKZQJ6vEzTdg9X6g8NGo2WjnfNw2wyVFqZlS0icjfnL6zErf6QIpOoazq0ewIf794EX/iVS5c3EXJpHXZ2bmRnJ9Kvz1ycHBtVY9RCiJJIInULUGYzhh/WoRKSsXrmXrTu+fMo9IN6YD56DuOvW9H+q+q78s3RCRh+3Ya6HAmAae9R9PcMRteicZXXXduYzh/AuOYjNE3boRs5BW2bOy2b32obt8bw4ywMi55Cd9fjmA9vQNfrHjRuPjUctagIs8lAyrkNODXvh74Mw52EqEnGvEwu/LWQ7Ew7jIbWlh6iqOC1ODg3wcWz6DmagT1eQWflQOixb0lPukSnwR9i6+Be6LqEiP2c2P4WGo2WrsMX4eZTdXsY3aqLTWRlJ7Jn/2zquwbg5TGuxOsb1A+kV48Z3NHlZUKvbOFiyFqaNh6EX7NhVR+sEKJMJJGqYcpkxvjrNsznQtFPGIyu5T89TxprK/QThmD4agXG7QfBvWpW8VPZuRi37MW09yjY2aK/bygaR3uMv+3AsHgZps6tsBozAE09xyqpv7ZR6UkYV85F49Ucqyf/i8bKpsB5rV8nrKf+H4YfZmDasAhsHdENerRmghWVJj10D6bsFOq3GVvToQhRKsps4sSOGSSE7wFg14+/0qjlCBp4dyM59jj+3V68ac+RRqvD/47nqdcggFO73ubA6ofoOOQjHF2akZZ4jtSEc6QmnCHu8g6c6reg05CPsa/nXZ1P75aglJm9+9/GaMym353vcSkkudT3Wlk5ENDybgJa3l2FEQohKkISqRqkMrIwfP875pBwdAO7o+/VqdA1uoCmmDq3wrT9ENbD7yh4v8mMiruKOTIWFRGLOSoOjaM9+lH90Ho0KFUM5pBw8n5cB+mZ6Hp2RD/8Tsuy61r/phh3HMK04xC5Zy6hH9YbXZ/OhYb7KaUwHz+PcedfaOo5oPHxQuvjidbXC12uAdOFK/nxRcahktOwemgUWvfa+Yu9UgrjqnmQk4n+qU8LJVHXaFy9sHpuMaZt36LxbY3GwbmaIxWVLfnMb+gdPXBs0qOmQxGiVC4c/pyE8D206v06MYkKe/MZIoPXEn52JRqNjkb+Je8V5eU3CAeXJhzbOp1Dvz8Bymw5Z+vYEN/W9xDQ/eVKmQt1Ozp3fjlR0Qfo2e1NXJz9gKCaDkkIUYkkkSqBUgoVFoPpwHHMoZHoBvVA36N9hcs1R8aRt2QNpGdiNXEEujva3vRaq7EDyT0XSosNh8jZfOS6Qsz5/wHYWKHx9sQcEkHeh0vQ9e+G/q4eaGyKHreulMK09yjGtTvQuLnmz8vybVjgGo21FVbD+qDr0gbjmu0Y1+7EdOgUVvcMRtvcNz+E2ESMq//AHBKOxssNlZSG+dxlTH9PQA4Ero1a19R3RtO0ERq7opOL2sB8YDXm8wfQj52G1suv2Gs1VjboRzxfTZGJqmTIiCf9ygHc73gUjUwEF7cAs9lIxNlfuXxiKZ7NBtKi6zNYXbcKXNSFdVw+sRTf1hNo0uY+EoOCaN/lfgJ7Tif64gZ0elts7QsP1SuKU/0W9Bz/A6EnlqK3csDZvRX13AJvu01yK1tS8kWOHP0UX5++BPhPqOlwhKiTri2IU1ULQEkidRMqOxdT0BlMB06gYhLyE5X6LhhXbEZFxaEfOxCNvnDPjEpKBaOp2LLNYdH5S5w72GE99cFCCcyNNE4OWD99L9Hb9uDp6fnPCa0WrVcDND5eaNzro9FqUOmZGNb/iWn7QUxBZ7AaOxBt2xYFepGUwYhh1VbMh0+jbdMCq4dGorG9eXKjdXfF6ql7MJ8OwfDbdvI+/wVtl9ZonBww7Q4CG+v8YYk9OqDRalG5eajoeMwRcUReCcO3R2e03p635AbDymyGnAw09kVvbHjT+7LSQKtDY/vP0vTm2FCMGz5HG9gTbU8ZilGXJJ9dD8qMa5vCE/OFqCo5WQlY27gUmhuUHHuCs/veJ/1qMI71WxB2ehkxl7YS2ONlGrYYTkrcSU7vfo8G3t1o1etfBe61tnUu1xLiVjb1COg2tULPpzZJSr7I9p3TsLZ2ok/Pt2UVTyGqWXxaKIcv/crxsPXYWjnR1W88nZuOwcmu8A9EmbnJ2Fk7o9WUfTHzWyqRWrduHV988QUGg4FHH32Uhx56qFrrV0qhwmMwHTiB6dg5MBjR+Hiiv3cIuk6twMoK44Y/Me06jDk6AetHx6JxcshPuo6ezU+6ouNLVZfGzwfrR8aWeo8obZNGxHVqgU+XLsWX6+SA9cQRmLu3x/DrNgxL14Jeh6aRB1ofTzTenpgOnkBFxKIf2hvd4F5otCW/wWs0GnTtWqINaIrxj4OYdv4FJhO67u3Qj+yHxvGf5XE1NtZomvmgbebDVQdo6t+0VM+xupmjL2L87RNU2Ck0zTqg6zYGbbv+Nx2Op8xmVMgRTIfWYj67F8wmNG6+aHwC0HgHYD66GWwd0E94Uz40awlTThrGrCRs6je96TVKKZLP/I6DTxdsXHyrLzhRJ5mM2cRc2kbEudWkxp9Co7XCqX4L6rm1wtm9FSlxp4i68HuBjW7TEs9xdu98Tu6cRcT5NWSmXMHO0YsOg+aj1d6aCzTczq6E/cGe/W9jZeXAoAELsK3jPXNCVJXE9DDSsgt+707NiiXo8hrCEo+j0+hp5T2AzNxk/jj9OTvOfEVgo77Y5TUj9cwRopPPEZ18lvScRPq1eoK72r5Q5hhumUQqLi6OBQsWsHr1aqytrXnggQfo3r07LVq0qNR6VGY25sg4nC/HYuLsP8czsjD9dSq/98naCl2X1uh6dijUW2Q1ZgBaH08MyzeT+8lSdP5NMZ0IhjwDGm8P9OMGoXGyv7Hagqz0aFv5VenS4lo/H6xfeQTz6YuYw6JREbGYjp6F/cfBxhqrx8eja9uyzOVqrK2wGnEnuu7tICcPrbdH5QdfSZQhF3X5BJoG3lC/kSW5UdnpGLf+H+YDa8C+Hro7H8B8di/G5e/Cuk/RdR6Os8Eak/bqP2Ulx2I6vB6SosHeGV3vCWDnhIoKxnz5JBzP379E/9iHaJzq18jzFZVDKUVW9AmSTq0m9cIfKFMuth6BNGh3D86BQ9FZX9cLacgmNWQneSkReHR/sgajFrWZMS+D1MTzxF3eQfTFDRjzMnBwbkLLO17AmJdBWuI5YkO3EXl+NRqNjmYdHqF55yct+z85u7emx7jviDz/Gxf+WoRSJjqP/hprW5mbWZnMZhPHTizm5OkluLu3Z2DfD7Ev5fBIIWqD9OwEolPOk2PIKPniG+i11ni5tKS+g2+xP0YbTDmcifyDw5dWE371eJHXNHBszND2L9Op6WgcbPJ/yEhMD+NI6GqOXVlHVt4ONAka3Jya4ufRjUaurejQpOQ5oUXGXa67qsD+/fvp0aMHLi4uAAwdOpTNmzczZcqUm96jjCZMQWcgO6f4wg1GzNEJqIjY/KF3gA9g2H+mwGUabw/0E4ag69yq2KFuus6t0Xg2wLDkN0wngtF1CkTXsyMaX69bqidCo9Oi6xCArkMAAMqsUInJaOxtC/QglYe2gUslRFi1VPgZDN++kv/AzhFNowC0ns0wndwOmaloe4xDP+RJNPb1UCOeR4UexXTod0wHfsXXZMR4qGB5Gr+O6IY+hbZtPzT6gnPPVEYyKjMVrWfT6nlyosIMmYmkBm9FqX+G4poNOaRe2Ebu1UtorR1wbTMKG9emJJ9ZS9T294jZvQDngMHoEhK4cOZ9cpMugzKjt2+Ac8tBNfhsRGXLTA0nPuzPct2bFRfJ5ZNnS76wGGaTgYykEFITz5OVGgaARmuFl98gfFvdg6tXpwKfN0opstMj0epssHUo/AOXRqPFt9XdeDUfjMmQU+RS5aL0EhLPEBd/tMCxqOgDRMccxL/FeHp0ex1dMXtridotJO4gcSkXy3xfREokOcEVe++obpeTLnJu7/dEJ58jPSexwuXZWjnS0CWQRq6tcLJ1K3AuJSuGE2EbyTakUd/Rl6HtX8LbtQ1c915orbejkUurQt/H3ZyaMKzDNO5q+wJ/HlpPnzuGYmNVulFhxdGo0m5LXsW++uorsrKymDZtGgArV67k5MmTvPvuu5ZrgoJktZvarEsJwxZLIu2jdpP2IYoj7UMUR9qHuBlpG6I4JbWPW6ZHqqh87sZssqKNXdRu0j5EcaR9iOJI+xDFkfYhbkbaRt1W9uUpqoinpyeJif90CcbHx+PhcevOvxFCCCGEEELUXbdMItWrVy8OHDhAUlIS2dnZbN26lb59+9Z0WEIIIYQQQghRyC0ztM/T05Np06YxefJkDAYDEyZMoH37im98K4QQQgghhBCV7ZZZbEIIIYQQQgghbhe3zNA+IYQQQgghhLhdSCIlhBBCCCGEEGUkiZQQQgghhBBClJEkUkIIIYQQQghRRpJICSGEEEIIIUQZSSIlhBBCCCGEEGUkiZQQQgghhBBClJEkUkIIIYQQQghRRpJICSGEEEIIIUQZSSIlhBBCCCGEEGUkiZQQQgghhBBClJEkUkIIIYQQQghRRpJICSGEEEIIIUQZSSIlhBBCCCGEEGUkiZQQQgghhBBClJEkUkIIIYQQQghRRpJICSGEEEIIIUQZSSIlhBBCCCGEEGUkiZQQQgghhBBClJEkUkIIIYQQQghRRpJIVYJDhw4xatSomg6jTAICAkhKSuLrr79m7NixjB07lk6dOjFw4EDL4/Dw8JoO87Z3O7aNayIjI+nUqROQ/zzat2/P2LFjGTduHGPHjuXuu+9mx44dNRzl7el2axdFxbtkyRL69u3L+fPniYyMpFWrVpb3jrFjxzJ48GAmTZpEREREDUV9e7od20ZAQACvvfZaoXOTJk2yvIc888wzvPjiiwXOZ2ZmMnjwYFauXFktsdYWt2MbCQwMZN++fQWOz5kzh4ULFwLwxhtv8O2339ZEeLVKbWwbABEREUybNo277rqLMWPGMGHCBFatWlXd4RZJX9MBiJr19NNP8/TTTwP5H3oPPfQQw4YNq+GoxK2ocePGrF271vL4/PnzTJw4ke3bt1O/fv0ajExUtwULFrB161Z++eUXvL29iYyMxNbWtkD7UErxn//8hwULFvDJJ5/UYLSiqrm7u7Nr1y6ys7Oxs7MDICoqisuXL1uumTdvHmPGjGHt2rWMHTsWgLlz59K+fXvuvffeGolbVB+9Xs/rr7/O77//Lp8XooCS2kZkZCQPP/wwL7/8Mp988gkajYa4uDj+9a9/ERYWxvTp02sg6n9IIlWEzMxM3nzzTcLCwtBqtbRp04Y5c+ag1WpZtWoVS5YsQavV4urqyvvvvw9AVlYW06ZNIzQ0lNzcXP7zn//QtWtX8vLy+Oijjzh8+DAmk4nWrVszc+ZMHB0dGThwIKNGjWLXrl2kpKQwdepUjh49ypkzZ9Dr9XzxxRd4enoSFxfHnDlziImJwWAwMHLkSJ599tlCcb/44ouEhYUVOObj48Pnn39eLa9bXSBt4x+BgYHY2toSFRVV5z8Y60q7MJvNzJkzh/Pnz/Pzzz/j6up609ckNzeXhIQEGjRoUIFX9vZXF9qGi4sLvr6+/PHHH4wePRqA3377jdGjR7Ns2TIA6tevz/z583nllVfo0aMHZ8+e5ciRI6xevbqyX/LbTl1oI02bNqV9+/a89dZbfPnll1XzQtZC0jbg66+/ZtSoUYwfP95yzNPTkwULFjBo0CAmTZqEh4dHRV/q8lOikDVr1qjHH39cKaWU0WhUM2bMUFeuXFHnzp1T3bt3V9HR0UoppZYsWaJmzZqlDh48qFq1aqWOHz9uOT558mSllFILFy5U8+fPV2azWSml1Mcff6zefvttpZRSAwYMUHPnzlVKKbVhwwYVGBiozp07p5RS6vnnn1dffPGFUkqpSZMmqe3btyullMrJyVGTJk1SGzZsqNBz9Pf3V1evXi1w7OGHH1abNm2qULm1XV1oG9dERESojh07KqWUOnjwoBo5cmSB81u2bFG9evVSWVlZlVLf7ay2t4uDBw+qoUOHqldeeUX5+/urXbt2FTgfERGhAgMD1ZgxY9SoUaNUz5491bBhw9Qnn3yiMjIyyl1vbVAX2sbIkSPV5s2b1RNPPGE5PnLkSHX69GnLe8g18+bNU48//rgaNGiQJb66rq60kczMTDVkyBD1ww8/KKWUeuedd9Rnn32mlFLq9ddfV//3f/9X7jpqK2kbSo0aNUrt3LmzyPvHjx+vtm7dWu76K4P0SBWhS5cuLFiwgEmTJtGrVy8eeeQRmjRpwpIlS+jTpw8NGzYE4NFHHwXyx3j6+vrSoUMHIP+X+l9//RWAXbt2kZ6ezv79+wEwGAwFfqEdMmQIAL6+vri5uREYGAjkD6NKTU0lKyuLw4cPk5qayqeffgrk/9pw/vx5RowYUSBu6ZGqenW5bYSHh1uG5BiNRry8vFi8eLFlKE9dVhfaxeXLl+ncuTPvv/8+b7zxBqtXr7Y8L6DA0L49e/bw6quv0rt3bxwcHMrxitYedaFtAAwYMIDZs2dz9epVrly5gp+fH87OzoWue+WVVxg5ciT33XefJb66rq60EXt7ez7++GMeeeQRunXrVu7Xqy6RtpHPaDQWeV9eXh4ajeYmr171kESqCL6+vmzbto1Dhw5x8OBBHnvsMWbOnIlOpyvwD5aTk0NUVBQAVlZWluMajQalFJA/FOatt96iX79+QH43bW5uruVaa2try9/Xl3GN2WxGKcWyZcssX1iTkpKwsbEpdO1nn31WkactSqG2t40jR47g6emJr68vSin0+n/eIm6cIyX+UdvbBeQPv5g7dy4AR48eZerUqfz8888F4rnmzjvv5LHHHuOVV15h06ZNODk5lbqe2qYutI1rdQ8ZMoT169cTEhJSYBjOjdd5eXnRuHHjMpVfm9WVNgLQtm1bnnvuOaZPn0779u3LfH9dI20DOnXqxKFDh7jrrrsKXB8XF0dkZCTt2rUrc12VSVbtK8LPP//Mm2++SZ8+fXj11Vfp06cPFy9epHv37hw4cID4+HgAli1bxocfflhsWX369OGnn34iLy8Ps9nMrFmzyjTx2tHRkY4dO7JkyRIA0tLSLBP8RfWr7W1j9erVll+vzp49i6+vb7nLqktqe7uAgh+sM2bMwGQyMWfOnJte//jjj1OvXr06/wNPXWgb14wbN441a9Zw+PBh7rzzzkopsy6oS20E4IknnsDNzY3ff/+90sqsraRtwHPPPcemTZtYs2aN5VhsbCyvvPIKEydOxNPTs9LqLw/pkSrCuHHj+OuvvxgxYgR2dnY0atSIyZMn4+zszKuvvsqTTz4J5K9UNHfuXK5cuXLTsp5//nnef/99xo8fj8lkolWrVrzxxhtliuejjz7i3XffZfTo0eTl5TFq1CjGjBlTkacoyqm2t43nnnuO1157jVGjRqHRaIr9oiz+UdvbxY1sbGz49NNPGT9+PO3ataN3796FrrGysmLWrFk8+eST3Hvvvfj7+1da/beTutQ2OnXqRHZ2NgMHDizQmy2KV5faCOT3krz//vuFylywYAGLFi2yPB4wYECdX/FT2gY0bNiQ5cuX89///pcvv/wSvV6PjY0N9913H/fff3+l1V1eGnWtz08IIYQQQgghRKnI0D4hhBBCCCGEKKMqTaQyMjIYNWoUkZGRAOzfv5/Ro0czZMgQFixYYLnu3Llz3HPPPQwdOpQZM2bcdHUOIYQQQgghhLgVVFkideLECSZOnGgZr5mTk8Nbb73F4sWL2bhxI6dPn+bPP/8E4NVXX2XWrFls2bIFpRQrVqyoqrCEEEIIIYQQosKqLJFasWIFb7/9tmW34ZMnT9KkSRN8fX3R6/WMHj2azZs3ExUVRU5ODh07dgTg7rvvZvPmzVUVlhBCCCGEEEJUWJUtq/Pee+8VeBwfH4+7u7vlsYeHB3FxcYWOu7u7ExcXV2SZQUFBVROsqHFdunSpcBnSPmovaR+iONI+RHGkfYibkbYhilOa9lFt65MWtTjg9RuF3Xj8Ziqj0UN+w6+ssqqLxFwyaR8Sc3GkfUjMxZH2ITEXp7K+dMvrXD2qM25576i7MVfbqn2enp4kJiZaHsfHx+Ph4VHoeEJCgmU4oBBCCCGEEELciqotkerQoQOXL18mLCwMk8nE+vXr6du3L97e3tjY2Fi6Rn/77Tf69u1bXWEJIYQQQgghRJlV29A+Gxsb5s+fz9SpU8nNzaVfv34MGzYMyN8peebMmWRmZtK6dWsmT55cXWEJIYQQQgghRJlVeSK1Y8cOy989e/bk999/L3RNYGAgq1atqupQhBBCCCGEEKJSVNvQPiGEEEIIIYSoLSSREkIIIYQQQogykkRKCCGEEEIIIcpIEikhhBBCCCGEKCNJpIQQQgghhBCijCSREkIIIYQQQogykkRKCCGEEEIIIcpIEikhhBBCCCGEKCNJpIQQQgghhBCijCSREkIIIYQQQogykkRKCCGEEEIIIcpIEikhhBBCCCGEKCNJpIQQQgghhBCijPTVXeHKlSv58ccfLY8jIyMZO3Ys2dnZBAUFYWdnB8CUKVMYPHhwdYcnhBBCCCGEECWq9kTq3nvv5d577wXg4sWLvPDCC0yZMoVHHnmEH3/8EQ8Pj+oOSQghhBBCCCHKpEaH9s2ePZtp06Zha2tLdHQ0s2bNYvTo0Xz22WeYzeaaDE0IIYQQQgghbqrGEqn9+/eTk5PD8OHDuXr1Kj169GDu3LmsWLGCI0eOsGrVqpoKTQghhBBCCCGKpVFKqZqo+MUXX2TIkCGMGjWq0Llt27bx22+/8fnnnxc4HhQUVF3hiWrWpUuXCpch7aP2kvYhiiPtQxRH2oe4GWkbojilaR/VPkcKIC8vj8OHDzN//nwAgoODuXLlCkOHDgVAKYVeX3RoldHoIb/hV1ZZ1UViLpm0D4m5ONI+JObiSPuQmItTWV+65XWuHtUZt7x31N2YyzW075lnnqlQpcHBwTRt2hR7e3sgP3GaO3cuqampGAwGli9fLiv2CSGEEEIIIW5Z5eqRuv/++ytUaUREBF5eXpbHgYGBPP3000ycOBGj0XjTIX9CCCGEEEIIcSsocyJlMBgYOHBghSodMWIEI0aMKHDsoYce4qGHHqpQuUIIIYQQQghRHUoc2nfkyBEWL15MXl4e48ePp2vXrmzcuLE6YhNCCCGEEEKIW1KJidSHH35Ix44d+eOPP3Bzc2PDhg3873//q47YhBBCCCGEEOKWVGIiZTKZ6NWrF/v37+euu+7Cx8dHNssVQgghhBBC1GklJlJms5mTJ0+ya9cuevfuzYULFzAYDNURmxBCCCGEEELckkpcbOLZZ59l+vTpTJgwAR8fHwYOHMiMGTOqIzYhhBBCCCGEuCWVmEh5eXmxbds2y+Nt27Zx6NChKg1KCCGEEEIIIW5lN02kzp49i1KK119/nY8//hilFABGo5GZM2eyY8eOagtSCCGEEEIIIW4lN02kfvnlF/bt20d8fDxTpkz55wa9nqFDh1ZLcEIIIYQQQghxK7ppIvXuu+8CMGPGDN57771qC0gIIYQQQgghbnUlrtoXFBRUHXEIIYQQQgghxG2jxETK29ubo0ePyt5RQgghhBBCCPG3Elftu3TpEg8++CB6vR5ra2uUUmg0Go4ePVod8QkhhBBCCCHELafEROqnn36qjjiEEEIIIYQQ4rZRqqF9p06dYsWKFdSvX59jx47h7e1dHbEJIYQQQghRIUk5ecRn5dZ0GKIWKrFH6uuvv2bfvn3Exsby6KOPsmjRIsLCwnjhhRfKXenkyZO5evUqen1+9XPmzCE8PJwvvvgCg8HAo48+ykMPPVTu8oUQQgghhDApxdQ/T+LrZMf8Xm1qOhxRy5TYI7Vhwwa++eYb7OzscHV1ZcWKFaxfv77cFSqlCA0NZe3atZb/vLy8WLBgAT///DNr165l+fLlhISElLsOIYQQQgghdkYmEJqWxV2+HjUdiqiFSuyRurbIxDX16tWz9CSVR2hoKBqNhqeeeoqrV69y33334eDgQI8ePXBxcQFg6NChbN68ucBGwEIIIYQQQpSWWSn+dzacZvXsGejjVtPhiFpIo5RSxV0wZcoUJkyYwKeffsry5cv59ttvOXHiBF9++WW5Kjx27Bi//PILs2fPJicnh8mTJzN8+HDy8vKYNm0aACtXruTkyZOWTYGvkT2taq8uXbpUuAxpH7WXtA9RHGkfojjSPuquoxlG/hefy2MeNnRxLNwJIG1DFKc07aPErqVZs2bx2muvERwcTMeOHenQoQMfffRRuYPq1KkTnTp1AsDe3p4JEyYwb948nn322QLXaTSaIu+vjEYP+Q2/ssqqLhJzyaR9SMzFkfYhMRdH2ofEXJzK+tItr3P1OHzkCDtzdDR1sueJvl3Q3eR7ZWWQ9466G3OJiZSnpyeLFy9Gq9ViMpnIzc2lQYMG5a7wyJEjGAwGevbsCeTPmfL29iYxMdFyTXx8PB4eMpZVCCGEEEKU3fFME5fTcpnTPbBKkyhRt5W42MTGjRsZP348dnZ2JCQkMGrUKHbs2FHuCtPT0/nggw/Izc0lIyODNWvW8OGHH3LgwAGSkpLIzs5m69at9O3bt9x1CFHXHbsaxtDNH7Pkwh7yTMYqr89gNlV5HUIIIURpmJViU3IeTZ3sGejrXtPhiFqsxETqyy+/5PvvvwegWbNmrF69moULF5a7wgEDBtCvXz/GjRvHPffcwz333EOXLl2YNm0akydPZty4cYwaNYr27duXuw4h6rp14SdIyc1i8bmdTNz5JQfjL1VZXUGJV+i/YT5X0hNLvlgIIYSoYjsjE4kxKB5r3Vh6o0SVKnFon9lsxsvLy/K4YcOGmM3mClX68ssv8/LLLxc4Nnr0aEaPHl2hcoUQYFJm9sReYLBPG0b4tOejU5uZeuAnBjZsxb/aDcPdzqlS6/sj6ix5ZhPbo8/yRID0JAshhKg5+Sv1heFppWGQ9EaJKlZij1T9+vVZtmwZRqMRk8nEqlWrcHOTJSSFuFWdTIogJS+L/l6B9PJswbIBz/Jc4AD2xV3knWNrK72+gwn5vV1/xgRXetlCCCFEWZxMTCM0LYuhLlbSGyWqXImJ1Jw5c1ixYgUdOnSgffv2rFixgtmzZ1dDaEKI8tgVE4yVVkdPj+YAWOv0PB5wJ4/49+ZQQijRWSmVVldkZhKRmcn4OLhyLjWG2OzUSitbCCGEKKszSWkAtLIv/56nQpRWiYlU06ZNWb16Nfv37+fQoUOsWLECX1/f6ohNCFFGSin+jAnmDrdmOFjZFDg3yrcDGmB9+PFKq+9gfCgAr7QdCsBu6ZUSQghRg84lZeBlb4OTTnqjRNUrMZG6xtnZGUdHx6qMRYg670xyFAtObyHbmFeu+y+lxxOVlUy/hgGFzjW0d6Gbux/rwk9gLn4f7lI7GH+JRvYu9PFsSTNHN3ZJIiWEEKIGnUtOp1X9yp0LLMTNlDqREkJUrd/DjvHU3u/4+dIhfrp0sFxl/BkTjAbo61U4kQIY06QTsdmpHE64XIFI8xnNJo4kXqaHR3M0Gg19GwZw9OoV0vKyK1y2EEIIUVYpuQaiM3No7SqJlKgekkgJUcOMZhMfnNzEu8fX0alBY3p5tOD7i/tIzMkoc1l/xgTT1tUbN9uie4/7eQVQz8qWdZUwvO9kUiSZxjx6uOfPxervFYBJKfbFhVS4bCGEEKKsziWnA0iPlKg2kkgJUYOu5mTw/P4fWHn5MA+36MmnPR7ilXZDyTOb+Cb4zzKVlWTM5lxqDP0aBt70GhudnmE+7dgZc67CPUcH4y+h02i4w70pAK1dvXGzceTP2PPlKu9oYhj37/iCVOnREkIIUQ5nk9LRAIGuMhVFVI9yJVLjx4+v7DiEqJNePvgLZ1Oi+U+Xu3mpzWD0Wi1NHBswoWlXfrtylNC0hFKXdSInDsjvdSrOmMYdyTOb2Bx5ukKxH4y/RFtXHxytbAHQajT0axjA/rgQck3GMpWVazLy3vF15JmN2OmsKhSXEEKIuulcUjpNnOxxsJIV+0T1KFci9e6771Z2HELUOREZSZxPjeGFVoMY6tO2wLknA/pip7dm4dk/Sl3eiew4mjq60dSp+H3eAlwaEuDsVaHhfcm5mZxPjaHH30usX9PXK4Bsk4HDCaFlKm/pxb2EZybxRvsRWOvkA1AIIUTZKKU4l5ROq/rSGyWqT7kSqbZt25Z8kRCiWHvjLgJwp5d/oXMuNvY87t+HvXEXS7UwRFpeNhdyr9K3iLKKMqZxR86nxhCcGlu2oP92KCEUBZa9qq7p6tYUB701u2JLv3rflfREvru4j6E+bel+Q3lCCCFEacRn55KUa5D5UaJa3fSn3zfffPOmN2k0GubOnVslAQlRV+yLu0hTRzd8HFyLPH+/X3dWXj7Cp2e28X2/p9AWs0P7vrgQzCj6FzM/6npDfdrx3zPbWBd+nIB2w8oc+8H4Szhb2RHo0rDAcWudnl6eLdkTewGTMqPTFP9bjVKK+Sc3YKuzYlqbIWWOQwghhID8/aMAWbFPVKubJlItW7YsdCw5OZmlS5fi7e1dpUEJUdtlGnIJSrzC/X7dbnqNjU7PC60HMitoDf8OWkODm6zEB3A44TL1tDa0cS3d/5vO1nb0bxjI5ohTjGvSiRb1PEsdu1KKg/GhdPPwKzJR6u8VwLaoM5xOjqJD/eI37z6YFUVQchhvdhhZ7PMTQgghinM2OR2dRkMLF/ksEdXnponU448/XuDx/v37ef311xk9ejQzZ86s8sCEqM3+SriMUZnp41n4B4vrDfFuy/boc+yJvVBimQMdmxTba3WjB/26szsmmIk7v6Kdqzfjm3ZhcKM22OqLX+zhYlocV3MzLMue36iXZwv0Gi07o88Vm0il5GaxKvUs7ev7MK5J51LHLYQQQtzobFI6LZwdsNHJgtSi+pQ4q9toNPLxxx+zZs0aZs+ezbBhZR8GJIQoaF/cRRz0NnRs0LjY67QaDR92u69UZQYFBZUphrb1fVg/9GU2RpxkzZWjzDn2O5+c2sK9ze7gMf8+2Omti7zvYPwlAHp4+BV53tHKlj5e/vwWdoxHWvbG1cahyOs+O/sH2WYjb3YYVaYEUAghhLieWSnOJ6UzpLFHTYci6phiE6mwsDCmTZuGvb09a9asoWHDhsVdXmqLFi1i06ZNAPTr14/XXnuNN998k6CgIOzs7ACYMmUKgwcPrpT6hLiVKKXYF3eRHh5+6LW6Go3FxdqeB5v3YKJfd45dDWfl5cMsubiXjZEneaXtUAY0DETzd5JjNJv4MzaYNWFHae7kgYddvZuW+3yrAUyMDeb/gnfzavvhhc4HJV5hXfhxhjo1p0U9+eATQghRfhEZ2WQaTbLQhKh2N02kVq1axQcffMBjjz3Gc889V2kV7t+/n71797JmzRo0Gg1PPvkk27Zt4/Tp0/z44494eMiXKlG7BafGkpibQe8ShvVVJ41GQ2e3JnR2a8L9V8P54OQmXj+8kh7ufjzq34cD8ZdYF36cpNxMvOycmdZ2QLHlNXNyZ1yTzvx6JYj7/LrRxLGB5Vyeycj8ExtpZO/CSKdb5zUQQghxezqXlA4giZSodjdNpGbOnIlWq+Xrr7/mm2++sRxXSqHRaDh69Gi5KnR3d+eNN97A2jp/2FDz5s2Jjo4mOjqaWbNmER0dzeDBg5kyZQparYxzFbXPtWXPe3m0qOFIitaxQWO+7/cUqy4f5svzu3h23/do0dDHqyV3N+1CD4/mJa7GB/BUQD82RZxi0dntBYYnfh+ynysZiXza40GsI1Or8qkIIYSoA84lpWOr09LUyb6mQxF1jEYppYo6ERUVVeyNlbFy35UrV3jggQf4+eef+fjjj5kzZw729vY888wzjBo1ivvuKzg3pKxzQMTto0uXLhUu43ZpH+/H78OsFG969qnpUEqUasrhTE4CrWzccNXblfn+jWkX+T3tAtPde9LSpj5xhkzejdtNRztPnmxQ+gUm6lL7EGUn7UMUR9pH7fdRVDY6DUxrVLbPKWkbojilah+qhly4cEENGDBArV69utC5rVu3queff77Q8SNHjlRa/ZVZVnWRmKuvrqqKOyknQ93x2zvq63O7Kr3sW7F9ZBvy1IjNn6jJu75RJrNZPbf3e9Vv/XyVkJ2mlJL2UZ0k5uqrS17r6nE7tg95nSufwWRSfVftUZ8eDylwvLrilveOuh1zjYydCwoK4tFHH2X69OmMHz+e4OBgtmzZcn1yh15f4oKCQtx29seFoIA+XnVjbpCt3ornWg3kbEo0bx1ZxeHEy0xpPQg3WxnHLoQQouIupWaRZzbTSjbiFTWg2hOpmJgYXnjhBT766CNGjhwJ5CdOc+fOJTU1FYPBwPLly2XFPlEr7Y27SAMbRwKcK2cFzNvBcN92+Dt7sT36HG1dvbm7acWHUgghhBAA55JloQlRc6q92+fbb78lNzeX+fPnW4498MADPP3000ycOBGj0ciQIUMYNWpUdYcmRJUymk0cjL/EwEat6tS+STqNln+1G8rc4xt4S/aMEkIIUYnOJaVTz1qPt4NtTYci6qBqT6RmzpzJzJkzizz30EMPVXM0QlSf41fDyTDm3lLLnleXTg2asHLQ8zUdhhBCiFrEaFbsi0mio5uzZc9DIaqTrC8uRDVQSvHthT04W9vRw6N5TYcjhBBC3PYOxiZxNSePkU09azoUcQs4EpvNrL1xZBrM1VanJFJCVIP98SEcSbzCUwH9sNdb13Q4QtxSDsVH8szudcRnZ9Z0KEKI28i6y7G42ljRq2H9mg5F1LC/YrJ4dXcsOyIy2Xg5vdrqlURKiCpmNJv59MwfNHaoLwstiBphMJtQRW8ZeEv46mwQx67G8urBreQYjTUdjhDiFmFWCqO56PeupJw89sUkMbyJJ3qtfJ29nRhMCnMlfiYdjs3i9T1xNHaywt/VmjUX06rtM09anhBVbF34cS6nJ/BC60FYaXU1HY6oZVJyczCpmw9jyDDkMXrzL2xKi63GqErvfEoip5Pj6duwCedTEnn36J+3dNInhKgeRrOZqX+eZMqfJzAV8Z6wOSwek1KMbuZVA9GJ8so1mnl4YwQfHU4s031mpYjJMBRKrA/HZvHa7vwk6tOBDbk/wJmwNANH43IqM+ybkkRKVJnEnCRC88LJNlVPY74VZRnz+PL8TjrU92VAw8CaDkfUMqsvn2PEpp9YdPqvm16z9sp5knKz2Z4eT5bRUI3Rlc6q0LPY6vS83bkfz7e5g21RoXwbfKymwxJC1LAl58I5mpDKicQ0fg2JLnBOKcX6K7G0bVCPpvXsayhCUR4rL6QRmWFkfWg6keml+0zKyDPzxu44JqyLYPDKKzy1NYqPDify49kUXtsdh69jfhLlYqNjQGMHnK21/HoxtYqfST5JpESlM5gNfBeynDE7H2He1S+4c9M47vvzad4+/iG/hm3AaDbVdIjV5oeQ/STlZvJym8GyopAos2OJMYSlpxQ6nmcyMe/YHuYf34udXs+q0LMk5mQVus5oNrPs0mka2juSZTaxLiy4UuNLyc3haGJMue9Py8tlS2QIw3xb4GRtw+SWHRjh25KvzwXxR1RoJUYqKltcVhbBKSk1HYaopU4kpvLd2XCGN/Ggh5crX52+QnxWruX8maR0LqdlMVoWmbitJOeY+P5sMh3dbdFpNSw9k1ziPWFpeTy1NYqDMVk80saFe/zrYaPTsDUsnS9OJBVIogBsdFpGNXdib1QW8VlVP1RcEilRqQ4lHuP+3c/y2flv6e7WmWdcHuSJlhPxsvNgf8IR5p1aSHBaSE2HWS3is9P4IWQ/g73b0La+T02HI24zYekpPLtnPff+sZLn9qxnW+Ql8kwmEnOyeG7vetZcOc+j/h1Z0n8cRmXm+wsnCpXxR1QocdmZ/Kt9L/ysHfgl5DRGc+WsZnQ2OYGHd67m2T3r+TP6yk2vS8zJYmvkpSKH660Pv0CuycSEZq0B0Gg0vNmpD+3qe/BO0C72ZiQSmpZc7NBFUf2upKXz6I6dPLVzF3FZhRP4sghNS2NHVFSxwzlD09LYHB5OhuHW61EVlS/DYGT2ofM0dLBleqcWvNq5BSalWHD8kuWa9ZdjsdVpGeTrXoORiqLsjcokNDWvyHNLTieTY1S8eocb41o4seVKRrG9UnsiM3lySxTpeWY+G9iQp9vXZ0qnBiwa1IjN9zRlxWhfvh3qjattwWkT41rUw6xgbUhapT63olT7PlKi9lFKcTb1At9fWsm2mN342Dfkv3fMoa9nD4KCgugS0MVyXY45Fztd3dg076vzuzCZzTzfamBNhyJuQz+HnMJKq2Oyfwc2hF9gxuEduFjbotVoyDYamNdtEIO8/QAY4duSNZfPMdm/A262+cNclFL8dPEkTR1d6O3VmAv1QvgqMZRdMVe46+/7ymtD+AXmHdtLfRs7WtSrz3+O7aZNfQ9L3ddkGvKYum8jl9KSiclK5xH/jpZzZqX4NfQs7et74u/SwHLcRqfng+6DeXr3On5KCuen7eHY6fT4uzSgj1djJrfsIL27NehKWjrP7d6NAhTw1dmz/Ltr13KVdS45mal79pJuMDCySWNe79QJG13BL0Qbw8KYd/QYeWYztjodQ3x9GN+sGa1cXSv+ZMQt6cOjF0nIzuXLAR1xsNLjYKXnidZNWHzqMnuir9LVw4VtEQkM9HHHwUq+xt5Klp1PYeGxJByttPx3gBetGvzzfS8sLY/fQtIY07weTZ2teaiVC7+FpLP0TDIzengUKEcpxdIzKXxzKpnA+jbM7eOJp0PBf2utRoO3o1WRcTRytKJnI3vWXUrn0TauWOmq7jNDeqTquLS8dD48vZgLaZdKvvgGmcYsVoWt56E9LzBp71T2xB/iWf/JrOz3DX09exS6XqPR1Jkk6mRSBL+HH+d+v274OMgHviibpNxsNoZfZGTjljzdqgtrhjzAp72G0bGBF76O9fi231hLEgXwWECnQr1SRxKjCU69yoMt26HVaGhv54yvQz1+vHiy0K//BrOJhacPsTH8YrE9A0azmU9OHuCdoD9pV9+DpQPGMa/bIHJMRt4J2lVgFSaTMvPvIzu5kp5CxwZeLD5zmN0xYZbzf8VHEZGZxgS/1oXqaWBrz/K77mWmVyv+3bkfo5sEoBTsj40o1+spKseVtHSe35OfRC3ueyf3t2jBxrDwcg3xu5ZEOVpZ8WDLlmwIC+fZP3dberiMZjOfnDjBO0eCaNugPovu7MMQXx+2RkTy2M5dTNq+g6/jE3j9wEHLf79cvFi5T1hUmRyTie/Ph/NB0EXWhsYQnJyOwWxmS1gcW8MTeLx1E9o2qGe5fqK/N3717PnkWAgbr8SRZTQxqpkM67uVXEui7vS2p561lpd3xnLu6j9z5L88kYSNTsMT7fK/E7nZ6RnbvHCvlFKKr08m882pZIY0cWTxoIaFkqjSuLtlPa7mmNgdVbXbakgqX4cppXjv1Kdsi9nNmvBNvN1xOkMb9S903eHE4yy5tJwsY3aBe0PSL5NtyqGlkx9vtJ3CcO9BOFk5VOMzuDUZzSbmHt+Ap109ng7sX9PhiNvQqtCz5JpNPNiiHZD/y1tPT196evoWeb2PYz2G39Ar9dPFU9S3sWO4bwtLGRNbtOODE/s4fjWWTm4Ngfwk6o1Df7AnNhyANVfO8VqH3rR0/qeXyGg2sycmjB8unuR0cjwPNG/Li227o9dqcbWx45V2PZl3fC+/hJzioZbtAVh85jB7YsP5V/tejGkawLN71jPr8A7+r98YWjo3YNXls7ha2zKwUbMin5Neq8Xb2o4uTfwZ1cS/cl5YUW5h6flJlFnlJ1F+9erxSIA/v1+5wmcnT7Hozj6l7im8Pon6om9fGjrY07FBA2YfOcIjO3byZudOLA+5RFBCAve3aM6L7dqh12q5w8ODl9q3Z0t4BJsjIojPyiI9I8NSrqe9XVU9fVGJ9sVcZcGxS0Rl5mCv17EmNH+epZU2v/10cKvHI60aF7hHr9XyepeWPLPzBAuOX8LH0ZaObs7VHrso2rUkaoCvA7N7eZCYbWLq9mhe3hnLfwc0JNdkZndkFk+3dy0wDO/h1i6svZTO92eSeauHB0opvjmVzPdnUxjT3IlX73BDW84RCN0b2tHIQc/qC2kMauxYWU+1EEmk6rD1kdvYFrObyX73cjLlHG8encu5lItMbfU4Oo2O+OxEFpz7mi3Ru/Cy86CpQ8F5PsO8BzDOdxhtXQJlqM11fr50kEvp8XzU7X7ZfFeUWY7RyKrQs/T1akITJ5dS3/dYQEc2RVzkhwsnGNM0gP1xETzTqgs2un/e5kc19uerc0f46eIpOrk1xGA28eah7ZaEx1anZ9GZv5i8cw33+rVhTJMAtkZeYl1YMFdzs/Gwc+CdLv0Z3rhlgbrHNQ1kX1wEi88e5g53by6kXuWHiye5u1kr7vVrjUaj4cPug3ls11r+dXAr87vdxd6YcCb7d8BaJ1sC3OrOJiXxrwMHCiRRAE7W1jzZqhUfnzjB/tg4ejf8Zxlqo9nMl2fOcjg+nhbOzgS6utDK1RWzUryybz+OVlYs7nsnDR3yh4P2827Et079ee3AQV47cBBrrZa3u3ZhRJMmBWJxtLLinuZ+3NPcL3/oeBfZm+92EZ2ZzX+Ph7In+ipNnexZ2K8dXdxdiMrM4XxyBueT04nOzOHFDn7oivhO0d7NmbF+XqwNjWVUUy/53nGL2Jlsw5oLSfT/O4nSazV4OehZOKjR38lUDG52OjzsddwfUDD5vdYrtfpiGpPbuLLxcjpLz6QwuoJJFOT/eDiuZT0WH08iNCUPP5eq+T4midRtICUvjbePf8jVlKt8ZWyFg77iS32GZ0bx/unP6VK/PVNbPY5ZmfnozBd8H7qSC2mX6ObWmf+7+BNGZeQZ/0k80vw+bHU2lfBsareozGS+Dv6T/l4B9GsYUNPhiNvQhogLpOTl8FDLdmW6z9fRmeG+LVl9+RzhGanY6HTc06zgsDlbvZ57/drwf+ePEpKaxJdnj7A7NoxXO/TiXr82APRr1IQvzx5h+aXTLLt0Gi0aenn5Mr5pIL28fNFpCo8I12g0zOzUlwd3/Mqrh7aSmJNFV/dG/Kt9L8uXHXc7Bz7sMZin96zj6T3rABjfTLYEuNWtvxLG+8eO0cDWlk9697IkUdeM92vGikuXWHjqFN09PdBrtSTn5vLWoUMcTUikXf367IuNZX3YP8M6G9rbs7jvnTRyKDiCwa9ePZYM6M8PFy4wwNtb5kFVsyyjibf2nyXXZOLljs0JcHUqdM3ZpHQ+PX6J+Oxc/F0caVXfiQBXR5o42RGWns35pHTOJ2cQnJxBcm7BRQQMZjO2Oi0vtG/G/S29sfp7E10fRzt8HO24qxQLR7zQzo96VlaMb96wcp60KJM8kyIkJZfzSbkEJ+VxPimXkBR7+vs68M7fSdQ1+clUQ6Zuj+FKmoEZ3d2x1Rf+/HiolQtrQ9J5eWcMMZlGRjd34rUKJlHXjPJz4v9OJvPVySTe6+NZIL7KIonULe5C2iVeOTybhNwkTGYTUw69xaLucyuUTBnMRmYeex+dVse7nV5Dp9Gh0+h4s92LBNRrwfzTiziYeJQ+Ht15tc1z+Do0qsRnVHsppfjw1CZ0aJneflhNhyNuQyZl5ueLp2jj6k7HBmXfZPJar9S+uAju9WuNi03hOYkT/Frz/YUTPLX7dzKNhgJJFICztS2vd+zDmCYBnEyKo1/DpnjZlzwswsXGlre79GPqvk34OtRjXrdB6LUFPzRbubrz7879mHF4B3d6NaahfeEvauLWYDSb+fTkKVZcukRXd3fe694NF5vCP6ZZabW80LYNbxw8xLorYbRydeH1gwdJzsnl7a5dGdGkMUop4rKzOZ+cTERGJkN8ffC0L/ozzMnamufbtq3qpydukGU0MX3PaU4mpuJkrefxP44xvnkjnm6b3yOYmmvgi9OX+T00lvq21nR0c+ZCSga7o68WKsvX0Y72bvXwsLfh+q+tNjotY5o1xMO+/D/KOlnreb590cOBRdU6lZDDjH1xXM3O38LGxUZLgKsNgW7ZvNqrWZFJipeDFZ/f1YiDMVkMbVr054i7vZ6xLZxYeSGtUpMoAGcbHc92qM9nx67y7oF4ZvX0qPRk6pZKpNatW8cXX3yBwWDg0Ucf5aGHHqrpkKpFfHYiqYZ0mjk2Rq/9Z5jLluhdvHP8Y+pZO/Ftz4/Zf+YQ36T8wtRDM1jY/b1yJ1P/d/EnTqec5/3OM/GyK7hSyt1NRtDGJYCUvFS6u3eu0POqa7ZHn2NfXAjT2g7By07Gbouy2x0TRkRmGnPbDCrXsBVfR2eG+bZgU3gIE5sX3aNV38aOkY1bsubKef7VvmASdb1Wru60ci3b0sLdPXxY1HsETZ1ccLYuemGZwT7NcbWxw6+e9DbcqhKzs5n112GOJiYysUULprRrWygpvl7/Ro1o36ABi0+fJtdkwsXGhq/697P0KGk0Grzs7fG6SfIkalb2dUnU7O6B9PCqz9dnrrA6JJodkQl0sYXDmw+TYTByf0tvnmzTxLJaXobBSHByBhEZ2TR2tMPf1RFHWUmv1lkbksYnQYl42uv5T28PWjWwwdNej0ajISgoutjkxMNez5jm9W56HuCZDvXp4mlHb2/7Skuirrk/0BmjWbH4RBIaTTwzb1ghsKJumdYeFxfHggULWL16NdbW1jzwwAN0796dFi1a1HRoVcJoNrEv4S9+DdvI/vjDmDFjo7XGv15zWjm3xKRM/Bq+gY712/JB55m42dYn1y4TP7+3ePPY3BKTKZMyEZYRxaX0KxjVPxuSpeal8e3FXxjtM4TBjfoWeW+Ac/Mqec61lcFsIiQtjo9PbSbQuSH3NetW0yGJ29RPF0/RyN6J/g2blruMf7XvxX1+bfBxvPkH1yvtezK+WSsCXdzKXc/NdPPwLvGaru7Sy32rMSvF4fh41oRe5s/oaHQaDbPv6Mrwxo1LvFej0fBS+3Y8tXMXHd3ceK97N+rb1o0VWm9FRrPiSlomkZk5NHGyo7GTfZFzjiA/iXrluiRqcOP8L5nTO7VgdDMvPjwawh9X0+joVo/pnVvSwrngcExHKz1dPFzo4uFS1U9LlEKuyUxoSh4J2Sb8nK3xdtSX+KOcUorEbBMXk/Owt9Lg72qDvVX+Dyd5JsV/gxJZeymdHg3teLuXB/WsK39eq51ey50+VbdY2UOtXVDAFyeSgHhGVOJ0qVsmkdq/fz89evTAxcUFgKFDh7J582amTJly03sMZgMbIreTbsi46TU3E5kRydlLl8sbboWk5KWyMWo7cTmJuNnU57EW99PMsTHn00I4m3KB9ZHbyDJlc2+TUfyrzXNYaf9ZJ/+uRn1RwFvH5vLCobcY5NXHck6hiM9J5FzqRYJTL5Flyi6idmjq4MNrbZ+v6qdZ4zINuWyIOEmeuew7W0emR3IupOgN5SD/jScyK5lzKTGEpMVhMJuw0ur4uPsDxf5yK24diTlZbIu8VGDJ7tKKTIvj/MWTlRpPmiGXk0lxTG/fs0JtyMHKusSeJBudvkqSqNokPD2DPTEx5bo3MjWN8xdun6W4MwwG1kVFkxAWTj1ra+5t3py7/fxo7FT6la7a1q/P6mHDcLezrRPvgWeT0jmekFrm+yJTDAQHR1Z6PApFbFYu55PSuZiaSa7pn02s7XRa/F0dCXB1wsPOGs11A+52RydyKjGNt69Loq7xd3HkqwEd2HDwCKN6yP5tZfFXTBaXUm7+HeJmIpNsuHAupUz3mIGIdAPBSblcSsnDdN1HmpN1/vC7gPrWuNoUTIAyDGYuJOcRnJTL1RyT5bgGaFLPioD6NkSkGzh7NZeHW7nwdHtXdFUwx6i6PPx3MvXliSRiHRy4dMPr3NfX4ab7UhVHo4rbNKQaffXVV2RlZTFt2jQAVq5cycmTJ3n33Xct1wQFBdVUeKIaVHT1JWkftZu0D1EcaR+iONI+xM1I2xDFKal93DI9UkXlczf++iHLnIriSPsQxZH2IYoj7eP/27vz8Crqs//j7zlbFhKSAEmIrIKUqJSloCgKuCNCpChYlkdEFKQitrFFQMJTREWquFStVK310YpUUEFFRFF/WAUEjIobKCp7QsKWkPVs8/39ETkaIYGE5GTh87our8sz58zMJ+FOcu4zM/dIZVQfUhHVxsmt3hx/T05OZt++faHHubm5JCXV7AVhIiIiIiIiNaHeNFJ9+vRh7dq1HDhwgJKSEt5++2369Tv6MAQREREREZG6VG9O7UtOTiY9PZ0xY8bg9/sZNmwYXbt2retYIiIiIiIiR6g3wyZERERERBqTxx57jDfffBOA/v37c/vtt7NmzRruvfdevF4vAwcODA1a27RpExkZGRQWFtKrVy/uvPNOXC4XWVlZTJkyhf3793Pqqacyb948mjSpvXHhVcl82NSpU+nduzdXXXUVQL3O/M477/Doo49ijKF169bce++9xMXFVStzvTm1T0RERESksVizZg0ffvghS5YsYenSpXz11VcsW7aMO+64g8cff5zly5fz5Zdf8v777wMwZcoUZs6cyVtvvYUxhkWLFgFw5513MmrUKFasWEGXLl14/PHH603mnJwcJk6cyIoVK8ptp75mLiwsZNasWTz55JO89tprdO7cmUcffbTamdVIiYiIiIjUsMTERKZNm4bH48HtdtOxY0e2bdtGu3btaNOmDS6Xi7S0NFasWMHu3bspLS2le/fuAFx11VWsWLECv9/Phg0bGDBgQLnl9SEzwOuvv87FF1/MwIEDQ9uoz5n9fj+zZs0iOTkZgM6dO5OdnV3tzGqkRERERERqWKdOnUKN0bZt21i+fDmWZZGY+NNN05OSksjJySE3N7fc8sTERHJycjh48CAxMTG4XK5yy+tDZoAbb7yR4cOHl9tGfc6ckJDAJZdcAkBpaSlPPvkkl1xySbUzq5ESEREREaklW7ZsYdy4cUydOpW2bdse8bxlWRXeT/V47rNaG44nc0UaQuaCggLGjx9PamoqQ4cOrXZmNVIiIiIiIrUgMzOTsWPH8qc//YmhQ4dWeN/UXy7fu3cvSUlJNGvWjMLCQoLBYLnl9SFzRep75tzcXEaNGkVqair33HPPCWVWIyUiIiIiUsOys7OZNGkS8+bNY9CgQQB069aNrVu3sn37doLBIMuWLaNfv360atWKiIgIMjMzAVi6dCn9+vXD7XbTq1cvli9fXm55fchckfqcORgMMnHiRAYOHMiMGTNCR52qm1njz0VEREREatjdd9/Nyy+/XO40sxEjRtC+ffvQWO7+/fszffp0LMti8+bNZGRkUFRUxBlnnMG9996Lx+Nh9+7dTJs2jf3795OSksKDDz5IXFxcvch82LRp0zj77LND48/ra+Z33nmHyZMn07lz59Bru3Tpwj333FOtzGqkREREREREqkin9omIiIiIiFSRGikREREREZEqUiMlIiIiIiJSRWqkREREREREqkiNlIiIiIiISBWpkRIRERERaQBmz57NNddcE7pxLEAwGGTEiBE89NBDYc3yyiuvcMEFF3DDDTfU+LZ37tzJ5MmTa3y7NU2NlIiIiIhIAzB16lSKi4t54oknQsueeOIJnE4nt956a1izLF26lPT0dJ5++uka33ZWVhZbt26t8e3WNFddBxARERERkWOLiIjggQceYOTIkVx44YUYY3jhhRd4+eWXcTqdLF68mIULF2LbNvHx8cycOZOOHTuydetWZs+eTXFxMbm5uaSmpvLwww8TERFBly5duPjii9m8eTPz5s3j17/+dWh/BQUF3HnnnWzevBnLsujbty+33XYb9913H1988QW7du3i4MGDjB07tlzOl156iWeeeQaHw0FCQgJ//etfSUlJ4b333mP+/Pn4/X4iIyOZOnUqPXr04NFHH+Wzzz4jNzeXTp068cUXX5CTk8MNN9xQK41aTVEjJSIiIiLSQHTu3Jn09HRmzJiBbdvcc889JCcns379epYuXcqCBQuIioriww8/ZPLkySxfvpxFixbx29/+liFDhuD3+7nqqqtYtWoVAwYMwO/3c+GFF/K3v/3tiH3dfffdxMfH8/rrr+P3+/n973/Pv/71L+644w42bdrE6NGjufzyy8utc7ghW7JkCSkpKfzf//0f8+fPZ9y4cTz00EM899xzJCQksGXLFq6//nrefvttAHbv3s2yZctwuVysW7eOu+66q143UaBGSkRERESkQbn22mt566236NixI/379wdg1apVbN++nREjRoRel5+fT15eHlOmTGH16tU89dRTbNu2jdzcXIqLi0Ov69Wr11H389///peFCxdiWRYej4cRI0bw7LPPMmHChAqzrV27lvPPP5+UlBSA0NGqBQsWkJubW+7olWVZ7NixA4Du3bvjcjWs1qRhpRUREREREVq3bk3btm1Dj23bZsiQIUyZMiX0ODc3l7i4ONLT0wkGgwwcOJALLriA7OxsjDGhdaOjo4+6D9u2j3gcCAQqzeV0OrEsK/S4tLSU3bt3Y9s25557Lg8//HDouezsbJKSkli5cmWFGeozDZsQEREREWngzjvvPN544w1yc3MBWLhwIddddx0AH374IZMmTeKKK67Asiw2btxYbvJfRc4//3wWLFiAMQafz8eiRYvo06dPpev07t2btWvXhnL85z//4f777+ecc85h9erVfP/99wC8//77XHnllXi93iO24XQ68fv9Vfr664KOSJ2Aw+dvLlu2rK6jHLdVq1Yxf/58SkpKCAaDnHbaaUyfPp2WLVuya9cuLr30Un71q18dsd7ixYvxeDx1kLhhamy1ARAIBHj22Wd57bXXCAQClJSUkJqayp///Gc6dOhQx+kbloZUH4cOHeLaa68FoLi4mJycHE499VQA+vTpw+jRo4/4vVFcXEzLli2ZM2cObdq0qZPcDVlDqg+AXbt2kZaWxqefflpu+dNPP82WLVuYO3cujz76KAcPHuR///d/AfD5fPz5z39m3759zJ8/n7i4uLqI3iA1xvoAeOqpp1i2bBnGGGzbpm/fvqSnp+u9RxX07duX8ePHM27cOCzLIiYmhsceewzLskhPT2fSpEnExcURFRXFWWedFTqlrjIZGRncfffdpKWl4ff76du3LxMnTqx0nc6dOzNlyhRuvPFGABITE5kzZw7JycnMnj2b2267DWMMLpeL+fPnH/VIVKdOnXA6nQwbNozFixeTkZFBly5dGDlyZPW+ObXFSLV99NFHZtCgQXUd47jt2bPHnH322WbXrl2hZY8//rj53e9+Z4wxZufOnaZ79+51Fa9RaWy1YYwxt912m7n55pvNgQMHQstee+0107dvX1NYWBjWvA1dQ6uPw46W+2i/N2zbNrNnzzbp6enhjNdoNLT6qOhvxz//+U8zdepUY4wxjzzyiLnzzjuNMcYUFRWZcePGmUmTJpnS0tKwZm0MGmN9LF++3FxzzTWmpKTEGGNMaWmpmTBhgnnggQfCmlWkqnRE6meKioqYPn0627dvx+FwcOaZZzJ79mwcDsdRxzhC2Sev6enp/PDDD3i9Xu6++2569eqFz+dj3rx5bNiwgWAwyBlnnEFGRgYxMTFcdNFFDB48mFWrVpGXl8fkyZP55JNP+Oqrr0LdeXJyMjk5OcyePZvs7Gz8fj+DBg066qcAt956K9u3by+3rHXr1vz9738vt+zgwYP4/f5yFxded911nH766bXw3WxcTvba+PLLL1m7di3vvvsuUVFRodekpaWRn59PYWEhTZo0qbHvd0PT2OujqrxeL3v37qV58+YntJ3GQvXxk/z8fG666SZSU1P53//9XxwOXWGg+oC9e/cSDAYpLS0lMjKSiIgIZs6cyYEDB6r3TRUJl7ru5OqTJUuWmHHjxhljjAkEAmbGjBlm27ZtZtOmTaZ3794mKyvLGGPMM888Y2bOnGk++ugjc/rpp5vPPvsstHzMmDHGGGMeffRRM3fuXGPbtjHGmAceeMD85S9/McYYc+GFF5o5c+YYY4x54403TGpqqtm0aZMxxpibb77ZzJ8/3xhjzLXXXmveffddY0zZpzPXXnuteeONN07oa7z33nvNmWeeaQYOHGhmzJhhli1bZvx+vzGm7FOj1NRUc+WVV5b7b9asWSe0z8bgZK+NZ555xtxyyy0ntP3G7GSoD2MqPiJ1+PfG4MGDzbnnnmsuv/xy8+CDD+pI5Y8ae30c7xGpyZMnm7S0NNOjRw+zd+/eau+vsVF9GHPo0CFz/fXXmzPPPNNcc8015t577zXr16+v9j5FwkVHpH6mZ8+ePPTQQ1x77bX06dOH6667jnbt2vHMM88cdYzjunXraNOmDd26dQMgNTWVl19+GSi73qSgoIA1a9YA4Pf7y306e9lllwHQpk0bWrRoQWpqKgBt27YlPz+f4uJiNmzYQH5+fmiuf3FxMZs3b+aKK64ol7sqnwpNmzaNm266ifXr17Nhwwbuu+8+/v3vf7NgwQIAIiMjefXVV6v/TWykVBuUm8Dzww8/kJ6eDpTdrO/GG29k1KhRVf22NhonQ31U5ue/Nz744AOmTJnCeeedd1Ifpfy5xl4fFR1Vsm273HPvvPMOd955J59//jl/+MMfePbZZxvcqOPaoPqA2NhY/vWvf7Fz504++ugj1q9fz4QJExg1alRoAp1IfaTfYD/Tpk0bVq5cybp16/joo4+4/vrrycjIqHCMI4Db7Q4ttywrNErStm3uuOOO0Gz/oqKiclNJfn7x5M+3cZht2xhj+M9//hM6lerAgQNEREQc8dpHHnnkuL6+d999l7y8PK6++moGDBjAgAEDSE9P54ILLuDrr78mISHhuLZzMjrZa6NHjx7885//xO/343a76dChQ+iN87Rp0ygpKTmu/TRWjb0+qqJv375cf/313Hbbbbz55pvExsbW+D4amsZeH3FxcZSWluL1esttZ//+/cTHx4ceX3XVVQwfPpwrr7ySa665hrlz55KRkXFc+2jMVB9lgyZ69uzJb37zG9q0acPw4cP5+OOPGT9+vBopqdd0cvLPvPDCC0yfPp3zzz+fKVOmcP7557Nly5YKxzhW5vC4SJ/Ph23bzJw5kwcffPC4s8TExNC9e3eeeeYZoGxy1siRI3n33Xer/fU1adKEBx98kO+++y60bNeuXURERJS7D4Ec6WSvjW7dutG7d29uv/32cuesf/fdd2zatAmn01ntfTcGjb0+qmrcuHE0bdq0Vhq1hqix10eTJk3o2bMnzz77bGhZTk4OK1asCL2hh5/exEdERPC3v/2NJUuW8Nprr1V7v42F6qOsSXzggQfIy8sLvWbr1q2cccYZ1d6vSDjoiNTP/Pa3v2X9+vVcccUVREVFccoppzBmzBji4uKOOsZx27ZtFW7r5ptv5q9//StDhw4lGAxy+umnM23atCrlmTdvHnfddRdpaWn4fD4GDx7MlVdeWe2v75xzzmHmzJlMnTqVgoICnE4niYmJPP7448TFxVFQUEBpaSlDhgw5Yt25c+ee1EMpTvbaALj//vtZsGABEyZMIBAI4PP5iIuLY9SoUVx99dXV3ndj0Njro6rcbjczZ87kxhtvZPjw4Ue9pcLJ5GSoj3nz5jFnzhwGDRqEw+HA6XRy66230rt376O+vn379tx9991Mnz6dTp066e/LSV4fN998M5ZlMWLECCzLwrZtunTpUu7GrSL1kWXMz25rLCIiIiIiIsekU/tERERERESqqFYbqcLCQgYPHsyuXbsAWLNmDWlpaVx22WU89NBDoddt2rQpdJH7jBkzCAQCtRlLRERERETkhNRaI7Vx40ZGjhwZOpe3tLSUO+64g8cff5zly5fz5Zdf8v777wMwZcoUZs6cyVtvvYUxhkWLFtVWLBERERERkRNWa43UokWL+Mtf/kJSUhIAn3/+Oe3ataNNmza4XC7S0tJYsWIFu3fvprS0lO7duwNl41FXrFhRW7FEREREREROWK1N7bvnnnvKPc7NzSUxMTH0OCkpiZycnCOWJyYmkpOTc9RtZmZm1k5YqXM9e/Y84W2oPhov1YdURvUhlVF9SEVqojbk5Ba28edHGw7485vI/XJ5RWqq6DMzMxvcD5AyH5vqQ5kro/pQ5sqoPpS5MjXVkOn7HB4NNbc0LGGb2pecnMy+fftCj3Nzc0lKSjpi+d69e0OnA4qIiIiIiNRHYWukunXrxtatW9m+fTvBYJBly5bRr18/WrVqRUREROiw+dKlS+nXr1+4YomIiIiIiFRZ2E7ti4iIYO7cuUyePBmv10v//v25/PLLgbI7XmdkZFBUVMQZZ5zBmDFjwhVLRERERESkymq9kXrvvfdC/3/uuefy2muvHfGa1NRUXnrppdqOIiIiIiIiUiPCdmqfiIiIiIhIY6FGSkREREREpIrUSImIiIiIiFSRGikREREREZEqUiMlIiIiIiJSRWqkREREREREqkiNlIiIiIiISBWpkRIREREREakiNVIiIiIiIiJVpEZKRERERESkitRIiYiIiIiIVJEaKRERERERkSpSIyUiIiIiIlJFrnDvcPHixTz//POhx7t27WLIkCGUlJSQmZlJVFQUALfccguXXnppuOOJiIiIiIgcU9gbqeHDhzN8+HAAtmzZwqRJk7jlllu47rrreP7550lKSgp3JBERERERkSqp01P7Zs2aRXp6OpGRkWRlZTFz5kzS0tJ45JFHsG27LqOJiIiIiIhUqM4aqTVr1lBaWsrAgQPZv38/55xzDnPmzGHRokV8/PHHvPTSS3UVTUREREREpFKWMcbUxY5vvfVWLrvsMgYPHnzEcytXrmTp0qX8/e9/L7c8MzMzXPEkzHr27HnC21B9NF6qD6mM6kMqo/qQitREbcjJLezXSAH4fD42bNjA3LlzAfjmm2/Ytm0bAwYMAMAYg8t19Gg1VfSZmZkN7gdImY9N9aHMlVF9KHNlVB/KXJmaasj0fQ6PhppbGpZqndp30003ndBOv/nmG9q3b090dDRQ1jjNmTOH/Px8/H4/L774oib2iYiIiMgJC36zj+CXOXUdQxqhah2R+t3vfndCO925cyctW7YMPU5NTWXChAmMHDmSQCBQ4Sl/IiIiIiLHyxR68T21AWfXlji7JNd1HGlkqtxI+f1+LrroohPa6RVXXMEVV1xRbtno0aMZPXr0CW1XREREROQw/5tboDSA69LT6jqKNELHPLXv448/5vHHH8fn8zF06FB69erF8uXLw5FNRERERKRa7NxCgv/dhrNPWxwpsXUdRxqhYzZS999/P927d+edd96hRYsWvPHGG/zrX/8KRzYRERERqQG+f3+G7+Wv6jpGWPlf3QwuB+5Bnes6ijRSx2ykgsEgffr0Yc2aNVxyySW0bt1aN8sVERERaSBMsZ/g+l0EM3dTR3e9CbuoHC/2Z9m4LjsNKy6yruNII3XMRsq2bT7//HNWrVrFeeedx7fffovf7w9HNhERERE5QcGvcsA2kO/F5JXWdZxaZ4whef0hiIvAdVGHuo4jjdgxh01MnDiRP/3pTwwbNozWrVtz0UUXMWPGjHBkExEREZETZG/cAw4LbIPZehASouo6Uq0KfpJN9F4/7tHdsCLq5JapcpI4ZnW1bNmSlStXhh6vXLmSdevW1WooERERETlxxh8k+HUuzrNbE/x4N/a2gzh/c0pdxzou9s58sA2OdvHHvY7xBwm8uonSBBeR57SpvXAiVNJIff311xhjmDp1Kg888EDonNpAIEBGRgbvvfde2EKKiIiISNXZ3+wDbxDnb1IwuYXYWw/WdaTjYgq9eB9dC74gEX/sg6N9wnGtF3jne8z+YnIGNCPBYdVySjnZVdhILVy4kNWrV5Obm8stt9zy0wouFwMGDAhLOBERERGpvuDneyDCieNXLbA27yP4wTZM0MZyHvMy+TrlX7IJSgJYTSPwPrmByNv7YsVXfkqinVtIYMUWnD1SKGqlJkpqX4WN1F133QXAjBkzuOeee8IWSEREREROnLENwc9zcJ6ZjOV24mifQPC9HzC7D2G1ja/reBUKfruP4Ec7cV12Gs6zWuGd9yHeJzYQkd4Hy3P0t67GGPwvflE27nxYF/j+5Br1LnXjmB9HZGZmhiOHiIiIiNQge9tBKPDi6NYSAEf7+B+X59VdqGMw/iD+hZ9jNY/GNbATjlOa4rm+J2ZnPv7nPqtwfHtww27szftwX5mKFa9x5xIex2ykWrVqxSeffKJ7R4mIiIjUQ76Fn+N77lOMXb7JsD/fA04L55lJAFjNoqBpRL2+Tiqw8ntMbhHu3/06dPTJ+etkXENOJ/hpNoHl3x6xjiny4X/lK6x28Tj7tg9zYjmZHXNq3/fff8+oUaNwuVx4PB6MMViWxSeffBKOfCIiIiJSieDGPVDgxUqIwp2WWm65o1NzrCg3AJZl4WgfX3akqo4ZXxCCdigbgJ1TSOCtLTh7nhJq/g5zXdIRk11AYPm32NvycJ3fFkeXZCynA/+rm6DIj2dSVywNmJAwOmYjtWDBgnDkEBEREZEqMsU+KPBCbASBFVuwWsbgOqs19p4CTG4RrgvL35DWcWoCgc9zMEU+rCaesOe1d+YT+HA7wQ27wBvESmyCo20cVtv4siNobgfuq888Yj3LsnCP7IrVPJrA6h34nvwY4iJxdmtJcPUOXBd3wNEmLuxfj5zcjtlItWrVihUrVrBp0yYmTpzIu+++y+DBg8ORTUREREQqYXKKAPD8rguBVVvxP78RR4smBLfsA8pOi/u5w2PE7W15Rxz1qU2BdTsJrNqK2ZEPbgfOnq2wEptg78zD3noQk5kFUHZKX9zRr3Gy3E7cgzrjurwT9pe5ZQ3ZB9uwEqJwXdE5bF+LyGHHbKSefPJJVq9ezZ49exg7diyPPfYY27dvZ9KkSdXe6ZgxY9i/fz8uV9nuZ8+ezY4dO5g/fz5+v5+xY8cyevToam9fRERE5GRg5xQCYLVqimd8L7z3fYj3yQ1Y0W6sdvFYCeVHhjvaxoNVNoiiOo2UvfsQGIOj9fEf/Qlu2Y//uc+wUmJxD++C8+zWWNHucq8xhV7M/hKstsferuV04OzWEme3lpiDJeCwsCKP+ZZWpMYdc9jEG2+8wVNPPUVUVBQJCQksWrSIZcuWVXuHxhh++OEHXn311dB/LVu25KGHHuKFF17g1Vdf5cUXX+S7776r9j5ERERETgYmpxCcFlbzaKyYCDwTzwZvALOnEGfXlke83op0YaXEVvs6Kd+CjXj/trasgTnejDvzAYj4w7m4Ljj1iCYKwIqJwNEuHsuq2jVOVkJUhUewRGrbMRupw0MmDmvatGnoSFJ1/PDDD1iWxfjx47nyyit5/vnnWbNmDeeccw7x8fFER0czYMAAVqxYUe19iIiIiJwM7JxCrMQmoRvsOk6JxTOuJ1ZKLM5erY66juPUBOxteRWOEq+IMQaTXQDF/qNOCawwY3YBxHiwYiOqtD+R+s4yx/gpuuWWWxg2bBh/+9vfePHFF3n66afZuHEj//jHP6q1w08//ZSFCxcya9YsSktLGTNmDAMHDsTn85Geng7A4sWL+fzzz0M3BT5M97RqvHr27HnC21B9NF6qD6mM6kMq09jro+PLuXjjXey6uNlxrxP/bRGnfJjPd1cn4Ys7/g/HXUVBfvViDsXJHqJzfOT0imV/19hjrtd+2V6Mw2L7FS2Oe1/hUBO1ISe3Y/70zJw5k9tvv51vvvmG7t27061bN+bNm1ftHfbo0YMePXoAEB0dzbBhw7j33nuZOHFiuddVdGi3poo+MzOzwf0AKfOxqT6UuTKqD2WujOpDmStTUw1ZTWY2QZvS/1tOk97tSe55+nGvZ6cU4P1wFac3OQVXzzaVvvbnmYOb9+Ijh/jf9STwwTaSP91D24u7l113VVFGYyhduAJnr1Po2bPrcWc8UQ2xpqXhOWYjlZyczOOPP47D4SAYDOL1emnevHm1d/jxxx/j9/s599xzgbIfsFatWrFv377Qa3Jzc0lKCt8kGREREZGGxuwrBttgtYyp0npWyxiIdGFvy4PelTdS5fa3p2ywhaNlDJ6RXfFuPYjvmU+ImNYPK6KCt5T5XigJ4Gh57CNXIg3NMa+RWr58OUOHDiUqKoq9e/cyePBg3nvvvWrvsKCggPvuuw+v10thYSFLlizh/vvvZ+3atRw4cICSkhLefvtt+vXrV+19iIiIiDR2ocYmuYqNlMPC0S4ee2v5gRPGF6z0uic7pxAiXdA0AquJB/d1PTB7i/C//FXF62QXlO3zFDVS0vgc84jUP/7xD5577jkATj31VF555RVuvvlmLrroomrt8MILL2Tjxo389re/xbZtRo0aRc+ePUlPT2fMmDH4/X6GDRtG167hO/wrIiIi0tCERp9XsZECcLSPJ7DyewL/7wfsnfnYO/Mx2QU4e5yC54ajnxJncgqxkmNCl184f9UC1yWnEVj5HfZ57XC0iz9ynT1ljZQjRY2UND7HbKRs26Zly5/GZ6akpGDb9gnt9I9//CN//OMfyy1LS0sjLS3thLYrIiIicrIwOYUQF4EVdeQ48WNxdGwOb32H/6WvINaDo008WBbBb/dhjDnqtep2TiHOzuUHRrgu7kBg5XcEv9131EbKzi6AJm6I8RzxnEhDd8xGqlmzZvznP/9h2LBhWJbFkiVLaNGifk1dERERETnZ2DmFVT6t7zDHGYl4bjsPR7MoiI/EsiwC72/Fv+hLzMESrGbR5V5vSgOQV3rE0S8rNgIrsckRpwmG1ssuwJESW+X7Q4k0BMe8Rmr27NksWrSIbt260bVrVxYtWsSsWbPCEE1EREREjsYYEzrVrjosy8LZsVnZDW1/bHIOT98zO/KP3F9uxddjOTokYP9w4Ij7UhljsLMLsTRoQhqpYx6Rat++Pa+88gr5+fk4nU5iYqr3AysiIiIiNaTQB8X+ah+ROhqrVVNwWNg783F2Tyn3nL2n4uuxHKcmEFy3C7OvGCuxyU9P5HuhxK/ro6TROu67sMXFxdVmDhERERE5TqHGpoqjzytjeZxYKbHYO/KOeM7kFILDwmoRfcRzjg5lNwO2fziA42eNlP3joAlLjZQ0Usc8tU9ERERE6hdzeGJfUs2eKeRoE4e9I/+I0/TsnEKsFtFYbucR61gpsWX3pfrhF+PUszWxTxo3NVIiIiIiDYydUwhuB1ZCVI1u19E2Dgp9mLzScssrux7Lclg4Tk04YuBEaGJfrCb2SeOkRkpERESkgTF7frynk6Nmp+H9NHAi76d92QaTW1Tp9ViOUxMwWYcwJf6f1ssuwNFSE/uk8apWIzV06NCaziEiIiIix8mcwOjzyoQGTvxscp/ZXwwBu9IJgY4OzcCAvS2vbB1jsPcU6vooadSq1UjdddddNZ1DRERERI6D8QUxB4qrPfq8MpbHidUypnwjlXPswRaO9vFglQ2cAOCQt2yqoBopacSq1Uh16dKlpnOIiIiIyHEwe4vA1OzEvp9ztI3H3pkXGjhxeEKgo5LBFlaUG+uUpqHrpOwfB03UVkaR+qDC8efTp0+vcCXLspgzZ06tBBIRERGRih0+QlQbp/ZB2eS+4Ec74ceBEyanEGI8WDGVD41wnJpA8OPdZddUaWKfnAQqbKQ6dep0xLKDBw/y7LPP0qpVq1oNJSIiIiJHZ+cUggVWUpNjv7garLZl9w49fHrf8V6P5eiQQPDD7ZjsgrIjUtFuaBpRKxlF6oMKG6lx48aVe7xmzRqmTp1KWloaGRkZtR5MRERERI5k9hRiNYvC8lT4Nu6EOFo3LbveaWcepJQ1bs6uLY+93s9uzGv2FOJI0cQ+adyO+RMYCAR44IEHWLJkCbNmzeLyyy8PRy4REREROQq7kns61QTL48JKicXekY8zwQGFvuPan9UiGmI92D8cxM4uwNkjpdYyitQHlTZS27dvJz09nejoaJYsWUJKSs38QDz22GO8+eabAPTv35/bb7+d6dOnk5mZSVRU2Y3lbrnlFi699NIa2Z+IiIhIY2BsU3aq3Wlta3U/jjZxBL/ei6d9WQPlOI6hEZZl4ejQjOBXOZrYJyeFChupl156ifvuu4/rr7+e3//+9zW2wzVr1vDhhx+yZMkSLMvixhtvZOXKlXz55Zc8//zzJCUl1di+RERERBoTk18KvmCtDZo4zNE2nuC6XTTJLhswcbxHwBwdmmFv3FO2jhopaeQqbKQyMjJwOBw8+eSTPPXUU6Hlxhgsy+KTTz6p1g4TExOZNm0aHk/ZD2bHjh3JysoiKyuLmTNnkpWVxaWXXsott9yCw1Gt6exSR/wBg9ulc6FFRERqi/11LnD8jU11HR44Efd9CbgcWM2jj2s9R4eEn/5fjZQ0cpY5fJOAX9i9e3elK9bE5L5t27YxYsQIXnjhBR544AFmz55NdHQ0N910E4MHD+aaa64p9/rMzMwT3qfUjvwSD+9sbs+ZKftIbXmgyuv37NnzhDOoPhov1YdURvUhlWlM9RG510f75fsoaeFh+8Dm4Ki9Dy+tgE3qv/dgGShNcPHD0OM7Y8gKGDo/n41xWXwzuiXU42ETNVEbcnKr8IhUbY8437JlCzfddBNTp06lQ4cO/P3vfw89d+2117J06dIjGimouaLPzMxscD9A9TnzKx94CdoBvtidRI8z23BG+7LSCndm1YcyV0b1ocyVUX0oc2VqqiGr7nZMXgmlL3+AFR9F8/S+tIit/bHipStXYbILiG6fWKXc3rVrwGnRs1evWkxXuYZY09Lw1Mm5c5mZmYwdO5Y//elPDB06lG+++Ya33nor9LwxBperdkZ6Ss0r8Rk+3RKge0cnrRIdLHzPS/Z+u65jiYiINArGF8D7xAYoDeCZeDZWGJooAMePp/dV9Xoszw098Vz/m9qIJFKvhL2Rys7OZtKkScybN49BgwYBZY3TnDlzyM/Px+/38+KLL2piXwPyybcBfAHo29XNdZdFEOmxePatUgpLyp81ui/fZu1Xfry+o55NKiIiIr9gbIP/uc8wO/PxXN8TxylNw7ZvR5uyRqqq12NZsRFYMboRrzR+YT/s8/TTT+P1epk7d25o2YgRI5gwYQIjR44kEAhw2WWXMXjw4HBHk2owxrD2az9tEh20TnQCcN2ACOa/Vsq/3y6lR4qDjd8HWLfJz/dZNk4HnJripGWz+nvOtIiISH0RePNbgp9m4/rt6Th/nRzWfTvOSMIXs5mIjs3Cul+RhiLsjVRGRgYZGRlHfW706NFhTiMn6vssm715hmsucIeWtU50cs0FEbzwrpcduadhGy8JMRYDernpleqiabSmMYqIiBxLIDOLwPJvcfZujeuSjmHfvyM5hu+uSabncU7sEznZ6EIkOSFrv/YTHQFdO5QvpW4dXRwqNmzcvI9Lz0mkUysnjlqcLiQiItKY2Dvy8P/7UxwdEnCP7IpVj6ffiZys1EhJteUV2ny9LUjfX7uPev+ovr92E+3LonOblDpIJyIi0jCZvFK8/9iAFRuBZ8JZWG5nXUcSkaPQOVZSbes3BzAGep+hflxERKQmGF8Q75MboMQf1gl9IlJ1aqTkuBwqtvEFfpq2F7QN6zcF6NzGSfOmKiMRkeoyXhtTEKjrGFIPGGPwP/8ZZkcenrG/wdEqfBP6RKTqdChBjmn1l35eW+PDsiAp3qJVCyceFxSUGM49UyUkIlJdJmDwPrgT+4dSnN1icPWLw9GlCZauKT0pBT/aSTAzC9eQVJzdWtZ1HBE5Br0LlkodbqJOb+vklBYOdu+1+XZXgMISaBFn8avWOm9bRKS6/G/sx/6+FGfvWIJfFxP8rBCrmQtX3zgcZzTB0ToCK6JhHvW38wKYg36cp0bVdZQGwXgD+F//Bqt9PK5LT6vrOCJyHNRISYUON1Fntncy+pIInD9+QmqMIb/I4HZZmsQnIlJNwS3FBJbtx9mnKRHjUjABQ/CzQgLv5+F/dT+8uh8ssE7x4GgXibNLE5xnxTaI6W2mIIB37g7MAT8R6a1xnt6kriPVe4H3foD8Utw39GwQ/8YiokZKKlBREwVgWRbxMfolL3Ky8+4MUvJ1kNi+bpzRtfs7wZdl490aJKaPq1G8yTTFQXz/zMZq4cYzMgkAy2Xh6hWLq1csdl4Ae2sJ9nYv9rZSgl8UEVxzCMf/y8MzKglHm8g6/goqZgIG7/wsTF4Aq4Ub7/wsIme0w5Hsqeto9ZbJLyXw9nc4uqfg1M1vRRoMNVJSzsECm9Vf+fng8wBntncy6uLyTZSICEDhej/7X/Rh/HDofT8JQzzEnOUqd21PsNhQ9HEA7w4bT4qFp42TiDYOHFEWxhgC+w2+HTbenTbGa2h6sRt38yNPYyv6JMC+BV6c8RYx57qgEfxK8i3IwRwMEDG1LVbUkadIO+JdOHrEQo9YAIxtCK45hO+lvZTO3o7ronjcQ1pgRdf+6dX2Hh+Bdw/iuigeR0rlE+SMMfgW5GB/W4JnfAqODpGU3rMD76O7ibyjbVjyNkT+N76BgI17yOl1HUVEqkCN1Elm7Vd+vt0VJKW5g9aJDlq3cNAkyuKbHUHWbQrwzc4gAL/p5OLqfh5czkbwjkVEaowJGg6+6uPQqgCRpzmIu9xD3hs+9i/wUbgmQLPhHowfCtcEKPokgPGDowkUrQfwA+BqYWEXG+ziHzfqBMsBhesCxF3mpulFbhxuC2w48KqPQ+/6iejgIHFcRKMYwhD46BDBdQW4hzTH2fH4rh+yHBau8+Nwdo/B/+o+Au/lEVhfgLNbDI52ETjaReJoE4HlrrnrqYzXxv/GfgJvHYAgBL8pJjKjHZan4n0E3s0j+EE+rkHNcPUumzgX8ftT8D64E+8TWUTc2hpLf1fKsbMLCK7ZgfOCU3Ek6RRIkYZEjdRJ5NMtAZau9hEbbbFpRxDz4zRzlxMCQWgabXHRb9yc1dlFQmzDvLhZRGpPsMCw95lSSr+zie3votlvPVhOi8jTIincEODgqz6y7ysFwPJAk7NcxPZxEdHWSbDA4NsVxLvDxrfbxhFtEdHGgaeNA0+Kg2Ch4cBSH3lv+ClcHyAhzUPsu605lOUn9nwXza7yYB3lxt/1SfDbYvxL9pEc6cFO8eI4pfzRG/uAn+CH+fjfPojjtChcVzSv8j6sGCee0cm4zovD//o+gp8VEvwwv+xJJ1jxLvj5qY8WWMkeHO0jQw2XlVD56ZHGGIKfFOJ/MRdzIIDz3KY4z4jG9/Qe/C/txTMq+ehf/5dF+F/MxdkjBveQFqHlzs7ReEYn43suB//ivXhGJFX5627M/Eu/hkgX7st/VddRRKS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", 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "schemes = df.loc[(df.scheme.notnull()) & (df.year_from < 2022)].groupby([\"year_from\", \"scheme\"], as_index=False).size()\n", "g = sns.FacetGrid(schemes, col=\"scheme\", hue=\"scheme\", col_wrap=6, height=2, ylim=(0,100))\n", @@ -494,36 +313,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of US certs: 937 (19.18%)\n", - "Number of DE certs: 908 (18.59%)\n", - "Number of FR certs: 905 (18.53%)\n", - "Number of JP certs: 549 (11.24%)\n", - "Number of CA certs: 443 (9.07%)\n", - "Popular schemes constitute: 3742 (76.60%)\n", - "\n", - "Latex commands:\n", - "\n", - "\\newcommand{\\fractionCertsUS}{$19\\%$}\n", - "\\newcommand{\\fractionCertsDE}{$19\\%$}\n", - "\\newcommand{\\fractionCertsFR}{$19\\%$}\n", - "\\newcommand{\\fractionCertsJP}{$11\\%$}\n", - "\\newcommand{\\fractionCertsCA}{$9\\%$}\n", - "\\newcommand{\\fractionCertsPopularSchemes}{$77\\%$}\n", - "\\newcommand{\\numCCActiveCerts}{$1650$}\n", - "\\newcommand{\\numCCArchivedCerts}{$3490$}\n", - "\\newcommand{\\fractioncertspopularcategories}{$74\\%$}\n", - "\\newcommand{\\fractionCertsSmartcards}{$32\\%$}\n", - "\\newcommand{\\numCCBulkArchivedTwentyNineteen}{$1186$}\n" - ] - } - ], + "outputs": [], "source": [ "total_certs = schemes[\"size\"].sum()\n", "\n", @@ -556,43 +348,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/1h/qt5h035n5rzcjdmfl295hrk80000gn/T/ipykernel_74632/1990204984.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " interesting_schemes.scheme = interesting_schemes.scheme.cat.remove_unused_categories()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "interesting_schemes = schemes.loc[schemes.scheme.isin({\"NL\", \"SE\", \"NO\", \"UK\"})]\n", "interesting_schemes.scheme = interesting_schemes.scheme.cat.remove_unused_categories()\n", @@ -611,21 +369,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-19T15:30:37.742909\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", "st_keywords_df = dset.get_keywords_df(\"st_keywords\").drop(columns=[\"crypto_scheme.MAC\"])\n", @@ -665,33 +411,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\newcommand{\\numShaTwoFourteen}{$66\\%$}\n", - "\\newcommand{\\numShaTwoTwentyOne}{$78\\%$}\n", - "\\newcommand{\\numShaOneTwentyOne}{$57\\%$}\n", - "\\newcommand{\\numSearchedPQKeywords}{$17$}\n", - "\\newcommand{\\numCertsWithPQKeywords}{$8$}\n", - "\\newcommand{\\numSearchedECKeywords}{$12$}\n", - "\\newcommand{\\numECNISTTwentyOne}{$43\\%$}\n", - "\\newcommand{\\numECBrainpoolTwentyOne}{$13\\%$}\n" - ] - } - ], + "outputs": [], "source": [ "frac_sha2_2014 = df_keywords.loc[(df_keywords.year_from == 2014) & (df_keywords.SHA2)].shape[0] / df_keywords.loc[df_keywords.year_from == 2014].shape[0]\n", "frac_sha2_2021 = df_keywords.loc[(df_keywords.year_from == 2021) & (df_keywords.SHA2)].shape[0] / df_keywords.loc[df_keywords.year_from == 2021].shape[0]\n", "frac_sha1_2021 = df_keywords.loc[(df_keywords.year_from == 2021) & (df_keywords.SHA1)].shape[0] / df_keywords.loc[df_keywords.year_from == 2021].shape[0]\n", "\n", - "pq_keywords = [x.split(\".\")[-1] for x in extract_key_paths(cc_rules_get_subset(\"pq_crypto\"), \"pq_crypto\")]\n", + "pq_keywords = [x.split(\".\")[-1] for x in extract_key_paths(rules_get_subset(\"pq_crypto\"), \"pq_crypto\")]\n", "n_positive_pq_certs = df_keywords[pq_keywords].any(axis=1).sum()\n", "\n", - "ec_keywords = [x.split(\".\")[-1] for x in extract_key_paths(cc_rules_get_subset(\"ecc_curve\"), \"ecc_curve\")]\n", + "ec_keywords = [x.split(\".\")[-1] for x in extract_key_paths(rules_get_subset(\"ecc_curve\"), \"ecc_curve\")]\n", "frac_nist_2021 = df_keywords.loc[(df_keywords.year_from == 2021) & (df_keywords.NIST)].shape[0] / df_keywords.loc[(df_keywords.year_from == 2021)].shape[0] \n", "frac_brainpool_2021 = df_keywords.loc[(df_keywords.year_from == 2021) & (df_keywords.Brainpool)].shape[0] / df_keywords.loc[(df_keywords.year_from == 2021)].shape[0] \n", "\n", diff --git a/notebooks/cc/vulnerabilities.ipynb b/notebooks/cc/vulnerabilities.ipynb index 7dcd9b43..3b7ed226 100644 --- a/notebooks/cc/vulnerabilities.ipynb +++ b/notebooks/cc/vulnerabilities.ipynb @@ -6,64 +6,49 @@ "source": [ "# Vulnerability analysis\n", "\n", - "This notebook contains analysis of vulnerabilities in Common Criteria certificates. \n", - "\n", - "## Executive summary\n", - "\n", - "- We exclude the current year (2022) from analyses when possible, search for `2022` to see the relevant places\n", - "- We exclude the vulnerabilities that affect a product *prior* to certification in the following cases:\n", - " - Correlation analysis of SARs, EALs vs number/severity of vulnerabilities\n", - " - CWE analysis\n", - " - Time from CVE to maintenance update\n", - "\n", - "- Weak correlations (in a sensible direction, i.e., negative correlations) between expert-selected SARs that could correspond to more secure products and number/severity of vulnerabilities. Same correlations exist on more coarser scale of EALs\n", - "- Maintenannce updates very rarely used to address vulnerabilities. Instead, they often make claims about vulnerabilities not being applicable to the *certified version* of the product. The rationale is often mentioned in the non-public Impact Analysis Report\n", - "- Approximately 50% of vulns. exist prior to certification, these are seldomly referenced in the certificatation documents (TODO: double-check). \n", - "- In combination with reference map between certificates, the ecosystem is quite fragile, as a single vulnerability on average affects X certs directly and Y certs indirectly\n", - "- Some categories heavily underrepresented in the CPE/CVE landscape, e.g., smartcards" + "This notebook contains analysis of vulnerabilities in Common Criteria certificates. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", + "import itertools\n", + "\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", "import seaborn as sns\n", - "from typing import Optional\n", "from tqdm.notebook import tqdm\n", + "\n", "from sec_certs.cert_rules import cc_rules\n", - "import itertools\n", - "from scipy import stats\n", - "import functools\n", "\n", - "from typing import Set\n", - "from sec_certs.sample.sar import SAR\n", "plt.style.use(\"seaborn-whitegrid\")\n", "sns.set_palette(\"deep\")\n", - "sns.set_context(\"notebook\") # Set to \"paper\" for use in paper :)\n", - " \n", - "from sec_certs.dataset import CCDataset, CVEDataset, CCDatasetMaintenanceUpdates, CPEDataset\n", - "from sec_certs.sample import CommonCriteriaCert\n", - "from sec_certs.utils.helpers import download_file\n", - "import datetime\n", - "import tempfile\n", - "from bs4 import BeautifulSoup\n", + "sns.set_context(\"notebook\") # Set to \"paper\" for use in paper :)\n", "\n", - "from pathlib import Path\n", "import itertools\n", - "\n", - "from sec_certs.utils.pandas import compute_cve_correlations, find_earliest_maintenance_after_cve, expand_df_with_cve_cols, filter_to_cves_within_validity_period, compute_maintenances_that_come_after_vulns, move_fixing_mu_to_directory, prepare_cwe_df, get_top_n_cwes, get_sar_level_from_set, discover_sar_families\n", - "\n", "import warnings\n", + "from pathlib import Path\n", "\n", - "warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)\n", + "from sec_certs.dataset import CCDataset, CPEDataset, CVEDataset, CCDatasetMaintenanceUpdates\n", + "from sec_certs.utils.pandas import (\n", + " compute_cve_correlations,\n", + " compute_maintenances_that_come_after_vulns,\n", + " discover_sar_families,\n", + " expand_df_with_cve_cols,\n", + " filter_to_cves_within_validity_period,\n", + " get_sar_level_from_set,\n", + " get_top_n_cwes,\n", + " move_fixing_mu_to_directory,\n", + " prepare_cwe_df,\n", + ")\n", + "\n", + "warnings.simplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)\n", "\n", "RESULTS_DIR = Path(\"./results\")\n", - "RESULTS_DIR.mkdir(exist_ok=True)" + "RESULTS_DIR.mkdir(exist_ok=True)\n" ] }, { @@ -75,23 +60,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "parsing cpe matching (by NIST) dictionary: 100%|██████████| 398394/398394 [00:48<00:00, 8269.99it/s] \n", - "Building-up lookup dictionaries for fast CVE matching: 100%|██████████| 197102/197102 [00:13<00:00, 14175.63it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# Local instantiation\n", - "dset = CCDataset.from_web_latest()\n", + "dset: CCdataset = CCDataset.from_web_latest()\n", "# dset.process_maintenance_updates() # Run this only once, can take ~10 minutes to finnish, fully processes maintenance updates\n", - "main_dset = dset.mu_dataset\n", + "main_dset = CCDatasetMaintenanceUpdates.from_json(dset.mu_dataset_path)\n", "cve_dset: CVEDataset = dset._prepare_cve_dataset()\n", "cpe_dset: CPEDataset = dset._prepare_cpe_dataset()\n", "\n", @@ -114,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -124,205 +100,39 @@ "source": [ "df = dset.to_pandas()\n", "\n", - "if 'n_maintenances' not in df.columns:\n", + "if \"n_maintenances\" not in df.columns:\n", " n_maintenances = main_dset.get_n_maintenances_df()\n", - " df = pd.concat([df, n_maintenances], axis='columns')\n", + " df = pd.concat([df, n_maintenances], axis=\"columns\")\n", "\n", - "if 'maintenance_dates' not in df.columns:\n", + "if \"maintenance_dates\" not in df.columns:\n", " main_dates = main_dset.get_maintenance_dates_df()\n", - " df = pd.concat([df, main_dates], axis='columns')\n", + " df = pd.concat([df, main_dates], axis=\"columns\")\n", "\n", "# Expand DataFrame with CVEs that affect some certificate\n", "cves = list(itertools.chain.from_iterable([x.heuristics.related_cves for x in dset if x.heuristics.related_cves]))\n", "cve_dict = {x: cve_dset[x] for x in cves}\n", - "cve_dset.cves = cve_dict # Limit cve_dset to CVEs relevant to some certificate\n", + "cve_dset.cves = cve_dict # Limit cve_dset to CVEs relevant to some certificate\n", "df = expand_df_with_cve_cols(df, cve_dset)\n", "\n", - "df_cves_within_validity_period = filter_to_cves_within_validity_period(df.loc[(df.not_valid_before.notnull()) & (df.not_valid_after.notnull())].copy())\n", + "df_cves_within_validity_period = filter_to_cves_within_validity_period(\n", + " df.loc[(df.not_valid_before.notnull()) & (df.not_valid_after.notnull())].copy(), cve_dset\n", + ")\n", "df_cves_within_validity_period = expand_df_with_cve_cols(df_cves_within_validity_period, cve_dset)\n", "\n", "df_cpe_rich = df.loc[~df.cpe_matches.isnull()].copy()\n", - "df_cve_rich = df.loc[df.n_cves > 0].copy()" + "df_cve_rich = df.loc[df.n_cves > 0].copy()\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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related_cvescve_published_datesearliest_cveworst_cve_scoreavg_cve_score
ebd276cca70fd723{CVE-2019-4513, CVE-2013-6745, CVE-2017-1732, ...[2019-08-26, 2013-12-22, 2018-08-17, 2015-01-2...2013-12-2210.05.800000
3dc6e1ebe7dd5584{CVE-2022-40707, CVE-2022-40709, CVE-2022-4070...[2022-09-28, 2022-09-28, 2022-09-28, 2021-03-0...2021-03-037.84.640000
b7f814ed16f2ecca{CVE-2018-8753}[2018-08-15]2018-08-155.95.900000
c290ee3692a00006{CVE-2019-7487, CVE-2019-12258, CVE-2019-12257...[2019-12-19, 2019-08-09, 2019-08-09, 2019-08-0...2019-08-099.88.075000
4fc1208e4c800aa6{CVE-2017-14615, CVE-2017-14616}[2017-09-20, 2017-09-20]2017-09-207.56.800000
..................
fca440a973659369{CVE-2013-6725, CVE-2015-1920, CVE-2008-4111, ...[2014-01-16, 2015-05-20, 2008-09-16, 2011-03-0...2006-08-1410.05.425000
bf0f130ebce2e124{CVE-2011-1315, CVE-2007-3263, CVE-2007-5483, ...[2011-03-08, 2007-06-19, 2007-10-16, 2014-09-2...2005-11-0410.05.710390
686005d0b5ff5c5c{CVE-2011-1315, CVE-2007-3263, CVE-2007-5483, ...[2011-03-08, 2007-06-19, 2007-10-16, 2014-09-2...2005-05-0210.05.904348
5f1df5ad8e51ba75{CVE-2006-2462, CVE-2005-1749, CVE-2004-2424, ...[2006-05-19, 2005-05-24, 2004-12-31, 2008-07-2...2003-08-2710.05.594565
a092aebf5a286ded{CVE-2004-2558}[2004-12-31]2004-12-317.57.500000
\n", - "

376 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " related_cves \\\n", - "ebd276cca70fd723 {CVE-2019-4513, CVE-2013-6745, CVE-2017-1732, ... \n", - "3dc6e1ebe7dd5584 {CVE-2022-40707, CVE-2022-40709, CVE-2022-4070... \n", - "b7f814ed16f2ecca {CVE-2018-8753} \n", - "c290ee3692a00006 {CVE-2019-7487, CVE-2019-12258, CVE-2019-12257... \n", - "4fc1208e4c800aa6 {CVE-2017-14615, CVE-2017-14616} \n", - "... ... \n", - "fca440a973659369 {CVE-2013-6725, CVE-2015-1920, CVE-2008-4111, ... \n", - "bf0f130ebce2e124 {CVE-2011-1315, CVE-2007-3263, CVE-2007-5483, ... \n", - "686005d0b5ff5c5c {CVE-2011-1315, CVE-2007-3263, CVE-2007-5483, ... \n", - "5f1df5ad8e51ba75 {CVE-2006-2462, CVE-2005-1749, CVE-2004-2424, ... \n", - "a092aebf5a286ded {CVE-2004-2558} \n", - "\n", - " cve_published_dates \\\n", - "ebd276cca70fd723 [2019-08-26, 2013-12-22, 2018-08-17, 2015-01-2... \n", - "3dc6e1ebe7dd5584 [2022-09-28, 2022-09-28, 2022-09-28, 2021-03-0... \n", - "b7f814ed16f2ecca [2018-08-15] \n", - "c290ee3692a00006 [2019-12-19, 2019-08-09, 2019-08-09, 2019-08-0... \n", - "4fc1208e4c800aa6 [2017-09-20, 2017-09-20] \n", - "... ... \n", - "fca440a973659369 [2014-01-16, 2015-05-20, 2008-09-16, 2011-03-0... \n", - "bf0f130ebce2e124 [2011-03-08, 2007-06-19, 2007-10-16, 2014-09-2... \n", - "686005d0b5ff5c5c [2011-03-08, 2007-06-19, 2007-10-16, 2014-09-2... \n", - "5f1df5ad8e51ba75 [2006-05-19, 2005-05-24, 2004-12-31, 2008-07-2... \n", - "a092aebf5a286ded [2004-12-31] \n", - "\n", - " earliest_cve worst_cve_score avg_cve_score \n", - "ebd276cca70fd723 2013-12-22 10.0 5.800000 \n", - "3dc6e1ebe7dd5584 2021-03-03 7.8 4.640000 \n", - "b7f814ed16f2ecca 2018-08-15 5.9 5.900000 \n", - "c290ee3692a00006 2019-08-09 9.8 8.075000 \n", - "4fc1208e4c800aa6 2017-09-20 7.5 6.800000 \n", - "... ... ... ... \n", - "fca440a973659369 2006-08-14 10.0 5.425000 \n", - "bf0f130ebce2e124 2005-11-04 10.0 5.710390 \n", - "686005d0b5ff5c5c 2005-05-02 10.0 5.904348 \n", - "5f1df5ad8e51ba75 2003-08-27 10.0 5.594565 \n", - "a092aebf5a286ded 2004-12-31 7.5 7.500000 \n", - "\n", - "[376 rows x 5 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Take a look at columns related to CVEs\n", - "df.loc[~df.related_cves.isna(), ['related_cves', 'cve_published_dates', 'earliest_cve', 'worst_cve_score', 'avg_cve_score']]" + "df.loc[\n", + " ~df.related_cves.isna(), [\"related_cves\", \"cve_published_dates\", \"earliest_cve\", \"worst_cve_score\", \"avg_cve_score\"]\n", + "]\n" ] }, { @@ -339,141 +149,77 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# distribution of categories in CPE-rich vs. all certificates\n", - "categories_cpe = df_cpe_rich.category.value_counts().sort_index().rename('Category distribution CPE-rich')\n", - "categories_cve = df_cve_rich.category.value_counts().sort_index().rename('Category distribution CVE-rich')\n", - "categories_all = df.category.value_counts().sort_index().rename('Category distribution all')\n", + "categories_cpe = df_cpe_rich.category.value_counts().sort_index().rename(\"Category distribution CPE-rich\")\n", + "categories_cve = df_cve_rich.category.value_counts().sort_index().rename(\"Category distribution CVE-rich\")\n", + "categories_all = df.category.value_counts().sort_index().rename(\"Category distribution all\")\n", "\n", "categories_merged = pd.concat([categories_all, categories_cpe, categories_cve], axis=1)\n", "categories_merged = categories_merged.div(categories_merged.sum(axis=0), axis=1)\n", "\n", - "categories_merged.plot.bar(title='Categories (without smartcards) comparison between CPE-rich, CVE-rich and all certificates')\n", + "categories_merged.plot.bar(\n", + " title=\"Categories (without smartcards) comparison between CPE-rich, CVE-rich and all certificates\"\n", + ")\n", "plt.show()\n", - "plt.savefig(RESULTS_DIR / \"categories.pdf\", bbox_inches='tight')\n", + "plt.savefig(RESULTS_DIR / \"categories.pdf\", bbox_inches=\"tight\")\n", "\n", "# distribution of years in CPE-rich, CVE-rich, and all certificates\n", - "years_cpe = df_cpe_rich.year_from.value_counts().sort_index().rename('Year distribution CPE-rich')\n", - "years_cve = df_cve_rich.year_from.value_counts().sort_index().rename('Year distribution CVE-rich')\n", - "years_all = df.year_from.value_counts().sort_index().rename('Year distribution all certificates')\n", + "years_cpe = df_cpe_rich.year_from.value_counts().sort_index().rename(\"Year distribution CPE-rich\")\n", + "years_cve = df_cve_rich.year_from.value_counts().sort_index().rename(\"Year distribution CVE-rich\")\n", + "years_all = df.year_from.value_counts().sort_index().rename(\"Year distribution all certificates\")\n", "\n", "years_merged = pd.concat([years_all, years_cpe, years_cve], axis=1)\n", "years_merged.index.name = \"year_from\"\n", "years_merged = years_merged.loc[years_merged.index < 2022]\n", "years_merged = years_merged.div(years_merged.sum(axis=0), axis=1)\n", - "years_merged.plot.line(title='Years comparision between CPE-rich, CVE-rich and all certificates')\n", + "years_merged.plot.line(title=\"Years comparision between CPE-rich, CVE-rich and all certificates\")\n", "\n", "plt.show()\n", - "plt.savefig(RESULTS_DIR / \"cve_cpe_certs_time_evolution.pdf\", bbox_inches='tight')\n", + "plt.savefig(RESULTS_DIR / \"cve_cpe_certs_time_evolution.pdf\", bbox_inches=\"tight\")\n", "\n", "# distribution of security levels in CPE-rich, CVE-rich and all certificates\n", - "levels_cpe = df_cpe_rich.eal.value_counts().sort_index().rename('EAL distribution CPE-rich')\n", - "levels_cve = df_cve_rich.eal.value_counts().sort_index().rename('EAL distribution CVE-rich')\n", - "levels_all = df.eal.value_counts().sort_index().rename('EAL distribution all certificates')\n", + "levels_cpe = df_cpe_rich.eal.value_counts().sort_index().rename(\"EAL distribution CPE-rich\")\n", + "levels_cve = df_cve_rich.eal.value_counts().sort_index().rename(\"EAL distribution CVE-rich\")\n", + "levels_all = df.eal.value_counts().sort_index().rename(\"EAL distribution all certificates\")\n", "\n", "levels_merged = pd.concat([levels_all, levels_cpe, levels_cve], axis=1)\n", "levels_merged = levels_merged.div(levels_merged.sum(axis=0), axis=1)\n", - "levels_merged.plot.bar(title='EAL comparision between CPE-rich, CVE-rich and all certificates')\n", + "levels_merged.plot.bar(title=\"EAL comparision between CPE-rich, CVE-rich and all certificates\")\n", "plt.show()\n", - "plt.savefig(RESULTS_DIR / \"eal_distr_all_cpes_cves.pdf\", bbox_inches='tight')\n", + "plt.savefig(RESULTS_DIR / \"eal_distr_all_cpes_cves.pdf\", bbox_inches=\"tight\")\n", "\n", "# distribution of most common (top 20) vendors in CPE-rich, CVE-rich and all certificates\n", "top_manufacturers = list(df.manufacturer.value_counts().head(20).index)\n", - "vendors_cpe = df_cpe_rich.loc[df_cpe_rich.manufacturer.isin(top_manufacturers), 'manufacturer'].value_counts().sort_index().rename('Common vendors distribution CPE-rich')\n", - "vendors_cve = df_cve_rich.loc[df_cve_rich.manufacturer.isin(top_manufacturers), 'manufacturer'].value_counts().sort_index().rename('Common vendors distribution CVE-rich')\n", - "vendors_all = df.loc[df.manufacturer.isin(top_manufacturers), 'manufacturer'].value_counts().sort_index().rename('Common vendors distribution all certificates')\n", + "vendors_cpe = (\n", + " df_cpe_rich.loc[df_cpe_rich.manufacturer.isin(top_manufacturers), \"manufacturer\"]\n", + " .value_counts()\n", + " .sort_index()\n", + " .rename(\"Common vendors distribution CPE-rich\")\n", + ")\n", + "vendors_cve = (\n", + " df_cve_rich.loc[df_cve_rich.manufacturer.isin(top_manufacturers), \"manufacturer\"]\n", + " .value_counts()\n", + " .sort_index()\n", + " .rename(\"Common vendors distribution CVE-rich\")\n", + ")\n", + "vendors_all = (\n", + " df.loc[df.manufacturer.isin(top_manufacturers), \"manufacturer\"]\n", + " .value_counts()\n", + " .sort_index()\n", + " .rename(\"Common vendors distribution all certificates\")\n", + ")\n", "\n", "vendors_merged = pd.concat([vendors_all, vendors_cpe, vendors_cve], axis=1)\n", "vendors_merged = vendors_merged.div(vendors_merged.sum(axis=0), axis=1)\n", - "vendors_merged.plot.bar(title='Common vendors comparison between CPE-rich, CVE-rich and all certificates')\n", + "vendors_merged.plot.bar(title=\"Common vendors comparison between CPE-rich, CVE-rich and all certificates\")\n", "plt.show()\n", - "plt.savefig(RESULTS_DIR / \"top_vendors_vulns.pdf\", bbox_inches='tight')" + "plt.savefig(RESULTS_DIR / \"top_vendors_vulns.pdf\", bbox_inches=\"tight\")\n" ] }, { @@ -485,38 +231,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "20582f364586433ab62d0b424ce24fd4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00100 CVE-rich certs, second-most-popular value with >= 40 instances)\n", "cve_rich = df_cves_within_validity_period.loc[df_cves_within_validity_period.related_cves.notnull()].copy()\n", @@ -527,195 +244,41 @@ "\n", "sars = cve_rich[families].notnull().sum().to_frame(\"support\")\n", "sars.index.name = \"family\"\n", - "sars[\"second_most_popular\"] = sars.index.map(lambda x: 0 if not len(cve_rich[x].value_counts()) > 1 else cve_rich[x].value_counts().iloc[1])\n", + "sars[\"second_most_popular\"] = sars.index.map(\n", + " lambda x: 0 if not len(cve_rich[x].value_counts()) > 1 else cve_rich[x].value_counts().iloc[1]\n", + ")\n", "sars = sars.loc[(sars.support >= 100) & (sars.second_most_popular >= 40)].sort_values(by=\"support\", ascending=False)\n", "sars[\"range\"] = sars.index.map(lambda x: len(cve_rich[x].value_counts()))\n", "\n", - "df_corr = compute_cve_correlations(df_cves_within_validity_period.loc[df_cves_within_validity_period.category != \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"], sar_families=sars.index.tolist(), filter_nans=False)\n", + "df_corr = compute_cve_correlations(\n", + " df_cves_within_validity_period.loc[\n", + " df_cves_within_validity_period.category != \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"\n", + " ],\n", + " sar_families=sars.index.tolist(),\n", + " filter_nans=False,\n", + ")\n", "df_corr[\"range\"] = sars[\"range\"]\n", "df_corr.loc[\"eal\", \"range\"] = len(cve_rich.eal.cat.categories)\n", - "df_corr = df_corr.sort_values(by=\"avg_cve_score_corr\").rename(index={\"eal\":\"EAL\"}).drop(columns=[\"worst_cve_score_corr\", \"worst_cve_pvalue\"])\n", + "df_corr = (\n", + " df_corr.sort_values(by=\"avg_cve_score_corr\")\n", + " .rename(index={\"eal\": \"EAL\"})\n", + " .drop(columns=[\"worst_cve_score_corr\", \"worst_cve_pvalue\"])\n", + ")\n", "df_corr = pd.concat([df_corr.loc[\"EAL\", :].to_frame().transpose(), df_corr.drop(\"EAL\")])\n", "df_corr.range = df_corr.range.astype(\"int\")\n", "df_corr.support = df_corr.support.astype(\"int\")\n", "df_corr.index.name = \"EAL/SAR family\"\n", "df_corr.index = df_corr.index.map(lambda x: x.replace(\"_\", \"\\_\"))\n", - "df_corr.to_csv(RESULTS_DIR / \"certs_without_smartcards_sar_cve_corr.csv\", float_format=\"%.2e\")" + "df_corr.to_csv(RESULTS_DIR / \"certs_without_smartcards_sar_cve_corr.csv\", float_format=\"%.2e\")\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - 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n_cves_corrn_cves_pvalueavg_cve_score_corravg_cve_pvaluesupportrange
EAL/SAR family
EAL-0.0267507.929923e-02-0.3489142.518593e-07277914
ALC\\_CMC-0.1027653.222375e-07-0.3673244.387737e-0823365
ALC\\_CMS-0.0990417.843342e-07-0.3664784.721080e-0823425
AVA\\_VAN-0.1086675.797582e-08-0.3508031.888384e-0723674
ATE\\_IND-0.0833901.772333e-06-0.3284772.325445e-0830833
ATE\\_COV-0.0205691.621168e-01-0.3176712.010572e-0522993
ADV\\_FSP-0.0790444.737295e-06-0.2794932.081353e-0631326
ASE\\_OBJ-0.1150483.066473e-07-0.1365624.254182e-0218692
ASE\\_REQ-0.1174901.336536e-07-0.1190726.443006e-0219072
ALC\\_FLR0.0509429.789388e-010.0994478.829933e-0115923
\n", - "
" - ], - "text/plain": [ - " n_cves_corr n_cves_pvalue avg_cve_score_corr \\\n", - "EAL/SAR family \n", - "EAL -0.026750 7.929923e-02 -0.348914 \n", - "ALC\\_CMC -0.102765 3.222375e-07 -0.367324 \n", - "ALC\\_CMS -0.099041 7.843342e-07 -0.366478 \n", - "AVA\\_VAN -0.108667 5.797582e-08 -0.350803 \n", - "ATE\\_IND -0.083390 1.772333e-06 -0.328477 \n", - "ATE\\_COV -0.020569 1.621168e-01 -0.317671 \n", - "ADV\\_FSP -0.079044 4.737295e-06 -0.279493 \n", - "ASE\\_OBJ -0.115048 3.066473e-07 -0.136562 \n", - "ASE\\_REQ -0.117490 1.336536e-07 -0.119072 \n", - "ALC\\_FLR 0.050942 9.789388e-01 0.099447 \n", - "\n", - " avg_cve_pvalue support range \n", - "EAL/SAR family \n", - "EAL 2.518593e-07 2779 14 \n", - "ALC\\_CMC 4.387737e-08 2336 5 \n", - "ALC\\_CMS 4.721080e-08 2342 5 \n", - "AVA\\_VAN 1.888384e-07 2367 4 \n", - "ATE\\_IND 2.325445e-08 3083 3 \n", - "ATE\\_COV 2.010572e-05 2299 3 \n", - "ADV\\_FSP 2.081353e-06 3132 6 \n", - "ASE\\_OBJ 4.254182e-02 1869 2 \n", - "ASE\\_REQ 6.443006e-02 1907 2 \n", - "ALC\\_FLR 8.829933e-01 1592 3 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_corr" + "df_corr\n" ] }, { @@ -732,157 +295,71 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of certificates with >0 CVEs in validity period but 0 maintenance reports: 0\n", - "Number of certificates with >0 CVEs in validity period and >0 maintenance reports: 14\n", - "Number of vulnerable certificates with a maintenance report *after* CVE: 7\n" - ] - } - ], + "outputs": [], "source": [ "df_main_after_vuln = compute_maintenances_that_come_after_vulns(df_cves_within_validity_period)\n", "\n", - "n_vuln_certs_wo_maintenances = df_cves_within_validity_period.loc[(df_cves_within_validity_period.n_cves>0) & (df_cves_within_validity_period.n_maintenances==0)].shape[0]\n", - "n_vuln_certs_w_maintenances = df_cves_within_validity_period.loc[(df_cves_within_validity_period.n_cves>0) & (df_cves_within_validity_period.n_maintenances>0)].shape[0]\n", - "n_certs_with_main_after_vuln = df_main_after_vuln.loc[df_main_after_vuln.earliest_maintenance_after_vuln.notna()].shape[0]\n", - "\n", - "print(f'Number of certificates with >0 CVEs in validity period but 0 maintenance reports: {n_vuln_certs_wo_maintenances}')\n", - "print(f'Number of certificates with >0 CVEs in validity period and >0 maintenance reports: {n_vuln_certs_w_maintenances}')\n", - "print(f'Number of vulnerable certificates with a maintenance report *after* CVE: {n_certs_with_main_after_vuln}')\n", + "n_vuln_certs_wo_maintenances = df_cves_within_validity_period.loc[\n", + " (df_cves_within_validity_period.n_cves > 0) & (df_cves_within_validity_period.n_maintenances == 0)\n", + "].shape[0]\n", + "n_vuln_certs_w_maintenances = df_cves_within_validity_period.loc[\n", + " (df_cves_within_validity_period.n_cves > 0) & (df_cves_within_validity_period.n_maintenances > 0)\n", + "].shape[0]\n", + "n_certs_with_main_after_vuln = df_main_after_vuln.loc[df_main_after_vuln.earliest_maintenance_after_vuln.notna()].shape[\n", + " 0\n", + "]\n", + "\n", + "print(\n", + " f\"Number of certificates with >0 CVEs in validity period but 0 maintenance reports: {n_vuln_certs_wo_maintenances}\"\n", + ")\n", + "print(\n", + " f\"Number of certificates with >0 CVEs in validity period and >0 maintenance reports: {n_vuln_certs_w_maintenances}\"\n", + ")\n", + "print(f\"Number of vulnerable certificates with a maintenance report *after* CVE: {n_certs_with_main_after_vuln}\")\n", "\n", "updates_that_should_fix_vulns_path = RESULTS_DIR / \"updates_that_should_fix_vulns\"\n", "updates_that_should_fix_vulns_path.mkdir(exist_ok=True)\n", - "mu_filenames = move_fixing_mu_to_directory(df_main_after_vuln, main_dset.to_pandas(), updates_that_should_fix_vulns_path, \"/Users/adam/phd/projects/certificates/sec-certs/datasets/cc_04_10_2022/certs/maintenances/reports/pdf\")" + "mu_filenames = move_fixing_mu_to_directory(\n", + " df_main_after_vuln,\n", + " main_dset.to_pandas(),\n", + " updates_that_should_fix_vulns_path,\n", + " \"/Users/adam/phd/projects/certificates/sec-certs/datasets/cc_04_10_2022/certs/maintenances/reports/pdf\",\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "df_main_after_vuln.loc[df_main_after_vuln.earliest_maintenance_after_vuln.notna(), [\"name\", \"cpe_matches\", \"related_cves\", \"not_valid_before\", \"not_valid_after\", \"cve_published_dates\", \"maintenance_dates\", \"earliest_maintenance_after_vuln\"]].to_csv(\"/Users/adam/Downloads/vulns_before_mu.csv\")" + "df_main_after_vuln.loc[\n", + " df_main_after_vuln.earliest_maintenance_after_vuln.notna(),\n", + " [\n", + " \"name\",\n", + " \"cpe_matches\",\n", + " \"related_cves\",\n", + " \"not_valid_before\",\n", + " \"not_valid_after\",\n", + " \"cve_published_dates\",\n", + " \"maintenance_dates\",\n", + " \"earliest_maintenance_after_vuln\",\n", + " ],\n", + "].to_csv(\"/Users/adam/Downloads/vulns_before_mu.csv\")\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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maintenance_datesearliest_cveearliest_maintenance_after_vuln
dgst
c57b9308028caccf[2019-12-18, 2018-08-03]2019-01-242019-12-18
822d871f3bbd06d7[2021-09-29]2020-07-172021-09-29
b3f6140720637831[2013-12-06, 2014-12-08, 2014-12-08]2014-03-182014-12-08
d7496d93563c3bea[2014-12-19]2013-10-132014-12-19
16f1a518cd842fcb[2014-05-14, 2013-05-30, 2013-10-10]2013-07-182013-10-10
44fafadf91cbbc96[2019-08-20]2018-04-302019-08-20
c25126449c82fe65[2018-12-08]2016-06-042018-12-08
\n", - "
" - ], - "text/plain": [ - " maintenance_dates earliest_cve \\\n", - "dgst \n", - "c57b9308028caccf [2019-12-18, 2018-08-03] 2019-01-24 \n", - "822d871f3bbd06d7 [2021-09-29] 2020-07-17 \n", - "b3f6140720637831 [2013-12-06, 2014-12-08, 2014-12-08] 2014-03-18 \n", - "d7496d93563c3bea [2014-12-19] 2013-10-13 \n", - "16f1a518cd842fcb [2014-05-14, 2013-05-30, 2013-10-10] 2013-07-18 \n", - "44fafadf91cbbc96 [2019-08-20] 2018-04-30 \n", - "c25126449c82fe65 [2018-12-08] 2016-06-04 \n", - "\n", - " earliest_maintenance_after_vuln \n", - "dgst \n", - "c57b9308028caccf 2019-12-18 \n", - "822d871f3bbd06d7 2021-09-29 \n", - "b3f6140720637831 2014-12-08 \n", - "d7496d93563c3bea 2014-12-19 \n", - "16f1a518cd842fcb 2013-10-10 \n", - "44fafadf91cbbc96 2019-08-20 \n", - "c25126449c82fe65 2018-12-08 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_main_after_vuln.loc[df_main_after_vuln.earliest_maintenance_after_vuln.notna(), [\"maintenance_dates\", \"earliest_cve\", \"earliest_maintenance_after_vuln\"]]" + "df_main_after_vuln.loc[\n", + " df_main_after_vuln.earliest_maintenance_after_vuln.notna(),\n", + " [\"maintenance_dates\", \"earliest_cve\", \"earliest_maintenance_after_vuln\"],\n", + "]\n" ] }, { @@ -898,31 +375,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jOUJyhOSIy+cINzc3evbsyVdffQWUFRWTJ09m69atlbZ1uX3y73//mw4dOvDll1/y2muvce7cOdvrqvoOX45KpWLKlCm89NJLjB07tsribuDAgaxZswaj0YjRaGTjxo1A2ZXamJgYnn/+eYYPH05aWhqJiYlV5sbafsZNmVyhasZefvllFi9ezD333INGo8FkMnHbbbfx9NNPV1huypQpTJo0iTfeeKPSOg4ePMi4ceMqTNNoNKxatYrnn3+eN998k/fffx+1Ws1TTz1FixYtKix7zz338PzzzxMREYFGo6FPnz789NNPtgPYycmJKVOmkJeXx4gRI5g4caLttYcOHWL58uVYrVbee+89PD09GThwII888ggPPfQQKpUKNzc3Fi1ahEqlYvr06Tz55JN4enri7OzMDTfcQGJiYq3339tvv81LL73Ed999R2BgIC1atKhwlvFqvfvuu8yaNct2lrBt27ZAWde4P/30E6NHj0an0zFgwAByc3NtZ447d+6Mp6dnhSYsTzzxBC+99BLLli2z3dzbt2/fCtu73HovVX5z8Ouvv85LL73EG2+8QUREBGazmRtvvJG//e1vNb6/p556ijfeeINx48ZhsVgYPXo0w4cPZ9CgQbz99tuMHz8ei8VCly5dqu3CddCgQbz22mtXvE//85//MG/ePJYuXYpKpeKNN96ge/fu3HLLLYwaNQoPDw9atmxJ+/btSUhIoGXLlgwbNowpU6awePFi23puuukmHnjgAe6//36sVis+Pj58+umnlZrICNGUSI6QHCE54vI5YsGCBbz22mtERERgMpkYO3Yst99+e6XtVLdPtm/fzi+//MK6devw8PDg/vvv55///KetOKnqO1yT8ePHX7bXxXvuuYfExETGjh1b4cSgp6cnjz76KOPHj8fLywtvb2969epFQkICAwYMqJPPuClTKdI2RTiomTNn0qFDBx5++OFK8zp16sSePXts97TYw8cff8zw4cNp164d+fn53H777Xz++ee0b9++QeNITExk2rRpbN682dbsQAghmjrJEVdGckTjVNvv8I8//sjq1av54osv6ikyURW5QiVELbVu3Zrp06ejVquxWCw88sgjDZ4oP/jgA5YvX85LL70kiVIIIRyI5AjR0KZNm0ZmZiYffvihvUNpduQKlRBCCCGEEELUktwAIIQQQgghhBC1JAWVEEIIIYQQQtRSk72Hymq1UlhYiE6na/Z94wshhKNQFAWz2Yyrq2uz6SVR8pEQQjiWus5FTbagKiwstI3RIIQQwrF07NixwgCuTZnkIyGEcEx1lYuabEGl0+mAsh2l1+vtHM3lxcTEEB4ebu8watQY4mwMMQLQpg3Exdk7iho1hv3ZGGIEibOcyWTi1KlTtr/RzUFN+aixfDcamuyXqsl+qUYjyav2IN+Zyuo6FzXZgqq8WYVer69ypGhH0xhihMYRZ2OIkfPnoTHESePYn40hRpA4L9acmr5dST5qLN+Nhib7pWqyX6rQiPKqPch3pmp1lYuaRwN2IYQQQgghhKgHUlAJIYQQQgghRC1JQSWEEEIIIYQQtSQFlRBCCCGEEELUkhRUQgghhBBCCFFLUlAJIYQQQgghRC1JQSWEEEIIIYQQtSQFlRD1ZP2us6zcdpr1u87aO5RmLTk5mfDwcMaNG8e4ceMYMWIEzzzzDJmZmfW2zYKCAiZMmMC4ceOIq8OBJmfNmsW5c+cAeOSRR0hLS8NisfDwww8zZswYvvzyS1566aVarXvatGm2x+PGjauTeEXj0ZSOk3IffPABW7duveLl8/PzeeKJJwBIS0vjkUceqfOYmqpNkXGs3hHLpkgZWFc0T012YF8h7M1ktmA0W+wdhgACAgJYu3YtAIqi8O677/LMM8/w3Xff1cv2jh8/jl6v5/vvv6/T9e7bt48nn3wSgM8//xyAlJQUTp48yW+//XZN696/f7/tcfm+Es1LUzlOyj377LNXtXxubi4nTpwAIDAw0HaMiZqVmCwUG0vtHYYQdlPvV6gKCgoYO3YsycnJ7Ny503b2a9y4cfTv35/HHnsMgEWLFjFkyBDbvG+//RYo+4M7ceJERowYwUsvvURpqRywQojaU6lUPP3005w+fZoTJ05QWlrKnDlzuPvuu7n11lv529/+RklJCe+//z7vvvuu7XWzZs1i48aNFdaVmZnJY489RkREBOPHj+fXX3/lwoULzJ49m5MnT/L3v/+9wvJGo5HZs2czYsQIxo4da1tfdHQ0kydPZvz48Tz00EMkJSUBZVeNnnrqKUaMGMFnn31Geno6jz76KNnZ2QwdOpTk5GQee+wxcnJymDBhAvv27bNdaTp+/Dh33XUXERER3HvvvaSmplb7Xl9//XUA7rrrLgA6deoEQHFxMf/85z8ZO3YsERERrFmzBoBVq1Yxffp0HnroIYYNG8Yrr7xStx+SsDt7HicWi4X58+cze/Zsbr/9dr7++msA/ve//zF16lQUReHgwYMMHz6cgoICpk2bxssvv8z48eMZPXq07eTCzJkzWbVqFcnJyYwcOZLJkyfzwAMPUFBQwDPPPMPdd9/NkCFDmDFjBoqi8Prrr5Oens6TTz5JcnIyQ4cOrTZ+gA8//JA5c+Ywbdo0hg4dyscff1xfH4cQwsHVa0F15MgRJk+eTHx8PACDBw9m7dq1rF27li+++AI3NzdmzZoFQExMDO+++65t/tSpUwGYMWMGc+fOZcuWLSiKwvLly+szZCFEM6DX62nVqhVnz57l8OHD6HQ6li1bxs8//4zRaGTnzp1MnDiRDRs2oCgKRUVF7Nmzh9tuu63Cel577TX69+/P+vXrWbhwIbNnz7b9MAsPD+eTTz6psPzSpUspKipi06ZNfPXVV3z00UeYTCbmzJnDf/7zH1avXs2DDz7I3Llzba/p1KkTW7Zs4dFHHyUgIIDPPvsMb29v2/yPP/6YgIAAVq1aVWFbzz//PE888QTr169n9OjRLFmypNr3OmfOHAB++OGHCuv48MMP8fb2ZsOGDSxZsoQPP/zQdgb/8OHDLFy4kHXr1rF9+3ZOnjx57R9MPbr45N7Fvv322wrNHVNSUpg6dSojR47k8ccfp7CwEIC8vDweffRRRo0axdSpU8nIyGjQ+O3BXsdJeZ5/8803WbFiBVu3buXgwYNMmzYNtVrNd999x5w5c5g/fz5ubm4AmEwmVq9ezX/+8x9mzpyJyWSqsM64uDjeeecdvv76a3bs2EGXLl1YtmwZW7Zs4ffff+fYsWPMmTOHgIAAPvrooxrjL28KefLkSb788kt++OEHPvvsM/Ly8ur0MxBCNA712uRv+fLlvPzyy7zwwguV5v373//mnnvuoXXr1kBZQfX555+TlJTEDTfcwIsvvkhmZiYlJSX07NkTgAkTJrBw4UKmTJlSn2ELIepaeDgcO1b36+3WDZYsqdVLVSoVTk5O3HDDDXh5efHtt99y9uxZ4uPjKSoqIiwsjNDQUA4cOEBKSgqDBw9Gr9dXWMfevXttV3fCwsLo0aMHR44csf3Iu9SBAweYNGkSarUaf39/fvzxR06dOkVSUhKPP/64bbmCggLb4+7du1/1e8vKyiIjI4MhQ4YAVPibWdV7rc7evXt58803AfDx8eHWW29l//79uLm5cf3119veZ1hYGLm5uVcdZ0M5cuQIc+bMsZ3cKxcbG8unn35Kq1atbNPmzZvHlClTGDNmDB999BGLFy9mxowZvP/++/Tp04fPPvuMNWvW8MYbb/D+++/XfbD1eazExFz1y+xxnOzZs4fjx4+zfft2nJ2dKSoq4uTJk/Tp04c333yTiIgIJk+eTO/evW2vmTRpEgBdunTB39+/UoHv6+tLixYtABg7dizR0dF8/fXXnD17lpycHIqKivDy8qoynuriB+jXrx96vR5fX1+8vLzIz8/Hw8PjKveyEKKxq9eC6o033qhyenx8PPv377fNLywspEuXLrz44ouEhoYyc+ZMFi9ezC233IK/v7/tdf7+/qSlpVVaX15eXqWzQhaL3LsihMOoxQ+5KxYVddUvMZlMxMXF0b59e7Zu3crChQu57777mDBhAtnZ2SiKAmA7+56SksLTTz9daT3ly138/HJ/e7Tain9yExISsFqttGjRwnbvisViqdARgJOT01W/P51OV+G50WgkPT2dU6dOVfteq3K592cwGGzTVSrVZddjb1Wd3DOZTPzrX//i2WeftTVlNJvNHDhwwHaFYsKECdx7773MmDGDHTt22Jqijx07lldffRWz2VxpX19zPqrPY+Uq2es4sVgszJgxA19fX3r37k1WVhYuLi5A2RVENzc3/vjjDxRFQaVSAaDRaGyvt1qtlY61i4+jpUuXsmXLFiZNmsSNN97IqVOnmsVxIISoP3bplGLZsmVMmTLFdhbL1dW1ws2fDz30ELNnz2bw4MGVXlv+x/NiS5YsYdGiRRWm+fn5sXDhQmIcKDldTlQtfhTaQ2OI0xFi1Gg0pKbmU2w042zQ8fvvhRV+QPTGMeK8Eo0hzsvFmJGRgdFotC1jtVpZvnw5bdq0ISMjg7Vr19K9e3dat27N+fPniYyMJCQkhKioKAIDA9mxYwdOTk6UlpZW2k6nTp147733GDNmDGlpaezfv58JEyZw6tQp8vPzKy0fFBTEt99+i6enJ3l5ecyePZt33nmHzMxMvv32Wzp37sy2bdvYvXs3c+fOJT8/n1OnTtl+HJaWlnLkyBHS0tIwGo22v2/l7698u6dOncLT05Ovv/6a6667jm3btnH8+HHc3Nyqfa9qtZr9+/fbfphGRUXRpk0bFi9ezP33309eXh4bN25k+vTpxMfHc+HCBdv7uzROR1PVyb3//Oc/TJw40XbVAiA7Oxs3Nzfb+7j4JF56errtBJ9Wq8XNzY2srCwCAwMrrLe2+cjex5kjHSehoaF88cUX/POf/2T37t289NJLPPzww7Rt25ZZs2bxzDPPsGrVKt5++22GDRtGfn4+X331Fffeey9nz54lMzOTgoICLly4QHx8PC4uLhXe28aNGxkwYAChoaEcP36cP/74gxMnTpCbm0txcTFRUVEV9kd18aekpAB/fXblx2RVJ37rmr2/LxfT6XSkpuZRWGzC1VlPdHQRZrO5weNoTHnVHmTf1C+7ZL+tW7fy5Zdf2p6npKQQGRnJnXfeCZSd/dFqtQQGBlY4U5uRkUFAQECl9d1///2MHz++wrTys7zh4eEVziA5oqioqApNFxxVY4jTkWI8k3Uao9mCQaehZ88OleY7SpyX40j7szo1xZicnExubi6vvvoqUPZDsUuXLnz++ed4eXnh5ubG888/z9GjR9Hr9dxwww1oNBrbOvv160fHjh2r3MaCBQv417/+ZeuU4a233uK2225j3759uLu7V3hNVFQUL774Iq+//jrz5s0D4NVXX2XgwIEsXryYN954A6PRiJubGwsXLqRly5a4u7tX2PaIESNYuHAhX3zxBQaDgfDwcKDsLHnv3r0pLS21bXfRokW88sorrF69Gm9vb/7973+TnZ1d7Xu97bbbmDdvnu1+qt69e9OpUydeeeUVXnnlFSwWC88++yx33nknq1atIj093RbXpXFezsWFoL3s3r2b8+fPM2vWLPbt22ebXtXVhapO4pVTqyvfhlybfOQIx5mjHCdQ1sz17bffZvbs2eh0OqZMmcK0adN45ZVXGDFiBJMmTeKmm25i0qRJTJ06FXd3d0wmk61Z3kcffUTPnj1ZuXIlrVu3tu338u0888wzvPLKK2zfvh1XV1d69+6Ni4sLQ4YM4b///S/vv/8+8+fPt72muviPHz8O/PW3vPyYvLhIrw+O8H251JmsWIqNpTgbtHTv3t5ucTjafnEUjvidsbc6z0VKAxgyZIiSlJSkKIqiXLhwQRk4cGCF+RcuXFD69u2rJCYmKlarVZk1a5byySefKIqiKGPGjFEOHjyoKIqivPTSS8rnn39+RdssKSlRDh48qJSUlNThO6kf5e/P0TWGOB0pxhVbTynfbj6urNh6qvLMhjn0rpkj7c/q1FeMVqtVyc/PV0aPHq2kp6df8/oaw75UlPqP055/m8tz0cyZM5WRI0cqt99+u3LbbbcpPXv2VJ599lnFZDIp119/vVJaWqooiqKkpKQoQ4cOtb32/PnziqIoitlsVq6//nrFZDJd0XZres+N5btRlbo+Ti52pfvl3nvvVfbu3Vun23Zkjvh9WbX9tPLt5uPKqu2n7RdEI8mr9uCI3xl7q+tc1OAD+yYnJxMUFFRhmo+PD6+++iqPP/44I0eORFEUHnzwQaDszNb8+fMZNWoUxcXF3HfffQ0dshCiGTp69ChDhw5l0qRJFe7lFI3f/Pnz2bRpE2vXrrX1NPf++++j0+no06ePrdvvNWvWMGjQIKCsl9rye602btxInz59Kt0/1RzJcSKEEA3U5G/btm22x927d6+y6/MRI0YwYsSIStM7d+7MihUr6jU+IYS4VPfu3SsMdiuah5dffpmZM2fy8ccfExwcbBtj6dlnn2XmzJmMGTMGd3d3FixYYOdIHYMjHCdLly616/aFEMIx7yAWQggh6tjFJ/fK9evXj379+tmeh4aGVvkD3cvLq9J4SUIIIQTU88C+QgghhBBCCNGUSUElhBBCCCGEELUkBZUQQgghhBBC1JIUVEIIIYQQQghRS1JQCSGEEEIIIUQtSUElhBBCCCGEELUkBZUQQgghhBBC1JIUVEIIIYQQQghRS1JQCSGEEEIIIUQtSUElhBBCCCGEELUkBZUQQgghhBBC1JIUVEIIIYQQQghRS1JQCSGEEEIIIUQtSUElhBBCCCGEELUkBZUQQgghhBBC1JIUVEIIIYQQQghRS1JQCSGEEEIIIUQtSUElhBBCCCGEELUkBZUQQgghhBBC1FK9F1QFBQWMHTuW5ORkAGbNmsXw4cMZN24c48aN4+effwYgMjKSiIgIhg8fznvvvWd7/fHjx5k4cSIjRozgpZdeorS0tL5DFkII0cRcmouWLVvG2LFjiYiIYNasWZhMJqD6nJOSksLUqVMZOXIkjz/+OIWFhXZ7L0IIIRxLvRZUR44cYfLkycTHx9umxcTE8M0337B27VrWrl3LsGHDKCkpYfbs2SxevJiNGzcSExPDzp07AZgxYwZz585ly5YtKIrC8uXL6zNkIYQQTcyluSguLo4vv/yS77//nnXr1mG1Wvnuu++A6nPOvHnzmDJlCps3byY8PJzFixfb6+0IIYRwMPVaUC1fvpyXX36ZgIAAAIqKikhJSWHu3LlERESwcOFCrFYr0dHRtGrVirCwMLRaLREREWzevJlz585RUlJCz549AZgwYQKbN2+utJ28vDySk5Mr/EtNTa3PtyaEEKKRuDQX6fV6XnnlFdzc3FCpVHTs2JGUlJRqc47ZbObAgQOMGDGiwnQhhBACQFufK3/jjTcqPL9w4QL9+/fn1VdfxcXFhccee4wVK1bg4uKCv7+/bbmAgADS0tJIT0+vMN3f35+0tLRK21myZAmLFi2qMM3Pz4+FCxcSExNTx++qfkRFRdk7hCvSGOJ0hBg1Gg2pqfkUG804G3T8/nshFovFNr83FePUaDS2xxcv5wgcYX/WpDHECBKnvVyai0JDQwkNDQUgKyuLb7/9lvnz51ebc7Kzs3Fzc0Or1VaYXpW8vDzy8vIqTHO0Y1oIIUTdqteC6lJhYWF89NFHtufTpk1jzZo1jBw5stKyKpUKRVGqnH6p+++/n/Hjx1eYZrFYyMzMJDw8HIPBUAfR15+oqCh69+5t7zBq1BjidKQYz2Sdxmi2YNBp6NmzQ6X5F8e5ftdZTGYLep2GiIFtGzLMy3Kk/VmdxhAjSJzljEajw5zoSktL429/+xsTJ06kX79+HDp0qNIyV5OLoPYn+JpaEVtXZL9UzZH2i06nIzU1j8JiE67OeqKjizCbzQ0ex6UnKkVFsm/qV4MWVCdPniQ+Pt7WbEJRFLRaLYGBgWRmZtqWS09PJyAgoNL0jIwMW5ONi3l4eODh4VFhmtForPBaIRyZyWzBaJaz2EI0lDNnzvDII49w77338tBDDwFUm3N8fHwoKCjAYrGg0WiqzUVQuxN8jaXYbmiyX6rmiPvlTFYsxcZSnA1aundvb7c4HG2/OApH/M7YW12f3GvQbtMVReHNN98kNzcXs9nMsmXLGDZsGD169CAuLo6EhAQsFgsbNmxg0KBBhIaGYjAYbFX1mjVrGDRoUEOGLIQQookpKCjg4Ycf5tlnn7UVU0C1OUen09GnTx82btxYYXpVPDw8aNGiRYV/QUFB9f+mhBBC2E2DXqHq3Lkzjz76KJMnT6a0tJThw4czduxYAN566y2efvppjEYjgwcPtjUDXLBgAXPmzKGwsJCuXbty3333NWTIQgghmpgVK1aQmZnJf//7X/773/8CMHToUJ599tlqc87LL7/MzJkz+fjjjwkODubdd9+151sQQgjhQBqkoNq2bZvt8dSpU5k6dWqlZQYMGMC6desqTe/cuTMrVqyo1/iEEEI0feW56IEHHuCBBx6ocpnqck5oaChLly6tz/CEEEI0Ug16hUoI8ZeV204D4Oass3MkQgghGgudTnKGEI5GCioh7KS8Ewq9rkFvZRRCCNFIbYqMIz4xjzNZsTjpNYy6sY29Q7oqmyLjKDFZGmXsQlyOFFRCCCGEEI1AiclCYbGJYmOpvUOplRKTpdHGLsTlyKlxIYQQQgghhKglKaiEEEIIIYQQopakoBJCCCGEEEKIWpKCSgghhBBCCCFqqcaCqri4mN9//x2AJUuWMGvWLFJSUuo7LiGEEKICyUdCCCEcUY0F1axZs9i6dSvR0dH873//IyQkhLlz5zZEbEIIIYSN5CMhhBCOqMaCKikpiX/+859s376d8ePH8/TTT5OTk9MAoQkhhBB/kXwkhBDCEdVYUJnNZgB+++03+vfvj8VioaioqN4DE0IIIS4m+UgIIYQjqnFg3169ejF69Gg0Gg29evXi/vvvZ8CAAQ0RmxBCCGEj+UgIIYQjqrGgmjt3LocPH6ZTp06o1WoefvhhBg8e3BCxCSGEEDaSj4S4cpsi4ygxWXDSaxh1Yxt7hyNEk1Ztk7/ynpQ0Gg19+vTB3d0dgFtuuYWVK1c2SHBCCCGE5CMhrl6JyUKxsZQSk8XeoQjR5FVbUM2bN8/2+O67764w79tvv62/iIQQQoiLSD4SQgjhyKotqBRFsT02Go3VzhNCCCHqk+QjIYQQjqzagkqlUlX5uKrnQoir99uRFNKyiuQHoRA1kHwkhBDCkdXYKYUQ4trodWrW7zqLyVzWjv3MuRxeAM5lFJCUls+R006Et/UlxN/NvoEKIYRotMo7oQDwdNXbORohmpdqC6qSkhL++OMPFEWp8Lh8nhDiypnMFoxmC/Hn89hz9DwvABNuaU9iah6nk3LYefgcg68PpU2Ip71DFcLhSD4SomblnVAAOOk1do7m8pz0GumFUDQp1RZURqORp556yvb84sdX08SioKCAe+65h08++YQWLVqwbNkyli5dikqlIjw8nHnz5qHX61m0aBErV67Ew8MDgEmTJjF16lSOHz/OnDlzKCgooE+fPsybNw+tVi6sicYnJaOAvTHnCfZzBUCnVdOuhRfXtfNl5fYzHDieRosAdztHKYTjqYt8dGkuioyMZP78+RiNRkaNGsX06dMBqs05KSkpzJgxgwsXLtCmTRsWLFiAq6tr3b5RIRxYXRdAFxeAQjR21VYm27Ztu+aVHzlyhDlz5hAfHw9AXFwcX375JatWrcLV1ZWZM2fy3Xff8cADDxATE8O7777L9ddfX2EdM2bM4PXXX6dnz57Mnj2b5cuXM2XKlGuOTYiGZLUq7D2WiqebgRH9WlaYp9GoufG6YDbuiefQyXTuGd7JTlEK4ZiuNR9dmotKSkqYPXs2S5cuJTg4mMcee4ydO3cyePDganPOvHnzmDJlCmPGjOGjjz5i8eLFzJgxow7enRCNgxRAQlSv2k4p7rjjDn744YdKPSpdjeXLl/Pyyy8TEBAAgF6v55VXXsHNzQ2VSkXHjh1JSUkBICYmhs8//5yIiAheffVVjEYj586do6SkhJ49ewIwYcIENm/eXOt4hLCXcxkFGE0WurfzQ6+r3BQjwMeFDmFeHI/P4mRClh0iFMJxXWs+ujQXRUdH06pVK8LCwtBqtURERLB58+Zqc47ZbObAgQOMGDGiwnQhhBACLlNQPf7442zevJnBgwfz1ltvkZiYeNUrf+ONN+jTp4/teWhoKDfeeCMAWVlZfPvtt9x6660UFhbSpUsXXnzxRVavXk1eXh6LFy8mPT0df39/2+v9/f1JS0urtJ28vDySk5Mr/EtNTb3qeIWoL7HJORh0Gltzv6r06OCHi5OWRT8cwVxqbcDohHBs15qPLs1Fl+aWgIAA0tLSqs052dnZuLm52ZqbV5eLQPKREAAlxlJOJ2VzIbfY3qEI0SCqbfI3YsQIRowYwblz51i+fDn33nsvnTp1YurUqdxyyy3XtNG0tDT+9re/MXHiRPr16wfA559/bpv/0EMPMXv2bAYPHlzptVW1l1+yZAmLFi2qMM3Pz4+FCxcSExNzTbE2lKioKHuHcEUaQ5yOEKNGoyE1NZ9ioxlXFwOJafmE+OhITT2P0cMZwHZ11tvDGaPZSlGxkU6hThw+k8dXK3fRu71j9PrnCPuzJo0hRpA4a6uu81FVQxWoVKqrnl6V2uYjR9vnjkL2y190Oh2pqXlAWf5wddYTHV2E2Wy2zSssNgFg9nbDaLaQV1BcYbnq1lvucsuUr//S9V08r9Sq5m+/nSItu2yeVqPith4e9O3khkGvty13NfFdqd7I9+VyZN/Urxp7dwgNDWX69Ok888wzbN++nR9++IH58+ezZcuWWm3wzJkzPPLII9x777089NBDQNkfhsjISO68806gLNlptVoCAwPJzMy0vTYjI8PWZONi999/P+PHj68wzWKxkJmZSXh4OAaDoVaxNpSoqCh69+5t7zBq1BjidKQYz2Sdxmi2kJSWj6JAeIcQfDyccHcpS14hISEAuLvoMJmtGM0WWmvV5BlTOBRXyt/u6oVabd8xdhxpf1anMcQIEmc5o9FY6xNddZWPLs0t6enpBAQEVJtzfHx8KCgowGKxoNFoqs1FULt81Fi+Gw1N9ktlZ7JiOROXSEhICM4GLd27t68wr/weJ293AyUmC24epZWWu9SVdjZRvv6q1ncmK5ZjZy9w8EQaPp5O3DuyHS0C3PnlQCKbD6WRkqfjX3+7njNZcRQbS68qvqsh35eqybFU2bXkoqpU2+TvUikpKRw9epTTp0/TunXrWm2soKCAhx9+mGeffdZWTAE4OTnxzjvvkJSUhKIofPvttwwbNozQ0FAMBoOtql6zZg2DBg2qtF4PDw9atGhR4V9QUFCtYhSirp1OysHLzYC3e82FvUqlYvzg9pzLKODg8aqbFAnR3F1rPurRowdxcXEkJCRgsVjYsGEDgwYNqjbn6HQ6+vTpw8aNGytMr4rkI9HYlHc2UT6GVW0cOZ3BvmOpBPi48N5zg7l7WCdu6hHCvx7ux5N39iA6NpMftp6qw6iFcCyXvUJlMpnYvHkzP/zwA2fOnGHChAl8+eWXhIWF1WpjK1asIDMzk//+97/897//BWDo0KE8++yzvPrqqzz++OOYzWZ69erFgw8+CMCCBQuYM2cOhYWFdO3alfvuu69W2xbCHvKLTKRlFdG7c8AVd+98U48Qlmz8g1U7YunbTX6ICQF1m48MBgNvvfUWTz/9NEajkcGDBzNy5Eig+pzz8ssvM3PmTD7++GOCg4N599136/T9CVFXFEUhM7eYrDwjnq56nA31O9TM1gOJRJ1Ip1WQO0N6h+Hp9tfJQ5VKxcgBrTl29gIrt8UyblDbKjtmEqKxq/Yo+9e//sWmTZto06YNU6ZMYfTo0ej1tRt5u7zL2wceeIAHHnigymXK28hfqnPnzqxYsaJW2xXC3uJSytq7tw298gF7tRo1tw9sx5frYjiVmE3Hlt71FZ4QjUJd5aOLu18fMGAA69atq7RMdTknNDSUpUuXXvU2hWgoiqLwR9wFTiflUFRS1vRPpYIgH1fahnjSo6N/DWu4eodOpvPh8t8J9nOlX3hwtc3UH7q9Gwf+SCXy6HkGXx9a53EIYW/VFlRms5mvvvqK8PDwhoxHiCZDURTiU/II9XfF1UmH0XzlzSmG92vJ9z+dYNWOWGbed0M9RimE45N8JETNYs5e4MjpTAJ9XOjbNRAng5aE1HySUvOZ82kkA3uG8vDt3fD1dK6T7cUm5/DWkv2EBbpzc48QLNbKnbeU83Z3YtrornyyKprk9AJ8PJzqJAYhHEW191DNnz8fZ2dnkpOTbdM2bNhAbGxsgwQmRGOXk2+ksMRMuxZeV/1aFycdIwe0Zk90CqkXCus+OCEaEclHQlze7iMpHPgjjbBAd4b0bkHXNr6E+LnRs4M/E4a0Z8rwTuyNOc9T72zn18PJNa+wBmlZRbz6xV5cnfW88kj/apvxbYqMY/WOWFbviMWgVePuouNUYs41b18IR1NtQRUVFcXkyZOJi4uzTUtISODee+/lyJEjDRKcEI1ZalYRACGXGXvqciIGtkWtVrF+19m6DEuIRkfykRDVS8ks4N3vogjwdqZ/eFCl+3W1GjWTR3Rm0fNDaBHgxjvfRPH2/w6QnV9Sq+0VlZh55fM9mEqtvPJI/8te8Srv8KLYWIrZYqVDmDfp2UXkFtRukG4hHFW1BdX777/PBx98wMCBA23TnnzySf7973/LzbhCXIG0rCJcnLS2btKvlq+nMzdeF8LWA4mU/NkVrhDNkeQjIaq3anssCjC0TxhaTfWdN4f4u/HWkzczbVQX9sak8sTb2/hpX0KV46xVJ6/QxIbf4riQW8ycB/vSKsjjqmLtEOaFCjiRkH1VrxPC0VV75OXn5zNgwIBK0wcNGkRWVla9BiVEY6coCqkXCvH3dr7i3v2qMvqmNhSWlLLz8Lk6jE6IxkXykRBVy8orYeuBJG67oSUuTjWfvNNo1Ey6rSML/3kLrYI9+HD576z59cyf4yVWX1gpikJCah4/70+k1GLlzcdvJryd31XH6+qsI8jPlRMJ2VivopATwtFV2ynF5Q4stfqKh68SollKTi+gxGQhwNvlmtbTtY0PrYM92Lg7juH9Wl5TcSZEYyX5SIjKnPQa3v+/Q1gsVoJ8qs81TnpNpcF7wwLdefPxm/j1cDKfr43htyMpHDmdQV6hib7dggj2c8XT1UBmTjFnzuWwZucZ/ojLwsNVz/B+rWgf5lXruNuGeLI7OoVz6QX4edVNBxlC2Fu1BVWrVq2IjIzkxhtvrDA9MjISP7+rPyshRHMSc/YCwFUXVHqdmvW7zmL6s0dAvU7D6Btbs3hlNCcTsunc2qfOYxXC0Uk+EqIyo9lCzNkLhAW64+ykvezAvOX3Ml1MrVZxS+8wLuSWcDIxm+T0fFbtiGXl9rLOXlQqKD+X4e1u4MbrggkLdMfVuXbN2MuFBrjhpNdwOilHCirRZFRbUD377LM88MADTJw4kV69eqEoCocPH2bFihV88sknDRmjEI1OzJlMnA21u3/KZLZU6GL91hta89WGP/gxMk4KKtEsST4SorLjcVmYS610aXNteUGtVtE62IPrO/qTU2AkKa0Ac6mFsEB3/L1daBPsQZtQTzbujqtUlNWGRq2iXagnJxNzsFit17w+IRxBtQVVu3btWLJkCZ9//jnvvPMOarWanj178t1339GyZcuGjFGIRkVRFGLOXCDQx6VOmui5OOm4tU8Ym/cm8HBEOF7uhppfJEQTIvlIiMpOJ5dd4anLMZ2c9FpaBLjhbNAy/pb2dbbeS7UK9uBYXBYZ2cW4OV/9IN1COJpqCyqAtm3bMn/+/IaKRYgmIfVCEVl5JXRs6VVn6xx1Y2s27I7j5/0J3HVrxzpbrxCNheQjIf5SWGwmI7uYPl0C7B1KrYT6u6FRq0jJKKRNiKe9wxHimsndvELUsZgzmQAE+dRu/KmqtAzyoHt7Pzbvib/saPRCCCGavqT0fABaB19dt+WOQqdVE+TrSkpmgb1DEaJOSEElRB2LOXsBD1c9nm5124xh9I1tSM8uJup4Wp2uVwghROOSnFaAr6cTHq6Ntwl4WKAb+UVm8gplkF/R+FVbUP38888AmEymBgtGiKYg5kwm3dr61sn9U+W9/q3cdpqUjAJcnLT8GBlXB1EK0XhIPhLiLyazlYycYtqGNu6mci0C3IGyYUaEaOyqLagWLlwIwN13391gwQjR2KVnFZGeXUx4O986W2d5r39mi5WOYd4cOpEuzSREsyL5SIi/ZOaV9bTXrpEXVB6uetxd9CSlST4TjV+1nVK4uroyYsQI0tLSiIiIqDR//fr19RqYEI3R8fgsALq18eX3Uxl1vv5ubX04EpvBwu8Pc1OPUCIGtq3zbQjhaCQfCfGXjLxS3F30eLsbMJobd7fjIf6uxCblUGIsxclw2X7ShHBo1X57v/jiC44fP85LL73E3LlzGzImIRqt2OQc9Fo1rYI96qWgcnXW0SrIg9NJOVzfqXH27iTE1ZJ8JESZ0lIruUUWOrX0qFWzcie9hk2RcbZBgD1d7dtleZCvKycTsjmRkEXPjpLTRONVbZM/Nzc3brjhBj799FO6desGQGlpKV27dqVv374NFqAQjcnppBzahHqi1dRffy+dWnljKrUSl5JXb9sQwpFIPhKiTFp2EYoCgb6170W2xGSh2FhKsbG0wiDy9uDv5YxKBTFnLtg1DiGuVY3XV/Pz85k2bRp+fn5YLBbS0tL45JNP6NWrV0PEJ0SjYbEqnEnO4ba+9TvQaJCPCx6uek7EZ6EoSp10fiFEYyD5SDR35zMLUVFWiDQFOq0aX09nYs5KQSUatxoLqrfffpsFCxbQv39/APbs2cNbb73F8uXL6z04IRqT5PR8SkwWOoR51et2VCoVHVt6c/B4GsfOXiC8nV+9bk8IRyH5SDR35y8U4u6iRqdtOqPeBPm6cCI+G6PZgkGnsXc4QtRKjUdkQUGBLXkBDBgwgOLi4itaeUFBAWPHjiU5ORmAyMhIIiIiGD58OO+9955tuePHjzNx4kRGjBjBSy+9RGlpWQ82KSkpTJ06lZEjR/L4449TWFh4VW9OiIZ0OjEHgA5h3vW+rTYhHhh0GtbsPFPv2xLCUVxLPhKisSsqMZOZU4yXa/133lB+r9XqHbGs3hHLtgOJ9batIB9XSi1WTiVm19s2hKhvNRZUarWac+fO2Z4nJyej0dR8BuHIkSNMnjyZ+Ph4AEpKSpg9ezaLFy9m48aNxMTEsHPnTgBmzJjB3Llz2bJlC4qi2M42zps3jylTprB582bCw8NZvHhxbd6jEA0iNjkHZ4OGEH+3et+WVqOmUytv9v+RSkqGdDkrmofa5qPqrF27ljFjxjBmzBjefvttQE7wCcf1R1wWigJerg1zFaeh7rUK9HWR+6hEo1djQfXkk09y9913M2PGDGbMmMGkSZN4/PHHa1zx8uXLefnllwkIKOu1JTo6mlatWhEWFoZWqyUiIoLNmzdz7tw5SkpK6NmzJwATJkxg8+bNmM1mDhw4wIgRIypMF8JRnU7Kpl0LLzTqhrmnqXMrHzRqNWt/latUonmobT6qSnFxMW+88QZLly5l7dq1HDx4kMjISDnBJxzWkdMZqNUqPFyaVrM4g05Dm2BPYs5k2jsUIWqtxuvGt912G23btmXv3r0oisLf//532rVrV+OK33jjjQrP09PT8ff3tz0PCAggLS2t0nR/f3/S0tLIzs7Gzc0NrVZbYXpV8vLyyMur2OOZxWLfnmtE82L+s9e9sTc33LhQLk5abunVgl8OJDF1ZBc87Nz9rRD1rbb5qCoWiwWr1UpxcTEuLi6Ulpai1WorneBbuHAhd911FwcOHOCjjz6yTb/33nuZMWNGpfVKPhL1JTo2kwBv5wY7aVcbF3fLfjVdsoe382Xz3gTMpdYmdX+YaD6uqCFu27Ztadv22n4oKopSaZpKpbrq6VVZsmQJixYtqjDNz8+PhQsXEhMTU8uIG1ZUVJS9Q7gijSFOe8SYkmXCXGpFY84iKioKjUZDamo+xUYz3h7OGM1WioqNAHh7lPXOlJKSYntePr+qZat77u3hjFJqwWS28MonO+jW2oPe7Zwq/Hi7uDlUbX/UyWdedyTOa1cX+QjKumJ/9tlnGTVqFE5OTvTt2xedTnfNJ/hqm48ceZ/bk+yXMkVGK3Hncmkf8lf+MHu7YTRbyCsorvAYqHZeXSzn6qwnOroIs9kMgE6nIzU1j8JiU4Xl/K9wW67OerxcNZjMFjb8speW/oZa7aPeyPflcmTf1K8GG5Y6MDCQzMy/Luemp6cTEBBQaXpGRgYBAQH4+PhQUFCAxWJBo9HYplfl/vvvZ/z48RWmWSwWMjMzCQ8Px2Co3cHZUKKioujdu7e9w6hRY4jTXjGm74kH0hkxuBdBf44PcibrNEazBXcXHSaz1dYG3d1FB0BISIjtefn8qpat7rm7iw4vs5WgxGKSMo307+5Nz56dKsS1ftdZTGYLep2GiIFX/yNUPvO6I3GWMRqNDnGi68SJE6xcuZLt27fj7u7O888/z+7duystd7Un+GqTjxrLd6OhyX75y56jKSik0KF1IIV5FwgJCcHb3UCJyYKbR2mFx0C18+piOWeDlu7d21eI70xWLMXG2m3L2aBlaJ8wvtuxmVKdH717d6z1fpLvS9XkWKqsrnNRg11X7dGjB3FxcSQkJGCxWNiwYQODBg0iNDQUg8Fgq5zXrFnDoEGD0Ol09OnTh40bN1aYXhUPDw9atGhR4V9QUFBDvTUhiE3Kwd1FR6CPS4Nvu0trH0pMFk4n51SaZzJbMJrLrmIJIf7y22+/MWDAAHx9fdHr9UyYMIF9+/bVeILv4ulVkXwk6kN0bCYGvQZ/76Yx/tSlPN0MtAhw43h8lr1DEaJWaiyoli5dSkHBtfciZjAYeOutt3j66acZPXo0bdu2ZeTIkQAsWLCA+fPnM2rUKIqLi7nvvvsAePnll1m+fDmjR4/m4MGDPPfcc9cchxD14XRSNu1beNllkN1AHxd8PZ04euYCpRZrg29fiIZSV/kIoHPnzkRGRlJUVISiKGzbto2+ffte8wk+IepDdGwmXVuXdUTUVHVp7WMbsF6IxqbGJn+nTp1ixIgR3HLLLdxzzz1cd911V7WBbdu22R4PGDCAdevWVVqmc+fOrFixotL00NBQli5delXbE6KhGc0WElLzuWGofc5Cq1Qqwtv6svPwObYfTGJYv1Z2iUOI+nat+ehiN998M3/88QcTJkxAp9Nx3XXX8eijjzJs2DDmzJlDYWEhXbt2rXCCb+bMmXz88ccEBwfz7rvv1tXbEuKysvNLSEzNZ0jvMHuHUq86t/bh5/2JnMsooEWAu73DEeKq1FhQvfbaa7z44ousX7+eefPmoSgKkydPJiIiwuHvTRKiIcSdy8VqVWjfwstuMQT7ueLr6cTyracY2icMjabpnsUUzVdd56NHH32URx99tMI0OcEnHE1MbNn4TN3b+3HsbNMdq6lLax8ATsRnSUElGp0r+tXl5ubGyJEjGTt2LDk5OXz33XeMHDlSxoUSAjidlANAx5ZedotBpVLRvb0fqReK2Hn4XM0vEKKRknwkmpvoM5m4OGlpF+pp71DqVai/G27OOo7HZ9s7FCGuWo1XqCIjI1m+fDl79uxhxIgRfPTRR3Tu3JnExESmTJliuw9KiObqdFI23u4GfDyc7BpHy0B3Wgd7sPyXUwzu1cKhxyoRojYkH4nmKPp0Bt3a+jb5lgdqtYrOrX2kYwrRKNV4dL766qv06tWLX375hVdffZXOnTsD0LJlSyZNmlTvAQrh6E4n5dAhzNsuHVJcTKVScfewjpzLKCDySIpdYxGiPkg+Es1NZk4xKZmFdG/vX/PCTUCX1j4kpeWTX2SydyhCXJUaC6p169bh5eWFu7s7GRkZfP3111itZT2JPfPMM/UeoBCOrKjEzLmMAjrYsbnfxW68LoSwQDeW/XISq1V6ShJNi+Qj0dxEx5Z149+9vZ+dI2kY5fdRnUyQZn+icamxoHrttdfYsWNH2cJqNVFRUbz55pv1HZcQjcKZ5FwUBbt2SHExtVrFpFs7kpCaz75j5+0djhB1SvKRaG6Oxmbi7qKjdbCHvUNpEB3CvFCrVdLsTzQ6NRZUhw8ftnUP6+vrywcffMC+ffvqPTAhGoPyDik6hHnZNY6LDewZSrCfK9//fErG8xBNiuQj0ZwoisKR2AzC2/mhbib3xDoZtLQN8eCEFFSikamxoDKbzZhMf7VlLS0trdeAhGhMTidlE+DtjKeb4wwhoNGomXRrB86eyyU5vW4GQRXCEUg+Es1JWlYRGdnF9Ggmzf3KdW7tw8nEbCwyUL1oRGrs5e+WW27h4YcfZty4cahUKjZs2MDgwYMbIjYhHF5sclmHFADrd53FZLYA4Oass2dY3NI7jP/7+RRHYjO57YamPRikaD4kH4nmpPz+qeuaWUHVpbUPG36LI+58nsM0pxeiJjVeoXrhhRcYNmwYW7duZceOHQwbNox//OMfDRGbEA4tr9BE6oUi2v/Z3M9ktmD885+p1GLX2LQaNXcO7UBmTjGpF4rsGosQdUXykWhOok9n4uVuICyweQ1y26W1L4A0+xONSo1XqDQaDffddx/33XdfQ8QjRKMRm5wDONb9Uxe77YYwvt5wjGNnLzSbG5pF0yb5SDQlmyLjKDFZcNJrGHVjmwrzFEXh6JkMurfzs/uQHA3N39sZP08njsdlMfbmtvYOR4grUmNBtXHjRhYsWEBubm6FG9wPHTpUr4EJ4ehOJ5V16+qoTRJ0Wg3XtfVj3x+ppF4otHc4QlwzyUeiKSkxWSg2Vn0fYHJ6AVl5Rrp3aF7N/cp1bu3D8QS5QiUajxoLqg8++ICZM2fStWvXZneWRIjLOZ2YQ6i/K652vl/qcjq09OJIbAZHTmfaOxQhrpnkI9FcHD3TfO6fctJrbFfryp93ae3Db0dSyMwpxs/L2c4RClGzGgsqDw8Phg8f3hCxCNGoxCbncF07x052Wo2azq19+P1UBifis+j856CJQjRGko9EcxF9OhM/L2eCfV3tHUqDuPRqXbe2f95HlZDFzV6h9gpLiCtWY6cUPXr0YOfOnQ0RixCNRlZeCRdyS2wdUjiyDi28MOg0fP/zSXuHIsQ1kXwkmgOLVSE6NpPu7Zvf/VPl2oZ6otdpZIBf0WjUeIVq586dfPPNN+h0OnQ6HYqioFKppM26aNZiHXBA3+potWq6tfUh6kQ6sUk5jaIIFKIqko9Ec3AmOYf8IhPXdwqwdyh2o9Wo6djSS3r6E41GjQXV119/3QBhCNG4nErKRq0qO4vWGHRu5cPJxByW/XKSlx7sB1QcN0uv0xAxUHpTEo5N8pFoDg6dTEelgus7+ts7FLvq0tqHVdtjKTGV4qSv8eeqEHZVY5O/0NBQjh49yvLly/Hx8eHw4cOEhkp7VtG8nU7MoWWQR6P5I6/Xabh9YFv2xqQSl5ILXDJultm+42YJcSUkH4nm4NCJdNq38MLTzWDvUOyqc2sfLFbF1iJECEdWY0H12Wef8X//939s3ryZkpISFi1axEcffdQQsQnhkKxWhZMJWXRq5W3vUCrQ69Ss33WWldtOs3Lbabbsia8wP2JgW5wNGpb/cso+AQpxjSQfiaauoMjEyYQsejXj5n7lvf6d+bOQkvuoRGNQY0H1448/8vnnn+Ps7Iy3tzfLly9nw4YNDRGbEA4pOT2fwpJSOrdyvB7zKlx1Kq141cndRc+Ym9qyOzqFpLR8O0UoRO1JPhJN3ZHTmVgV6NX5r4JqU2Qcq3fEsu1Aoh0j+0t5wbN6R2y9xVVisqAAnq56TsRn1/n6hahrNbZX0mq16PV623MPDw+02to3c/rhhx/45ptvbM+Tk5MZN24cxcXFREVF4excNt7AU089xbBhw4iMjGT+/PkYjUZGjRrF9OnTa71tIerC8T//uHdp43gFVU3uGNyO9b+d5Yetp2gV5GHvcIS4KnWdj7Zt28aiRYsoKiri5ptvZs6cOdXmnOPHjzNnzhwKCgro06cP8+bNu6ZtC1GVqBNpuDpp6dTyrxYQ5V2KO+k1doysoou7Oa/PuAJ8XDgen2XrgEYIR1XjFarg4GB27NiBSqXCZDLx8ccfX1Ob9bvuuou1a9eydu1aFixYgK+vL0899RQxMTF88803tnnDhg2jpKSE2bNns3jxYjZu3EhMTIx0mSvs7mRCFu4ueqKOp1XbvM5ReboZGDWgNTsPnyOv0GTvcIS4KnWZj5KSknj55ZdZvHgx69ev548//mDnzp3V5pwZM2Ywd+5ctmzZgqIoLF++vC7fmhAoisLhk+n07BiARlPjz7NmIdDHhfwiEymZhfYORYjLqvGInTt3Ll999RUnT56kZ8+e/Prrr8ydO7dONv7KK68wffp0nJycSElJYe7cuURERLBw4UKsVivR0dG0atWKsLAwtFotERERbN68udJ68vLySE5OrvAvNTW1TmIU4lIn/rx/ylxqrbZ5nSMbf0t7NGoVR89k2jsUIa5KXeajn3/+mdGjRxMUFIROp+O9997D2dm5ypxz7tw5SkpK6NmzJwATJkyoMheB5CNRe4lp+WTmljTr7tIvFeDtAsDxOLmPSji2GtsrBAYGsmTJEoqLi7FYLLi5udXJhiMjIykpKWHUqFEkJSXRv39/Xn31VVxcXHjsscdYsWIFLi4u+Pv/1W1oQEAAaWlplda1ZMkSFi1aVGGan58fCxcuJCYmpk7irW9RUVH2DuGKNIY46zPGYpOVpLQCOgVrSU1NpdhoBsDbwxmj2UpRsbHS86rmAaSkpFzRslez3uqeOxt0/P57IRZLWeHXq50r+0/m4ONsxkmvrjT/Ys39M69LEue1qct8lJCQgE6n4+GHHyYjI4MhQ4bQoUOHKnNOenp6hen+/v5V5iKofT5y1H1ub011v+h0OlJT8ygsNuHqrCc6uohtv18AwEW5wP/WpVFYZMLP25XsvBLyCooxe7th/LNH1pSUFNvzi+flFRQDVDuvrper7225OOlw1qvZdfAU3pqMy+7T3jTd70tdkH1Tv2osqL766qsqpz/44IPXtOHvv//eto6wsLAKPTVNmzaNNWvWMHLkyEqvq6oN7f3338/48eMrTLNYLGRmZhIeHo7B4Nhdj0ZFRdG7d297h1GjxhBnfcd48HgakMKQAV05nZhjS27uLjpMZmuVz6uaBxASEnJFy17Neqt7btBp6Nmzg+19hLUpYv+bP5Ndoqd364BK88vJZ153JM4yRqOx1ie66jIfWSwWDh48yNKlS3FxceGJJ56w3cN7MZVKhaIoVU6vSm3yUWP5bjS0pr5fzmTFUmwsxdmgpXv39vxvx690CPNi4IBeZO6IRWsoxc3dgNZgwc2jFG93AyUmC3kFiYSEhNieXzzPzaPsvqbq5tX1cvW9LWeDlm6FOtKyiq7ou9CUvy/XoqkfS7VxLbmoKjUWVKdO/dXFsslkIioqin79+l3TRk0mEwcOHOCtt94C4OTJk8THxzNixAigrB2xVqslMDCQzMy/miWlp6cTEFD5UriHhwceHhVvsDcajRVeK0RdOJGQhVoFHcK8OZ2YY+9wai3Ax4W2IZ6cSc4hvK0PBp3j3OwsRHXqMh/5+fkxYMAAfHzKOpe59dZb2bx5MxrNX8dCec65NBdlZGRUmYtA8pGoney8Ek4lZjN1RGd7h+JwOrf25uDxNAqKTLi56Gt+gRB2UGNBNX/+/ArPs7KyeOGFF65poydPnqR169a4uJS1jVUUhTfffJP+/fvj4uLCsmXLGD9+PD169CAuLo6EhARatGjBhg0bmDhx4jVtW4hrcTI+m9bBnjgbGn/vXte18+PMuVxOJuZwQ5dAe4cjRI3qMh8NGTKEF198kby8PFxdXdm1axcjR47ks88+q5RzQkNDMRgMtrO8a9asYdCgQXXxloQAYP8fqSgK9AsPtncoDqdL67KTHicSsukjuUo4qKv+Vejj48O5c+euaaNJSUkEBQXZnnfu3JlHH32UyZMnU1payvDhwxk7diwAb731Fk8//TRGo5HBgwdX2QxQiIZgsSqcTMxiSO8we4dSJ7zcDbQIcONUYjY9O/jZOxwhrtq15KMePXrwt7/9jSlTpmA2m7npppuYPHkybdu2rTLnLFiwgDlz5lBYWEjXrl2577776vKtiGZu37FUAn1caBXkbu9QHE7HMG80ahV/xF2Qgko4rKu6h0pRFGJiYvD19b2mjY4ePZrRo0dXmDZ16lSmTp1aadkBAwawbt26a9qeEHUhMTWPYqOFzq0b3/hTep2a9bvOYvrz/io357L7uLq28SE5vYCTCTJwonB8dZ2P7rzzTu68884K06rLOZ07d2bFihW13pYQ1TGXWjlyKoORA1rLWEtVcDJo6djSm+jT0mxWOK6ruocKysYBudYmf0I0Rif+LDo6t2p8BRWA6c8u3qGswALw9XQm0MeFY3FZmMwW9HIvlXBgko9EU3QuowBTqZV+4UE1L9xMdW/vxw9bT1FYbMb1zxOCQjiSq76HSojm6kR8Fl5uBoJ8XewdSp3q2saH7VHJ/Lw/kTE3tbF3OEJUS/KRaIoSU/Nxc9bRtc21tf5pynp08GfZL6c4dvYCfbtJ4SkcT40F1bRp0y57Cfp///tfnQYkhKM6Hl82oG9Ta5IR6ONCgLczK7aeYni/lui0cpVKOCbJR6KpsVoVktLyualHCFqN2t7hOKxOrbzRa9Ucic2Qgko4pBoLqvDwcM6cOcOkSZPQ6XSsXbuW0tJSxowZ0xDxCeEQMnOKOZ9ZyOgbm94VHJVKRc8O/vy0P5FfDiQxakBre4ckRJUkH4mmJj27CKPZQn/p3a9KTnoNmyLjKDFZ8PVyZveRFB4Zd529wxKikhoLqkOHDvHdd9/ZxuYYOHAgkyZNso0ZJURzcOR02QjtPZpob3jBfq50auXND1tPcdsNLdFp5UypcDySj0RTk5xegEat4vpO/vYOxWGVmCwUG0vx93ImOjaTnHwjXu6VB8gWwp5q/NWUlZWFyWSyPS8sLKSkpKRegxLC0fx+OgNPNz2tgjxqXrgRUqlUTB7eiYzsYrYdTLJ3OEJUSfKRaEoURSE5vYAWAW446Rv/2Ib1LdCn7P7lo2ektz/heGo8gseOHcukSZMYNmwYiqKwadMmGX9DNCuKohB9OoPu7f1Rq5vW/VMX69UpgA5hXvyw9RS33hAm7fmFw5F8JJqSjJxiio2ltApumifq6pqPhxM6rZojpzMY2DPU3uEIUUGNv5ieffZZnnnmGXJzczEajbz66qtMmTKlIWITwiEkpxeQlWdsss39yqlUKu4Z3om0rCJ+2Z9o73CEqETykWhKEs7noVJBWKAM5nsl1GoVQb6uHD6VgaIo9g5HiAqu6BR0YGAgHTp04LnnnkOnk/7/RfPy1/1TTb+N+w1dAunS2odvt5ygqMRs73CEqETykWgKFEUhITWfQB8XDDL+3xULC3QjPauIxNR8e4ciRAU1FlQrV65k1qxZfPHFF+Tn5/PEE0+wfPnyhohNCIdw5HQGgT4uBPm62juUeqdSqXj49m7k5BtZtT3W3uEIUYHkI9HYbYqMY/WOWH7Yeoq8QhMtAuTq1NVo+efVvH3HUu0ciRAV1VhQffPNNyxbtgw3Nzd8fX1ZtWoVS5YsaYjYhLA7i8XK0djMZnF1qlynVj4M6hnK6p1nyC0qtXc4QthIPhKNXXmPdScTsgFoEeBm54gaFxcnHR1berHv2Hl7hyJEBTUWVGq1Gje3vw744OBgW5e1QjR1Z87lUlhS2uTvn7rUfWO6YrUqbDuSZ+9QhLCRfCSairMpuQR4O+NskN79rlbfbkGcSswhK096+BSOo8aCysvLi+PHj9tGp1+3bh2enp71HpgQjuD7n08CkJiaz/pdZ+0cTf3R69Ss33WWldtOs37XWQJ9XLh9YFuOxBURm5xj7/CEACQfiaahoMhEZk6J9O5XC056DWazBYCv1sfYORoh/lLjqZHZs2fz7LPPkpiYyM0334zBYGDx4sUNEZsQdpecVoCnmx61WoXpzz/iTZXJbMF40Xu867aObN5zlo9WHGHB0wPRSDfqws4kH4mmIDmjAKDJjmtY31ycdLg66zhzLveyy22KjKPEZMFJr2HUjW0aKDrRXNVYUJWUlLB27Vri4+OxWCy0adNGelYSzYLRbCE9u4j2LbzsHYpduDnrGN3HixW7s1ix/TR339bJ3iGJZk7ykWgKktMK8PV0wsNVT7FR7lO9WiqVilB/N84k51BiLMWpmmaT5ferCdEQaiyonn/+eTZt2kS7du0aIh4hHMaRUxlYrArBfk2/d7/qhLdyIbXAie9/OknfrkG0CSlrXrV+11lMZgt6nYaIgW3tHKVoLiQficau2FhKRk4xN3QJtE1z0mtsV1MAPF319gqv0QgLcONUYjZ7Ys4zpHeYvcMRouZ7qDp16sT69etJSUkhJyfH9k+Ipqj8PqKV206zYtspdFo1AT4u9g7Lrv4+oTtuznre/7/DmEutwF/NA5t6M0jhWCQficYuIbWso582oRWb+5VfTSk2llZoei2q5u/tjLuLXgahFw6jxitUW7duZfPmzRWmqVQqjh8/Xm9BCWEv5YWC1apwNiWPsAB3NOqyG+DLO24wmS24OTefZkaebgaeuLMHb369n2U/n+TeUV3sHZJopiQficYu4Xw+bs46fD2cMJqt9g6n0VKpVHQI8+LQyXRSLxQ2i3EihWOrsaA6evRoQ8QhhEPJyCnGaLLQMqjioIvlBZde17w6aBhwXTC33hDGsl9O0a6F9Kom7EPykWjMCovNnM8spGNLL1tPlaL22od5cfhUOlsPJDF1ZGd7hyOauWp/Fc6dO9f2OCsrq043et999zFmzBjGjRvHuHHjOHLkCOvXr2f06NEMGzaMb7/91rZsZGQkERERDB8+nPfee69O4xCiOsnp+WjUZTe+ijKPT+xBx5ZevPvdIRn/QzSo+sxHAG+//TYzZ84E4Pjx40ycOJERI0bw0ksvUVpadlN7SkoKU6dOZeTIkTz++OMUFhbWeRyiaTtwPA2rotAi0L3mhUWN3Jx1XN8xgK0HE7FaFXuHI5q5aguqmJi/+vd/+OGH62yDiqJw9uxZ1q5da/sXFBTEe++9x3fffcfatWtZtmwZsbGxlJSUMHv2bBYvXszGjRuJiYlh586ddRaLEFVRFIXktAJaBLih0zavK1GXY9BpeOnBfrg669h6IFF6TxINpr7yEcCePXtYvXq17fmMGTOYO3cuW7ZsQVEUli9fDsC8efOYMmUKmzdvJjw8XLprF1dt79HzOBu0+Hk62TuUJuO2vi3JyC7myOkMe4cimrlqfy0qilLl42t19uxZVCoVjzzyCLfffjvffPMNkZGR9O/fHy8vL1xcXBgxYgSbN28mOjqaVq1aERYWhlarJSIiolL7eSHqWlZeCUXGUtqEyBghl/LxcGLOg/0oMVn47fdztk4qhKhP9ZWPcnJyeO+99/j73/8OwLlz5ygpKaFnz54ATJgwgc2bN2M2mzlw4AAjRoyoMF2IK2U0Wzh4Io1WQe7S3K8O9Q8PwsNVz5pfz9g7FNHM1XgPFVCnB39eXh4DBgzglVdeoaSkhPvuu49Ro0bh7+9vWyYgIIDo6GjS09MrTU9LS6tynXl5eRWmWSzSS46onaT0AlQqKt0/Jcq0D/NiYM9Qdh5KZntUEhOHtken1dg7LNFM1GU++te//sX06dM5f/48QKWc4+/vT1paGtnZ2bi5uaHVaitMr4rkI1GV30+mYzRZaBUsJ+rqkk6rYcIt7fn6xz/sHYpo5qotqKxWK7m5uSiKgsVisT0u5+XlVasNXn/99Vx//fUAuLi4cOeddzJ//nzbGcJyKpWqyjORVSXTJUuWsGjRogrT/Pz8WLhwYYWmIo4sKirK3iFckcYQZ21j1Gg0pKbmE38uB08XDXk5WRjNVoqKjQB4ezjbnl/8+NJ5V7IslN2TUdfrrYtlnQ06fv+90PYj8PfffyfqTAklRjMAnu7OOGuMdAx14uS5QmZ/uJW7bvax9YZoD43hewkSZ23VRz764YcfCA4OZsCAAaxatQqo+urX1eQiqH0+crR97iiayn5ZvycLJ70alTmflJQszN5uGM0W8gqKKzwGqp1X/hjK8se1rKOulrPntlyd9URHFxHsYsTFUNbgqvz7otPpSE3No7DYZFvObDbX++fsyJrKseSoqi2oTp06Rf/+/W2JpF+/frZ519JN7cGDBzGbzQwYMAAoS2ChoaFkZmbalklPTycgIIDAwMAqp1/q/vvvZ/z48RWmWSwWMjMzCQ8Px2Aw1CrWhhIVFUXv3r3tHUaNGkOc1xpjVEIMxaYsurUNICAgAJPZaktg7i462/OLH18670qWBQgJCanz9dbFsgadhp49O9j2Z8+ePTmTdbrCss6uVry8ffHyymXfsVR2noB/TO1ll6KqMXwvQeIsZzQar/pEV33ko40bN5KRkcG4cePIzc2lqKgIlUpVIedkZGQQEBCAj48PBQUFWCwWNBqNbXpVapOPGst3o6E1lf1isVj5z5rN9L8uhJAQT4qNpXi7GygxWXDzqPgYqHZe+eO8gkRCQkKuaR11tZw9t+Vs0NK9e3sAUotPw3xw9W1D59Y+AJzJiqXYWHG55qqpHEt1qTa56HKqLahOnDhRZxu5WH5+PgsXLuT777/HbDazevVq3nnnHWbMmEFWVhbOzs789NNPvPbaa3Tq1Im4uDgSEhJo0aIFGzZsYOLEiZXW6eHhgYdHxcvoRqOxQmIU4krEnstBpUJ6YbpCXVr70KW1D1//+AcGvYan7uqJ2o5XqkTTVB/56KuvvrI9XrVqFfv372f+/PmMHTvW9uNjzZo1DBo0CJ1OR58+fdi4cSMRERG26VWRfCQuFXP2AvlFZm68LpjUC0X2DqdJGn1jGwCWbjrO63+/Ue5TEw3uiu6hqktDhgzhyJEj3HHHHVitVqZMmULv3r2ZPn069913H2azmTvvvJPu3bsD8NZbb/H0009jNBoZPHgwI0eObOiQRTNRarESm5xDiJ8bzoYGPzQapbLxuFT0aO/Hz/sTOZ9ZyJtP3FQhmZUPhqzXaYgY2NZ+wQpxBRYsWMCcOXMoLCyka9eu3HfffQC8/PLLzJw5k48//pjg4GDeffddO0cqGos9R8+j12m4vlMAmyLj7R1Ok+T0Z86Ojs3kl/2JDOvXys4RiebGLr8an3vuOZ577rkK0yIiIoiIiKi07IABA1i3bl0DRSaas/3HUikxWmTg2qtkMlvo0saHErOFmLMXWLrpOPeN7lphfnlzQSEc0YQJE5gwYQIAnTt3ZsWKFZWWCQ0NZenSpQ0dmmjkrFaFvTHn6d05ACe9nKirb+HtfPliXQw9O1bdJFeI+iJHtxB/+mlfAi5OWoJ9Xe0dSqOjUqm4vqM/ahX8sPU0cedyCW/nh5uzzt6hCSGE3ZxOyuZCbgn9w4PtHUqz8Ozd1/P0gu18uPwwPTv61/wCIeqIFFRCAOnZRRw6mU73dn5yD1AtqVQqbu4RQrHRwsET6ej1GsLb+to7LCGEsJs9R8+jUavo2zXQ3qE0C0G+rjwwthufrIpGUaBLG58aX7MpMo4SkwUnvYZRf96LJcTVkoJKCOCX/YkAdAjzsm8gDkajubrxpcqLqsJiM3uPpuLr4YSvp3M9RSeEEI5LURT2HD3Pde39cHPR2zucZmP0ja05k5zDz/sT0evUhLfzu+zyJSYLxcbSBopONFVqewcghL1ZrAo/70/k+o4BkvQo62hi/a6zrNx2mt1/5LNlT/xVvV6rUTPw+lBcnXVs2ZdATr6xfgIVQggHFpucQ0pmITf3CLF3KM2KSqXiiTt7EOrvxoHjaSSm5ts7JNEMSEElmr3DJ9PJzClmuPQKZFPekUSx0Yyp9Oo7lDDoNNzSKxSNWsXWg0mYpFMKIUQzs+NQMlqNmpu6S0HV0LQaNUP7tMDb3YmtBxPZeSjZ3iGJJk4KKtHsrd4Ri7e7gb7dguwdSpPi5qJnWN+WFBSb2HP0PNY/B2UVQoimzmKxsuvwOW7oGigtH+xEp9UwtE8LAn1c+M93Ufz421l7hySaMCmoRLN29Ewm0bGZ3Dm0AzqtHA51LcjXlX7dgkjJLOT3Uxn2DkcIIRrEkdhMsvON3NKrhb1DadZ0Wg3D+7Wib9cgPll9lI9WHMFcarV3WKIJkl+Qoln7vy0n8fEwMGJAa3uH0mR1aulN21BPomMz2XM0xd7hCCFEvdt5KBlXJy19ukjvfvam1aiZ9UBfJg5pz+Y98bz08W4yc4rtHZZoYqSgEs3W0dhMjp7JZOLQDhh0V9ebnbhyKpWKPp0D8PNy4r3/O0RSmtwgLIRoukpMpew5msJNPULRS25xCBq1igfGduOFe/sQl5LL0wu2s/uInOATdUcKKtFsfffTibKrU/1b2zuUJk+jUTOkVxgGnZY3vtpHYbHZ3iEJIUS9iIxOodho4Zbe0tzPETjpNWyKjGP1jlgKik188I9bCPZz5a3/HeCD7w9jrkXHS0JcSgoq0SxFx2YQc+YCdw7tKFenGoirs44X7+tD6oUi/vNdFFardFIhhGhaFEVh/a6zhAW6y8DmDqR8rKkSk4UQfzf+/fRAJt3Wka0HE1n761ku5EoTQHFtZGBf0exYLFa+3vDHn1enpKv0hqLXqYlLyaNPl0D2HUtl3hd7mffogGter0ajYf2us7au2fU6DRED217zeoUQ4modj88iNjmXJ+7sgUqlsnc4zc7qHbEAeLpevmdFrUbNtFFduL6jP69/tZ+f9ydyXTs/xt7cBp1WTrKKqydXqESzs2bnGU4n5fBQRLi0b29gJrOFNiEetAnx4NDJ9DobG6R83Cyj2SJjXgkh7Gb9rrO4OusYIr372UWxsZRiYynGK8wD4e38uGNwO1oHexAdm8n093YSm5RTv0GKJkkKKtGsJKXl8+2WE/QPD2LQ9aH2DqdZUqlU3NA1kEAfF97//jAxZzLtHZIQQlyzzJxiIo+eZ3i/VjgZpAFQY2HQaegfHsywvi3JLzLzz4W/8s2m45hLrbZ7rzZFxtk7TOHg5IgXzYbFqvDBssMYdBqemPhXc4zy5mJuzjo7R9h8aNRqhvYO49ffz/HGV/v599MDCQt0t3dYQghRaxt+OwuKwpib2tg7FFELYYHuPBTRjc/XxrDsl1PsO5ZKjw5+uDjV/rfBpsg4SkxlV8uc9BpG3SjfjaZKrlCJZmP9rjOcTMjmsfHX4e3hZJte3lzMJD39NCiDXsMrj/RHq1Ez74u9Mi6IEKLRysguZv2uswy6vgWBPi626eVXOFbviGXbgUQ7RiiuhJuLnumTezH34X7kFRpZt+ss0bGZlFpqNxhweWcY5R1iiKZLCirRJK3fdZaV206zftdZAGLOZLLkx+P07RrEYGnb7jCCfF3/TFwmXli0i+R0GaNKCNH4LN30BwowbVSXCtMv/kF9pff1iCtzcXfodV2s9u0axKIZQ2kb6smxsxdYtT2W3UdSUBTpnVZUTQoq0SSZLuqgICktnze+2k+QrwvTJ18vPS85mI4tvZn/xE2YzVZeXPQbpxKz7R2SEEJcsdikHLZHJTNuUDsCLro6JepfecFaH8Wqu4uewde34NY+Yei0at763wGe+c8OdkQl1fqK1eXI/VqNm9xDJZq0YmMpr3yxF61Gzct/64+by+W7UhX20a6FF28/dTNzP9vDSx/v5qm7ejLo+lApfoWoJbl3o2FYrQpfrIvBw1XPnUM7AH/t+5q67haNQ4CPC+MGtcPTzcCqHaf5z3eH+GT1Ufp0DqRXZ39aBnoQGuCG8zV2RFJeHIrGyS4F1aJFi9i0aRMAgwcP5oUXXmDWrFlERUXh7OwMwFNPPcWwYcOIjIxk/vz5GI1GRo0axfTp0+0RsmgkNJq/ukE3mS3sPJRMfrGZNx+/iSBfVztGJmoS4u/Gv5+6mTe/3s+Cb6PYHpXE4xN7VLgfQYi6VFUuqi7nHD9+nDlz5lBQUECfPn2YN28eWq3jnpOUH2cNY/WOWI6dvcBTd/XE9c+Ojcr3vZNehuVoKlyctJhLLdx2Q0vSs4ooMVvYfyyNnYf/GvrDoNfg5qxDUcrGXfR01ePmosfNWSd5rBlo8GwQGRnJb7/9xurVq1GpVPztb3/j559/JiYmhm+++YaAgADbsiUlJcyePZulS5cSHBzMY489xs6dOxk8eHBDhy0cXHlPfamp+aQVx1NUYmbrwSRyC4zMebAfHVt62ztEcQV8PZ3599OD+PG3syzddJwn39nGHYPaMaJ/a/y9nat8zcVFtBBXqqpctGHDBhYsWFBlzpkxYwavv/46PXv2ZPbs2SxfvpwpU6bY+20IO9kUGUdCaj4bI+O4qXsIw/u1tHdIop6VmCyUmCwE+roy/pb2WKwK59LzSUovYPvBJAqLzSiKQn6xmWJjKSmZheQX5QCg06ppF+pJny6B0qNtE9XgBZW/vz8zZ85Ery+7FN6uXTtSUlJISUlh7ty5pKSkMGzYMJ566imio6Np1aoVYWFhAERERLB582YpqEQl5fdMFRvNZOQUsTEynhJTKbfd0JK+3YLsHZ64Chq1itsHtaP/dcF8sTaG5VtP8cPWU4QFetC+hSchfq54uhkwlVoxmS0UFxpxdnWqecVCXKSqXBQfH19lzmnfvj0lJSX07NkTgAkTJrBw4UIpqJqxnHwj26OScHXW8dSkntI8uZEo78iivDnsxc0yLzevKhq1ipZBHrQM8iA9q4hiYyne7gbbFUpvdwN5hSaS0wuIP5/H6aQcnl6wnTsGt+OeYZ1krLImpsE/zQ4dOtgex8fHs3HjRr777jv279/Pq6++iouLC4899hgrVqzAxcUFf39/2/IBAQGkpaVVWmdeXh55eXkVplks0ptOc5RfbGH/rrNYrTC0Txghfm72DknUUoC3C7Mf6EvqhUK27E1gw29nSUjNQ6dV0yrInbBAd/y9XTCZzDhLa05xlarKRdOmTasy56Snp1eY7u/vX2UuAslHzUFhsZkt+xIoMVkYc2NrGcOwkbm4OeylzTIvN6829DoNwX6uBPu5MuC6YDKyi1m5PZbd0SnMebAfrYI9rnqd5UWf3BvpWOxWHp8+fZrHHnuMF198kbZt2/LRRx/Z5k2bNo01a9YwcuTISq+r6izQkiVLWLRoUYVpfn5+LFy4kJiYmLoPvh5ERUXZO4Qr4ohxajQaUlPzSblQzLHEYvRaNX07eWAszCa1tIDffy+s9gdN+WuLjWa8PZwxmq0UFRsBLvv8WpcFSElJqfP11sey6enp9bJeZ4OOo0dLqv1sLp7eI1RDXjcPUrJKyMwrJTE1j9jkXHQaFS0DnAjOM6HBYlvv5T5ze3KU40ej0RB1poQSoxkAJ4OO3u2cbPvMUeJsCBfnIq1WS1xcxR62VCpVlV0lV3dForb5qC73uU6nIzU1j8JiEwCuznqio4swm811to2G4mjfRaPZyrc7LpCVZ6RbS2csxjyio6Nt+/bifW/2dsNotpBXUDbG3sXPq3t8JctBWf64lnXU1XKOtK3y/VLf27r0eKruM790fa7OeoZ196G1TxDLfs3gH+/v4M6bfOgY6lxhHZc7XnU6HfGJFy3nln/Fx7WjHUtNjV0KqqioKJ555hlmz57NmDFjOHnyJPHx8YwYMQIARVHQarUEBgaSmZlpe116enqFe6zK3X///YwfP77CNIvFQmZmJuHh4RgMhvp9Q9coKiqK3r172zuMGjlynFuPRRGTkI2TXs34Wzqg02owmi0YdBp69uxw2deeyTqN0WzB3UWHyWy1JazLPb/WZQFCQkLqfL11vWxKSgoBAQH1FkN8jgrTn8/dnHW2Znx6nYaIgW0rfU6+/mXLujppiT+fx8mEbM6mFnDmfDEtAtzo0cEPfy+XGj9ze3C046f8ew9UOE7qO06j0egwJ7ouzUX79++vMudcmosyMjKqzEVQu3xUH/v8TFas7Uy7s0FL9+7t63T9DcHRjpm8QhOv/3cfyZlGhvQKI9DXpcp9W77vy5t/uXmUfQ4XP6/u8ZUsl1eQSEhIyDWto66Wc6RtwV95tT63daWfeVXrS8gBq17P+Fu8+C36PP/36wWemdST23q3sq2jpuP1Spe7mKMdS46grnNRgxdU58+f58knn+S9995jwIABQFkB9eabb9K/f39cXFxYtmwZ48ePp0ePHsTFxZGQkECLFi3YsGEDEydOrLRODw8PPDwqXjY1Go0VEqBoujZFxrHjUDJ+Xk50DNLi6lz2w104vvJ736CsV6SLC67LUatVtAgoa/KXk32B8zkqYs5mci69gPZhXgy9IQxvd7mvSlSvqlxUXc4JDQ3FYDDYfpSsWbOGQYMGVbleyUdNx8X305jMFrZHJZOeXcQL024gPbtIelEUV628SaGzQcvbT5b1artw+e+A3IPX2DV4QfXll19iNBp56623bNPuueceHn30USZPnkxpaSnDhw9n7NixALz11ls8/fTTGI1GBg8eXGUzQNE8KYrC9z+d5LufTtIiwI0B1wWTnpZaYRm9Tm3rARAqXgUpfy4aN4NOTc+OfrQJ9eDY2Sxik7J58t/beXxCd27uGSI3i4sqVZeLqss5CxYsYM6cORQWFtK1a1fuu+8+e4UuGkj5j9+M7CJ2R5/HalUY1rclRpMUUs3Z1XZeUd06tkclcV17P9Kyivhg2WEG9gyhRUDj6AFQxrmrrMELqjlz5jBnzpwq502dOrXStAEDBrBu3br6Dks0Mharwqero9kUGc/QPmG08HfDXM3I5Ze7CqLXqRssZlG/nPRaencOoGsbH/6Iu8C/vznI7ugQHp/YHU+32jX7vbgYr6oZomi8LpeLqso5nTt3ZsWKFfUdlnAwsck5RB1Pw91Fz603tMSg11S4il4XP65F41MXnVeUmCyYS63c1COEPUfPs+v3FPp1C6JbW9+6DLVeyDh3lUmfjcJhVfdj1lxq4T/fHmJ3dAoTh7Tn/jFdWbU9FhyvDwJhB15uBv791EBW7Yjluy0nORZ3gemTe9GrU9X3vFzOxcV4YyAFoBB1o8RYyq7fz3E6KYcgXxdG39gaUFX5I7Kue4YTzYtWo2b0ja3Z8Fsc+46lotfJd6gxkoJKOKyqfswWlZh546v9RMdm0qdLIO4uen7am2CnCEV9qaqp5tXQaNTcdWtHbugaxDvfHOTlz/Yw/pb2TBvVBZ226V6VrKkALN+n0tRViOolnM/j7aUHSEoroFtbX8Lb+eKk19quQglR17QaNbf1bcmWvQns+v0cfboEMLSPDBbdmEhBJepVXZ4xz84vYd4Xe4lPyWNgjxBaBLpjNFuk2V4TdWlTzdpoHezBu88N5st1MazeEcuR0xk8d8/1tAnxrMtQG43yfSrHjBCVKYrCT/sS+Wx1NC7OOkb2b4W3h3RuIxqGVqNm0PWh/HYkhfe/PwyoGNonzN5hiSskWVXUq/IfcEazxVZY1UZOgZEXP/yNpLQC5jzUj3YtvOouSNGkGXQanpjYg5ce7EtWbgn/eH8n//fTSUqruedOCNH85BYYeeebKBb98Dtd2/qy8J+3EOIvA8OLhqXVqBl2Q0u6t/fj/e8PsSkyruYXCYcgV6hEnavrZkXnMwvZHZ2Cq5OONx6/kc6tfEg4n1cn6xbNR//wYLq28eWz1Uf5bssJIqNTeCiiG9fX4t4qIUTTYLUqbD2QyFcbjlFsLOW+0V2YOKQDarX0DirsQ6tVM+ehfrz9v4MsXhlN6oUi7h/TVb6TDk4KKlHn6qpZkaIonErM5tDJdLzcDCx4ZhABPi51FKVoqi69/+ripqYerno6tfJGrYb9f6Txr8/2EOrvyvUdA/Dzcm7SHTlcyC3mfGYhJaZSDHoNpaUKzk5a3FzkfirR/CiKQtSJdJZuPM7ZlFy6tvHhyTt7cOzsBdb+ekZ66xN25aTXMufBvny25iirdsRyLqOAZ+6+Hg/5XjosKaiEQ8otMLI9KpnEtHxC/d24pVeoFFPiil2ucwaT2UKQrytjbmpNUlo+h09msGF3HH5eTnRq6cNtfVvibKj7P40aTcP23FRUUsqG385y5HQGpxJzyMorqXI5rUaFt7sTFqvCkN7SXr8purhr7+Y+ZozVqrDvWCqrtp/mREI2gT4u/GNKLwZf3wK1WkXUiXSKjaXSW5+wO41Gzd8ndKdFgDv/XR/DM//Zzj+n9K7VunQ6OXFW36SgEg5nb8x5Fv3wO/lFZnp28KdTa290WkluonYuvmJ1cTNUjVrNde38aBviybG4LM4k57I7OoVDJ9Po0yWI3p0DKDaW1lkzi6gzJZzJOn3VV8GupmMXc6mFM8k5xJ/PJyO7CAUI9nWle3s/OrT0IvF8PlZFwd1Fh8lsJSOnmOz8ErLyjCzddJxvNh+ndYCBEm0K/a8LRlOL9y5dtzum5j5uzIZdZ4g5m8WxsxfIKTAS4OPC3yd0Z3i/Vk2650/RuKlUKiIGtqVLGx/eWXqQ2R/vpmNLb8Lb+V7Vib/f40s4kxULXN1AvHIi5spJQSUcRsyZTJb/corDpzJoG+rJLb1a4OJU9gO4uh/FQlyJyzVD1Wk1dGntQ+dW3uQWmCi1WDl4PI1dv58DwMvdQKC3C6EBbhQUmXBzqV2TixKjuVZjWtXUFbqiKJxMzOanvQlsj0qi1KLg7qKnewc/Hr3jOloGediWXbntNEazxVZQubvqAU8MOg039Qhh28EkNu2O5a3/HSDEz5UJQzowtE+Lqzqh0djG7hJNW2GxmU174ln+yymKjaX4eTkxrG9LQv3dMJkt7DqcjNFsocRkkWZ+wmG1b+HF+/+4he+2nGDtr2dIOJ9Hz47+jLmpzRWNW1VYZEJruPoTKs39RMzVkIJK2FVeoYlDJ9PZvCeeY2cv4OVm4MGxXYkY2I51v56p8MNMunwW9UmlUhHo48LEoR2wWhXiUnJZ8uMfpGQWEpucw8nEbLZHJdEmxJPr2vnRvb0f4e18bUV/Q1+ZySs0sT0qiZ/2JZCYmo+TXkObEE9aB3vg6+mEk15boZiqSZCvK1NGdKaDTwEmfQg/bDvFoh9+57stJ+gY5kW7Fp7otBq56iQahXW/nuHAH2kci7uAudRKiJ8r/cOD6NzKG6PZWmEg3vIfjdLMTzgyZ4OWh28PByAy+jz7jqXyyJu/MOm2jgzv17JRtORpyle8pKASDUZRFFIyC4g7l8eZczlEx2ZyOjEbqwJ+nk48ckc4w/u1wkkvX0thX2q1inYtvOjRwZ/OrX2wWMp+gKVkFnIuvYD1v51l7a9n0KhVdG7tw/Ud/cnON+LmokOtqr+emMylVnYeSmbX7+eIOpFOqcVKp5bePHVXTwb2DGFTZPw1Xx3S6bTc0COEG7sH8/upDFZsO83eY6kcOplOhzAvurbxraN3I+qDoijEnLnAzkPJ5BaaKDaW4umqJ9TfjUBfl3q5P9CRKIrCzkPJfLvlBEUlpYQFutO/WyDurgaKjaWo6vH4FKIh+Hg4cesNYeTkG0lIzeOTVdGs3H6au27tyNA+YRiu4IqVvTTlK15N+y+rsKvCYjPx5/NIzy4mO99ITr6RJRuPA3/+YA315O5hnejTJZD2Lbz4cXccP/5WNuaCNOsTjkSjURPs54qvpzMdW3pjsVjJLTDh7qrn8Kl0vtl8AgC9Vk2gryuhfq7EpeTSMsijVvchlVMUhZwCI4mp+aReKCQtqwiLVcHHw4kxN7Xhtr4taR1c9VWoS3s7vNwxdfGyqan5JOWfJWJgW67vFMD1nQL4ZFU0R05ncCwuiz/isog7n8fI/q3o1TmgUZwVbS5OJmTx5bpjHI/PwkmvwcPVgJ+nEwXFZg4cT0Olgi6tfRjerxWuTfBv7NcbjrHr93OkZxfj5+nEjd1D8PdyxtvdQIlJmqGKpiXYz5XHJ3bn91MZfLv5BItXHOGbTccZNaA1o29qg089DUptLrVyPrOQcxkFlJhKsVgUElPzcXHW4u/lTLtQL9qGejbJvzGXIwWVqFMZ2cUcOZ1BQmoeWXlGoGygOm93A+1beDKsXyvahnjSMsi9Urvfi++9kGZ9wpGVF1gTh3bg/jFdyS0w8tmaoySl5ZN6oYiktHz2HkvF1UlLuxZetAxyJzGjBJOqCHcXHdn5Jbi76FEUsCoKpaVWcgqMZOWWkJlbTML5PM6ey+VsSi65BSYA3Fx0dAjz4oGx3ejS2sfWWcbl7i28mmOqfNlio7nSINz+Xs4M7BlKQbH5z9hyeP2rNJwNWnp1DqBftyC6t/fD19O5LnavuEqKorAxMp4v1h7F083A38dfR5GxFHNp2eDV3u4GMnOKOXQygz/isvj721t5elJP+nYNsnPkdSO3wMiSH//g5/2JOOk1DOndgtbBHlJEiUbpanrlVKlUXN8pgJ4d/flyXQyHT2aw7JdT/LD1NIN6hTL2pjZ0bOl9zTHlFZr4cXcch06kc/RMBsXGsmNLq1HjZNCQU2CkqMRsm65WQde2vvQPD2Zgz9B6K+4ciRRUok7En89j1fbT/Hr4HBargq+nE327BhLs54qzQYtKpcKg0zC8Xyt7hypEnfN0M9A2xJNQfzcURcFcaqVlkAd/xF3g7LlcftmfSInJwh+JRQCs/fXsZdenVqnwcjcQ4udG1zZlVxncXPQYdBq6ta3Y5K4h7y10c9bRq1MA8x4dwO+nMtgbc579x1LZfSQFAH9vZ1yddHh7GPDzdKZloFule8uG9w2t9zibkxJTKR+vjGbbwST6dAnkn1N64eaiZ/WOWFtBBeDmoueGroF0bePD0TOZvPblPiIGtuXBsV0b7VVGi1Vhy954lm48TrGxlPB2vnRu5U2At4sUU6JRu9qmcSqVCl9PZ27qEUL3IhNnz+WyL+Y8O6KSCQt0w0Vnpb3edMXjWJUYSzl6JpM9R8+TlJ5PQZEZgEAfF27pHYbZbMHL3YBOq8HZoGX8Le0ByMk3cvZcLn/EXWDfsVS+WBvDV+uP0S88iLE3N+17b6WgErVW3lZ/5fbTRJ1Ix0mvYczNbdBr1Oh0GltPYhefIZculUVTp1KpcHfRM7RPGEP7lI3rZLUq/GfJTjQGd6xWhS6tfSgoNoOqrPt2jVqFp5sBX08nfDyc2HP0PKUWa6VjyBHodWo2RcZjMlsI9nXlzqEd6Nzah+PxWZxMKBuIO/58HlDWtNfHw4CXmxM+HgYCfVwotTjOe2nsUi8U8ubX+4k/n8eU4Z24e1inGrv59/NyZsEzg/j6xz9Yv+ssx85cYMa03rQIcG+gqOvGyYQsPl4VzZnkXLq39+Ox8dfZxpASojlzd9HTPzyYl//Wn12/n2PrgaSyv8/Jcbi76AjydUWvVRPs54arsxadVkNeoZHsP4uh00k5nEzIptRiRaMu66wpvK0vD47tRrCfKyqVitU7Yit07HLpFbVenQO4d1QXzmUU8NPeBH7en0hk9HmCfF3o2sa32qbqjZkUVOKqWawKe46msGp7LKeTcvByM3DvqM6MvrEN7i56W9fMVZEulUVzpFarcDFo8PZ1xaDTMKaGM3X7j6VSasfDpKZhCi49jju29LY1K1m57TTZ+UYu5BaTV2giPauIhNQ8YpPLrpZEHkni6Yim0dTMng4eT+M/30ahAP96uD99ugRe8Wv1Og2P3nEdPTv48/73h3nuvZ08Mu46hvVtWWfjrtWXuJRcvttygr0xqfh4lHWB3iLAjdikHHuHJoRDcXHSMaJ/a0b0b827S3aSZ9JzPrOQpLR8Pll9tMrX6LRq2oZ4MvbmNvTuHMDppBzMpVacDVpC/N2q3VZ1V9RC/d0I8nVh/C3tSEzNZ9+xVLYdTCLI14X2YV5c186vzt6vvUlBJa6YuVRhY2Qca3ac4fyFQkL8XHnyzh4M7RN2ReMgCNGU1HZstMZypfZamhK6OGlxcXK3XWErMZVSUGwmv9BEsK8BMNV9wM2E1aqw7JdT/N9PJ2gd7MGs+/sS7OcK/NUl8ZWOp9S3WxAfPn8L7353iEU//M5P++J5ZNx1dG7tU+NrG7L7Y4tVIepEGj/+Fsehk+m4OmmZMqIz4wa1ZcveBIqNpXKiTjQ7F18ZqumYdzZoCAoqO/HlpNcwsGcLMnOKKSwxYy614O6ix9PNQLCfK1rNX3/z41LyKjQdro0SkwVzqZWubXxoGeROzJkLHI/PYvbi3XRv78fk4Z0IbwKFlRRU4rKsVoVjcRf49fA5dkadp9h0jk4tvXlgbFf6hQdfUw9mQjR2tS06mtuV2vJmkH6ezoy9qSUxMTH2DqlRSsks4KMfjhAdm8mQ3i144s4eFYaZqM14Sr6ezrz22I1sj0rifxuPM+PDXXRu5c3QG1rSt2sgPh5OVXY1Xt/dH5tLLcSnGYlac5RtB5MoLDbj8mchFXFzm1oPsC1EU1KbY16lUuHv7Yy/d+VOhMoLNKBWA11fXORVtQ6tRk2nVt6Et/PFoNOwYttpZjWRwkoKKlGByWwhKS2fEwnZnIjPIjo2k6y8Egx6DR2CDUwd04tubX1lLA8hhGggRSVm1u86y/JfTqHVqnnqrp4M79cSlUp11VelqqJWq7j1hpbc1D2ETXvi2XogkcUrjrAY8HIzEBbojrurDlcnHVqtGo1KRVxKLhargk6rJr/IhFqtwkmvxVmvwdlJi5Nei5NBi4uh7H8nfdnN684GLTqtGpVKhaIoGM0WcvKNZOcZScksICE1nzPJOZxIyMZktqDTXiDYz5WeHfzpEObFxKEd6m7HCuHgaipQruR1nq56NJorK7guPlFS24Gur2QdWo2a2we1Y8SA1mzZE28rrK5r58eYm9vQr1tQhStljYEUVE1UqcVKYbGZwhJz2f/FZgqLSyksMVNUUva4qMT85/NSsvNKSMsqIjvfaFuHs0FLgLczPTv68/iE7hyLOUJ4O79G0VxJiNqobTM+IepDUlo+26OSWPvrGUxmK21CPHj5b/0rdE9fmzPUF7v0x5qTXsPQPmEUFJvxcjdw9lwuKRll911cyC3BalVQKGu9YLUqABw7e4E/H14RlQo0ahUWq4Jyyet0WjUtg9zp3MobLSX079HW9h5dnLTXfAZdiMamtkXOtf5tuNjVNC+8GgadxlZYbd4Tz5odsby15ABebgZu7hFC325BhLfzQ6d1/OKqURRU69ev5+OPP8ZsNvPAAw8wdepUe4dUK4qiUGqxYjJbMZeW/7OQmWchNjkHi8VKqUXBYv3z/yqeFxvLiqLiklIKS0opKjZTZCy1FU8Xcooxmi2UWmrObjqtGp1WjUGnoUWAO707BxLo60KInytnz+XaziIadBqcDH99VS4d2+ZKBw+9lPx4FY6oLrohr+q4UKmrXl9tB+CtaVlHpNep2XogkcBGPFxVfeQjq6KQnV/ChZwS4s/nciY5lyOxGSSlFaBSQasgDzq29KZFgFudj/V16Y+1EpOFElPZPRW3D2xXYdnynr3KB8q9+HFRiRmLVUGrUTO0TxjFxlKKjaXsOnyOwhIzOo2avCITRSWllFqs6LRq2xloD1c9g3u1IMC7LP9oNGpW74jlTFxijfEKIRpGfRVo5c/HDWrH2JvbcuhEGks3HWfTnng27I5Dr9PQsaUXXVr70DLIg6TUPJwMWtycdfV+/+bVcPiCKi0tjffee49Vq1ah1+u555576NevH+3bt7+i1y//5RQWpewPt0ajRqtRoVGX/a/Vqm2PoeyMm8WqYFUU22OLRcFssWAuL4IsVkxmS4WC6K/HZc9N5Y/NFsyWssclJovtbF7Vzl/VflGrywodb3fDnzeA62gR4IZBp0GjVuHmokOjUqNSg16rwdNNb2ump9Oq8fEwYC4ta25h0GkqNaPIyC6udI9HdZeMr2VA3oYcQ0eIhlSbQXXretmGcLUnRsxmKzTSgupa89Gz7+6g0KigVqnQaFQoVsVWjFiVc7blnPQaOrXyZsyNbeh/XTC/Hj5n+yFTl/c4XO71V7stlUqFVqPC293A4ZPptvUH+LhUKsKACs+dDVp6d77yXgqFELV3NQMH16eq7sPUqFXc0DWI5PQC8otMZOcZcXHS8kd8Fiu3x1b4He3uoqOg2Mxdt3Zs6NCr5PAFVWRkJP3798fLywuAESNGsHnzZp566inbMnl5eeTl5VV4XWlp2Yd04Ng5coosKFfTHqEqqrLBNjVqFWq1Gq2GPwuWsmnlV3P0GnA1aFCrygazVWtU6LVqW7MGtRqc9DpbEeek15CTl4+LsytqNTgbdGXFnFKWdF0NGtSasqYRLk66sitPioJGrcbFSYtKBWZLWQ8szgYtRSWlmM1WXJy0ZcWcuWzepc+ddSq0agWtSoWLk5pf9p6psB5nPWj/LMDK559PKyGnNK7CPCctaFRVP7/cvPpa1stVb/cYrmRZY3AwrnrH2GeXW/bS/emI8WqcdQ79nSx/7uGixVXvGPusro8hs8WCVmVFjaXGz8LyZw2oXNrWqxG41nzUsYU7BcayE3ZatQorZXkFq4neXUPxcjcQ7OtGXErunz1rKZyMT8dFX5ZvnHRlBbVGVfa3WoWlwjw1lR8DleaVr+Pi119uuavdVlXrv1xM7i4adkTFY/qzeHNz0eKiLyu6ruZ9Xe1ydbEOe2zL292Am1P9xtsY96ExOBg3p6b3vupiOS83PU4X7ZvyY9RJ99exV37c2eN9GXRgNP51y0lZHFrC/J1RqVW0CnTBag3BbFE4n1nAhdwSCorNGLQVX3c1TKay3mbrKhepFAfPap9++ilFRUVMnz4dgB9++IHo6Ghee+012zIffvghixYtqvC6jh078sorrzRkqEIIIa5Qx44dcXdvXIPJSj4SQoimJTQ0lKCgax8b0eGvUFVV713aw9z999/P+PHjK0xLTU3llVde4cMPPyQ4OLheY7wWqampTJkyhe+++65OPtD68v/t3XlcVOUex/EPm1ghmt1ErdxyS40oTR0XDFBkFVFTFNHs5lpuZQFKkiYKanlRS+3atSzN3cwlNUm0K7hbuSGlIi4oKoiCwAzD7/7hy3NBFpMQZup5/zcPc57zPc85c36cZc6YQ05zyAgqZ3kyh4ygchYkIhgMBh577LGH0v/DVNZ6lJ+fT5UqVahRo0aR95vLtlHR1LgUT41L8dS4lEyNTfFSUlIYPXo0n3zySbn0Z/IHVA4ODhw8eFB7nZqaSq1atQq9x97eHnt7+yLTJiYmYm1tja2t7UPPWVZWVlZcu3YNKysrlfNPMoeMoHKWJ3PICCrnvapWrfrQ+n6Y/kw9Kom5bBsVTY1L8dS4FE+NS8nU2BTP2tqaxMRELEt4YNSDqvxvNN9Hhw4diI+PJy0tjezsbLZv346zs3Nlx1IURVH+ZlQ9UhRFUYpjFleoxo8fz6BBgzAYDPTp0wdHR8fKjqUoiqL8zah6pCiKohTH5A+oAHx9ffH19a3sGIqiKMrfnKpHiqIoyr1M/pa/srK3t+ett956oHvZK4PKWX7MISOonOXJHDKCyqmUTI158dS4FE+NS/HUuJRMjU3xyntcTP6x6YqiKIqiKIqiKKbqL3uFSlEURVEURVEU5WFTB1SKoiiKoiiKoihlZPYHVCdOnKBVq1baa71ez7vvvounpyf+/v6cPn0auPODjFFRUXh4eODl5cWhQ4e0af7zn//g4eFB9+7d2b59e7nmO3jwIL169cLX15cRI0aQkZEBwM2bNxk2bBienp4EBgZy9erVMucvD4cOHaJ37974+fkxePBgLl68aJI5AaKjo5k3b5722hQzlmTjxo14eXnRrVs3li1bVmHzLSgzMxMfHx8uXLgAQFxcHL6+vri7uzNnzhztfSdPnqR37950796dSZMmkZeXB8ClS5cIDAzEw8ODkSNHkpWVVe4Z58+fj7e3N97e3sycOdNkc0ZHR+Pl5YW3tzdLliwx2ZwAUVFRhISElClLSZ8x5f5SU1MZNmwYPXv2JCAgQPvcmdN+62Ey9Rpe0cypFpsSU6itFc1c6mRlqfCaJ2bs9u3b0q9fP2natKnWtnjxYnn//fdFRGT//v3Sp08fERH5/vvvZejQoWI0GuXMmTPStWtXMRgM8ssvv4ifn5/k5OTItWvXxM3NTdLT08stY9euXeW3334TEZFZs2bJRx99JCIiU6ZMkUWLFomIyPr162Xs2LFlyl9eXFxc5OTJkyIisnr1ahkxYoTJ5bx586aEhoaKo6OjzJ07V2s3pYyluXz5sri4uEh6erpkZWWJr6+vtm1UlJ9//ll8fHykZcuWcv78ecnOzpYuXbpIcnKyGAwGef311yU2NlZERLy9veXIkSMiIhIaGirLli0TEZFhw4bJpk2bRERk/vz5MnPmzHLNuGfPHunXr5/k5uaKXq+XQYMGycaNG00u5759+yQgIEAMBoNkZ2drnyFTyykiEhcXJ+3atZPg4OAyZSnpM6bc3+DBg2X58uUiIrJ8+XJt7Mxlv/UwmUMNr2jmUItNjSnU1opmLnWyslRGzTPrK1SRkZG89tprhdpiY2Pp0aMHAC+//DLp6elcunSJXbt24eXlhaWlJQ0bNqRu3bocOXKE3bt3061bN2xtbXniiSdo27YtsbGx5ZZxy5YtNG7cGIPBwJUrV7SnicTGxmqP3vXx8WH37t0YDIYHzl8e9Ho9Y8eOpXnz5gA0a9aMlJQUk8sZExNDgwYNGDJkSKF2U8pYmri4ONq3b0+NGjV49NFH6d69O1u3bn3o8y1o1apVhIeHU6tWLQB+/fVX6tevzzPPPIO1tTW+vr5s3bqVixcvkpOTg5OTEwC9evVi69atGAwGDhw4QPfu3Qu1l6cnn3ySkJAQqlSpgo2NDc8++yxJSUkml7Nt27YsXboUa2trrl+/jtFo5ObNmyaX88aNG8yZM4cRI0YAlClLSZ8xpXRpaWkkJCQQEBAAQO/evRk3bhxgPvuth8kcanhFMpdabGpMobZWNHOpk5Whsmqe2R5QxcTEkJOTg4eHR6H21NRUnnzySe31k08+yeXLl0lNTdX+ifwj7eXFxsaGU6dO0aVLF/bt24e3t3eRnNbW1tjZ2ZGWlvbA+ctDlSpV8PPzAyA/P5/58+fTtWtXk8vZs2dPhg0bhpWVVaF2U8pYmnvz1KpViytXrjz0+RYUERFBmzZt7pupuLG7cuUK6enp2NnZYW1tXai9PDVp0kTb8SUlJbFlyxYsLCxMLifc+XzPnTsXb29vdDqdSY7n5MmTGT9+vHYypyxZSvqMKaU7f/48devWZfr06fTo0YMxY8ZgY2MDmM9+62ExlxpekcylFpsaU6itFc2c6mRFq6yaZ/I/7Pv9998zY8aMQm2NGjUiMzOTL7744g/1YWlpiRTzdPjS2ssr5xdffEGzZs2Ii4tjxYoVjB8/nhUrVpSY80Hzl2dOvV5PSEgIeXl5DB8+vMQ+HnbO0jL+URUxlg+quPlaWFg89PmWpqRMD9r+MPz2228MHz6c4OBgrK2tOXv2rEnmHDNmDEOHDmXEiBEkJSU9UJ6HnXP16tXUqVMHnU7HunXrgAdf5yWpiM+MOSluv1W/fn1OnDjB6NGjmTRpEqtXryYkJISvvvqq2D5Mcb/1Z5lLDa9o5lCLzYUp1taKYi51sqJUZs0z+QMqT09PPD09C7WtXr2aRYsWERgYqLX5+fmxbNkyatWqxdWrV6lfvz4AV69epVatWjg4OBT6Ullp7Q0bNiyXnLm5uezYsUM7w9SjRw+ioqKAO2cOrl27Ru3atcnLyyMzM5MaNWo8cP7yyAmQlZXFyJEjqVGjBgsWLNDOolZGzpIylqSyxvJBOTg4cPDgQe31vWcQK4ODgwPXrl3TXt/NdG/73TGqWbMmmZmZGI1GrKysHtrYHTp0iDFjxjBx4kS8vb3Zv3+/yeU8ffo0er2e5557jkceeQR3d3e2bt1a6ApqZefcsmULV69exc/Pj4yMDG7fvo2FhcUDZynpM6b8X3H7reTkZPz9/XFxcQHu3Doybdo0wHz2W3+WudTwimYOtdhcmGJtrQjmUCcrWmXWPLM8XfHqq6+yY8cONmzYwIYNGwDYsGEDdnZ2dOnSRWs7ePAgtra21K1bF2dnZzZu3IjRaOTcuXMkJSXx/PPP4+zszPbt28nOziYtLY29e/ei0+nKJae1tTVTpkzh2LFjwJ0zUi+99BIAXbp04dtvvwXubABt2rTBxsbmgfOXl3fffZf69esTHR1NlSpVtHZTy1kcc8gI0KFDB+Lj40lLSyM7O5vt27fj7Oz80OdbmhdeeIGzZ89y7tw5jEYjmzZtwtnZmaeeegpbW1vtyVDffvstzs7O2NjY0KZNG7Zs2VKovTylpKTw5ptvMnv2bO0WWVPMeeHCBcLCwtDr9ej1emJiYggICDCpnEuWLGHTpk1s2LCBMWPG4OrqyowZMx44S0mfMaV09erVw8HBgV27dgGwc+dOWrZsCZjPfuthMJcaXhnMuRZXFlOsrQ+budTJilaZNc9CirvmZWaaNWvGqVOngDtXhSZPnsyxY8eoUqUK06ZNo2XLlogIM2fOZPfu3QCEhobSqVMn4M4jV9euXUteXh4jR46kZ8+e5Zbt4MGDTJ8+HaPRiIODA1OnTqV27drcuHGDkJAQzp8/T7Vq1Zg9ezZPP/10mfL/WSdOnMDf35/GjRtr95LWqlWLf//73yaV8667j0wfPXo0gElmLMnGjRtZtGgRBoOBPn36MHTo0AqZ771cXV1ZunQpTz/9NPHx8cyYMYPc3Fy6dOlCaGgoFhYWJCQkEBYWRlZWFi1atGDGjBlUqVKFixcvEhISwvXr16lTpw4ff/wx1atXL7ds06ZNY+3atdSrV09rCwgIoEGDBiaVE2Du3LnaVSl3d3dGjx5tcuN517p169i/fz+RkZEPnKWkz5hyf2fOnCE8PFy7Xz8yMpIGDRqY1X7rYTP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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ratio of CVEs appearing before (or exactly on) certification date: 32.14%\n", - "Ratio of CVEs appearing after certification date: 67.86%\n" - ] - } - ], + "outputs": [], "source": [ - "exploded_cves = df_cve_rich.explode(\"related_cves\").loc[:, [\"related_cves\", \"not_valid_before\", \"not_valid_after\"]].rename(columns={\"related_cves\": \"cve\"})\n", + "exploded_cves = (\n", + " df_cve_rich.explode(\"related_cves\")\n", + " .loc[:, [\"related_cves\", \"not_valid_before\", \"not_valid_after\"]]\n", + " .rename(columns={\"related_cves\": \"cve\"})\n", + ")\n", "exploded_cves = exploded_cves.dropna()\n", "exploded_cves[\"cve_published_date\"] = exploded_cves.cve.map(lambda x: cve_dset[x].published_date)\n", "\n", @@ -930,7 +391,9 @@ "exploded_cves.not_valid_after = exploded_cves.not_valid_after.dt.normalize()\n", "exploded_cves.cve_published_date = exploded_cves.cve_published_date.dt.tz_localize(None).dt.normalize()\n", "\n", - "exploded_cves[\"n_days_after_certification\"] = (exploded_cves.cve_published_date - exploded_cves.not_valid_before).dt.days\n", + "exploded_cves[\"n_days_after_certification\"] = (\n", + " exploded_cves.cve_published_date - exploded_cves.not_valid_before\n", + ").dt.days\n", "exploded_cves[\"n_days_after_expiry\"] = (exploded_cves.cve_published_date - exploded_cves.not_valid_after).dt.days\n", "\n", "plt.rcParams[\"figure.figsize\"] = [12, 4]\n", @@ -940,24 +403,34 @@ "exploded_cves.to_csv(RESULTS_DIR / \"exploded_cves.csv\")\n", "\n", "hist = sns.histplot(exploded_cves.n_days_after_certification, kde=True, ax=axes[0])\n", - "hist.set(xlim=(-4000,4600), title=\"CVEs appearing n days after certification\", xlabel=\"Number of days after date of certification\", ylabel=\"Frequency of CVEs\")\n", + "hist.set(\n", + " xlim=(-4000, 4600),\n", + " title=\"CVEs appearing n days after certification\",\n", + " xlabel=\"Number of days after date of certification\",\n", + " ylabel=\"Frequency of CVEs\",\n", + ")\n", "hist.axvline(0, color=\"red\", linewidth=\"1\", label=\"Day of certification\")\n", "hist.legend(loc=\"upper right\")\n", "# plt.savefig(RESULTS_DIR / \"cves_n_days_after_certification.pdf\", bbox_inches='tight')\n", "# plt.show()\n", "\n", "hist = sns.histplot(exploded_cves.n_days_after_expiry, kde=True, ax=axes[1])\n", - "hist.set(xlim=(-6200, 4000), title=\"CVEs appearing n days after certificate expiry date\", xlabel=\"Number of days after certificate expiration\", ylabel=\"Frequency of CVEs\")\n", + "hist.set(\n", + " xlim=(-6200, 4000),\n", + " title=\"CVEs appearing n days after certificate expiry date\",\n", + " xlabel=\"Number of days after certificate expiration\",\n", + " ylabel=\"Frequency of CVEs\",\n", + ")\n", "hist.axvline(0, color=\"red\", linewidth=\"1\", label=\"Day of expiration\")\n", "hist.legend(loc=\"upper left\")\n", - "plt.savefig(RESULTS_DIR / \"cves_vs_certificate_lifetime.pdf\", bbox_inches='tight')\n", + "plt.savefig(RESULTS_DIR / \"cves_vs_certificate_lifetime.pdf\", bbox_inches=\"tight\")\n", "plt.show()\n", "\n", "n_cves = exploded_cves.shape[0]\n", "ratio_before_cert = exploded_cves.loc[exploded_cves.n_days_after_certification <= 0].shape[0] / n_cves\n", "ratio_after_cert = exploded_cves.loc[exploded_cves.n_days_after_certification > 0].shape[0] / n_cves\n", "print(f\"Ratio of CVEs appearing before (or exactly on) certification date: {100 * ratio_before_cert:.2f}%\")\n", - "print(f\"Ratio of CVEs appearing after certification date: {100 * ratio_after_cert:.2f}%\")" + "print(f\"Ratio of CVEs appearing after certification date: {100 * ratio_after_cert:.2f}%\")\n" ] }, { @@ -969,514 +442,38 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "How many CVE-rich certificates were revoked in <365 days after certification: 2\n", - "How many certificates were revoked in <365 days after certification: 39\n", - "How many certificates have expiry date same as certification date: 21\n" - ] - } - ], + "outputs": [], "source": [ - "maybe_revoked = df_cve_rich.loc[~df_cve_rich.not_valid_after.isna(), [\"not_valid_before\", \"not_valid_after\", \"n_cves\", \"worst_cve_score\", \"avg_cve_score\", \"related_cves\"]]\n", + "maybe_revoked = df_cve_rich.loc[\n", + " ~df_cve_rich.not_valid_after.isna(),\n", + " [\"not_valid_before\", \"not_valid_after\", \"n_cves\", \"worst_cve_score\", \"avg_cve_score\", \"related_cves\"],\n", + "]\n", "maybe_revoked[\"validity_n_days\"] = (maybe_revoked.not_valid_after - maybe_revoked.not_valid_before).dt.days\n", "maybe_revoked = maybe_revoked.loc[maybe_revoked.validity_n_days < 365]\n", - "print(f'How many CVE-rich certificates were revoked in <365 days after certification: {maybe_revoked.shape[0]}')\n", + "print(f\"How many CVE-rich certificates were revoked in <365 days after certification: {maybe_revoked.shape[0]}\")\n", "\n", "df_w_validity_dates = df.loc[~df.not_valid_after.isna()].copy()\n", - "df_w_validity_dates.loc[:, \"validity_n_days\"] = (df_w_validity_dates.not_valid_after - df_w_validity_dates.not_valid_before).dt.days\n", + "df_w_validity_dates.loc[:, \"validity_n_days\"] = (\n", + " df_w_validity_dates.not_valid_after - df_w_validity_dates.not_valid_before\n", + ").dt.days\n", "df_w_validity_dates = df_w_validity_dates.loc[df_w_validity_dates.validity_n_days < 365]\n", - "print(f'How many certificates were revoked in <365 days after certification: {df_w_validity_dates.shape[0]}')\n", - "print(f\"How many certificates have expiry date same as certification date: {df_w_validity_dates.loc[df_w_validity_dates.not_valid_before == df_w_validity_dates.not_valid_after].shape[0]}\")" + "print(f\"How many certificates were revoked in <365 days after certification: {df_w_validity_dates.shape[0]}\")\n", + "print(\n", + " f\"How many certificates have expiry date same as certification date: {df_w_validity_dates.loc[df_w_validity_dates.not_valid_before == df_w_validity_dates.not_valid_after].shape[0]}\"\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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namenot_valid_beforenot_valid_aftervalidity_n_daysrelated_cves
602f65f51a07adafBrocade Communications Systems, Inc. Brocade D...2015-03-252016-03-21362NaN
a7120da6bf4874faDigiSAFE Data Diode model 3282 version 2.2, mo...2019-06-172020-06-10359NaN
e2f3a1cdd592ab05Cisco 800, 1900, 2900, 3900 Series Integrated ...2011-07-312012-07-20355NaN
f410930a2a47d3d0Avaya VSP 4000, VSP 7000 and VSP 80002017-03-102018-02-26353NaN
16bdbde359584f99Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...2019-01-252019-12-03312NaN
0c7ef6c32cbdee47Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S...2019-01-252019-12-03312NaN
3c24bbe7724dbfddMcAfee® Email Gateway (MEG) software v7.0.1, r...2012-10-162013-08-21309{CVE-2012-4581, CVE-2012-4583, CVE-2012-4585, ...
9e0ec9db58b5c8ecRICOH MP C306Z J-1.012016-04-252017-02-13294NaN
5068c3b7fefae589bizhub C652 / bizhub C652DS / bizhub C552 / bi...2010-06-292011-04-15290NaN
25fcafd838cecd84bizhub C360 / bizhub C280 / bizhub C220 / bizh...2010-06-292011-04-15290NaN
2748f920f4df8eafIDeal Pass v2 - SAC/EAC JC ePassport 4.0.02013-12-202014-09-09263NaN
13e306eb69c8bf95ALE Omniswitch 6250, 6350, and 6450 with the A...2017-10-172018-06-27253NaN
a0176ceabc4bfc0aCisco Expressway X12.52019-08-192020-02-26191NaN
fd31c2a2bbfcc4a9Pro 8100/8110/8120 (Ricoh/Savin/Lanier/nashuat...2016-08-102017-02-13187NaN
5a22fab9376989efCisco Email Security Appliance v9.12015-09-182016-03-21185{CVE-2016-1480, CVE-2016-6356, CVE-2020-3447, ...
7f1129858dd504a3FortiSwitch blade appliances with FortiTRNG ru...2014-11-072014-12-2245NaN
124272e23ec6ac09Samsung Galaxy Note 7 MDFPP2016-10-142016-11-1431NaN
a606e94c8e0e3028Samsung Galaxy Note 7 VPNCPP2016-10-172016-11-1428NaN
dc7ac9298e12f7eeNPCT7xx TPM2.0 rev1.38 Hardware version LAG019...2019-01-182019-01-180NaN
3746512864b941d4NPCT6xx TPM 2.0 Hardware version FB5C85D and F...2017-09-252017-09-250NaN
22ede463dbf1a105ST33TPHF20I2CTPM Firmware version 74.052017-07-192017-07-190NaN
4f953ae00a24eb85ST33TPHF2EI2C mode TPM 2.0TPM Firmware version...2017-07-192017-07-190NaN
0bf7a19b22163465ST33TPHF20SPI TPM Firmware version 74.002016-12-132016-12-130NaN
a5898123ec124b34ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...2016-12-132016-12-130NaN
4e7b5a210809acf9TPM 2.0 Hardware version FB5C85D, Firmware ver...2016-07-222016-07-220NaN
158950ed4bc35274ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio...2016-07-042016-07-040NaN
eff64d1800f26807NPCT7xx TPM 2.0 Hardware version LAG019, Firmw...2018-01-192018-01-190NaN
66838f2e4bb6ecccST33TPHF20 TPM Firmware versions 74.08 (0x4A 0...2018-09-242018-09-240NaN
d4647f5217d84ba3CELES-c002 Machine Readable Electronic Documen...2020-11-022020-11-020NaN
a334142059d865a2NPCT7xx TPM1.2 rev 116; Hardware version LAG01...2019-08-142019-08-140NaN
e2f5592cc95676e7Huawei FusionDirector 1.5.1.SPC12020-10-232020-10-230NaN
23a85a7f9c07412fMicrocontrôleurs sécurisés ST33G1M2A et ST33G1...2017-02-162017-02-160NaN
17b5257b3755a07cPlate-forme STSAFE-J, en configuration fermée,...2017-03-232017-03-230NaN
e27937cf16c20f8fST33JM2M0 B02 including optional cryptographic...2017-09-052017-09-050NaN
10b17081dd7cad8fST33TPHF2E mode TPM 2.0 TPM Firmware versions ...2018-09-242018-09-240NaN
58ece75f7f2b5099J-SIGN version 1.8.92019-01-112019-01-110NaN
2731d8ddba404fadJSAFE3_EPASS EAC (Rev. I)2019-04-092019-04-090NaN
f34df0dd7011366eJSAFE3_EPASS BAC (Rev. M)2019-04-092019-04-090NaN
bad93fb821395db2ST33TPHF20SPI2016-05-302016-05-300NaN
\n", - "
" - ], - "text/plain": [ - " name \\\n", - "602f65f51a07adaf Brocade Communications Systems, Inc. Brocade D... \n", - "a7120da6bf4874fa DigiSAFE Data Diode model 3282 version 2.2, mo... \n", - "e2f3a1cdd592ab05 Cisco 800, 1900, 2900, 3900 Series Integrated ... \n", - "f410930a2a47d3d0 Avaya VSP 4000, VSP 7000 and VSP 8000 \n", - "16bdbde359584f99 Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S... \n", - "0c7ef6c32cbdee47 Xaica-α PLUS ePassport on MTCOS Pro 2.5 with S... \n", - "3c24bbe7724dbfdd McAfee® Email Gateway (MEG) software v7.0.1, r... \n", - "9e0ec9db58b5c8ec RICOH MP C306Z J-1.01 \n", - "5068c3b7fefae589 bizhub C652 / bizhub C652DS / bizhub C552 / bi... \n", - "25fcafd838cecd84 bizhub C360 / bizhub C280 / bizhub C220 / bizh... \n", - "2748f920f4df8eaf IDeal Pass v2 - SAC/EAC JC ePassport 4.0.0 \n", - "13e306eb69c8bf95 ALE Omniswitch 6250, 6350, and 6450 with the A... \n", - "a0176ceabc4bfc0a Cisco Expressway X12.5 \n", - "fd31c2a2bbfcc4a9 Pro 8100/8110/8120 (Ricoh/Savin/Lanier/nashuat... \n", - "5a22fab9376989ef Cisco Email Security Appliance v9.1 \n", - "7f1129858dd504a3 FortiSwitch blade appliances with FortiTRNG ru... \n", - "124272e23ec6ac09 Samsung Galaxy Note 7 MDFPP \n", - "a606e94c8e0e3028 Samsung Galaxy Note 7 VPNCPP \n", - "dc7ac9298e12f7ee NPCT7xx TPM2.0 rev1.38 Hardware version LAG019... \n", - "3746512864b941d4 NPCT6xx TPM 2.0 Hardware version FB5C85D and F... \n", - "22ede463dbf1a105 ST33TPHF20I2CTPM Firmware version 74.05 \n", - "4f953ae00a24eb85 ST33TPHF2EI2C mode TPM 2.0TPM Firmware version... \n", - "0bf7a19b22163465 ST33TPHF20SPI TPM Firmware version 74.00 \n", - "a5898123ec124b34 ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio... \n", - "4e7b5a210809acf9 TPM 2.0 Hardware version FB5C85D, Firmware ver... \n", - "158950ed4bc35274 ST33TPHF2ESPI mode TPM 2.0 TPM Firmware versio... \n", - "eff64d1800f26807 NPCT7xx TPM 2.0 Hardware version LAG019, Firmw... \n", - "66838f2e4bb6eccc ST33TPHF20 TPM Firmware versions 74.08 (0x4A 0... \n", - "d4647f5217d84ba3 CELES-c002 Machine Readable Electronic Documen... \n", - "a334142059d865a2 NPCT7xx TPM1.2 rev 116; Hardware version LAG01... \n", - "e2f5592cc95676e7 Huawei FusionDirector 1.5.1.SPC1 \n", - "23a85a7f9c07412f Microcontrôleurs sécurisés ST33G1M2A et ST33G1... \n", - "17b5257b3755a07c Plate-forme STSAFE-J, en configuration fermée,... \n", - "e27937cf16c20f8f ST33JM2M0 B02 including optional cryptographic... \n", - "10b17081dd7cad8f ST33TPHF2E mode TPM 2.0 TPM Firmware versions ... \n", - "58ece75f7f2b5099 J-SIGN version 1.8.9 \n", - "2731d8ddba404fad JSAFE3_EPASS EAC (Rev. I) \n", - "f34df0dd7011366e JSAFE3_EPASS BAC (Rev. M) \n", - "bad93fb821395db2 ST33TPHF20SPI \n", - "\n", - " not_valid_before not_valid_after validity_n_days \\\n", - "602f65f51a07adaf 2015-03-25 2016-03-21 362 \n", - "a7120da6bf4874fa 2019-06-17 2020-06-10 359 \n", - "e2f3a1cdd592ab05 2011-07-31 2012-07-20 355 \n", - "f410930a2a47d3d0 2017-03-10 2018-02-26 353 \n", - "16bdbde359584f99 2019-01-25 2019-12-03 312 \n", - "0c7ef6c32cbdee47 2019-01-25 2019-12-03 312 \n", - "3c24bbe7724dbfdd 2012-10-16 2013-08-21 309 \n", - "9e0ec9db58b5c8ec 2016-04-25 2017-02-13 294 \n", - "5068c3b7fefae589 2010-06-29 2011-04-15 290 \n", - "25fcafd838cecd84 2010-06-29 2011-04-15 290 \n", - "2748f920f4df8eaf 2013-12-20 2014-09-09 263 \n", - "13e306eb69c8bf95 2017-10-17 2018-06-27 253 \n", - "a0176ceabc4bfc0a 2019-08-19 2020-02-26 191 \n", - "fd31c2a2bbfcc4a9 2016-08-10 2017-02-13 187 \n", - "5a22fab9376989ef 2015-09-18 2016-03-21 185 \n", - "7f1129858dd504a3 2014-11-07 2014-12-22 45 \n", - "124272e23ec6ac09 2016-10-14 2016-11-14 31 \n", - "a606e94c8e0e3028 2016-10-17 2016-11-14 28 \n", - "dc7ac9298e12f7ee 2019-01-18 2019-01-18 0 \n", - "3746512864b941d4 2017-09-25 2017-09-25 0 \n", - "22ede463dbf1a105 2017-07-19 2017-07-19 0 \n", - "4f953ae00a24eb85 2017-07-19 2017-07-19 0 \n", - "0bf7a19b22163465 2016-12-13 2016-12-13 0 \n", - "a5898123ec124b34 2016-12-13 2016-12-13 0 \n", - "4e7b5a210809acf9 2016-07-22 2016-07-22 0 \n", - "158950ed4bc35274 2016-07-04 2016-07-04 0 \n", - "eff64d1800f26807 2018-01-19 2018-01-19 0 \n", - "66838f2e4bb6eccc 2018-09-24 2018-09-24 0 \n", - "d4647f5217d84ba3 2020-11-02 2020-11-02 0 \n", - "a334142059d865a2 2019-08-14 2019-08-14 0 \n", - "e2f5592cc95676e7 2020-10-23 2020-10-23 0 \n", - "23a85a7f9c07412f 2017-02-16 2017-02-16 0 \n", - "17b5257b3755a07c 2017-03-23 2017-03-23 0 \n", - "e27937cf16c20f8f 2017-09-05 2017-09-05 0 \n", - "10b17081dd7cad8f 2018-09-24 2018-09-24 0 \n", - "58ece75f7f2b5099 2019-01-11 2019-01-11 0 \n", - "2731d8ddba404fad 2019-04-09 2019-04-09 0 \n", - "f34df0dd7011366e 2019-04-09 2019-04-09 0 \n", - "bad93fb821395db2 2016-05-30 2016-05-30 0 \n", - "\n", - " related_cves \n", - "602f65f51a07adaf NaN \n", - "a7120da6bf4874fa NaN \n", - "e2f3a1cdd592ab05 NaN \n", - "f410930a2a47d3d0 NaN \n", - "16bdbde359584f99 NaN \n", - "0c7ef6c32cbdee47 NaN \n", - "3c24bbe7724dbfdd {CVE-2012-4581, CVE-2012-4583, CVE-2012-4585, ... \n", - "9e0ec9db58b5c8ec NaN \n", - "5068c3b7fefae589 NaN \n", - "25fcafd838cecd84 NaN \n", - "2748f920f4df8eaf NaN \n", - "13e306eb69c8bf95 NaN \n", - "a0176ceabc4bfc0a NaN \n", - "fd31c2a2bbfcc4a9 NaN \n", - "5a22fab9376989ef {CVE-2016-1480, CVE-2016-6356, CVE-2020-3447, ... \n", - "7f1129858dd504a3 NaN \n", - "124272e23ec6ac09 NaN \n", - "a606e94c8e0e3028 NaN \n", - "dc7ac9298e12f7ee NaN \n", - "3746512864b941d4 NaN \n", - "22ede463dbf1a105 NaN \n", - "4f953ae00a24eb85 NaN \n", - "0bf7a19b22163465 NaN \n", - "a5898123ec124b34 NaN \n", - "4e7b5a210809acf9 NaN \n", - "158950ed4bc35274 NaN \n", - "eff64d1800f26807 NaN \n", - "66838f2e4bb6eccc NaN \n", - "d4647f5217d84ba3 NaN \n", - "a334142059d865a2 NaN \n", - "e2f5592cc95676e7 NaN \n", - "23a85a7f9c07412f NaN \n", - "17b5257b3755a07c NaN \n", - "e27937cf16c20f8f NaN \n", - "10b17081dd7cad8f NaN \n", - "58ece75f7f2b5099 NaN \n", - "2731d8ddba404fad NaN \n", - "f34df0dd7011366e NaN \n", - "bad93fb821395db2 NaN " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df_w_validity_dates.loc[:, [\"name\", \"not_valid_before\", \"not_valid_after\", \"validity_n_days\", \"related_cves\"]].sort_values(by=\"validity_n_days\", ascending=False)" + "df_w_validity_dates.loc[\n", + " :, [\"name\", \"not_valid_before\", \"not_valid_after\", \"validity_n_days\", \"related_cves\"]\n", + "].sort_values(by=\"validity_n_days\", ascending=False)\n" ] }, { @@ -1488,34 +485,40 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_cves_top_20_certs = df_cve_rich.n_cves.sort_values(ascending=False).head(20)[-1]\n", - "cves = df_cve_rich.loc[(df_cve_rich.year_from < 2022) & (df_cve_rich.n_cves < n_cves_top_20_certs) & (~df_cve_rich.eal.isna()), [\"related_cves\", \"eal\", \"category\"]].explode(\"related_cves\").rename(columns={\"related_cves\": \"cve_id\"})\n", + "cves = (\n", + " df_cve_rich.loc[\n", + " (df_cve_rich.year_from < 2022) & (df_cve_rich.n_cves < n_cves_top_20_certs) & (~df_cve_rich.eal.isna()),\n", + " [\"related_cves\", \"eal\", \"category\"],\n", + " ]\n", + " .explode(\"related_cves\")\n", + " .rename(columns={\"related_cves\": \"cve_id\"})\n", + ")\n", "cves[\"published_date\"] = cves.cve_id.map(lambda x: cve_dset[x].published_date).dt.tz_localize(None).dt.normalize()\n", "cves[\"base_score\"] = cves.cve_id.map(lambda x: cve_dset[x].impact.base_score)\n", "cves = cves.drop_duplicates()\n", "\n", - "g = sns.relplot(data=cves, x=\"published_date\", y=\"base_score\", hue=\"eal\", col=\"category\", col_wrap=3, height=4, kind=\"scatter\", facet_kws={\"sharey\": False, \"sharex\": False}, palette=\"viridis\")\n", + "g = sns.relplot(\n", + " data=cves,\n", + " x=\"published_date\",\n", + " y=\"base_score\",\n", + " hue=\"eal\",\n", + " col=\"category\",\n", + " col_wrap=3,\n", + " height=4,\n", + " kind=\"scatter\",\n", + " facet_kws={\"sharey\": False, \"sharex\": False},\n", + " palette=\"viridis\",\n", + ")\n", "g.set_titles(\"{col_name}\")\n", "g.set_xticklabels(rotation=60)\n", "g.set_axis_labels(\"Vulnerability publish date\", \"Vulnerability base score\")\n", "g.tight_layout()\n", - "plt.savefig(RESULTS_DIR / \"scatter_category_vs_cves.pdf\", bbox_inches='tight')" + "plt.savefig(RESULTS_DIR / \"scatter_category_vs_cves.pdf\", bbox_inches=\"tight\")\n" ] }, { @@ -1527,29 +530,25 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cves = cves.reset_index() # See https://github.com/mwaskom/seaborn/issues/2733\n", - "g = sns.displot(data=cves, x=\"base_score\", col=\"category\", col_wrap=3, height=3, kind=\"hist\", facet_kws={\"sharey\": False, \"sharex\": True})\n", + "g = sns.displot(\n", + " data=cves,\n", + " x=\"base_score\",\n", + " col=\"category\",\n", + " col_wrap=3,\n", + " height=3,\n", + " kind=\"hist\",\n", + " facet_kws={\"sharey\": False, \"sharex\": True},\n", + ")\n", "g.set_titles(\"{col_name}\")\n", "# g.set_xticklabels(rotation=60)\n", "g.set_axis_labels(\"Vulnerability base score\", \"Frequency\")\n", "g.tight_layout()\n", - "plt.savefig(RESULTS_DIR / \"hist_category_vs_cve.pdf\", bbox_inches='tight')" + "plt.savefig(RESULTS_DIR / \"hist_category_vs_cve.pdf\", bbox_inches=\"tight\")\n" ] }, { @@ -1564,215 +563,27 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_cve_rich[\"has_pp\"] = df_cve_rich.protection_profiles.isna()\n", - "corrs = df_cve_rich.groupby(\"category\")[[\"n_cves\", \"has_pp\"]].corr().iloc[0::2,-1].droplevel(level=1)\n", + "corrs = df_cve_rich.groupby(\"category\")[[\"n_cves\", \"has_pp\"]].corr().iloc[0::2, -1].droplevel(level=1)\n", "corrs.name = \"correlation\"\n", "support_counts = df_cve_rich.groupby(\"category\").size()\n", "support_counts.name = \"n certs in category\"\n", "pp_rich_counts = df_cve_rich.loc[df_cve_rich.has_pp == True].groupby(\"category\").size()\n", "pp_rich_counts.name = \"n certs with PP\"\n", - "pp_n_cves_corr = pd.concat([corrs, support_counts, pp_rich_counts], axis=1)" + "pp_n_cves_corr = pd.concat([corrs, support_counts, pp_rich_counts], axis=1)\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Boundary Protection Devices and Systems-0.2248672817
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Network and Network-Related Devices and Systems-0.44346110438
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Products for Digital SignaturesNaN10
Trusted ComputingNaN00
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" - ], - "text/plain": [ - " correlation \\\n", - "category \n", - "Access Control Devices and Systems 0.201211 \n", - "Biometric Systems and Devices NaN \n", - "Boundary Protection Devices and Systems -0.224867 \n", - "Data Protection 0.115856 \n", - "Databases 0.266130 \n", - "Detection Devices and Systems 0.043327 \n", - "ICs, Smart Cards and Smart Card-Related Devices... NaN \n", - "Key Management Systems 0.839556 \n", - "Mobility -0.042186 \n", - "Multi-Function Devices -0.238942 \n", - "Network and Network-Related Devices and Systems -0.443461 \n", - "Operating Systems -0.296302 \n", - "Other Devices and Systems -0.174534 \n", - "Products for Digital Signatures NaN \n", - "Trusted Computing NaN \n", - "\n", - " n certs in category \\\n", - "category \n", - "Access Control Devices and Systems 15 \n", - "Biometric Systems and Devices 0 \n", - "Boundary Protection Devices and Systems 28 \n", - "Data Protection 10 \n", - "Databases 7 \n", - "Detection Devices and Systems 7 \n", - "ICs, Smart Cards and Smart Card-Related Devices... 23 \n", - "Key Management Systems 4 \n", - "Mobility 11 \n", - "Multi-Function Devices 32 \n", - "Network and Network-Related Devices and Systems 104 \n", - "Operating Systems 61 \n", - "Other Devices and Systems 73 \n", - "Products for Digital Signatures 1 \n", - "Trusted Computing 0 \n", - "\n", - " n certs with PP \n", - "category \n", - "Access Control Devices and Systems 13 \n", - "Biometric Systems and Devices 0 \n", - "Boundary Protection Devices and Systems 17 \n", - "Data Protection 7 \n", - "Databases 6 \n", - "Detection Devices and Systems 5 \n", - "ICs, Smart Cards and Smart Card-Related Devices... 1 \n", - "Key Management Systems 3 \n", - "Mobility 2 \n", - "Multi-Function Devices 15 \n", - "Network and Network-Related Devices and Systems 38 \n", - "Operating Systems 38 \n", - "Other Devices and Systems 46 \n", - "Products for Digital Signatures 0 \n", - "Trusted Computing 0 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "pp_n_cves_corr" + "pp_n_cves_corr\n" ] }, { @@ -1789,738 +600,47 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering 2418 CVEs that have no CWE assigned. This affects 182 certificates\n", - "Still left with analysis of 4932 CVEs in 246 certificates.\n" - ] - } - ], + "outputs": [], "source": [ - "cc_df, cwe_df = prepare_cwe_df(df_cves_within_validity_period.loc[df_cves_within_validity_period.n_cves > 0], cve_dset, fine_grained=True)" + "cc_df, cwe_df = prepare_cwe_df(\n", + " df_cves_within_validity_period.loc[df_cves_within_validity_period.n_cves > 0], cve_dset, fine_grained=True\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cert_dgstealcve_idcategorycwe_id
03dc6e1ebe7dd5584EAL2+CVE-2022-40707Boundary Protection Devices and SystemsCWE-125
13dc6e1ebe7dd5584EAL2+CVE-2022-40709Boundary Protection Devices and SystemsCWE-125
23dc6e1ebe7dd5584EAL2+CVE-2022-40708Boundary Protection Devices and SystemsCWE-125
33dc6e1ebe7dd5584EAL2+CVE-2022-40710Boundary Protection Devices and SystemsCWE-59
177709221bfec82ad9EAL2+CVE-2019-9488Detection Devices and SystemsCWE-611
..................
151845f1df5ad8e51ba75EAL2+CVE-2007-4617Other Devices and SystemsCWE-399
151885f1df5ad8e51ba75EAL2+CVE-2008-0902Other Devices and SystemsCWE-79
151935f1df5ad8e51ba75EAL2+CVE-2008-0901Other Devices and SystemsCWE-255
151945f1df5ad8e51ba75EAL2+CVE-2008-0901Other Devices and SystemsCWE-200
152035f1df5ad8e51ba75EAL2+CVE-2008-0900Other Devices and SystemsCWE-264
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7524 rows × 5 columns

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cwe_namecwe_descriptiontypechild_ofurl
cwe_id
CWE-1004Sensitive Cookie Without 'HttpOnly' FlagThe software uses a cookie to store sensitive ...weakness{CWE-732}https://cwe.mitre.org/data/definitions/1004.html
CWE-1007Insufficient Visual Distinction of Homoglyphs ...The software displays information or identifie...weakness{CWE-451}https://cwe.mitre.org/data/definitions/1007.html
CWE-102Struts: Duplicate Validation FormsThe application uses multiple validation forms...weakness{CWE-20, CWE-1173, CWE-694}https://cwe.mitre.org/data/definitions/102.html
CWE-1021Improper Restriction of Rendered UI Layers or ...The web application does not restrict or incor...weakness{CWE-451, CWE-610, CWE-441}https://cwe.mitre.org/data/definitions/1021.html
CWE-1022Use of Web Link to Untrusted Target with windo...The web application produces links to untruste...weakness{CWE-266}https://cwe.mitre.org/data/definitions/1022.html
..................
CWE-994SFP Secondary Cluster: Tainted Input to VariableThis category identifies Software Fault Patter...categoryNaNhttps://cwe.mitre.org/data/definitions/994.html
CWE-995SFP Secondary Cluster: FeatureThis category identifies Software Fault Patter...categoryNaNhttps://cwe.mitre.org/data/definitions/995.html
CWE-996SFP Secondary Cluster: SecurityThis category identifies Software Fault Patter...categoryNaNhttps://cwe.mitre.org/data/definitions/996.html
CWE-997SFP Secondary Cluster: Information LossThis category identifies Software Fault Patter...categoryNaNhttps://cwe.mitre.org/data/definitions/997.html
CWE-998SFP Secondary Cluster: Glitch in ComputationThis category identifies Software Fault Patter...categoryNaNhttps://cwe.mitre.org/data/definitions/998.html
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1338 rows × 5 columns

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" - ], - "text/plain": [ - " cwe_name \\\n", - "cwe_id \n", - "CWE-1004 Sensitive Cookie Without 'HttpOnly' Flag \n", - "CWE-1007 Insufficient Visual Distinction of Homoglyphs ... \n", - "CWE-102 Struts: Duplicate Validation Forms \n", - "CWE-1021 Improper Restriction of Rendered UI Layers or ... \n", - "CWE-1022 Use of Web Link to Untrusted Target with windo... \n", - "... ... \n", - "CWE-994 SFP Secondary Cluster: Tainted Input to Variable \n", - "CWE-995 SFP Secondary Cluster: Feature \n", - "CWE-996 SFP Secondary Cluster: Security \n", - "CWE-997 SFP Secondary Cluster: Information Loss \n", - "CWE-998 SFP Secondary Cluster: Glitch in Computation \n", - "\n", - " cwe_description type \\\n", - "cwe_id \n", - "CWE-1004 The software uses a cookie to store sensitive ... weakness \n", - "CWE-1007 The software displays information or identifie... weakness \n", - "CWE-102 The application uses multiple validation forms... weakness \n", - "CWE-1021 The web application does not restrict or incor... weakness \n", - "CWE-1022 The web application produces links to untruste... weakness \n", - "... ... ... \n", - "CWE-994 This category identifies Software Fault Patter... category \n", - "CWE-995 This category identifies Software Fault Patter... category \n", - "CWE-996 This category identifies Software Fault Patter... category \n", - "CWE-997 This category identifies Software Fault Patter... category \n", - "CWE-998 This category identifies Software Fault Patter... category \n", - "\n", - " child_of \\\n", - "cwe_id \n", - "CWE-1004 {CWE-732} \n", - "CWE-1007 {CWE-451} \n", - "CWE-102 {CWE-20, CWE-1173, CWE-694} \n", - "CWE-1021 {CWE-451, CWE-610, CWE-441} \n", - "CWE-1022 {CWE-266} \n", - "... ... \n", - "CWE-994 NaN \n", - "CWE-995 NaN \n", - "CWE-996 NaN \n", - "CWE-997 NaN \n", - "CWE-998 NaN \n", - "\n", - " url \n", - "cwe_id \n", - "CWE-1004 https://cwe.mitre.org/data/definitions/1004.html \n", - "CWE-1007 https://cwe.mitre.org/data/definitions/1007.html \n", - "CWE-102 https://cwe.mitre.org/data/definitions/102.html \n", - "CWE-1021 https://cwe.mitre.org/data/definitions/1021.html \n", - "CWE-1022 https://cwe.mitre.org/data/definitions/1022.html \n", - "... ... \n", - "CWE-994 https://cwe.mitre.org/data/definitions/994.html \n", - "CWE-995 https://cwe.mitre.org/data/definitions/995.html \n", - "CWE-996 https://cwe.mitre.org/data/definitions/996.html \n", - "CWE-997 https://cwe.mitre.org/data/definitions/997.html \n", - "CWE-998 https://cwe.mitre.org/data/definitions/998.html \n", - "\n", - "[1338 rows x 5 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "cwe_df" + "cwe_df\n" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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frequencycwe_namecwe_descriptionurltype
cwe_id
CWE-119875Improper Restriction of Operations within the ...The software performs operations on a memory b...https://cwe.mitre.org/data/definitions/119.htmlweakness
CWE-269757Improper Privilege ManagementThe software does not properly assign, modify,...https://cwe.mitre.org/data/definitions/269.htmlweakness
CWE-20652Improper Input ValidationThe product receives input or data, but it doe...https://cwe.mitre.org/data/definitions/20.htmlweakness
CWE-787650Out-of-bounds WriteThe software writes data past the end, or befo...https://cwe.mitre.org/data/definitions/787.htmlweakness
CWE-200612Exposure of Sensitive Information to an Unauth...The product exposes sensitive information to a...https://cwe.mitre.org/data/definitions/200.htmlweakness
CWE-125326Out-of-bounds ReadThe software reads data past the end, or befor...https://cwe.mitre.org/data/definitions/125.htmlweakness
CWE-264287Permissions, Privileges, and Access ControlsWeaknesses in this category are related to the...https://cwe.mitre.org/data/definitions/264.htmlcategory
CWE-362279Concurrent Execution using Shared Resource wit...The program contains a code sequence that can ...https://cwe.mitre.org/data/definitions/362.htmlweakness
CWE-416196Use After FreeReferencing memory after it has been freed can...https://cwe.mitre.org/data/definitions/416.htmlweakness
CWE-399161Resource Management ErrorsWeaknesses in this category are related to imp...https://cwe.mitre.org/data/definitions/399.htmlcategory
CWE-79160Improper Neutralization of Input During Web Pa...The software does not neutralize or incorrectl...https://cwe.mitre.org/data/definitions/79.htmlweakness
CWE-668143Exposure of Resource to Wrong SphereThe product exposes a resource to the wrong co...https://cwe.mitre.org/data/definitions/668.htmlweakness
CWE-59131Improper Link Resolution Before File Access ('...The software attempts to access a file based o...https://cwe.mitre.org/data/definitions/59.htmlweakness
CWE-190119Integer Overflow or WraparoundThe software performs a calculation that can p...https://cwe.mitre.org/data/definitions/190.htmlweakness
CWE-862108Missing AuthorizationThe software does not perform an authorization...https://cwe.mitre.org/data/definitions/862.htmlweakness
CWE-476105NULL Pointer DereferenceA NULL pointer dereference occurs when the app...https://cwe.mitre.org/data/definitions/476.htmlweakness
CWE-28795Improper AuthenticationWhen an actor claims to have a given identity,...https://cwe.mitre.org/data/definitions/287.htmlweakness
CWE-86395Incorrect AuthorizationThe software performs an authorization check w...https://cwe.mitre.org/data/definitions/863.htmlweakness
CWE-40477Improper Resource Shutdown or ReleaseThe program does not release or incorrectly re...https://cwe.mitre.org/data/definitions/404.htmlweakness
CWE-2274Improper Limitation of a Pathname to a Restric...The software uses external input to construct ...https://cwe.mitre.org/data/definitions/22.htmlweakness
CWE-9472Improper Control of Generation of Code ('Code ...The software constructs all or part of a code ...https://cwe.mitre.org/data/definitions/94.htmlweakness
CWE-66572Improper InitializationThe software does not initialize or incorrectl...https://cwe.mitre.org/data/definitions/665.htmlweakness
CWE-31071Cryptographic IssuesWeaknesses in this category are related to the...https://cwe.mitre.org/data/definitions/310.htmlcategory
CWE-40071Uncontrolled Resource ConsumptionThe software does not properly control the all...https://cwe.mitre.org/data/definitions/400.htmlweakness
CWE-102163Improper Restriction of Rendered UI Layers or ...The web application does not restrict or incor...https://cwe.mitre.org/data/definitions/1021.htmlweakness
\n", - "
" - ], - "text/plain": [ - " frequency cwe_name \\\n", - "cwe_id \n", - "CWE-119 875 Improper Restriction of Operations within the ... \n", - "CWE-269 757 Improper Privilege Management \n", - "CWE-20 652 Improper Input Validation \n", - "CWE-787 650 Out-of-bounds Write \n", - "CWE-200 612 Exposure of Sensitive Information to an Unauth... \n", - "CWE-125 326 Out-of-bounds Read \n", - "CWE-264 287 Permissions, Privileges, and Access Controls \n", - "CWE-362 279 Concurrent Execution using Shared Resource wit... \n", - "CWE-416 196 Use After Free \n", - "CWE-399 161 Resource Management Errors \n", - "CWE-79 160 Improper Neutralization of Input During Web Pa... \n", - "CWE-668 143 Exposure of Resource to Wrong Sphere \n", - "CWE-59 131 Improper Link Resolution Before File Access ('... \n", - "CWE-190 119 Integer Overflow or Wraparound \n", - "CWE-862 108 Missing Authorization \n", - "CWE-476 105 NULL Pointer Dereference \n", - "CWE-287 95 Improper Authentication \n", - "CWE-863 95 Incorrect Authorization \n", - "CWE-404 77 Improper Resource Shutdown or Release \n", - "CWE-22 74 Improper Limitation of a Pathname to a Restric... \n", - "CWE-94 72 Improper Control of Generation of Code ('Code ... \n", - "CWE-665 72 Improper Initialization \n", - "CWE-310 71 Cryptographic Issues \n", - "CWE-400 71 Uncontrolled Resource Consumption \n", - "CWE-1021 63 Improper Restriction of Rendered UI Layers or ... \n", - "\n", - " cwe_description \\\n", - "cwe_id \n", - "CWE-119 The software performs operations on a memory b... \n", - "CWE-269 The software does not properly assign, modify,... \n", - "CWE-20 The product receives input or data, but it doe... \n", - "CWE-787 The software writes data past the end, or befo... \n", - "CWE-200 The product exposes sensitive information to a... \n", - "CWE-125 The software reads data past the end, or befor... \n", - "CWE-264 Weaknesses in this category are related to the... \n", - "CWE-362 The program contains a code sequence that can ... \n", - "CWE-416 Referencing memory after it has been freed can... \n", - "CWE-399 Weaknesses in this category are related to imp... \n", - "CWE-79 The software does not neutralize or incorrectl... \n", - "CWE-668 The product exposes a resource to the wrong co... \n", - "CWE-59 The software attempts to access a file based o... \n", - "CWE-190 The software performs a calculation that can p... \n", - "CWE-862 The software does not perform an authorization... \n", - "CWE-476 A NULL pointer dereference occurs when the app... \n", - "CWE-287 When an actor claims to have a given identity,... \n", - "CWE-863 The software performs an authorization check w... \n", - "CWE-404 The program does not release or incorrectly re... \n", - "CWE-22 The software uses external input to construct ... \n", - "CWE-94 The software constructs all or part of a code ... \n", - "CWE-665 The software does not initialize or incorrectl... \n", - "CWE-310 Weaknesses in this category are related to the... \n", - "CWE-400 The software does not properly control the all... \n", - "CWE-1021 The web application does not restrict or incor... \n", - "\n", - " url type \n", - "cwe_id \n", - "CWE-119 https://cwe.mitre.org/data/definitions/119.html weakness \n", - "CWE-269 https://cwe.mitre.org/data/definitions/269.html weakness \n", - "CWE-20 https://cwe.mitre.org/data/definitions/20.html weakness \n", - "CWE-787 https://cwe.mitre.org/data/definitions/787.html weakness \n", - "CWE-200 https://cwe.mitre.org/data/definitions/200.html weakness \n", - "CWE-125 https://cwe.mitre.org/data/definitions/125.html weakness \n", - "CWE-264 https://cwe.mitre.org/data/definitions/264.html category \n", - "CWE-362 https://cwe.mitre.org/data/definitions/362.html weakness \n", - "CWE-416 https://cwe.mitre.org/data/definitions/416.html weakness \n", - "CWE-399 https://cwe.mitre.org/data/definitions/399.html category \n", - "CWE-79 https://cwe.mitre.org/data/definitions/79.html weakness \n", - "CWE-668 https://cwe.mitre.org/data/definitions/668.html weakness \n", - "CWE-59 https://cwe.mitre.org/data/definitions/59.html weakness \n", - "CWE-190 https://cwe.mitre.org/data/definitions/190.html weakness \n", - "CWE-862 https://cwe.mitre.org/data/definitions/862.html weakness \n", - "CWE-476 https://cwe.mitre.org/data/definitions/476.html weakness \n", - "CWE-287 https://cwe.mitre.org/data/definitions/287.html weakness \n", - "CWE-863 https://cwe.mitre.org/data/definitions/863.html weakness \n", - "CWE-404 https://cwe.mitre.org/data/definitions/404.html weakness \n", - "CWE-22 https://cwe.mitre.org/data/definitions/22.html weakness \n", - "CWE-94 https://cwe.mitre.org/data/definitions/94.html weakness \n", - "CWE-665 https://cwe.mitre.org/data/definitions/665.html weakness \n", - "CWE-310 https://cwe.mitre.org/data/definitions/310.html category \n", - "CWE-400 https://cwe.mitre.org/data/definitions/400.html weakness \n", - "CWE-1021 https://cwe.mitre.org/data/definitions/1021.html weakness " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print top-25 CWEs across dataset\n", "top_25_cwes = get_top_n_cwes(cc_df, cwe_df, n_cwes=25)\n", - "top_25_cwes" + "top_25_cwes\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2534,30 +654,20 @@ "\n", "# Same for EALs\n", "for eal in cc_df.eal.unique():\n", - " get_top_n_cwes(cc_df, cwe_df, eal=eal).to_csv(CWE_DIR / (eal + \".csv\"))" + " get_top_n_cwes(cc_df, cwe_df, eal=eal).to_csv(CWE_DIR / (eal + \".csv\"))\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('Tainted Input', 'SFP Secondary Cluster: Tainted Input to Command')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# For more coarse-grained description of CVEs, one could use so-called Security Fault Patterns (SFP) clusters.\n", "from sec_certs.utils.pandas import SFPModel\n", + "\n", "model = SFPModel.from_web()\n", - "model.search_cwe(626) # <-- This returns Primary Cluster and Secondary cluster" + "model.search_cwe(626) # <-- This returns Primary Cluster and Secondary cluster\n" ] }, { @@ -2571,145 +681,35 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cert_dgstealcve_idcategorycwe_id
03dc6e1ebe7dd5584EAL2+CVE-2022-40707Boundary Protection Devices and SystemsCWE-125
13dc6e1ebe7dd5584EAL2+CVE-2022-40709Boundary Protection Devices and SystemsCWE-125
23dc6e1ebe7dd5584EAL2+CVE-2022-40708Boundary Protection Devices and SystemsCWE-125
33dc6e1ebe7dd5584EAL2+CVE-2022-40710Boundary Protection Devices and SystemsCWE-59
177709221bfec82ad9EAL2+CVE-2019-9488Detection Devices and SystemsCWE-611
\n", - "
" - ], - "text/plain": [ - " cert_dgst eal cve_id \\\n", - "0 3dc6e1ebe7dd5584 EAL2+ CVE-2022-40707 \n", - "1 3dc6e1ebe7dd5584 EAL2+ CVE-2022-40709 \n", - "2 3dc6e1ebe7dd5584 EAL2+ CVE-2022-40708 \n", - "3 3dc6e1ebe7dd5584 EAL2+ CVE-2022-40710 \n", - "17 7709221bfec82ad9 EAL2+ CVE-2019-9488 \n", - "\n", - " category cwe_id \n", - "0 Boundary Protection Devices and Systems CWE-125 \n", - "1 Boundary Protection Devices and Systems CWE-125 \n", - "2 Boundary Protection Devices and Systems CWE-125 \n", - "3 Boundary Protection Devices and Systems CWE-59 \n", - "17 Detection Devices and Systems CWE-611 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "cc_df.head()" + "cc_df.head()\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'CWE prevalence in time')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cc_df[\"vuln_year\"] = cc_df.cve_id.map(lambda x: int(x.split(\"-\")[1]))\n", "cc_df = cc_df.loc[cc_df.vuln_year < 2022]\n", "top_10_cwes = set(get_top_n_cwes(cc_df, cwe_df, n_cwes=10).index.tolist())\n", "top_10_cwes = cc_df.loc[cc_df.cwe_id.isin(top_10_cwes)]\n", - "pivot = top_10_cwes.groupby([\"vuln_year\", \"cwe_id\"], as_index=False).size().rename(columns={\"size\": \"frequency\"}).pivot(\"vuln_year\", \"cwe_id\", \"frequency\").fillna(0)\n", + "pivot = (\n", + " top_10_cwes.groupby([\"vuln_year\", \"cwe_id\"], as_index=False)\n", + " .size()\n", + " .rename(columns={\"size\": \"frequency\"})\n", + " .pivot(\"vuln_year\", \"cwe_id\", \"frequency\")\n", + " .fillna(0)\n", + ")\n", "sns.lineplot(data=pivot)\n", "plt.xlabel(\"Year of vulnerability\")\n", "plt.ylabel(\"Number of vulnerabilities\")\n", "plt.legend(title=\"Weakness ID\")\n", - "plt.title(\"CWE prevalence in time\")" + "plt.title(\"CWE prevalence in time\")\n" ] }, { @@ -2721,58 +721,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\newcommand{\\numCCActiveVulnerable}{$106$}\n", - "\\newcommand{\\numCCArchivedVulnerable}{$270$}\n", - "\\newcommand{\\fractionCPEShort}{$4.47\\%$} % number CPE strings with <4 characters.\n", - "\\newcommand{\\numRegularExpressions}{$485$}\n", - "\\newcommand{\\numSmartcardsVulnerable}{$23$}\n", - "\\newcommand{\\numSmartcardsDistinctVulnerabilities}{$2$}\n", - "\\newcommand{\\numSmartcardsRocaVuln}{$16$}\n", - "\\newcommand{\\numSmartcardsReferencingRocaVuln}{$119$}\n", - "\\newcommand{\\numSmartcardsTitanVuln}{$7$}\n", - "\\newcommand{\\numSmartcardsMinervaVuln}{$0$}\n", - "\\newcommand{\\numSmartcardsTmpFailVuln}{$0$}\n", - "\\newcommand{\\numCertsWithMaintenance}{$361$}\n", - "\\newcommand{\\numVulnerableCertsWithMaintenance}{$24$}\n", - "\\newcommand{\\numCertsRevokedWithinYear}{$39$}\n", - "\\newcommand{\\numVulnerableCertsRevokedWithinYear}{$2$}\n", - "\\newcommand{\\numVulnerableCertsInValidityPeriod}{$50\\%$}\n", - "\n", - "Table of CWEs follows:\n", - "\n", - "CWE-119\t & Buffer overflow \t & 863 \\\\\n", - "CWE-200\t & Sensitive information exposure \t & 608 \\\\\n", - "CWE-20\t & Improper Input Validation \t & 582 \\\\\n", - "CWE-787\t & Out-of-bounds Write \t & 490 \\\\\n", - "CWE-269\t & Improper Privilege Management \t & 466 \\\\\n", - "CWE-264\t & Access control error \t & 287 \\\\\n", - "CWE-125\t & Out-of-bounds Read \t & 255 \\\\\n", - "CWE-362\t & Race Condition \t & 199 \\\\\n", - "CWE-399\t & Resource Management Errors \t & 161 \\\\\n", - "CWE-416\t & Use After Free \t & 155 \\\\\n", - "\n", - "Table of correlations follows:\n", - "\n", - "EAL & -0.03 & 7.93e-02 & \\cellcolor{green!15} -0.35 & \\cellcolor{green!15} 2.52e-07 & 2779 & 14 \\\\\n", - "ALC\\_CMC & \\cellcolor{green!15} -0.10 & \\cellcolor{green!15} 3.22e-07 & \\cellcolor{green!15} -0.37 & \\cellcolor{green!15} 4.39e-08 & 2336 & 5 \\\\\n", - "ALC\\_CMS & \\cellcolor{green!15} -0.10 & \\cellcolor{green!15} 7.84e-07 & \\cellcolor{green!15} -0.37 & \\cellcolor{green!15} 4.72e-08 & 2342 & 5 \\\\\n", - "AVA\\_VAN & \\cellcolor{green!15} -0.11 & \\cellcolor{green!15} 5.80e-08 & \\cellcolor{green!15} -0.35 & \\cellcolor{green!15} 1.89e-07 & 2367 & 4 \\\\\n", - "ATE\\_IND & \\cellcolor{green!15} -0.08 & \\cellcolor{green!15} 1.77e-06 & \\cellcolor{green!15} -0.33 & \\cellcolor{green!15} 2.33e-08 & 3083 & 3 \\\\\n", - "ATE\\_COV & -0.02 & 1.62e-01 & \\cellcolor{green!15} -0.32 & \\cellcolor{green!15} 2.01e-05 & 2299 & 3 \\\\\n", - "ADV\\_FSP & \\cellcolor{green!15} -0.08 & \\cellcolor{green!15} 4.74e-06 & \\cellcolor{green!15} -0.28 & \\cellcolor{green!15} 2.08e-06 & 3132 & 6 \\\\\n", - "ASE\\_OBJ & \\cellcolor{green!15} -0.12 & \\cellcolor{green!15} 3.07e-07 & -0.14 & 4.25e-02 & 1869 & 2 \\\\\n", - "ASE\\_REQ & \\cellcolor{green!15} -0.12 & \\cellcolor{green!15} 1.34e-07 & -0.12 & 6.44e-02 & 1907 & 2 \\\\\n", - "ALC\\_FLR & 0.05 & 9.79e-01 & 0.10 & 8.83e-01 & 1592 & 3 \\\\\n" - ] - } - ], + "outputs": [], "source": [ "def get_n_rules(dct):\n", " if isinstance(dct, list):\n", @@ -2781,14 +732,23 @@ " return sum([get_n_rules(x) for x in dct.values()])\n", " else:\n", " return sum([len(x) for x in dct.values()])\n", - " \n", + "\n", + "\n", "n_regular_expressions = get_n_rules(cc_rules)\n", "\n", "n_all_cpes = len(cpe_dset)\n", "n_short_cpes = len([x for x in cpe_dset if x.item_name is not None and not len(x.item_name) > 3])\n", "\n", - "n_smartcards_vulnerable = len(df_cve_rich.loc[df_cve_rich.category == \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"])\n", - "unique_smartcard_cves = set(itertools.chain.from_iterable(df_cve_rich.loc[df_cve_rich.category == \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"].related_cves.tolist()))\n", + "n_smartcards_vulnerable = len(\n", + " df_cve_rich.loc[df_cve_rich.category == \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"]\n", + ")\n", + "unique_smartcard_cves = set(\n", + " itertools.chain.from_iterable(\n", + " df_cve_rich.loc[\n", + " df_cve_rich.category == \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"\n", + " ].related_cves.tolist()\n", + " )\n", + ")\n", "\n", "df_cve_rich[\"roca\"] = df_cve_rich.related_cves.map(lambda x: \"CVE-2017-15361\" in x)\n", "df_cve_rich[\"titan\"] = df_cve_rich.related_cves.map(lambda x: \"CVE-2021-3011\" in x)\n", @@ -2821,12 +781,17 @@ "except KeyError:\n", " n_tmpfail_certs = 0\n", "\n", - "n_cves_in_validity_period = exploded_cves.loc[(exploded_cves.cve_published_date > exploded_cves.not_valid_before) & (exploded_cves.cve_published_date < exploded_cves.not_valid_after)].shape[0]\n", + "n_cves_in_validity_period = exploded_cves.loc[\n", + " (exploded_cves.cve_published_date > exploded_cves.not_valid_before)\n", + " & (exploded_cves.cve_published_date < exploded_cves.not_valid_after)\n", + "].shape[0]\n", "n_cves_all = exploded_cves.shape[0]\n", "\n", "print(f\"\\\\newcommand{{\\\\numCCActiveVulnerable}}{{${df_cve_rich.loc[df_cve_rich.status == 'active'].shape[0]}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numCCArchivedVulnerable}}{{${df_cve_rich.loc[df_cve_rich.status == 'archived'].shape[0]}$}}\")\n", - "print(f\"\\\\newcommand{{\\\\fractionCPEShort}}{{${(100 * n_short_cpes / n_all_cpes):.2f}\\%$}} % number CPE strings with <4 characters.\")\n", + "print(\n", + " f\"\\\\newcommand{{\\\\fractionCPEShort}}{{${(100 * n_short_cpes / n_all_cpes):.2f}\\%$}} % number CPE strings with <4 characters.\"\n", + ")\n", "print(f\"\\\\newcommand{{\\\\numRegularExpressions}}{{${n_regular_expressions}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numSmartcardsVulnerable}}{{${n_smartcards_vulnerable}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numSmartcardsDistinctVulnerabilities}}{{${len(unique_smartcard_cves)}$}}\")\n", @@ -2836,10 +801,14 @@ "print(f\"\\\\newcommand{{\\\\numSmartcardsMinervaVuln}}{{${n_minerva_certs}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numSmartcardsTmpFailVuln}}{{${n_tmpfail_certs}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numCertsWithMaintenance}}{{${df.loc[df.n_maintenances > 0].shape[0]}$}}\")\n", - "print(f\"\\\\newcommand{{\\\\numVulnerableCertsWithMaintenance}}{{${df_cve_rich.loc[df_cve_rich.n_maintenances > 0].shape[0]}$}}\")\n", + "print(\n", + " f\"\\\\newcommand{{\\\\numVulnerableCertsWithMaintenance}}{{${df_cve_rich.loc[df_cve_rich.n_maintenances > 0].shape[0]}$}}\"\n", + ")\n", "print(f\"\\\\newcommand{{\\\\numCertsRevokedWithinYear}}{{${df_w_validity_dates.shape[0]}$}}\")\n", "print(f\"\\\\newcommand{{\\\\numVulnerableCertsRevokedWithinYear}}{{${maybe_revoked.shape[0]}$}}\")\n", - "print(f\"\\\\newcommand{{\\\\numVulnerableCertsInValidityPeriod}}{{${(100 * n_cves_in_validity_period / n_cves_all):.0f}\\%$}}\")\n", + "print(\n", + " f\"\\\\newcommand{{\\\\numVulnerableCertsInValidityPeriod}}{{${(100 * n_cves_in_validity_period / n_cves_all):.0f}\\%$}}\"\n", + ")\n", "\n", "name_mapping = {\n", " \"Improper Restriction of Operations within the Bounds of a Memory Buffer\": \"Buffer overflow\",\n", @@ -2861,590 +830,11 @@ "print(f\"\\nTable of correlations follows:\\n\")\n", "\n", "for sar, row in df_corr.iterrows():\n", - " color_n_cves = \"\\cellcolor{green!15}\" if row['n_cves_pvalue'] < 0.01 else \"\"\n", - " color_avg_score = \"\\cellcolor{green!15}\" if row['avg_cve_pvalue'] < 0.01 else \"\"\n", - " print(f\"{sar} & {color_n_cves} {row['n_cves_corr']:.2f} & {color_n_cves} {row['n_cves_pvalue']:.2e} & {color_avg_score} {row['avg_cve_score_corr']:.2f} & {color_avg_score} {row['avg_cve_pvalue']:.2e} & {row['support']:.0f} & {row['range']:.0f} \\\\\\\\\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cert_idnamestatuscategorymanufacturerschemesecurity_levelealnot_valid_beforenot_valid_after...protection_profilescert_labyear_fromn_maintenancesmaintenance_datesn_cvescve_published_datesearliest_cveworst_cve_scoreavg_cve_score
ebd276cca70fd723BSI-DSZ-CC-0683-2014IBM Security Access Manager for Enterprise Sin...activeAccess Control Devices and SystemsIBM CorporationDE{EAL3+, ALC_FLR.1}EAL3+2014-12-05NaT...NaNBSI2014NaNNaN6[2019-08-26, 2013-12-22, 2018-08-17, 2015-01-2...2013-12-2210.05.800000
b7f814ed16f2eccaCSEC2016009Clavister cOS Core v12.00.00activeBoundary Protection Devices and SystemsClavister ABSE{EAL4, ALC_FLR.1}EAL42019-06-19NaT...NaNNaN2019NaNNaN1[2018-08-15]2018-08-155.95.900000
4fc1208e4c800aa6CSEC2015010WatchGuard Firebox Security Appliances with Fi...activeBoundary Protection Devices and SystemsWatchGuard Technologies, Inc.SE{ALC_FLR.2, EAL4}EAL42017-05-05NaT...NaNNaN2017NaNNaN2[2017-09-20, 2017-09-20]2017-09-207.56.800000
c8f2a5312b671aaeISCB-5-RPT-C069-CR-v1MarkLogic Server 8.0-4activeDatabasesMarkLogic CorporationMY{EAL2+, ALC_FLR.3}EAL2+2015-12-22NaT...NaNNaN2015NaNNaN11[2017-05-23, 2017-05-24, 2018-09-07, 2017-05-2...2017-05-239.68.309091
4c33266562bb9b24CSEC2017009HP LaserJet Managed MFP E62555/E62565/E62575/E...activeMulti-Function DevicesHP Inc.SE{ALC_FLR.2, EAL3}EAL32019-10-11NaT...NaNNaN2019NaNNaN3[2021-11-03, 2021-11-03, 2021-10-29]2021-10-299.86.600000
592a45cf16b66c1bCSEC2016006Intrusion Detection in the HP Enterprise Laser...activeMulti-Function DevicesHP Inc.SE{ALC_FLR.2, EAL2}EAL22019-05-24NaT...NaNNaN2019NaNNaN3[2021-11-03, 2021-11-03, 2021-10-29]2021-10-299.86.600000
43610aed20f29542CSEC2015002HP Color LaserJet Enterprise M553 Printer Seri...activeMulti-Function DevicesHP Inc.SE{ALC_FLR.2, EAL2+}EAL2+2015-10-06NaT...[IEEE Standard Protection Profile for Hardcopy...NaN2015NaNNaN2[2009-03-18, 2009-03-18]2009-03-187.66.350000
547e899b9b5eabaeISCB-5-RPT-C076-CR-v1Hewlett Packard Enterprise ArcSight Enterprise...activeNetwork and Network-Related Devices and SystemsHewlett Packard EnterpriseMY{EAL2}EAL22016-12-14NaT...NaNNaN20161.0[2017-07-14 00:00:00]9[2017-09-30, 2017-09-30, 2017-10-31, 2017-09-3...2017-09-309.86.644444
e75c31ba27f12f29ISCB-3-RPT-C073-CR-v1RSA Archer GRC Platform version 6.1activeNetwork and Network-Related Devices and SystemsRSA, The Security Division of EMCMY{ALC_FLR.2, EAL2+}EAL2+2016-09-14NaT...NaNNaN2016NaNNaN29[2022-03-30, 2022-03-30, 2020-05-04, 2019-03-1...2017-10-119.86.327586
a92fe4e09a4182882014-43-INF-1560HP Asset Manager v9.50 with Connect-It v9.60 b...activeNetwork and Network-Related Devices and SystemsHewlett-Packard Development Company, L.P.ES{ALC_FLR.2, EAL2+}EAL2+2016-05-27NaT...NaNNaN20161.0[2016-09-27 00:00:00]2[2020-02-04, 2016-04-05]2016-04-059.88.650000
f1d52a650e824104CSEC2014004Blue Coat ProxySG S400 and S500 running SGOS v6.5activeNetwork and Network-Related Devices and SystemsBlue Coat Systems, Inc.SE{}NaN2015-03-06NaT...[Protection Profile for Network Devices]NaN2015NaNNaN2[2017-06-08, 2016-01-08]2016-01-087.57.450000
15b121492722bffbBSI-DSZ-CC-0924-2014Red Hat Enterprise Linux on 32 bit x86 Archite...activeOperating SystemsRed Hat, Inc.DE{EAL4+, ALC_FLR.3}EAL4+2014-11-27NaT...[Operating System Protection Profile, Version ...BSI2014NaNNaN44[2000-11-14, 2000-09-30, 2001-01-09, 2017-10-0...1994-12-1910.06.738636
91cc0d74e99141f0ISCB-5-RPT-C107-CR-v1RSA Archer Suite v6.5activeOther Devices and SystemsRSA Security LLCMY{ALC_FLR.2, EAL2+}EAL2+2019-09-03NaT...NaNNaN2019NaNNaN22[2022-03-30, 2022-03-30, 2020-05-04, 2022-05-2...2019-09-189.86.550000
b891647233f3f188383-4-447Dell EMC™ VMAX® All Flash and PowerMax™ with P...activeOther Devices and SystemsDell EMCCA{ALC_FLR.2, EAL2+}EAL2+2018-10-15NaT...NaNNaN20181.0[2021-03-15 00:00:00]6[2022-01-21, 2022-01-21, 2020-06-23, 2020-06-2...2020-06-238.17.516667
98e92fd36fe22f33ISCB-5-RPT-C095-CR-v1RSA Archer Suite v6.3activeOther Devices and SystemsRSA Security LLCMY{ALC_FLR.2, EAL2+}EAL2+2018-05-30NaT...NaNNaN2018NaNNaN21[2022-03-30, 2022-03-30, 2020-05-04, 2022-08-2...2019-09-189.86.600000
\n", - "

15 rows × 33 columns

\n", - "
" - ], - "text/plain": [ - " cert_id \\\n", - "ebd276cca70fd723 BSI-DSZ-CC-0683-2014 \n", - "b7f814ed16f2ecca CSEC2016009 \n", - "4fc1208e4c800aa6 CSEC2015010 \n", - "c8f2a5312b671aae ISCB-5-RPT-C069-CR-v1 \n", - "4c33266562bb9b24 CSEC2017009 \n", - "592a45cf16b66c1b CSEC2016006 \n", - "43610aed20f29542 CSEC2015002 \n", - "547e899b9b5eabae ISCB-5-RPT-C076-CR-v1 \n", - "e75c31ba27f12f29 ISCB-3-RPT-C073-CR-v1 \n", - "a92fe4e09a418288 2014-43-INF-1560 \n", - "f1d52a650e824104 CSEC2014004 \n", - "15b121492722bffb BSI-DSZ-CC-0924-2014 \n", - "91cc0d74e99141f0 ISCB-5-RPT-C107-CR-v1 \n", - "b891647233f3f188 383-4-447 \n", - "98e92fd36fe22f33 ISCB-5-RPT-C095-CR-v1 \n", - "\n", - " name status \\\n", - "ebd276cca70fd723 IBM Security Access Manager for Enterprise Sin... active \n", - "b7f814ed16f2ecca Clavister cOS Core v12.00.00 active \n", - "4fc1208e4c800aa6 WatchGuard Firebox Security Appliances with Fi... active \n", - "c8f2a5312b671aae MarkLogic Server 8.0-4 active \n", - "4c33266562bb9b24 HP LaserJet Managed MFP E62555/E62565/E62575/E... active \n", - "592a45cf16b66c1b Intrusion Detection in the HP Enterprise Laser... active \n", - "43610aed20f29542 HP Color LaserJet Enterprise M553 Printer Seri... active \n", - "547e899b9b5eabae Hewlett Packard Enterprise ArcSight Enterprise... active \n", - "e75c31ba27f12f29 RSA Archer GRC Platform version 6.1 active \n", - "a92fe4e09a418288 HP Asset Manager v9.50 with Connect-It v9.60 b... active \n", - "f1d52a650e824104 Blue Coat ProxySG S400 and S500 running SGOS v6.5 active \n", - "15b121492722bffb Red Hat Enterprise Linux on 32 bit x86 Archite... active \n", - "91cc0d74e99141f0 RSA Archer Suite v6.5 active \n", - "b891647233f3f188 Dell EMC™ VMAX® All Flash and PowerMax™ with P... active \n", - "98e92fd36fe22f33 RSA Archer Suite v6.3 active \n", - "\n", - " category \\\n", - "ebd276cca70fd723 Access Control Devices and Systems \n", - "b7f814ed16f2ecca Boundary Protection Devices and Systems \n", - "4fc1208e4c800aa6 Boundary Protection Devices and Systems \n", - "c8f2a5312b671aae Databases \n", - "4c33266562bb9b24 Multi-Function Devices \n", - "592a45cf16b66c1b Multi-Function Devices \n", - "43610aed20f29542 Multi-Function Devices \n", - "547e899b9b5eabae Network and Network-Related Devices and Systems \n", - "e75c31ba27f12f29 Network and Network-Related Devices and Systems \n", - "a92fe4e09a418288 Network and Network-Related Devices and Systems \n", - "f1d52a650e824104 Network and Network-Related Devices and Systems \n", - "15b121492722bffb Operating Systems \n", - "91cc0d74e99141f0 Other Devices and Systems \n", - "b891647233f3f188 Other Devices and Systems \n", - "98e92fd36fe22f33 Other Devices and Systems \n", - "\n", - " manufacturer scheme \\\n", - "ebd276cca70fd723 IBM Corporation DE \n", - "b7f814ed16f2ecca Clavister AB SE \n", - "4fc1208e4c800aa6 WatchGuard Technologies, Inc. SE \n", - "c8f2a5312b671aae MarkLogic Corporation MY \n", - "4c33266562bb9b24 HP Inc. SE \n", - "592a45cf16b66c1b HP Inc. SE \n", - "43610aed20f29542 HP Inc. SE \n", - "547e899b9b5eabae Hewlett Packard Enterprise MY \n", - "e75c31ba27f12f29 RSA, The Security Division of EMC MY \n", - "a92fe4e09a418288 Hewlett-Packard Development Company, L.P. ES \n", - "f1d52a650e824104 Blue Coat Systems, Inc. SE \n", - "15b121492722bffb Red Hat, Inc. DE \n", - "91cc0d74e99141f0 RSA Security LLC MY \n", - "b891647233f3f188 Dell EMC CA \n", - "98e92fd36fe22f33 RSA Security LLC MY \n", - "\n", - " security_level eal not_valid_before not_valid_after \\\n", - "ebd276cca70fd723 {EAL3+, ALC_FLR.1} EAL3+ 2014-12-05 NaT \n", - "b7f814ed16f2ecca {EAL4, ALC_FLR.1} EAL4 2019-06-19 NaT \n", - "4fc1208e4c800aa6 {ALC_FLR.2, EAL4} EAL4 2017-05-05 NaT \n", - "c8f2a5312b671aae {EAL2+, ALC_FLR.3} EAL2+ 2015-12-22 NaT \n", - "4c33266562bb9b24 {ALC_FLR.2, EAL3} EAL3 2019-10-11 NaT \n", - "592a45cf16b66c1b {ALC_FLR.2, EAL2} EAL2 2019-05-24 NaT \n", - "43610aed20f29542 {ALC_FLR.2, EAL2+} EAL2+ 2015-10-06 NaT \n", - "547e899b9b5eabae {EAL2} EAL2 2016-12-14 NaT \n", - "e75c31ba27f12f29 {ALC_FLR.2, EAL2+} EAL2+ 2016-09-14 NaT \n", - "a92fe4e09a418288 {ALC_FLR.2, EAL2+} EAL2+ 2016-05-27 NaT \n", - "f1d52a650e824104 {} NaN 2015-03-06 NaT \n", - "15b121492722bffb {EAL4+, ALC_FLR.3} EAL4+ 2014-11-27 NaT \n", - "91cc0d74e99141f0 {ALC_FLR.2, EAL2+} EAL2+ 2019-09-03 NaT \n", - "b891647233f3f188 {ALC_FLR.2, EAL2+} EAL2+ 2018-10-15 NaT \n", - "98e92fd36fe22f33 {ALC_FLR.2, EAL2+} EAL2+ 2018-05-30 NaT \n", - "\n", - " ... protection_profiles \\\n", - "ebd276cca70fd723 ... NaN \n", - "b7f814ed16f2ecca ... NaN \n", - "4fc1208e4c800aa6 ... NaN \n", - "c8f2a5312b671aae ... NaN \n", - "4c33266562bb9b24 ... NaN \n", - "592a45cf16b66c1b ... NaN \n", - "43610aed20f29542 ... [IEEE Standard Protection Profile for Hardcopy... \n", - "547e899b9b5eabae ... NaN \n", - "e75c31ba27f12f29 ... NaN \n", - "a92fe4e09a418288 ... NaN \n", - "f1d52a650e824104 ... [Protection Profile for Network Devices] \n", - "15b121492722bffb ... [Operating System Protection Profile, Version ... \n", - "91cc0d74e99141f0 ... NaN \n", - "b891647233f3f188 ... NaN \n", - "98e92fd36fe22f33 ... NaN \n", - "\n", - " cert_lab year_from n_maintenances maintenance_dates \\\n", - "ebd276cca70fd723 BSI 2014 NaN NaN \n", - "b7f814ed16f2ecca NaN 2019 NaN NaN \n", - "4fc1208e4c800aa6 NaN 2017 NaN NaN \n", - "c8f2a5312b671aae NaN 2015 NaN NaN \n", - "4c33266562bb9b24 NaN 2019 NaN NaN \n", - "592a45cf16b66c1b NaN 2019 NaN NaN \n", - "43610aed20f29542 NaN 2015 NaN NaN \n", - "547e899b9b5eabae NaN 2016 1.0 [2017-07-14 00:00:00] \n", - "e75c31ba27f12f29 NaN 2016 NaN NaN \n", - "a92fe4e09a418288 NaN 2016 1.0 [2016-09-27 00:00:00] \n", - "f1d52a650e824104 NaN 2015 NaN NaN \n", - "15b121492722bffb BSI 2014 NaN NaN \n", - "91cc0d74e99141f0 NaN 2019 NaN NaN \n", - "b891647233f3f188 NaN 2018 1.0 [2021-03-15 00:00:00] \n", - "98e92fd36fe22f33 NaN 2018 NaN NaN \n", - "\n", - " n_cves cve_published_dates \\\n", - "ebd276cca70fd723 6 [2019-08-26, 2013-12-22, 2018-08-17, 2015-01-2... \n", - "b7f814ed16f2ecca 1 [2018-08-15] \n", - "4fc1208e4c800aa6 2 [2017-09-20, 2017-09-20] \n", - "c8f2a5312b671aae 11 [2017-05-23, 2017-05-24, 2018-09-07, 2017-05-2... \n", - "4c33266562bb9b24 3 [2021-11-03, 2021-11-03, 2021-10-29] \n", - "592a45cf16b66c1b 3 [2021-11-03, 2021-11-03, 2021-10-29] \n", - "43610aed20f29542 2 [2009-03-18, 2009-03-18] \n", - "547e899b9b5eabae 9 [2017-09-30, 2017-09-30, 2017-10-31, 2017-09-3... \n", - "e75c31ba27f12f29 29 [2022-03-30, 2022-03-30, 2020-05-04, 2019-03-1... \n", - "a92fe4e09a418288 2 [2020-02-04, 2016-04-05] \n", - "f1d52a650e824104 2 [2017-06-08, 2016-01-08] \n", - "15b121492722bffb 44 [2000-11-14, 2000-09-30, 2001-01-09, 2017-10-0... \n", - "91cc0d74e99141f0 22 [2022-03-30, 2022-03-30, 2020-05-04, 2022-05-2... \n", - "b891647233f3f188 6 [2022-01-21, 2022-01-21, 2020-06-23, 2020-06-2... \n", - "98e92fd36fe22f33 21 [2022-03-30, 2022-03-30, 2020-05-04, 2022-08-2... \n", - "\n", - " earliest_cve worst_cve_score avg_cve_score \n", - "ebd276cca70fd723 2013-12-22 10.0 5.800000 \n", - "b7f814ed16f2ecca 2018-08-15 5.9 5.900000 \n", - "4fc1208e4c800aa6 2017-09-20 7.5 6.800000 \n", - "c8f2a5312b671aae 2017-05-23 9.6 8.309091 \n", - "4c33266562bb9b24 2021-10-29 9.8 6.600000 \n", - "592a45cf16b66c1b 2021-10-29 9.8 6.600000 \n", - "43610aed20f29542 2009-03-18 7.6 6.350000 \n", - "547e899b9b5eabae 2017-09-30 9.8 6.644444 \n", - "e75c31ba27f12f29 2017-10-11 9.8 6.327586 \n", - "a92fe4e09a418288 2016-04-05 9.8 8.650000 \n", - "f1d52a650e824104 2016-01-08 7.5 7.450000 \n", - "15b121492722bffb 1994-12-19 10.0 6.738636 \n", - "91cc0d74e99141f0 2019-09-18 9.8 6.550000 \n", - "b891647233f3f188 2020-06-23 8.1 7.516667 \n", - "98e92fd36fe22f33 2019-09-18 9.8 6.600000 \n", - "\n", - "[15 rows x 33 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[(df.related_cves.notnull()) & ((df.not_valid_before.isnull()) | (df.not_valid_after.isnull()))]" + " color_n_cves = \"\\cellcolor{green!15}\" if row[\"n_cves_pvalue\"] < 0.01 else \"\"\n", + " color_avg_score = \"\\cellcolor{green!15}\" if row[\"avg_cve_pvalue\"] < 0.01 else \"\"\n", + " print(\n", + " f\"{sar} & {color_n_cves} {row['n_cves_corr']:.2f} & {color_n_cves} {row['n_cves_pvalue']:.2e} & {color_avg_score} {row['avg_cve_score_corr']:.2f} & {color_avg_score} {row['avg_cve_pvalue']:.2e} & {row['support']:.0f} & {row['range']:.0f} \\\\\\\\\"\n", + " )\n" ] } ], From 6d7a9073504e533271c3cfeefc10aac89f0c0247 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 15:41:42 +0100 Subject: [PATCH 50/81] random fixes in FIPS notebooks --- notebooks/fips/in_process.ipynb | 497 +++----------------------- notebooks/fips/temporal_trends.ipynb | 511 +++------------------------ 2 files changed, 103 insertions(+), 905 deletions(-) diff --git a/notebooks/fips/in_process.ipynb b/notebooks/fips/in_process.ipynb index 3632b7c0..00ff5eb1 100644 --- a/notebooks/fips/in_process.ipynb +++ b/notebooks/fips/in_process.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "4d7d7d8c", "metadata": {}, "outputs": [], @@ -37,38 +37,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "80f04ffe", "metadata": {}, "outputs": [], "source": [ - "iut_dset = IUTDataset.from_web_latest()http://localhost:8888/notebooks/notebooks/fips/in_process.ipynb#" + "iut_dset = IUTDataset.from_web_latest()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "47a4d967", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 727 entries, 0 to 726\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 timestamp 727 non-null datetime64[ns]\n", - " 1 displayed 721 non-null float64 \n", - " 2 not_displayed 721 non-null float64 \n", - " 3 total 721 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(3)\n", - "memory usage: 22.8 KB\n" - ] - } - ], + "outputs": [], "source": [ "iut_global_df = pd.DataFrame(iut_dset.snapshots, columns=[\"timestamp\", \"displayed\", \"not_displayed\", \"total\"])\n", "iut_global_df.info()" @@ -76,23 +58,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "776323d7", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "iut_melt = iut_global_df.melt(\"timestamp\", var_name=\"type\", value_name=\"count\")\n", "line = sns.lineplot(data=iut_melt, x=\"timestamp\", y=\"count\", hue=\"type\")\n", @@ -102,19 +73,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "0d1626ad", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['entries', 'timestamp', 'last_updated', 'displayed', 'not_displayed', 'total'])\n", - "IUTEntry(module_name='F5(R) vCMP Cryptographic Module', vendor_name='F5, Inc.', standard='FIPS 140-3', iut_date=datetime.date(2021, 12, 14))\n" - ] - } - ], + "outputs": [], "source": [ "s0 = iut_dset.snapshots[0]\n", "print(s0.__dict__.keys())\n", @@ -123,33 +85,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "97b726d6", "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All:\n", - "Average seen for 187.7822445561139\n", - "Average seen for (FIPS 140-2) 176.27710843373495\n", - "Average seen for (FIPS 140-3) 202.19622641509434\n", - "Average seen for[iut] 237.97319932998326\n", - "Average seen for[iut] (FIPS 140-2) 252.79819277108433\n", - "Average seen for[iut] (FIPS 140-3) 219.4\n", - "Only not present:\n", - "Average seen for 193.92161016949152\n", - "Average seen for (FIPS 140-2) 176.35151515151514\n", - "Average seen for (FIPS 140-3) 234.75352112676057\n", - "Average seen for[iut] 253.37076271186442\n", - "Average seen for[iut] (FIPS 140-2) 253.24545454545455\n", - "Average seen for[iut] (FIPS 140-3) 253.66197183098592\n" - ] - } - ], + "outputs": [], "source": [ "def iut_key(entry):\n", " return entry.module_name, entry.vendor_name, entry.standard\n", @@ -190,118 +131,52 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "5bcc6da5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=iut_local_df, x=\"iut_date\", y=\"seen_for_iut\", hue=\"standard\", style=\"present\", aspect=1)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "10bad193", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=iut_local_df, x=\"iut_date\", y=\"last_seen\", hue=\"standard\", style=\"present\", aspect=1)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "fbfbf88e", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "hist = sns.histplot(data=iut_local_df[~iut_local_df.present], x=\"seen_for_iut\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "98b27134", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "hist = sns.histplot(data=iut_local_df[~iut_local_df.present], x=\"seen_for\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "6b8e02d3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Cisco Systems, Inc. 30\n", - "Fortinet, Inc. 23\n", - "Juniper Networks, Inc. 22\n", - "Apple Inc. 20\n", - "Oracle Corporation 16\n", - " ..\n", - "Microchip Technology Inc 1\n", - "L3Harris Technologies, Inc. 1\n", - "Brocade Communications Systems LLC 1\n", - "SafeLogic 1\n", - "Nokia XHAUL 1\n", - "Name: vendor, Length: 225, dtype: int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "iut_local_df.vendor.value_counts()" ] @@ -316,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "46ce3c13", "metadata": {}, "outputs": [], @@ -326,28 +201,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "aa74b965", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 727 entries, 0 to 726\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 timestamp 727 non-null datetime64[ns]\n", - " 1 displayed 727 non-null int64 \n", - " 2 not_displayed 727 non-null int64 \n", - " 3 total 727 non-null int64 \n", - "dtypes: datetime64[ns](1), int64(3)\n", - "memory usage: 22.8 KB\n" - ] - } - ], + "outputs": [], "source": [ "mip_global_df = pd.DataFrame(mip_dset.snapshots, columns=[\"timestamp\", \"displayed\", \"not_displayed\", \"total\"])\n", "mip_global_df.info()" @@ -355,21 +212,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "dc3186f6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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kXGOsDGLtg2Lk6HMBsLGwKfCcq40zTwX15ZXmw3m34ziMkpEZOxey9/phcnW5FR2qIAiCcJ/Cw8Nxc3OjTp06lR1KpRI1BcXI1ZvWXrBSFlylTCaTMahhb/Pjlr5NOBh7jIUHf+K54IH0q1dw5IQgCIJQtYwZM4b4+HgWL15c2aFUOpEUFONeScF/tavenIOxxwD4+eRftKnWDDebh3siC0EQhEfdjz/+WGj5uHHjKjiSyieaD4qRqzMlBdYWxScFLfxC+GngfBp5BgIQceN0ucYmCIIgCGVJJAXFKE1NAYCtyobJ7V/DQmHBxZSr5RmaIAiCIJQpkRQUI6+joZXSqsT7qBQWBHs14GKSSAoEQRCEh4dICoqRq9cgk8lQKUo3iVJd15rczEwwz2UgCELVdTD1JMdvPt7z/wsCiKSgWFqDDpXcotQrI9Z1rQnAlku7kSSpPEITBKGM7Eo+wme7v67sMIRyMnHiRL777jveeOON+9o/MDAQvV5fxlGZXL9+vUotuSxGHxTDYDSgVJT+MtVyqY5CruD3yH9I12RgY2HNsEb9yiFCQRAEoThubm6FTkIk5CeSgmLojXqUMtNiRwaDkdjETDxdbLBS3fvSWSktmd19Kh9sn8/GizsBGFCvB1YWJe+bIAhCxQq/sJ3157cxvs0LeNq542BZuYvd6Aw6EjUpaPVaVEpVpcbyMDEajbz33nucP38eHx8fcnJMM9N26NCB3bt3Ex4ezg8//ICNjQ0Gg4FPPvkEpVLJ6NGjad++PefOnQNgwYIFeHp6mo+bkpLCO++8g1arJSsrixEjRtCvXz+6devGsmXL8Pf3B6BXr1589dVXaLVaZs+ejSRJGAwGJk+eTOPGjTl27BgffvghTk5O+dZZqApEUlAMvdGAUq4kISWbz387xpkrybQK8uK90S2L3beaky9TOrzGtG3zAEjJTcdHJAWCUGUtPb4KgPe2zgFgaofXqO7oh4uNU6XE89PxVWyN2cM5rvNWmxcqJYb7sf1oNFsOR5fLsbu3qEaXZtXuuc3+/fu5evUqa9asIScnh7CwMJ544gnz899++y0ff/wxISEhnD17lvj4ePz9/YmNjWXYsGHUq1ePL774gqVLl+ZbsC8hIYFBgwbRu3dv4uPj6devHwMGDODJJ59k7dq1jBs3jnPnzuHo6Ejt2rXp27cvixcvxt/fn4sXLzJhwgT++ecf5syZw8SJE+nQoQNLly4tl+t0v0SfgmIYjAYMBnh+5hbOXElGZaHgYOQtPl16mPm/RnDiQsI99w90C2BUkyEApOakV0TIgiCUgsFoMP/drlpzajj5mR9/tvtrXv5nKjuvHij1cTO1WegMugeK7VpqDADHb51Bf1ecwr1duHCBkJAQZDIZNjY2BAcH53v+iSeeYNKkSXzxxRdotVpat24NgJOTE/Xq1QOgadOmXLx4Md9+jo6ObNq0iWeeeYa3336btLQ0AIYMGcL69euRJIkNGzYwaNAg1Go1V69e5d1332X48OF8/PHH5ObmotPpOH/+PE2bNgWgVatW5Xw1SkfUFBRDZ9SjzjR9sD95uQ0qpYJJi/Zw4PRNlAoZB0/fZHTfhlyOTadX6xrU9ncqcIxgrwYALD68nNb+Tc0jEnzsPfGTXCvstQiCUFDej+0zjQcwoH4YGZpMTsWf5VZGImcSLnA59Tp7rx+hU83WJT5mtjaHl/6eir3Kli97fXBfzYaSJBGXEY9KZkGOLpeziRc5cuMk7ao1p65brVIfryJ1aVb83Xx5+m/nbqPRmO/xSy+9RP/+/dmzZw/Tpk2jf//+hIWF5dtPkqQCHcwXLlxI9erVWbBgAWq1mubNmwOmxZECAgI4evQoe/bsYcWKFRiNRiwsLFixYkWpYq1soqagGFm5GvR6qO3nSHAdd+rXdGH+mx34dUYvPnihFblaA9+sOcXmQ9eZ8eNBdHpjgWN42LribedBYlYyf5/bwvG4SI7cOMmvp9YSkX6Gv89tISEzCb3RwJ9RG0SNgiBUIJ3RlPRbyE33SPaWdrSt1pxBDXszvfNbBHkEkppbus9kck4qOoOOlJw0Im6eJkeXW+CHqThqTQbZuhxq2ZraqT/d9RUbL+5k3bnNpTrO46h27dqcPHkSSZLIzMzk5MmT5ucMBgPz58/HxcWFIUOG8PzzzxMREQFAeno6Z86cAeD48eMEBgbmO25qaqq5JuHvv/9GLpej1WoBeOqpp5g/fz7169fH1tYWe3t7/Pz82LVrFwDR0dEsWLAAgICAAE6cOAHA3r17y+9C3AeRFBRDq9eDJGdotztvjrrVnLG3UdGgpit1/J1o3cib90e3IEWt4cDpuALHsFBY8EXvD8yPFz7xMT8OmEd1R1+2JR3k55N/8s2RFWy+tIuVp/9mzZnwCnltgiCA3mAaamZRxCgjZ2vHEifqkiSh0WvJuGt+kvXntzH6r7f5fP//ShVXXEY8AAE2pqTAIJmSiisp5dNW/yhp164dPj4+DBkyhHfffZeQkBBzUqZQKHB1deXpp59m+PDh/Pbbb7z88ssAeHh4sHr1ap5++mmOHDnCyJEj8x13xIgRLFiwgOeffx4HBwfatGnDpEmTAGjfvj0xMTEMGTLEvP3s2bNZsmQJzz77LG+//TZt2rQB4J133mHWrFm8+OKLZGVllTphLE/l3nyg1WqZMWMGFy9eRK/XExgYyNixYxk0aBD169c3b/fmm2/SrFkzjhw5wvz589Hr9fj6+jJnzhwsLUs2xXC5xG/QgyTDxrLgpVJZKPj8rY4AGI0SXq42zP05gsux6fRpVwt3Z2vztnKZnE+7TcZGZY317arEt9q8QPjRLURprnAm4QJnEi4AcCnlWvm/MEEQgDvNB0p54V+HLtZOZGqziE67QTUn33sea/mJNWy8uIMmPo0AqOtaiwvJVwA4fOMEiVnJuNsW3WQoSRIx6XHsvHaQ+MxEAPysvfJtk5yTyi8n/8LB0p4+gV1LPYfK40ChUDB//vwC5YMHDwZg1KhRjBo1Kt9zsbGxyGQyPvjggwL7nT9/HoA2bdqwadMmc3m/fneGmUdGRuLv709ISIi5rEGDBvz6668FjteyZUvWr19vfvzWW2+V6HVVhHKvKdi9ezcqlYqVK1eyevVqrl+/zo4dO6hfvz4rVqww/2vWrBl6vZ5JkyYxb948Vq9ejYuLCytXrizvEO9JZ9AjGeVYW907f5LLZXRvUR2AP3de4u89lwtsU9u1Bj72d4a3+Dp40dSxAVM7vG4u6xbQniup0VxOuQ5UvfYmQXjU6IymmoKikoK6rqb2+z3XD9/zODm6XLZe2YtBMnL0hqm6+skGPc3Py5Cx69qhQvdNyEzig+2fM+7faczYuYD157dy5MZJnKwccFDa8l3/2XjauTOt05vYq2xZd24zK06uITkntdSvVyh7n3zyCZ988gmfffZZZYfywMq9pqBbt25069YNgMzMTDIyMggNDWXbtm0Ftj116hTe3t74+Zl6//bs2ZPFixcXqMJRq9Wo1ep8ZQZD+fTM1RtMzQfWhdQU/NfATgH4edixdtdl1u66TJdm/tT0cSx2Pw9bV/546hvA1GN517WDTN0yC18HLzI1WXzeazr2lTxeWhAeVXkjBIpqPgjyDMTB0o5sXU6Rx7iccp0fj/2ORq9hfJsX+GL/96Z9PQJ5t8M4ajr7MWPnQi4kFbxZAPj11FrOJpp6uivkCl5vOYq1ZzfxaosRpF9LxsnKga+e+BiAj7q8zc8n/+TYzUjSctRiefYy4ufnx+7du+9r3/fff7+Mo6k8FTb6YPLkyezatYsXXngBZ2dn4uLieOWVV0hNTaVRo0a8/fbbJCQk4ObmZt7Hzc2N+Pj4AsdatmwZixYtyleWN1tVZGTh85fndSQprazcbJDkXLwQRUJs8ZfLEmhZW8HZa/Dn5mN0auRQ7D7/ja2/RxeOpUdxRW0ajrTh0FYCbnc2qij3e73Km4irdERcxbuVmwTA9SvXURUxwlhhlBMbf8Mcd0zOLazkKtwtTT/IX15Zjsaopb5dLSziJZo7NSIm5yanT5qWT78UdxFHoy3nE65w9OhRZDIZ0dlxJOvSCHGoz7EbkTS0r42npRvVrb2xTVbyrMcTpF9LBgper4aKWhwjkogzx0m3TS6PyyI8piosKZg9ezaZmZm88soruLq68uqrr9KnTx+USiVTp05lyZIlBXp6AsjlBVs4Ro4cycCBA/OVGQwGkpKSCAoKKtAHISIigtDQ0PuKW35tHUgyWjZriq11yRZFCgV2R+0kXaMq9ryFxRZKKEMZQK4ul5F/TWDNrc20r96C11uOuq/XUFoPcr3Kk4irdERcJXMh6QrEQr26gYR4Nyx0G5eULahUVua4Z//+CgB/PPUN+6KPoLmkxdXGmffC3kSlVNGMZgWOkXwpk9MRF5hz+QcUMrm542CTeiHkXtbQsHp9BjfsXWC/wq6Xf1YyK2L/5oLxOn0b98TGwrrAfiWl0WiKvJkSHj/l3qcgMjKSy5dNVWZ2dnZ0796dU6dOMWjQICwtLVEoFISFhREVFYWXlxcJCXdS9YSEBLy8vAoc08HBAT8/v3z/CtuuLBiMBpBkWJWg+eBu1bztiUl4sBUSrSyseK3FSGq71ODIjZMYparTQ1UQHhX6YvoUANha2JCtzQZAq9eayzdd3MWiQ8sAmNl10j2nIq7lXN38d9963Rnc0DTD3saLOwBwsrIvccxOlqZtT8ef5/uIyu13JTxayj0piIqK4vPPPzd3mDt27Bi1a9dm2rRp5m327dtH/fr1adSoEQkJCURHm4bcrF+/nh49epR3iPdkkAzIUKCQl66HbzVPe5LScli76zK5mvtfXatDjZZ0rdWOHF0u/57fft/HEQShcMV1NASwUVmTdbtPwa3bowIAfji2EoPRwLsdxhU7FXItl2o823ggU9q/yjONBzA0qA8tfEM4eessAI5WxTc15lEpVYxt9iwA54vopyAI96Pck4JBgwbh5eXF0KFDeeqpp7C1tWXIkCEolUqGDBnCsGHDyMjIYOzYsSgUCmbMmMG4ceN48skn0ev1DBo0qLxDvCejZEAuK/1l6tDEDyd7S374O5Ih7/7LzmOx9x1DiLdpRsQVJ9eQnC16GwtCWdIZ8pICRZHb2FnYEJcRz4WkK+b5A14IHWZ+PsijbrHnkcvk9K/fg6a3hysC1HOvbf7b0bLkNQUA3QLaMbhhbxKzknln00wytVml2l8QClPufQoUCkW+WoE8hY0FBWjbti3r1q0r77BKzIgROUV/WRTF08WGH9/vzpbD0Xyz5hTzf4mgRQNPbKxK1i/hbi7WTgys35O/zm5k0uZP6VyzNc8FP1nq4wiCUJD29ugDy3tU/Xep1ZatV/by/ra55rL21VviZuOCXCa/r+XVAarfNe9BLZfq99iycA3c6wBwPS2W6LQ4GnjUua84BNi1axfBwcE4OTmVaPvr168zevRotm/fznfffUfdunXp1KlTqc751VdfodfrGT9+fOkDLoGJEyfSpk0bnnyy5L8XYkbDYhgxIL/Py2ShVNC7TU3aBfsA8Oum8xiN9zfvwLBG/RjS8Ak8bF35+9wWYtILzpwoCELpaQ2mPgKWiqKTgtquNWjm0zhfmbWFFU19GhXZObEkGrjX4amgvnzff849ayqKEuRZj18GL+S3IYtEQvCAli5dSnr6/U0xP3bs2FInBFWVWBCpGEbJiPI+mg/u9sqgYPaejGPd7su0bOhFo9puxe/0HzKZjCFBfehcsw2vrn+PEzej8Hf0eaC4BEEAze2Og/fqJAgwoe1YotNuMHXLLMLqdCyTcyvkCgYVMuKgNCwUpa99LG8Zp3aScbJ8+kDZB3fBvnGnYrc7cOAA33//Pd7e3pw/fx6lUskPP/xAeHg4v/32G1ZWVri6ujJz5kz++ecfjh49ysSJE/nss8+oXbt2occ8duwYH374IU5OTgQFBZnLp0yZQmhoKL179+btt99GrVZjMBho3bo1b731Fu+88w62trZcvnyZlJQUBg8ezOjRo/Md+48//mDVqlVYWlqiUqn48ssv2bhxI8eOHWPWrFkAhIeHs2nTJr788kvmzJnD6dOnMRgMNGzYkPfeew9Jknjvvfc4f/48Pj4+5OQUPbdGUURNwT1IkoQRPQoe7EPnYKviy/GmL5F3v9nHa3O389rc7Xy69DDGUs5Y6GbrQi3naoRf3E5ClhifLAgPSnO7psDqHjUFYOpzUMulGj8NnM/oJkMrIjThAcjlco4fP86bb77JqlWrsLCwYNOmTSxcuJBly5bxyy+/4O3tzU8//cQzzzyDu7s78+bNKzIhAJgzZw4TJ05k+fLleHh4FHh+//796HQ6fv31V1auXImbmxtarRaFQkFSUhIrVqzg119/5dtvvyU1NX//sNzcXBYvXszPP/+Mr68vf//9N0888QR79+4lK8vUXyQ8PJwhQ4awceNG0tLS+Pnnn/ntt99ISEhg8+bN7N+/n6tXr7JmzRrmzJnDhQsXSn3dRE3BPWj0GpBJKLj3l0VJ1LhrZkMXeyuMksSB0zdRSfYo7BIICnBFZVGy6sMhQX2YvWcx49ZPY1yr0dRy9ueXU2tRazJp4RtCZMJ5qjn68GzwwOIPJgiPubzmA1UxSUEeG9X9zwnwuLBv3KlEd/PlLSAgAHd3dwB8fHzMk+XZ2ZlmiG3dunWhaxMU5fz58zRt2hSAVq1asXz58nzPBwcHM3fuXN544w06dOhA//79UalM76uWLVsC4OjoSLVq1cyj7PJYWlryxhtvoFAouHHjBh4eHtja2tK1a1c2bdpEjx49uHTpEm3atOGzzz7j2LFjDB8+HDDNFnzjxg0AQkJCkMlk2NjYEBwcXNpLJpKCe8nW5QKglD14UqCQy/BxsyUpPZcPX2yFUYIJX+5iV6SaXZEHaFrPAxd7K9ydrenRsjpuTkV/8YT6NOKTru/w/ra5bLi4g0xNFjczE5DL5ObhSafiz/JM4wFisRRBKIZGr0UhUxQ6UZrwcFMq8//E/XctGUmS7vv/vbB1aTw8PAgPDyciIoJt27bx9ddfmzvO37290WjM990cGxvL559/zj///IOHhwczZ840Pzds2DBmzZqFhYUFffr0QS6XI5PJGDp0KM8//3y+8//www/5Ht/P6oviU3APeXOdK8ugpgBg3psd+HVGLxQKORZKOfPe7MCLYR70al2DY+cS2Hokmt82n+fdb/Zx5koyBkPR/6F13WrxZINeXEy+ys3MBAbW78nSgfP5tNtknmzQC4PRQEpOWpnELQhlRZIk4nITuKG+VdmhmGn0Wixk4v7ocZA3e2NmpmliuT179pjvpmUyGXr9veeUCQgI4MSJEwDs3bu3wPN79+5lz549tGzZknfffRcLCwvzHfyBAwcASE9PJzY2lho1apj3S09Px8XFBQ8PD1JSUti/fz9arakGq379+mg0Gn755RfzKILQ0FC2bt1qjnfx4sVcvnyZ2rVrc/LkSSRJIjMzk5MnT5b6GolPwj3kJQUWsrJZutneJn9yYWmhwNdVRZd29UnNyCWkrgfZuTqWh59lytd7aRXkxYjeDfD3LHz8cl3Xmua/e9XtjJWFFbVda2CQDPwZtYHd1w4x8K5V2gShskUmnGdF7N+siP2bb/p+iquNM1nabCRJws7SlhM3owi/sI2BDXpS371ietNrDFos7jFxkfDocHd354033mDUqFGoVCq8vb2ZOHEiAO3atePVV1/ls88+MzcR/Nc777zDjBkz8Pb2pmHDhgXuxGvXrs3kyZP54Ycf0Ol0dOjQgXr16gHg5OTEmDFjSEhI4PXXX8fB4c5kVfXr16dmzZoMGTKEatWq8dZbb/Hxxx/TsWNHmjVrRt++fdm+fTs+PqbO5T169ODkyZMMGzYMmUxGvXr1qFatGjVq1ODvv/9myJAh+Pj4EBISUuraAvFJuIc7SUHZ1BQUxc5GxXujTe1NkiTRqLYbR8/G8/uWCxw6c4vvpnbDy9W2wH5545p71u6E012zodV1rUWIVwPWntvE7muHsFFZM73TW/cchy0IFeHuobS/nV7H6y1H8fr698nW5fJS8+f49sgKADzs3Co2KRA1BY+cli1b8ttvv5kf5/XgBwqdFO+jjz4q0THXr19vfvzWW28VOPayZcsK3bdp06YMGTIkX9m4cePMfy9evDjfc927dwdMvwm7d+9m1KhR5udkMhmTJk0q9Dzz588v9nXci/gk3ENeUmCpsKqwc8pkMupVdyGwmjPVPO2Z+3MEs5cfIaxVDXq2rpFvWycrBxb0/ggPW9cCx3iqUT+WHl9l7mNwMfkKQZ71KuplCEKhbmUmopJZEFa3I+vPb6ND9Zbm6YPzEgKApOxUJEkiW5eDrcqmXGPS6rUoRVIg3DZ9+nSuXr1aoLx9+/aMHTu2QmOJiopi+vTptG3blvbt21fIOcUn4R6i02+AJMNablfh55bJZLQP8eXo2XiOnU9g5ZbzBZICAG/7gsNiAAJcqjOj60SupEQzZctnRCVewsPOvUACIQgVKSErGUcLe7rWass/57ey5MjPALhaOzOsUT8C3QNYdnwV5xIvsfL03/x1diNzerxHDWe/cotJY9BgcR8TBwmPpo8//rjMj3l3TUJpNGjQgNWrV5dxNPcmOhrew5n4Cyg0zhVaU3A3mUzGhGdCGdK1LsnpuaSqc0t9jJrO/gCsPvMvr69/n8j4c2UdpiCUmEavwVKuwut2MpuYnYKV0pJFfWbQsWYrvOzc8bLzIEuXw19nNwJw7ObpcosnS5tNYlYKlvKy6TckCA87kRQUQZIkotVxyHMdUSoq9zI1CjDNgLj7xI1S7yuTyXij1Rga3l6w5bdT6+5rmIoglAVTVb0CuUzOgPphdKnZhg87T0Bx1536oAa9GBEymMae9QHKbRSN0Whk7N9TuJWZiJVc9LcRBBDNBwUYjUbkcjmpOenk6HKxyLXDwqVyk4Javo40ru3Gig1nSUjJpm/7WoV2PCxKu+rNaVe9OTuu7OebIyvYH3OUdtVblGPEglA4rVFvrqp/pvGAQrexs7SlT2BX+gR25e0NH5OWoy6XWBKyktDdXgzpftYdEIRHkagpuEuWNpthq15j/fmtnLgVBYAx26HSawoAJj4bSi0fR/7ec4WXZ20jNaP0TQnta7TEy86dhQd/YvOlXeUQpSDcm9ZQuk59ztZOHL5xgkOxx/li//f87+ivXEi6UiaxxKpvmv/WGQ1lckxBeNhV/q9dFZJzewbD5SfW8O2RFVgrrdCrq0ZS4OxgxZxx7Rn/dBMMRonj5xNKfQylXMGHXSYQ6BbAj8f+ICk7pRwiFYSiaQ06lLKS35XnrUA4f993HIiJYMvlPfx7oWwW2snUZpv/1kv3nrRGePjkzSRYlF27dpGWlnbPbYYPH87+/fvLMKqqr/J/7aqoZj6NeafdK+RqJWysqk4rS6em/rg4WLF0fdR91Ra4WDsxpulTGCUjr/7zHtnaHAziLkmoILpSJgV9AruaZ+l0sLRDJpORqc0qk1jyhhwDOCgrfoSRUH4MBkOBcf//9SBLJT/Kyv3XTqvVMmPGDC5evIherycwMJCPPvqILVu2sHTpUnQ6HcHBwUyfPh2ZTMaRI0eYP38+er0eX19f5syZg6VlxfQMljDNTd0joAMvNHuazGwtcBZb66qzNKlcLmNQl9r8b20kkxftZerI5tS8vdiSJEnIZDKMRgmZjCLXPfB38Db/PeqvCQC42biwuO/MQrcXhLJiqiko3ddObdca1HatwbBG/Zi1+2tSc8rmizxvbZOJbV9Cuqkpk2MKVcO7777LjRs3GDNmDL179y52qeTr16/z7bffYmVlhcFgYM6cOfj5ld8w2Kqs3JOC3bt3o1KpWLlyJQDPPfcc27ZtY86cOaxduxZHR0deeukltm3bRqdOnZg0aRIrVqzAz8+Pjz76iJUrVzJy5MjyDhO4s2BFbdcaAGTmmDoh2VpVnaQAoF/7ADycbZj502G+WXOK2a+34999V/lj6wUa1nLl5MVE6lRz5qMXWxe6v1Kh5L2O45i56ytzWVJ2ijmpEITyojXoHmhOADtL23yzIj6IHF0OlgoVLfxCiIiPKJNjCia7rh5kx9XyqXbvXLMNHWu2uuc248aN48CBA3zyyScMGzaM8PBw7Ozs+Oyzz/jpp5944403+P7775k3bx7Vq1cnMjKSWbNmERAQwLfffssvv/zC5MmTyyX+qq7ck4Ju3brRrVs3wLS8Y0ZGBhcvXqRZs2Y4OprucMPCwti+fTsuLi54e3ubM7SePXuyePHiiksKbtcUyDD9MGblJQVVqKYgT6sgb0Y+0YBl/0bRb+Lf5vK9J01fmMfOJWAwSijkhf/IB3s1MP9dx6UGF1OukaPLFcvCCuXGYDRglIwPNHugncqWjDJqPsjR5WJtUTlzkAgVIyoqqkRLJdva2vLBBx8AkJiYSJMmTSo0zqqkwhrLJ0+ezK5du3jhhReQyWS4ubmZn3NzcyM+Pp6EhIRCy/9LrVajVucfpmQwPHi7eF5NQd7dclauKSmws6l6SQFAs/qeLPvXNEri1cHBhLWsjlwuY+vh6yz4/QTxKVn4uBXdVjqnx7tk63JJyk7h4qGlpGnUIikQyo329vA/RSn6FPyXvcqWXL2G2XsWM7n9qw8UT7ZeJAXlpWPNVsXezVeGwpZK1mq1vPPOO6xZs4aAgACWLl3KuXOP7yRvFZYUzJ49m8zMTF555ZUCczjfa03rwsqXLVvGokWL8pW5ubmxcOFCIiMjCz1ORETx1YMpWlNb5bVr17BNVnI2xtQRKfrqRbRp14vd/36VJLbCSJJEnxZO1PG2wlGVzPHjyQDkpJuW3Ny86zh1fa24GJdLw2rWRTYNJGSblrFdsW8Vjhb2+Fp5wH3GVN7u91qVNxFX8bL0eQuMKe87rgy16TMaEXeaJduWEWDjz9Uc06ReDewCsFKUvP/RraR4JIPRHEtVulb/VZVjq4rkcjl6vZ6goCBmzJhBZmYmdnZ2hS6VnJ2djUKhoFq1auTm5rJ161Y8PAqfPv5xUO5JQWRkJNbW1gQEBGBnZ0f37t1Zvny5+T8GICEhAS8vL7y8vEhISChQ/l8jR45k4MCB+coMBgNJSUkEBQUV6JgYERFBaGhosbHGqW9BNNSqWYvQ6qHEZV8BkmnVPARXx/K5gy5pbEVp1qxgWYhR4tddG/jncBpavWn2wpah7alX3aXQY1TPrslft7YSkX4GAE9LV74a8Ml9x1ReHvRalRcRV8kkZiXDNdPQ2PuNq05uXcLX7QZgW9JBtnHQ/FwsCUzr9FaJJiIySkZmX/qeII9AQkNDq9y1ult5x6bRaIq8mXpYeXh44Obmxuuvv87YsWOLXSq5X79+DBkyBF9fX15++WWmTJnCpk2bKvlVVI5yTwqioqLYtWsXixYtQiaTcezYMQYNGsTvv/9OamoqTk5OhIeHM2bMGBo1akRCQgLR0dFUq1aN9evX06NHjwLHdHBwyLcWNZje2ElJSQ8UqzGvT8HtG+pLsWk42Vvi4vBwVTEq5DJeHxJC5JUkktNzOXD6JheiU4tMCtxsXPhp4Hx0Rj3fR/zG6bjHt+pMKD9qTSYA1g+wloiDlT1zerzLpM2fmsuCvRrQ0i+E747+yh+R/9Chekvkcjk+9p5FHudgzHHANERXePQolUr+/vtOX6tnn322wDZ3L5XctGnTfM/t3bsXMPV3e9yUKClISkoyt/UfPHgQSZJo3brwnu3/NWjQIM6fP8/QoUORy+XUrl2b559/Hh8fH0aMGIFCoaB169bmJoUZM2Ywbtw4FAoFderUKXTd63JjygmQ3Z6+4VJsGrX9nB7KHvltg31oG+yDJEmM/Wwr/1sbSfi+q0x/oVWh/QxUShUqVDha2qMxaishYuFRl3p7DQM7xYMthVzD2Z+VQ77mSmo0+2Mi6BvYDUuFiu+O/sras5tYe9Z0h/dK8+G09m+KVSH9Bo7cOIGtyoZXWgx/oFgE4VFTbFLw5ZdfEhsby7x581i0aBHr16/H1dWV/fv38/bbbxd7AoVCwbRp0wqU9+/fn/79+xcob9u2bbEzUZUXo2SqapfJIEejJzY+g7aNfSollrIik8mYOrIF4fuvsu1IDP/uvcqLAxoVub2NygaNUYtRMiKXibmthLKTcnt+gZsJciIvJxEU4FbMHkWTy+Xm+QvyOFk5kJarpql3EMduRvLNkRXsvn6IaR3fLNA3KS1Xjb+Dd76FmARBKMGMhuHh4cycOROj0cgvv/zC//73P5YvX862bdsqIr5KoddJLPs3CqMEdas5V3Y4D6yWryOvDwmhTWNvth2NIVdb9JSuthamvhMf7fgCnUHHtdQY8/TPgvAgkrJTkCHj771ZTF28j7jEzDI9/uI+M/ltyCKmdHiNT7q+g6etG2cSLrD+wtYC26blqHGycizT8wvCo6DYpEClUmFpacnx48dxd3fH398fheLRzK51t4c1zvv1GP/uu4qbkzUhdd0rOaqy06t1DbJydLwxbyc/bzhrHoJ5N1uVqWr3bOIlnlvzJpM2f8rIP8ez+NDyMpteVng8HblxEiuDG3lfO5O/3suVG+nMWnaEqKumkTOXYtL46PuDpGWUfoZBpUJpvvOv61aLr/rMoKaTPz+f/IuohIv5tk3LTcfRyv7BXpAgPIKKTQrc3Nz4+uuvmTdvnrnH//79+7G1LfnSvQ+L9MzbX0S3fysXvt2pSiyGVFYa1nJlQMcALFUKft96gW1HYgpsY6W8M3JDKb/TurTz2gFe++f9QhMJQSiO3qDnhvoWVhpvqnuomPBMU9IyNLz5+U72nYpj8qK9pGVo+Gn9GY6ejef3LefL5Lx58xAsO7EKMHV23B8dQZYuBzebwjveCsLjrNhfvNmzZ5OVlUVYWBijR48GYOPGjXz44YflHVuFu/NzJ2Pa8y2xt1FVYjRlTyaT8Xy/IOaOa4+9jQULfj/O37svExOfYd6mgXsdgh3qsaTfLP7XfzZf9v6QBu51AMjR55KeWz5r2wuPtrS8943OEqVCRudQf6p7me7UmzcwjRJ4efY2Tl0yjSBav+8q/1t7mlzNg61eOLrpUACupsawP/ooUzd/xpcHvkchV9C2WiHjeQXhMVdsUuDp6cmkSZPo1asXZ8+eBeDjjz+mYcOG5R5cRTMaTR0N+7WvSYsGBedHeFRYWSqZ/bpptMf/1kXy6pzt/G/dacA05KunRzucrR2xsbDGx96Tca1G42HrCuRfg14QSirl9sgDSWuJhcI0muez19rx+8zeTH++FUO71aWOvxMWSjk9Wlanmpc9f++5wgufbuFybNp9n7e6kx+T2r2MSmHBlwd+IDE7hREhg1jQ60PcbEVNgSD8V7FJwbVr1xg8eDB9+vThxRdfBGDKlCns2LGj3IOraHdGHzw6TQZF8fe8056qVMj4d+9VDAZjodu62jjzSbdJyJCx4+qBCm9CyNJmk63NKX7DR1R8ZiLfHl5hnib4YZOty2Ha9nkAGDQqlLeTAnsbFTa3Fxsb3qs+M15qw5+z+zJuaAiLJnbmzadCSM/UcvRcwanOS6OZbzCL+36Kk5VpbpOw2h3xsLv/kQ+C8CgrdkjitGnTzMtP9urVC4DXXnuN1157jc6dO5d7gBXJKOVfEOlRN+GZpqSqc3G0s+TLlcd5fd4OPF1s8LTT8t8J1JysHBjUsDerz/zLgZhjDKwfxpCgPgBkarNIzEqhmqNPmQ/x0uq1jP7rbbztPBjh1a9Mj13VJGWnoJQrsVPZcisjgVuZiWy/so+jcacAaOUfSoh3g2KOUvUcvxlpTiQN2TYoXYr/fMlkMrq1qM736yJJUz/4ssYOlnYsemIGablqLBRVcy0TQagKik0KkpKS6N27N3BnoSB/f390uofzruVe8r645A/hZEX3o3OoP2CapAkgNiGT9EwN15USrxSy/ZP1e7L27Cb0Rj2rz4TTJ7AbXx1aytEbJwEY1WQIvet2KbCfJEnk6HOxUliSqcvGwbLoRZruZpSMzNn7LQA3MxOK2frhptZk8uo/7+Fs5UhYnY6sPP13gW1WnVnP1dRongjsiuoh+mE7m3AJgO/6z+bVmfvMzQcl4WRvSWrmgycFYJqgS9QQCMK9FZsU2NracvToUZrdNcn+8ePHzUtRPkruTHP86Dcf3C3A15GnutXF0c6SazfV7D9ZcFQCmIZ8LXpiBqfiz7L48HI2XNxhTggAbqhNCytpDTpydDn8dXYTmZosrqffIDk7lRZ+IWy/so//9Z+No5VDoee429bLezkVf9b8OFmb9mAvtArbefUAAKm56eaEoFPN1owIHkRcRjyz9yzmYvJVLiZfJfziDuq71ybIoy7darVHLpeTq9dgqVARn5mITCbDw7bq/PjFqG8S6BaAk5UDOr0BpaLkHXid7K2IvpVBfEo2Hs5FL+olCELZKDYpePfdd3njjTdwcnIiLi6OJ598kuTkZBYuXFgR8VWovDZ1+WP2vSOTyXiuV30Alq4/Q47WiCRJhX4Bu9g40dQ7CBky84/Xh53Hs+z4apKyU5EkiXc2flLonf32K/sA2HBxB51qtsHR0v6eS9cejj2BTCbjo84T+GjHF/wQvYYrijgG1A/Dy/7RWsVs97VDqBQW5n4DtV1q8GqLEQDUtazF2ObPsvpMONfTYknPVXMw5hgHY46RoclCKVfyy6m/qOtaiwvJVwCY2PYlqspsIrHqmzT3DUaSJLQ6g7lPQUm4O1lz5koyL8zcQvsQX4b3qo+HszWKR2iosCBUJcUmBU2bNmXr1q2cOHGClJQU3NzcCA4OLrAS4aPgTvPB4/uFY2ttgcEIGp0BK1Xhbw8HK3v61+/B2rObqOtai/rudXC1ceZqWgwLD/5YaEIwoc2LGCUjq878y59RG/kzaiPuNi580esDDJIRawsrjt+M5ED0MWwsrAj1bcyp+LMMafgE9dxr80HnCSzZv5ztV/ez5/phfhgwt9A57R826twMFHIF0ek3GNLwCawtrFl+YjWe/6nmbunXhJZ+TYiMP8e6c5t5pcUIXv57KldSo4mIM40cuZB8BX9HH+IzEzmbeIk6Rl90Bl2ltqGrczPI0GTi5+CN3iBhlChV88GoPg3QGYxciU1nz4kb7Dlxgz5ta/LSk43LMWpBeHwVmRTs3LmTTp068c8//+Qrj4+PZ/PmzQD07du3fKOrYMbHrE9BYexuz82wbH0Uo/o2xNKi8PvNZxoP4JnGA8yP67vX4WjcKfZFH6VrrXY0921ME+8gfju9DiulJa38TauQBboFcCbhAidvRbHn+mFG/fU2VkpL+tfrwS+n/sJWZUOWNptDN06gkMnpE9gNgHruATzn14/LljdZfeZfbmUmUsPZv3wvRjmLjD/Pxzu/ND+u7VqDJt5BNHCvXeTEOkGe9QjyrAdAoGstjtxuvulaqx3bruylV53O7Lp6gH8vbMNCpkQe/TtP1O3M0IZ9C8z/n8coGTFKElq9FkulCrlMXmbV9LG3m5T8HLzR6U0zhpampsDV0ZopI5ojSRLHzifw0z9nzH1gBEEoe0UmBRs2bKBTp0788ccfhT4vk8kewaQgb0ji45sU2NuY7irX77vK4ahbfP5WRxztiq8VeqJuFzzt3FDIFabmhdvX8O7EAUzDGzvUaImV0pI91w+jN+rJ1Or55dRfNPEO4u02L/L5ge+5mhpNx5qtCzQvNPNpzOoz/xKflfTQJQX/XWTq7jkfBjXoTbCnaWRBLZfqJTqenaVpVlELhQWjmwyhhV8wwZ4N0Og1nE++gk7Sg17Pn1Eb8bB1p221ZlgqC7bnf3f0V3PTDpgSvKkdXsNSoUJCuu+aM61ey6WUqwD4OXqh0ZU+Kcgjk8kIrefJ/lM3OXD6JkajhPxxa+cThApQZFIwe/ZsJEli/PjxBdaaflSJmgKo6eOIo60CBztrbiZl891fp2kb7IO1pZImgUW348vlclr4hZT4PHnV43e3gw8PeRKVUsWU9q8WuZ+XnWktijj1g41dr0h6o4HVZ/7lz6gNdK7Zxrxcb96EPh92Hk8Dj7qlPq6thWmdig7VW6JSqmjiHQRAj9odsFXZoI/P5bvrpqT+2yMr+CHiNxY+8TGuNncW+dLqteaEIK9Pw9nEi4xbP40mPkHsvnaInwcvRFnKoaaXU67z8c4vydHlUtPZHzcbF24lm9bOKE3zwX/5utuSka1l/Be7WPB2p/s+jiAIhbtnnwKZTMZ7773Hhg0bKiqeSpXXp+BxrinwdbdjfH9vQkND+eHvSNbuuszuEzcA+Gd+waWu71d1Jz/eafcywV4NOB1/jkDXWuY733uxUVnj5+DNX2c30rlma5ysq9ZKd5IkEZlgmre/gXsdFHIFB2OO8WeU6TO04+p+HK3skSSJc0mXcbVxvq+EAODZ4IF0qNGSxl7185VbKCzoVLM1ESkRdKjeEqXCNPfB3+c2cz3tBq42zkiSxOn4c8zc9RUAzwUPpHfdrpy6dZYcfQ6LD68wj4iIz0zE16FkM3xuvbwXGwsrdlw9YF5ds2ONVsCdtUVsre6/z06nUH9+Wh/Flbh0snN15smPBEEoG8V2NOzWrRsvvvgiHTt2xNEx/xfwo9d8YEoKFI9xR8O7De1WF3cna1Ztu0hapoYL0allupR0c99gAEJ9GpVqv041W/Hzyb8Y+/eUIudGqCi5ulz2pRwn9XI2XWu1Y9RfE8w/hgqZnA41WpGeq8bawoqG7nU5GneKtWc3mfev41Ljvs/tbO2IczFJ0eutRgGQlpPO3+c2cyszgUOxx/nq4E8AWCpVDGvUjw41WqKUK2jqY6ptuJwSzfrzpiWHV0Wup09gNwJcqhOfmcjF5Gu0r9GiwLnSc9V8d/QX8+MnG/TEz8GbVv6mmbBS1KbrYm9z/+MiXByseHdUCz5depin3gtn7IBGONlZEpuYSVir6rg4PPydTwWhMhWbFJw4cQKATZs25SsXfQoeffY2Kvp1CKBudWfeWbiHmT8d4qdpYZXeltuvXg8C3QKYtm0e55IuV2pSsOf6EfamRLA3JYKErGRzQgBgkIzsuLofgHpuAXQNaGeenVAhk2OQjAR7V8waIo5WDlgpLVl6fFW+8mCvBoVev6eC+uJj78GfURvZHxPB/piIfM9bW1jhZuOM3mjgsz1fM6PrRHMzhK2FNUq5kh4BHXGxcTLvk5x+OymwfrDBks3qe/LigCC2HIrmu7WnzeVRV5KZ8XKbBzq2IDzuik0KPvvsM/z8/AqU5yULJbFgwQL27duH0WikWbNmdO7cmQkTJlCrVi3zNp988gnVq1dnw4YNLF26FJ1OR3BwMNOnT6+wH+nHbUbDkqpX3YVXBwezePVJElKz8XKt/GWzA90CCPFqQHxmYqWc/1ziJVQKFSfjo1DJLdAadeYagCENn2Dzpd1MaPsiuXoN3xxeQefb8zLkeabxQLZd2UvvOhUzVbhMJuONVqO5lhaLjYU126/sJzr9BtUcfQrd3lKpoltAe7oFtCc67QYTN32S7/k5e7/J9/it8A8BU0Lwbb9ZhXZoXPKX6QfcxvLBauIslHL6tQ8gsJozs5YfpVNTPyRJ4q+dl8jM0RFzK4Prt9RYqhQ0qeuBk/2jN3xaEMpLsUnB2LFjCQ8Pz1eWnZ3NK6+8woEDB4o9wa5duzh69CgrV64EYOjQoXTq1In27dsza9asfNumpqYyZ84c1q5di6OjIy+99BLbtm2jW7dupXlN980o+hQUKW+Z25j4jCqRFAB42Xlw4lYUs/YsZmLbl0rdGe5+xWXEM337fPPjRvZ1Gd56CJM2zwSgbbVm5nUhwDS9L5jWNsjTt143+tarmPd1nma+wTS73WRzOv4c0ek38HXwLna/ak6+vNX6eWLVN+kW0J5Jm2ai1mSaazvA1GFUBrzX6Y1CE4LsXNOkTI0C3Mos6Q6s7sKP73dHJpNx7noKa3Zc4un3839X1fB24PO3OmChrCpTOZW94+cT0OgMtAoq/v9SEIpTZFKwatUq5syZQ0ZGBkFBQfmeMxqNJR6R0LZtW0JDQ81jpB0dHcnMzCx0271799KsWTNz34WwsDC2b99eIClQq9Wo1ep8ZQaDoUTx3Iu5pqCI8dyPs+peDigVMvaejEMCmtf3rPTkqVfdzqg1GeyPiSD8wjY2XdzFxHYvU9PZn8OxJ1h95l8AWviFMLjhE2V23rxOgxZyJXqjgUC7mtRw9mNK+1eJy4jH296z0P1crZ0Z1WQInrdHUFSmpxv1p6ZzNVr5NSnR9m2q3Znm/Lt+s5GQiM9KYsrmz5jY9iUae9UvchZMuNN00KNVdZDKbuRI3vkCqzlja6UkK1dPhxBf6lZ35sSFRI6ejeeXjecY1efRW+o9z6zlR8jO1TP79XY0qOla2eEIDzmZdI91cA0GA2PGjOHTTz/NV65UKvHw8Cj1j8LJkyd55513mDp1Kl988QWenp5kZGTQrl07XnvtNX788UeSkpKYPHkyALt372bZsmX88MMP+Y7z1VdfsWjRonxlbm5uDzz18v4bl9iTs5Mu1r1o7uv7QMd6FK3Zn8Lpa9kAhDV1pHU9+2L2KH+SJPFD9BqSdWkAqOQWPOnVnTU3N5vG6QMKmYKJAaO5lZuErdIae6Ut2YZcvrr6MwA+lh709erEtew4XFVO+FsX3dP+QuY1/rq1ldbOwbRzCcUgGbGQF1vh9liTJInwo2kcuZjF6G7uVPcon+r8tCw9V25paFLLxvzdtGpvMmeicxjWwZV6ftblct7KNmNlLAATBnhja3X/NSJBQUGP5Ey1Qunc89tMoVCwbNky4uPjuXXrVr678Rs3bpRq/oKjR4/y7rvvsmDBAlxcXBg7diy9evVCp9Px8ssvs2bNmgL7SJJU6F37yJEjGThwYL4yg8FAUlJSoW/siIgIQv+7FnAhrknZcBlqVK9OaHBwiV/bgyhpbBWpqJgC62v5c+cl/tlzhT1RWbz6dEfkchkXolOJvqWmVZC3eUbEiozLo7a3uc1ba9SxMs5UhfxOu5fZcfUAZ+LP80fSJq6m3Vno6e5JleI0CSy7sY5cvQZbC2t+HDi/yIT34ulYZPEy3uj2onmZ6Kr4fwhVJ67z11M4cnEPAJ3bNeXKhTPlFlfX/zz2rp7JS59tY/vpbIb0bo3q9gydf+64yIYD15g2piXVvByqzLUqTHGxSStvMLhLHTq0rV/kNvei0WiIjIy83/CER0yxtzizZs3i559/xt3dPd8XpUwmY9u2bSU6yeHDh5k+fTrfffcdNWrUAKBPH1Obq0KhoEuXLkRFRREaGkpUVJR5v4SEBLy8Ct61OTg44OCQf5U9jUZDUlJSieIpSt4qiWJIYuHsbFSM6N0AHzc7Fvx+nN0nbqDTGVj4xwkAUjM0DOl6f2PuH0Q1p4K1OqaploOJSrjIUf3JfAkBwK+n1iKTyVj25BdM3DiDhKxkALJ0OcRnJZGpyaK2a40Cx1XnZuKgsjMnBELxom9lAPBC/yCc7St2yKCPmx192tVk/d6rvPjpVj54oRWSJPHTetP3TOSVZKp5Fb9iZ1VlMBgxGiVUSvGdJZSNYpOCjRs3sm3bNjw9C28nLU5aWhrvvfceP/30k3kUw7p164iJieH111/HaDSyf/9+unbtSvv27Zk7dy6pqak4OTkRHh7OmDFj7uu898NovN2SIvoZ3lP9mqZ5+ef/kn+YWt449MrwcZeJnE+6zC+n/gKgye2hfnqjqQmhhW8ILzd/jmtpMfwZtRE3GxdqOvtjpbSkhrM/CVnJ1HGpwcWUa7zx73QABjfsTbYul/RcNc19Q/g98m9uZiRgryqfjpbnr6cQvv8aPu62PNUt0FxuMBi5EJ1mvu4Pm3PXU1Eq5PRpV6v4jcvBi/0b4WxvxYoNZ3nz8535notNKLx/08NCqzd19HyUO1IKFavYpMDDw+O+EwKA1atXk52dzdSpU81l3bp14/LlywwbNgyDwUBISAiDBg1CLpczfvx4RowYgUKhoHXr1rRv3/6+z116oqagJHzd7ZjwTFPC910lsLoLAzsFMHXxPtSZ2kqLqZ57APXcA+hbrxtpuWpcrJ0A6F+/B/aWtjzZoDdKuSLfgkJ5Xgh9mmqOPjT1bsS7W2eby1efudOTfV/0UfP7IkObVSYx6w1GTlxIpI6/E2evpTDzp8Pm5zYdvE5wbXcuxaZx7aapU+3iSV3w96z8fhylkZ2rY+uRaDqE+KKopPkt5HIZQ7vVNc0ieT0Vvd5IgJ8jUVdT+GfPFW4lZ9GmtgyjUeJ/605z6lISz/WsR+tGhQ/XvJfNh66z8cA1RvdtyMrN53FxtGJM34Zci1Pj52GPu3PZ9mvQ3l5PQmUhvrOEslFsUjBkyBAmTZrEE088gb19/i+kkvQpeOGFF3jhhRcKlI8cObLQ7fv370///mU3nW5pGMSQxBLrHOpP59A7CxI52KpQZ1VeUpBHLpObEwIANxsXhgbde5ItJysH8zb13AJIyEpmfs9pjP7rbWo6++Ni7URE3GkaeNTldPy5+47tqz9OkJ2rY9LwZshkMg6ducWsZUfMzwf4OfJcz/p89P1BElNz2Hok2twGDnD6chJWKmWZ/7CUpys30jEaJTo2LTjXSUV7qntgvsdxiZn8tP4MByNvcSQKVh/Yzo1EU83BgpXHWbn5Aq2CvBjctU6J7sTjkjJZ8tdptDoD7y6+s8DUzghTR8BqXvZ8/c79T7R1ITqV79dF0rddLdo3MTWZ6URNgVDGik0Kvv76a+RyOUePHs1XXpo+BQ8LSUxzfN8cbS1JTMtGozNw4Xoq9Wu6oFQ8fNfxvY5voJArUMoVLH3yc5RyJWvPbiQi7jT+Dt608A3Bx+HeNWep6lxytQaS0nKoX9OFXI2eSYv2EhNvalt/fYgeW2sLYm8/zjPqiQaE1PVgeK/61PZ3wsXBCj8PO+JTsnl51ja+WXOKbzhF49pu5Gj0BAW40TrIG7kccjR6GtR0zZdEPCid3kBCag6+7nb3fYwzV019NWr7OZVRVGXHx92O90a35GJMKj+tPYqNrR1dm/tTv4YL63ZfJiY+k183n2dHRCzTnm95z1qajQeu8fVq0zLWz/Wqx8rN57G0UNAuxJdNB68Dpr4Ve07coH1I6UY2afVGPv81gh23kwsrlcKcFGj1oqZAKFvFJgU7d+6sgDCqBvM0x+LzVWoeztYcjrrF8A82kqPR8+rgYHq1rgHAvpNxHDpzE7lcRuPabnRpVq1yg72HuyfesbEw3ZF3rNGKtBw1g4OewO4//QnSMjSsO5TC9qijTHimKbOWH+Fg5C3z877utvi425kTAoADp28SWN2ZW8nZONiqmPVaO5zsLbG/PXJjaLf8nTX/+6N86lISMhlcjEnjr52XzOUdQnxJy9RglCRefrIxiek6LkSnojcY0emNONiqqOlT+FoJeoORc9dSMBgl83YzfjjE8QuJrPrsCaxUpRt2qdMbOXc9hY37rxFY3blKzypYx9+ZQW1c8vXwDwpww2iU2HDgGt/+eYpfNp5jysjm5ueT03OwtFCYR9ts2H8NgE6hfgzuXIeerWqgNxixt1FhaaGgTWMf5qw4wsHIm6VOCo5dymLHsXTz4+MXEtmw/yoNarnmtXiiEjUFQhkp0Sd9165dbN26lezsbObPn8++ffto2rQp1tYPTzVmSdxZOllkBaU1pFtd0jI1ZOXoOH4hkc0Hr+Fkp2LjgescO59g3m7bkRhaBXk/VKvbedq5M7b5s4U+9+XKYxy/nA1kcyk2jbik/P0N4lOyuZGYhY+bLUO71eXLlcdZ8PtxAGytLQjwdSxRP4FPX2nL4ahbjO7TkOu31FTzcmDH0WgORt7C1tqC7Udj2H3iBi4OVmTm6Jj01R6yc/VA/omCavs58uawptTwvtPjPiY+g5k/HeJGoil2mQzeebYZxy+YppC+kZBJQCnu9DOztXzy02HOXDHVEvRtXzkdDB+UXC7jibY1OXzmFgmppvk5dHojV+PSeXvBbkLqujPjpTZcu6nmSlw6Q7rWYUTvBgA42t1Jgl4cYFrwq7afs7l/SEntPXmDjcfScbBV0ay+JyF13fn812MsXnMKuQw+eKE1ABaipkAoI8UmBYsXL2br1q3079+fFStWAHDq1CnWrl3L3Llzyz3AiiTWPrh/Lg5WTB5hupP6Y+sFVmw4y6dLTe3lgzrXpl2wL6cuJfLT+ihe/HQrtf2cGNSlNo62llhbKvFwsSn0uLlaPXGJWdT0qVrDxv7ccRFLlZKIcwnU9LTE0dGBE7d/RCc+G8rhM7cY0880AuLXTefp1rwajnZ3aiFqeDsQl5TFmL4lm2mvUW03GtV2AzDf7XdrUZ1uLaoDpvfuoTO3mPlKG3Yei+X3LRfwcLIgtIEvznaWNAxwZdexG2w+dJ1PfjyEr7sdDnYq0jI0RN9So87SMrpPA2r6OPLJT4eZ8/Od5sKFv5+gX4dadG1+7xoeSZL4Zs0pNhy4BkDrRt7YWVsUu19V5+JgxbHzCZy/nsIvG8+Zk6UTFxL59s9T7IwwDXdt0/jeHRNr+Dhw9Fw8Wp2hxM08G29fy+f7NTTXsCnlcn5cf4aktBxuJpn6QIghiUJZKTYpCA8PZ/Xq1VhZWZnXL3j55Zfp2bNnuQdX0URSUDaGdK1DSF13cnL1+Hna4epoqlFyd7ZmWfhZ1Flazl9P4b1vTCsIWqoU/PpxL4xGCXWWFr3RiK2VBdOW7OdqnOnOakzfhrhYPPhU1mXh9KUk8zh3gMY1bXh+cGsu30hHIZdR08cxX8e6cUNDANPQwraNffD3tGdw1zqoM7Vl1mnwpYGNGdG7AW5O1jwbVo8WDbxIvnmJ1i3vTMLVuLY7das5s/XwdSKvJKPVGXBxsMTX3Z6pI+tTr4ZpyOOCCR1JTsvF0lLBul2XibySzJcrj+PhbEOAnyM2Vhb8u/cKOVoDIXXc2XjwGs/3C+L05SRzQlDbz5G3hjV5qGqEiqK8/YM7ceGefOXVvOz5d99VAN5+NrTYfhM1vB0wGiUORd7Cz9OOap72KArpd2MwSly/qcbL1YZTl5IIrmmTr8mtfRNfnB0smbp4HwmpOQBl2pdEeLwVmxQYDAZUKtMdTl6vfIPB8EiuD2AUax+UCZlMRt1qzgXKHe0s+eKtjtjZWLD3RBw/rT8DgEZrYMHK4xw9F3+7yhu6NPM3JwQAP/5zBpkMjlyNYEjXOlT3rryag3/3X8VSpaBlAy9q+zvha5uGTCYr9kdBoZDna5cuy1EEttYW2FqbfoDzrn9EYsH3cVir6oS1qs7cn4+y+/gNRvVpmG8UCYCfhz1+HqYmjXojXEhR5/L8J5t595t9qJRyujavZv7xX3Z7H7lMxv7Tcfi62/LVxM6PVG94j9v/T68ODsbZ3pJ/915lcNc6NK7tRr+JfyOXy+jYpPh+AgF+phqevFqYQZ1r06WZP77udigUcgxGiVyNnrk/HyXi3J0mNy/ngolVXh+NvNESok+BUFaKTQratm3L2LFjGTx4MLm5uezYsYPff/+ddu3aVUR8FcooagrKXS1f0xdjj1bVOX05iY5N/Zj/SwS7T9ygXnVncrUGrt1Us/1oDDIZzHm9PY52lpyPTuXPrZHsOh7LvlNx/PJxz0q5C01V53Lw9E36tq/F8/1MC4VFREQUs1fV8/KTjenavBrBt5sk7sXFwYqPxrZm/i/HSFHnmhOCAD9HLseaOsDllc18pe0jlRAADOhYmxYNvMyJ6N2rES6d3gMo2TBmHzc7nutZj1vJ2dxIzGTNjkus2XGJYd0DaRXkxfvf7iczx7SaZIcmviSm5tC4jhu1nQvOi+F0e2bIQ2dMnVptrMX6G0LZKPadNHnyZFasWMHPP/+MpaUlP/74I127duXZZwvvePUwk0RHwwpjZ23BBy+0Qqc3su1INB7ONrw6OBiFXMbVuHTiErOo7n3njtXbzRZ7KZ4rafYsDz/LzaSsUnV+Kyv7TsVhMEqEtape4ecuS/Y2KpoGepR4+8a13Vn2QRgA566lkJmjI7SeB9duqpEk2HLoOjbWFlR/iKcMLoqFUl5kzVRe01hJ5c2VkKrOJepqCn9svcCRs7e4FJtmTgi6t6jGG0/dWb2ysKTT1kpJqyAvktJyeKJtLXzc7n/YqCDcrdikwMLCgjFjxlTodMOVReL2kERRUVBhLJRyZrzUJl9ZTR/HIofOhdbzZHn4WW4lZ1dKUpCYmoNKKX+gsfsPu7y+B3Cn0+NLTzaurHAeSs4OVrQN9uHaTTUrt5wH0nmqe11aNPAq0ftaJpPx3uiW5R6n8PgRt8R3yVv7QExeVHV5uZpGKVyKTSM9U8M9Vv7Ox2AwsjMihnPXU8xleoORrBydearYe5Ekif2n4vhz5yVsrC3ErJdCmcjrZwDQq3UN6lZzrrTpoAUBSjhPweNCIq+jofhQVlU2VhaoLBSs3n6R1dsvMrxX/QKT/dxNkiSi4zN4fe4Oc9kzPQJpE+xjLnO0U9G3fS0CfJ0IqeuO8nanr7u/nNfuusyP/5g6RqZlaMrp1QmPm6AANwKrOTN2YKNSN0UIQnkoMim4dOkStWvX5vz58wQGBha12SNFTF70cBj5RH32HL9BWqaGI1G3GNylToFE7mZSFhsPXGPnsdgCqzf+uvk8246axpa7OVqRlJ7LzxtMaxrU8HYgpK47247EEFrfAxtLJfVrunL2WgqCUNbsrC2Y92aHyg5DEMyKTApefPFFtm7dyvjx4wkPDy9qs0eKmKfg4dCvfQD92geweM1JNuy/xrQl+5n5Slvz85nZWqYt2U9SWg6G201CI3rXZ0jXuuw5cYMf/45EnaWlcW03Zr7SlpVbznPyYiIZWVqu3VSbZ53LW8gm/PYUtq0beZOdqyO4jnvFvmBBEIQKUmRS4O/vT6NGjZAkiaCgoHzPSZKETCYjMjKy3AOsSHfmKRBJwcNgSJe6RN/K4NSlJOasOErDmi50CvUnfP814lOymTuuPdW87Ik4l0C7YNNsc+1DfAvMPT+seyDDugcSeTmJqbdXt/tpWg9uJmfhYKNi0aoTnLueSsNarvTvEFDhr1MQBKGiFJkULF26lPj4eEaMGMHSpUsrMKTKY+5TIJoPHgruztZ8NLY1X686wY6IWPacuMGek3Fk5ehoWMvV3Eu+pAvQNKjpystPNqaOvxNuTta4OZnaeD94oRVHzyWUeiEbQRCEh02Rv35yuRxvb282bdqEk5MTMTExnDx5khs3buDq6oqv76P3BSmJGQ0fOpYWCsY/3dT8+MyVZK7dVBNar+Rj8PPkLYDz39kY7WxUdGrqJ3qFC4LwyCt29MHBgwcZP348np6eODo6kpqaSlpaGgsWLMi31Oi9LFiwgH379mE0GmnWrBlTpkxh+fLl/Pvvv+Tm5hIWFsarr74KwIYNG1i6dCk6nY7g4GCmT59eYcO/8poPFKJPwUNFJpPRu00NDpy+Sc/WNZABYa1qVHZYgiAID51ik4I5c+bw448/0rDhndXcjh8/zieffMKaNWuKPcGuXbs4evSoeTGloUOHsmLFClatWsWaNWuQyWQMGTKELl264OnpyZw5c1i7di2Ojo689NJLbNu2jW7duj3ASyw50dHw4fXKoGBeGRRc/IaCIAhCkYpNCnJzc/MlBABNmjQhK6vgfNyFadu2LaGhoeYqeUdHR7Kysujatat5oaWuXbuybds2qlWrRrNmzXB0NE3oERYWxvbt2yswKcib0VA0HwiCIAiPn2KTAmdnZzZs2ECvXr3MZRs3bsTJyalkJ1AqsbMzTQl78uRJYmJi8PLyokGDBuZt3NzcOHv2LFZWVri5ueUrj4+PL3BMtVqNWq3OV2YwPPiyunlz44nRB4IgCMLjqNik4MMPP2TChAm8//77ODo6kp6ejo+PD59//nmpTnT06FHeffddFixYwKpVq/JNTytJUqGd+4oqX7ZsGYsWLcpX5ubmxsKFC4scJlmSlexSUlNBDlGRkcTZWBW7fVmpiqvsVcWYQMRVWiKukquKMeWpyrEJj5Zik4LAwEDWr19PbGwsycnJuLq64u/vX9xu+Rw+fJjp06fz3XffUaNGDby8vEhIuLNeeEJCAl5eXnh5eREVFVWg/L9GjhzJwIED85UZDAaSkpIICgrC0tIy33MREREl6hS5Of4CZEBwcDBuDhWz4E1JY6tIVTEmEHGVloir5KpiTHnKOzaNRvPIzTkj3L8SNZ7LZDL8/f0JCQkpdUKQlpbGe++9x/fff0+NGjWAO30INBoNWq2WrVu30r17d9q3b09ERASpqalIkkR4eDg9evQocEwHBwf8/Pzy/SsseSgtMU+BIAiC8Dgr9wWRVq9eTXZ2NlOnTjWX9evXj4EDBzJ48GDkcjmDBw8mIMA0U9z48eMZMWIECoWC1q1b0759+/IO0UzMaCgIgiA8zso9KXjhhRd44YUXinzuv/r370///v3LO6wiiHkKBEEQhMfXPevJjUYjs2fPrqhYKp1RzGgoCIIgPMbu+esnl8s5c+YM0dHRFRVPpRKTFwmCIAiPs2KbD+zt7RkwYAA1atQoMDfBjz/+WF5xVYq8yYsUoqZAEARBeAwVmxR069atwmYUrGxGQJJAVBQIgiAIj6Nik4K8+QDi4+NJSUmhfv365R5U5ZEAWYUtwCQIgiAIVUmx9eTXr19n8ODB9OnThxdffBGAKVOmsHPnzvKOrcIZJANIiKRAEARBeCwVmxS8//77jBkzhiNHjmBvbw/Aa6+9Vuppjh8GmYY0JI0NYpoCQRAE4XFUbPNBUlISvXv3Bu7cQfv7+6PT6co3sgqUkJXMurObSNbfQMpxFDUFgiAIwmOp2JoCW1tbjh49mq/s+PHj5pUPHwVpOemcunUWBUoMaZ6VHY4gCIIgVIpiawreffdd3njjDZycnIiLi+PJJ58kOTmZhQsXVkR8FaKuWy2+6jODFRvOsjrlYmWHIwiCIAiVotikoGnTpmzdupUTJ06QkpKCm5sbwcHBBVYifBRk5+iwUikqOwxBEARBqBQlWvvg0KFDnDhxArVajaOjIwaDgdatW5d3bBXuVko2Xi62lR2GIAiCIFSKYvsUfPDBB8ycORO1Wo29vT2pqalMmzaNmTNnVkR8FSItQ8PvW89zKTYNT1ebyg5HEARBECpFsTUFu3fvJjw8HGtra3NZVlYWffr04b333ivX4CrK9VtqVm6+gMFopHFtt8oORxAEQRAqRbFJgZubGwpF/nZ2CwsL3N3dyy2oihZcx52/5vSt7DAEQRAEoVIVmRT8888/ALRs2ZLhw4cTFhaGs7Mz6enpbNy4kQ4dOlRYkIIgCIIglL8ik4I//vjD/LdKpWLHjh3mxxYWFhw8eJDXX3+9fKMTBEEQBKHCFJkUrFixosxOkpiYyMSJE9Fqtfz222/ExsYyYMCAfIsrvfnmmzRr1owjR44wf/589Ho9vr6+zJkz55Ec/igIgiAIVU2xfQpOnjzJ8uXLSUhIwGg05nvul19+KdFJJkyYQPv27fPVNtSvX79A4qHX65k0aRIrVqzAz8+Pjz76iJUrVzJy5MgSnUcQBEEQhPtXbFIwfvx4+vTpQ6dOnQp0OCypb775hjNnzuRLCgpz6tQpvL298fPzA6Bnz54sXry4QFKgVqtRq9X5ygwGw33FJgiCIAiCSbFJga2tLRMmTHigkxS2TkJcXByvvPIKqampNGrUiLfffpuEhATc3O4MCXRzcyM+Pr7AvsuWLWPRokX5ytzc3Fi4cCGRkZGFxhAREfFAr6E8VcXYqmJMIOIqLRFXyVXFmPJU5diER0uxScGoUaNYsmQJXbt2xcYm/8Q+Pj4+93VSJycnXn31Vfr06YNSqWTq1KksWbKEwMDAAtvK5QXnVxo5ciQDBw7MV2YwGEhKSiIoKKhAH4SIiAhCQ0PvK9byVhVjq4oxgYirtERcJVcVY8pT3rFpNJoib6aEx0+xSUF0dDTff/89X375Zb4faJlMdt9vJDs7OwYNGmR+HBYWxh9//EHHjh1JSEgwlyckJODl5VVgfwcHBxwcHPKVaTQakpKS7iseQRAEQRBKkBSsWrWKdevWUbt27TI76YEDBwgPD2fGjBkA7Nu3j/r169OoUSMSEhKIjo6mWrVqrF+/nh49epTZeQVBEARBKFqxSUGdOnWoVq3afZ8gLi6OyZMno1ariY2NZfjw4XTs2BGlUsmQIUNQKBT4+/szceJEFAoFM2bMYNy4cSgUCurUqZOvRkEQBEEQhPJTbFLQpEkThg0bRtu2bbG1zb+C4Msvv1zsCXx8fEo150Hbtm1Zt25dibcXBEEQBKFsFJsUxMXFERgYSFJSkmizFwRBEIRHWLFJwZw5cyoiDkEQBEEQKlmxScG0adMKLTcYDHz66adlHpAgCEJFU8UcJ9fDBiv/+sVvXIEkowF5ZhKSXodMaVHZ4QiPgYKTAPyHp6dnvn92dnYcO3YMZ2fniohPEASh3Nme2UDc8vcrO4x8JEni5i8f4bj3OxLDv63scITHRLE1BYWthDhu3DimTJlSLgEJgiAIoE+LJzf6DADaxOhKjkZ4XBSbFBTGxsaGW7dulXUsgiAIVYak14FCiSErDbnKCrnKutzOpT6+FU3cReSW1tgHd0FubY/m5mUA9A6eyNMTijmCIJSNYpOC999/H5lMZn4sSRJXrlzB2rr8PiCCIAiVIXHDEgwZqajc/Ujb/xcKexcMGSnILCzxf/krlA6uZX5OSZJI3roUJAlJl0v6oX/yPa9zC0B5ZT9GTTZyS5vCDyIIZaTYpOC/0wzL5XKaNGlCz549yy0oQRCEiiIZ76ywmnFsMwDZF48AYMhIwaFZL9RHN5AZtRenVv3L7rySEYwGjDotkjYHl24jsa4ehObGRfTqJLKvnMCpZT+uXLsGgD49EZVH9TI7vyAU5r76FAiCIDwqJIMeAJfOz+HQtAeZZ/cjGfRIeh0Wrj7Y1mlGxqmd6NXJpTuu0QCShExR8Gs298YFEv9eiD4jBfuQbgAoHdyw9KqFpVet2/E8C4AxPg2Am79+jF2jDlg4eWLftAdGTQ5ZZ/ZgF9QRuaWouRXKRpFJwfDhw/M1G/yXTCZj2bJl5RKUIAhCRZH0OgBkSgvkVrY4NOleYBu5lS1GTVaJj2nU5BD99csYc7Pxe2kBKlfTirLG3CzSIzaRcWIr+jTTsvDqI/8CoHT0KPxY1o4AGLLSSD/4NwBJG/9nfl6vTjYnEILwoIpMCsaNG1doeWJiIl999RU6na7cghIEQagoeTUFyIuuOFVY2WDMLXlSoEu9hTEnE4Ck8G+xDWyBXp1MztWTaBOuI1NY4DFgPCgU6JJuILe0wdK7VuHxqWyQ2zhgzFbjOegdEv7+CkmXi0Pz3qiPhJN95YRICoQyU+SnoEWLFvkea7VafvrpJ37++Weee+45Ro8eXe7BCYIglDtDXk1B0UmB3NIWY252yQ+ZmXL7Lxm50WfMQwsB3Pu8hn1wl5LHJ5NRY/xP5oc167W66zk5Gcc2I0lGZLJip50RhGKVaEji5s2bmTt3Ls2bN+fPP//E3d29vOMSBEGoEHk1BTJF0TMGyq1s0affWfsl/q/PyTp3CCvfOhg1WejTk7Cq1oCca5EgGZH0WgD8X19MztVTKKwdkAw6LL1qYuHiU2axq9z8kfRa9OmJWDh5ltlxhcfXPZOCc+fOMXPmTAAWLFhAgwYNKiQoQRCEiiLl1RQU0iEwj9zKDu3Foxi1OcgsLMm+eBSMenJjziKzsERuZUf2xaMoHd2xbdCW9ANrAVDaueBwuyNheVDYOACUqmlDEO6lyE/B+++/z5EjR5gwYQJhYWEVGZMgCEKFkfTF1xRYuHgDkHFyOzZ1miPpNDi06IOlV03sGrTFqNOSfng9NrWCsfKrh32jjmjiLt0z0SgLeesh5HWWFIQHVeQ7dvXq1QC8+eabBUYhSJKETCbj7Nmz5RudIAhCOZOMeUlB0T/gzu0Goz62ieTNP5K2/y8A7Bq2x8qnNgAKhQUuHZ4yb69yr4bKvVo5Rm0is1ABmJsrBOFBFfkpOHfuXJmdJDExkYkTJ6LVavntt98A2LBhA0uXLkWn0xEcHMz06dORyWQcOXKE+fPno9fr8fX1Zc6cOVhaWpZZLIIgCHe7e0jivbj3epnUfWvQ3DiPyrMmlt4BFRHePckUIikQylaFdFedMGECbdu2NT9OTU1lzpw5fPfdd/z555/ExcWxbds29Ho9kyZNYt68eaxevRoXFxdWrlxZESEKgvCYutOn4N5JgU2dUHxHfUq1N77HZ9Sn95zHpaKI5gOhrFVIUvDNN98QHBxsfrx3716aNWuGo6NpUo6wsDC2b9/OqVOn8Pb2xs/PD4CePXuyffv2ighREITHlHmeghK2/yvtnZErVeUYUcnJbzcfGEVNgVBGyrcXzG12dnb5HickJODm5mZ+7ObmRnx8fJHl/6VWq1Gr1fnKDAZDge0EQRCKk1f1XlV+6EtDphTNB0LZqpCkoDiSJCGXF15pUVj5smXLWLRoUb4yNzc3Fi5cSGRkZKHHiYiIePBAy0lVjK0qxgQirtIScRVPdeMCtsCZ8xcwxiRWdjiFKup6ybTZOAHRVy6jMThXaEzCo6lSkgIvLy+ioqLMjxMSEvDy8sLLy4uEhIQC5f81cuRIBg4cmK/MYDCQlJREUFBQgY6JERERhIaGlvGrKBtVMbaqGBOIuEpLxFUyapJIOg2NQkJR2le9H9Z7XS+jNodr28HPxwun+7ymGo2myJsp4fFTKfNitm/fnoiICFJTU5EkifDwcHr06EGjRo1ISEggOjoagPXr19OjR48C+zs4OODn55fvX2HJgyAIQnGMeg1wp33+YXKn+UB0NBTKRrnXFMTFxTF58mTUajWxsbEMHz6cjh07Mn78eEaMGIFCoaB169a0b98egBkzZjBu3DgUCgV16tRh0KBB5R2iIAiPMUlnao+XWTx8Q59lcgXIFUg6TWWHIjwiyj0p8PHxYcWKFYU+179//wJlbdu2Zd26deUdliAIAgCSToMkk5f77IPlRSZXoLl1tbLDEB4RYlktQRAea0a9FoqZo6AqU9g4kHPlONlXTlR2KMIj4OFMjQVBEMqIpNMgyR/er0Kvp6ehVydhXaNRZYciPAIe3k+CIAhCGZD0WqSHuKZA5eaHys2vssMQHhGi+UAQhMeapNOUeDZDQXjUiaRAEITHmlGneahrCgShLImkQBCEx5qkzRVJgSDcJpICQRAeW5LRgCFbjWRhVdmhCEKVIJICQRAeS5IkEf31q+iSbyAprSs7HEGoEkRSIAjCY0mfnohBnWR6UMSCbILwuBGfBEEQHku6pJg7D8TSw4IAiKRAEITHlCE7w/y3TCQFggCIpEAQhMeUUZNt/lvrH1J5gQhCFSKSAkEQHktGbS4ANSb/hs6jTiVHIwhVg0gKBEF4LEnabJArkYk5CgTBTCQFgiA8loyaHOSWVshkssoORRCqDJEUCILwWDJqc5CrxPwEgnA3kRQIgvBYMtUUiKRAEO5WaUuDHTp0iAkTJlCrVi1z2SeffEJUVBRLly5Fp9MRHBzM9OnTRfWeIAhlTtLmIBM1BYKQT6WuF9q+fXtmzZplfpyamsqcOXNYu3Ytjo6OvPTSS2zbto1u3bpVYpSCIDyKjJoc5NZ2lR2GIFQpVWoR8b1799KsWTMcHR0BCAsLY/v27QWSArVajVqtzldmMBgqLE5BEB5+enUS1u7VKjsMQahSKjUpiIqK4sUXXyQjI4N27dphZWWFm5ub+Xk3Nzfi4+ML7Lds2TIWLVqUr8zNzY2FCxcSGRlZ6LkiIiLKNvgyVBVjq0oxqaIjQKYA/5AqFdfdRFylU+lx6TU4Z6WRkCsRczuWSo/pHqpybMKjpdKSgho1ajB27Fh69eqFTqfj5Zdfpm3btvm2kSQJeSELlYwcOZKBAwfmKzMYDCQlJREUFISlpWW+5yIiIggNDS37F1EGqmJsVS2mKxs/BUyzzlWluPJUteuVR8RVtOzLx7kF1AxuiW3d0CoRU1HKOzaNRlPkzZTw+Km0pMDT05M+ffoAoFAo6NKlCytWrKBx48bmbRISEvDy8iqwr4ODAw4ODvnKNBoNSUlJ5Ru0UKEkvY7EfxffKTCKJiKhbGRG7UVubYdNrZDKDkUQqpRKG5K4bt06cxOA0Whk//79PP3000RERJCamookSYSHh9OjR4/KClGoZOmH15MZudv82P7QCm79/im3fv+U1N1/IElSJUYnVDSjJhvNrasYi1i8SJKMGLLSS3QsXcotVB7VkSnFbIaCcLdKqyno1q0b77//PsOGDcNgMBASEsKoUaNwdXVlxIgRKBQKWrduTfv27SsrROEB6TNTkVtYmceCS0YDKTt+wb5RJyzcfJG0ucitbM3bG7IzSN23GptaISjtXVEf24xV9YZ4Pz2NxPAlpF07iz4zFcmgI/tSBDkxUTi3G4KlVy3kltYYstXI5ApkKitkckVlvWzhPhhyMtEmXCfj1A6s/AKxD+4CgDEnE0N2OoacDG6umA6AfZMeuPd+ybyvUZODZNBx/YvRAFR783uUds5Fnkufnog+LR7rWsHl+IoE4eFUaUmBra0tX3zxRYHy/v37079//0qISCgrkkFH6u4/SNv/J3IbB3xHforC1pHcmLOkH1xH5umdqDxrkHPlJDXfXU32xaMkb/kRY242xtxM1IfXm4/l0uU5ZAoLPPq+TkxEBHVDQ5EkiZTtK0g/uI6b104D4NzxaVJ3/QaAfZPuuPd+uTJeunAfss4dJH7NXPPjzFM7TAmeQknKtuUFttfGX8WozcGQlU7OlZMkbfwOha2j+fmEv77ArkFb7Jv2yDfHiWTQkbB2AVnnDgBg4exdjq9KEB5OVWpIovBouPXHbHKuHAfAmK0m5pvXkSlVSLerffO+zAES1n5BVtQ+AKxrNsa+SXfzceRKS6xrNy1wfJlMhmvXEVj61CFl2zL06YnmhAAg4/gWrGsGY1O7KXILywL7P2wkSSL9wFokgw67Rp1QOrgWqAnJuXYaXVoCNgFNAQnFPe6UK5MhJwObyHASYvdi6RuIQ2iYOSFwaBqGpX89EtctIDNyt3lpY7vGndDeuoZN7aYYstVknNjKtbnP5Tuu3NLW3HSgjb9KUvQZ0o+GY+lVC0O2GseWfUndswpN7Dksfevi1Ko/1jVFTYEg/JdICoQyl5cQeA19l1t/mEYOyC2tMei1WFVrgFOrAeTGniVt/19kRe1D5VUL5/ZDsandtFTV/nb1W2NXvzWaW1fRJsVgUCeTsuNnABL+nAcyOXYN2mLXuNND1aFMkiR0STGoT2xD0mRjyEon+5JpSFrq7t8BTNXrMjkOoT2xcPHi5i8f5juGx4DxQNWbrS/r/GEsY0+QnWxP5pm9KGzszc85d3jKXKOUcWwzAK7dR+PYoo95m7RDf5v/Vjp7YeHijWv3Mahcfczlkl5H6r41ZJ7ZY+6TknPlhPl572c+QK6yKq+XKAgPNZEU3CdJMmLMyURubS+mYcb0Q2bMVqM+vgUA5/ZPYVMnFMdW/ZApVbh0fBrNzcso7JxR2rtgUycUS99AAGzqhCKT3X+fV0uvmlh61QRMP5bpR8JJPxqOpMkm69xB9JkpJUoKDDmZyC0sK6XzmSQZST/0D3JLW4zabFK2LgNAYe8CyLBr3AlDRio50WfAoCfrwhGMuVkgk5Fz9aT5OFbVG6JXJ5O0+QdsXGqRYZmFXYO290y2cq5HYsjJwK5e63J6bRJIRrQJ10kK/xaj0hL/0bOI/d8EEv76HORKqr9xpwnApcMwrHzrIreyw6ZO/qF49kEdyYraj03tpji1G1LoZ0+mtMCl4zBcOg4j+9IxkrcuxbFFH2QWKixcfERCIAj3IJKCUsiNPU/mmb3YBDQh7cBacqPPYNugLR4Dxhf65SRJUqkThvvZpzJJRgPIZLfb+E13cXIrW3MnLteuI83bWnoH5NvXtm7zMo9HYeuIS6encen0NAAJ6xaQEx1lilUyoj4SbqqRUFmTcWwLhpwMZHI5CjtnUravQOnshf/LCyu8o2La3tXmWoC8am3fF+Zj6Vkj33ZGnQbNzUtYV2tI7PcTyTy9C0mvxbFVf6z862MTEEJuzDkS//0GVewJEmNPkLhuAdXGfYfSwbXAeY3aXG7+/AEAmpb9UDq6kxtzFusajbBv0r3Ae1GbEE1uTBQOoT1L/NpSti8n/eA/WPrUNiUHPkFYOHvhM/JTNDcuYOHqk69PgMLWEfvGnQs9lsLWEd/Rswp9rjA2tZtiU0gTlCAIhRNJQTEkg570w+uR9FoyTm5Hn56I+mg4AJa+dcmK2oeu7WCUzp7kXj9D5pk9yC1tyDyzB2NuFtUnLEVhbV/4sW8PqctLBHRpCcR8/QqegydjG9ii5DFKRiS9DplCaf4xy7p4FIW1HVZ+9R7wCvwnXqMemcICyaDHqMk29/gGsPSujWPrAdjWa/lAd/5lSenshSFyD8lbfsKQm0XmqR0kb/nJ/LzM0gZJmwuSEQB96i2yLhzGtnYzc42BUadBplSVW7Kmz0wjbf9f5sc5V09i17hTgYQAQG5hiXW1hqbX5uCGNv4qAM5tB5lHcljXaES11xZz7MBunLYvACB+zVxs6jTDqc1AZHIFkiSRc/UUt36bYT52+l1V81ln9yMZjTg2u/Pjn33pGLd+n2k6R60QLJxNc4hkRO5Ge+sKln6BGDLTUVjZYtugDelH/sXC2ducLGriLuLafTSXFKYOfpaeNQp9jYIgVB6RFBQj/dA/5nZqMP3I6FNv4dTmSeyDuxDzzevE/m98kfvHr56LpXcAmZG7cOv5EpqbFwHTHVpm1D5sbT2J3vM1huwM8w9T/OrZ1Jy8EkNOJtkXDmMX3JnMUztJ3fMHrt1GoXRwxdKvnml7mZxbv31CztWTKOxdsWvYDplcbv6Rce4wDIemPZDb2KNXJ5F5ahd2Ddth4VK6nteZZ/aSfng9mltXULlXQ5sUg6XXnTt/maUNjq37Y1e/fKqg75d19Yaoj4ajPrHVXGbpXRvbhm1ReVTHpmYwCf98Teap7Vj6BqJPTyRhjak/gnXNYIzabDSx57Fr3AmPvuPKJCbJaCDj1E4sPWuSGbUH9ZENSJIRr2emk/jP18jkCpxa9iv2OEondwAsXH3yDe00n0dli12jTmSe3ok2MQZN3EWsqwehz0wl6d/FGLW5yFSWuHR6FrtGHdElxpCwbgH2wV3IjYkidfdvGDJME4Llxl0i9/ZID4CYxa9hH9INha0jafvWmAoP/WN+XvbvYnPHUgC5pQ1uPcdi27AtHDt+P5dNEIQKIJMekRlg8qbqfJBpjo3aXK5/MRqbus1xbj8UQ2YqN3/5EEufOrh0fhZDthqbWiFkXzqGbcO2yGRyMiP3oM9IBkAyGlHY2GPpU4fsS8dI2/MHkkGX/yQyOeRN3WzQFxmLdc3gfG3FxbHyr48u9RaGnIzCjy2Tm5MOANsGbbGu0Rhjbiape1ehcq+O7yhTp8C862XUabjx/dvYh3QjZccv+faXKVVIkhGVqy++z8+tkOr28pruVZsQTfxf83HrORaZ0oLc6Chyrp0yj5AAkKmscGrzJDYBTdAmRGPXqKO55qC4uLSJMWRG7UNhY4919UbEfv92vmupdPTA7YmXsSllb3h9eiKZZ/dj5VcPK7/AAs/fHZcuLZ6Yr18FuRKMd94bNoEt8Ro8qcC+ubHnuPnbJ+b3r0wmxy6oA67dRpJ14TDqoxvRxJkSXOuajfEc9A4pu38n5+pJdIkxADg0fwLJoCPn6im8n55mrlmoilMKV8WY8lTUNMeFfXcKjx9RU3AXQ2YKkl5LVtQ+8zA5ANt6rbCu0cj82C6ofaF/383SswbObZ8kM2qfqTPVbR7938SuYTvz47wPvCEnk4wTW7ENbEni+q/JuXoKCzc/bOs2R2Zh6hhl1GQjU1ogaXPJOnfQ3P4L4NZrLKpCVnzLjT1H4j9fI+m12DXuhMqjBqm7fivwGjU3zpNzPRLr6kHmsqyz+9Gl3CRl+wrA1GtbkowYMlKwa9z5oer7cC8qj2r4v7TA/NjKty5OrQeQfeUkSjtnDNnp3Fo5k9Sdv5K681fANATQwtUHCxcfHHd8Q1JyR2xqNSF52zJ0SbFY+tTBpnYoyGRknd2PNuF6oee2qdsCryGT7ytupaM7Tq1KNqeH0tH9drOPDku/QGxqhpB24C8cmz9R6PZWfvWo+c7PhT5n36gT9o06oU2OI+fyMewbdzbVBHQ3NSXlxl1CJpNj6V3rvl6XIAiVRyQFdzFqc/M9Vji44diiD47Ne9/3Me0atMW2fhsyjm3GqM3BNrBlodsprO1waj0AAJ8RnxR7XNfuo5Eko7njY1H9Fqz86uH/ylf5ymwDW3Djh0kYNdm49X6ZrLP7yTixlZs/f4BL5+ewOXecqzu+RLo9Ttyp3RBT/4TqDR+rmQJt7prxruaUleTeuGiaSfHKCTJP7zQ/JwfUR8JRHzH1NbHyr4/m5mXznTSAa4/nMWSmkLb/L+RWtviM/JTsi0exD+laIa9FJpPjMWA82qQYHJr2QGHjgHOHoQ90TJWrT76hgHmsfGo/0HEFQag8Iim4S15S4P3MB1hVbwgyeZncDctkMhxCwx74OAWPK8c+qEPp95Mr8HtxvvmxdY0g9OpEcq6cJGXHz1gCEqZqbY8Bb5ZpZ8WHmZVvHax860DHYUhGA7rkGySs/ZJMLKk7Yho3f/0YXUocnoMnFWjjl8kVSEYDCjsXLJw8Ubn5oXLzq9D4beu1xJbCk1JBEAQQSUE+0u2k4HGbO18mV+D99HQkvQ5DTgbnNv9Bgz4jTNehiowiqGpkcgUq92r4vjCfYxERyC1t8Bn1GUjGIt87MrnigWqdBEEQyptICu5i1JmSgsd1chOZ0gKlvQuaGs2RW9pUdjgPBZlMBrdrk0x/Pz7JpCAIjx5xG3iXu2sKBEEQBOFxI5KCuxi1OQDIVVVvznhBEARBKG+i+QDTEKqkf79GmxANPL7NB4IgCMLjTdQUYBoOqPKseXtiIQUyRcUviCMIgiAIla1K1hQsX76cf//9l9zcXMLCwnj11VfL9XwWzl549HsD976vY9TklOu5BEEQBKGqqnJJwfnz51m1ahVr1qxBJpMxZMgQunTpQr165T9WXiaToyhkDnlBEARBeBxUueaDHTt20LVrV1QqFRYWFnTt2pVt27ZVdliCIAiC8MircjUFCQkJBATcWX3Pzc2Ns2fP5ttGrVajVqvzlRkMhgqJTxAEQRAeVVUuKQC4e+FGSZKQy/NXaCxbtoxFixblK3Nzc2PhwoVERkYWesyIiIiyD7SMVMXYqmJMIOIqLRFXyVXFmPJU5diER0uVSwq8vLxISEgwP05ISMDLyyvfNiNHjmTgwIH5ygwGA0lJSQ+0dHJlqIqxVcWYQMRVWiKukquKMeWpqKWTBQGqYJ+CvD4EGo0GrVbL1q1b6d69e75tHBwc8PPzy/fvv4mDIAiCIAilU+VqCgICAhg4cCCDBw9GLpczePDgfH0MBEEQBEEoH1UuKQB44YUXeOGFF0q1T14/BK1WW+jzGo3mgeMqL1UxtqoYE4i4SkvEVXJVMaY85Rlb3nfm3X25hMeXTHpE3gkZGRlcuHChssMQBEF4KNWtWxd7e/vKDkOoZI9MUmA0GsnKysLCwsK0hO1tt27d4plnnuHXX3+tcv0OqmJsVTEmEHGVloir5KpiTHkqIjZJktDpdNja2hYY6SU8fqpk88H9kMvlhWa5CoWCpKQkFApFgVEJla0qxlYVYwIRV2mJuEquKsaUp6Jis7ISi8AJJiItFARBEAQBEEmBIAiCIAi3iaRAEARBEATgMUgKHBwceP3113FwcKjsUAqoirFVxZhAxFVaIq6Sq4ox5anKsQmPpkdm9IEgCIIgCA/mka8pEARBEAShZERSIAiCIAgCUMXnKViwYAH79u3DaDTSrFkzpkyZwvLly/n333/Jzc0lLCyMV199FYBff/2VP//8E6VSib+/PzNnzkSlUrFhwwaWLl2KTqcjODiY6dOn55vcKM+1a9d48803qVOnDvPmzTOXF3a+sogrMzOTadOmERERwe7duwt9/ZIk8cUXX3D48GGys7MZPnw4Q4YMASAxMZGJEyei1Wr57bffqkRMeT799FPOnj3LihUrKj2unTt38sMPP5i3S0xMpEWLFri6ulZIXKV9b0HZvO+LKr+bVqvl448/5vLly2RnZzN+/Hg6depUaMxVIaY8r7/+OnZ2dsyaNavS41q1ahV///23ebsbN27w1FNPkZubWyFxAZw6dYq33nqLvn37Mn78eKBkn1NBKJRURe3cuVN67rnnJIPBIBkMBmnQoEHS8uXLpT59+kgajUbSarVS//79pbNnz0oXL16UevToIWk0GkmSJGncuHHS6tWrpZSUFKlTp05SWlqaJEmSNHbsWGnLli0FzpWVlSU99dRT0vz586W3337bXH7u3LkC5/v5558fOK68v3/++Wepffv2RV6Dbdu2SWPGjJGMRqOkVqulzp07S8nJyZIkSdJzzz0nLVmyRBo2bFiZXKuyiEmSJOnw4cPSM888Iz333HNVKq48I0aMkH755ZcKi6s0762zZ8+WyTW7V8x3W758uTRt2jRJkiQpLi5O6tSpk6TVagvEXBViyrN27Vpp2LBh0uTJk6tUXJIkSRqNRho4cKC0bt26Covr+vXr0ujRo6Vp06ZJn3/+ubm8pJ8HQfivKtt80LZtW7755hvkcjlyuRxHR0eysrLo2rUrKpUKCwsL8zLLtWrVYvXq1eYs2snJiYyMDPbu3UuzZs1wdHQEICwsjO3btxc4l0ql4qeffqJGjRr5ynfs2FHgfCkpKQ8cF5jupjt27HjPa7Bjxw569uyJTCbD3t6eFi1amO9Iv/nmG4KDg8vsWpVFTNnZ2cybN4/JkydXqbjy/Pvvv9SsWZOhQ4dWWFyleW9t27atTK7ZvWL+bwy9evUCwNvbGz8/P06ePFkg5qoQE0B8fDy//fYbr7zySpWKK8/SpUvp1asXvXv3rrC43N3d+d///oe7u3uBeIv7PAhCYapsUqBUKrGzswPg5MmTxMTEEBMTk+/N7+bmRnx8fL4pjmNiYti7dy+9evUiISEBNze3AtsXdi5ra+sC5QkJCQXOl5iY+MBxAeZj3Eth509ISCiwf1lcq7KIac6cOYwZMwYXF5cqFReYqlOXLFnCK6+8UqFxlea9FR8fXyax3Svm/8ZQ2OfjvzFXhZgAPvjgA959913zdL9VJS6AnJwc/vzzT5577rkKjcva2hqFQlFovPf6PAhCUapsUpDn6NGjvPPOOyxYsABLS8t8y3tKkpRvAY/Lly/z4osv8vHHH+Pp6VngWP/dviSKOl9ZxlWa8xfWHyJPZca0b98+MjMzCQsLq1Jx5dm9ezc+Pj75jllRcZU03rvPVxaxlSTmin5/3W9Mq1atIjAwkMaNG1epuPL89ddftGvXLl8yVVFxFaU0/7eCkKdKdzQ8fPgw06dP57vvvqNGjRp4eXnly3YTEhLMK4ddunSJV199lblz55qr1b28vIiKiiqwfWRkJLNnzwZgxIgRdO/evdDzF3W+B42rKP+Ny9PTs8Bx69WrVy7X6kFj2rBhA5cuXWLo0KFotVqio6OZNGkSgwcPrhLXasuWLfnutCrqepX2vVVWsRVWfnenywkTJpivWd26dQsc978qO6bFixeTmprKgQMHyMzMJCUlhblz59KxY8cqca22bNnCa6+9VuHXq0mTJoX+f5Xmu0MQ8inPDgsPIjU1VerWrZsUExNjLrt06ZLUu3dvKTc3V9JoNNITTzwhXbp0yfz3yZMn8x0jPT1d6tixo5SSkiIZjUZp1KhR0u7du4s855o1a/J1BivsfMePH3/guPLExMTcs5Parl27pJEjR0pGo1FKTU2VOnfubO40KUmSdPDgQWnYsGFlcq3KKqa8Yzz33HNVKq6wsDDp3LlzkiSVzXurpHHlKcl769KlS2USW3Ex5/n111+lKVOmSJIkSdHR0VKXLl0kvV5fIOaqFJMkmd73kydPrjJxGY1GqXHjxlJGRoYkSWXz/ippXHkWLlyYr6NhST6nglCYKltTsHr1arKzs5k6daq5rF+/fgwcOJDBgwcjl8sZPHgwAQEBbNiwgVu3bjF37lzztm3atOGVV15h/PjxjBgxAoVCQevWrWnfvn2Bc506dYq5c+eSmJiIWq1m+PDhDBo0iAEDBhQ439GjRx84rueff57nn38ejUZDSkoKw4cPp2HDhkyZMiVfXO3bt2ffvn0MGDAASZKYMGECjo6OxMXFMXnyZNRqNbGxsQwePJjk5ORKjam8/g/LKq74+HjznVlFxlWa91ZAQADff//9A8dWo0aNIj8Pdxs0aBARERE8+eSTSJLEJ598gkKhKBDz4MGDSU1NrdSYClMW/49lEVdqamq+fgQVGde2bdtYunQpN27cQJIkjh07xssvv1yqz6kg3E1McywIgiAIAvy/vfsHpX+P4zj+9OUkpc6gY8MkKZ2MBqUYMFhOKRaxKROFwWQ5pYxCGQ3nh/xN6fhbBgsLShkMJwYRio5Ijt9wzx1uv1u/273CzfOxv+v93l69P5/v58v/4KKhJEn6GIYCSZIEGAokSVKWoUCSJAGGAkmSlPVlP0mUvrqKigpKS0sJgoB0Ok1lZSU9PT1UV1f/tvbw8JD8/HwflJH0pbgpkP6D6elpkskku7u7xGIxuru72d/f/23d/Pw8p6enH9ChJP1zbgqkdxAEAc3NzVxfXzM6OsrMzAzPz88MDQ1xfHzMy8sLjY2NDA4OkkgkWF5eZnt7m9vbWzo7OxkfH2d1dZW3tzdqa2sZHBwkFAp99liSvhk3BdI7qq+v5+joiKenJxKJBHd3d6ytrbG4uMjCwgIHBwe0t7cTjUbp7++nq6uL9fV1kskkc3NzrK6ucn5+zuzs7GePIukbMhRI76i4uJhMJsPj4yOdnZ1MTEwQBAHhcJjy8nIuLi5+qdna2qKlpYXCwkLy8vJobW1lY2PjE7qX9N15fCC9o6urK0KhEOFwmLOzM0ZGRkilUuTk5HB5eUksFvul5ubmhr29Pebn5wF4fX2lqKjoo1uXJEOB9J6SySQ1NTXk5uYyPDxMVVUVk5OTBEFAe3v739ZEIhHq6uro6Oj44G4l6a88PpDeyebmJlNTU/T29gJwf39PVVUVQRCws7NDKpXi8fERgLy8PB4eHgBoaGhgeXmZdDoNwI8fP1haWvqUGSR9b/4lUfqX/nynIDc3l5eXF0pKSujr6yMajQJ/bA3i8TgFBQU0NTURiUQYHx9nbGyMk5MTRkdHaWtrY2BggImJCVZWVshkMpSVlRGPx4lEIp88oaTvxlAgSZIAjw8kSVKWoUCSJAGGAkmSlGUokCRJgKFAkiRlGQokSRJgKJAkSVmGAkmSBBgKJElS1k891zGt+09gAwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "mip_melt = mip_global_df.melt(\"timestamp\", var_name=\"type\", value_name=\"count\")\n", "line = sns.lineplot(data=mip_melt, x=\"timestamp\", y=\"count\", hue=\"type\")\n", @@ -379,19 +225,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "f433f0c5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['entries', 'timestamp', 'last_updated', 'displayed', 'not_displayed', 'total'])\n", - "MIPEntry(module_name='Dell BSAFE Crypto Module for C', vendor_name='Dell Australia Pty Limited, BSAFE Product Team', standard='FIPS 140-3', status=, status_since=datetime.date(2022, 10, 7))\n" - ] - } - ], + "outputs": [], "source": [ "m0 = mip_dset.snapshots[0]\n", "print(m0.__dict__.keys())\n", @@ -400,141 +237,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "205b9a97", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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namevendorstandardstatusstatus_sincefirst_seenlast_seenpresentseen_for_statusseen_for
0Red Hat Enterprise Linux 7 NSS Cryptographic M...Red Hat(R), Inc.FIPS 140-2MIPStatus.IN_REVIEWNaT2020-10-222020-12-22FalseNaN61
1FortiAP-U221EV, FortiAP-U223EV, FortiAP-U321EV...Fortinet, Inc.FIPS 140-2MIPStatus.IN_REVIEWNaT2020-10-222021-04-28FalseNaN188
2SBx908 GEN2, AT-x950-28XTQm, AT-x550-18XTQ, AT...Allied TelesisFIPS 140-2MIPStatus.IN_REVIEWNaT2020-10-222020-10-27FalseNaN5
3Juniper Networks MX240, MX480, MX960 3D Univer...Juniper Networks, Inc.FIPS 140-2MIPStatus.IN_REVIEWNaT2020-10-222021-02-10FalseNaN111
4Cisco Network Convergence System 1001 Cryptogr...Cisco Systems, Inc.FIPS 140-2MIPStatus.COORDINATIONNaT2020-10-222021-02-10FalseNaN111
\n", - "
" - ], - "text/plain": [ - " name vendor \\\n", - "0 Red Hat Enterprise Linux 7 NSS Cryptographic M... Red Hat(R), Inc. \n", - "1 FortiAP-U221EV, FortiAP-U223EV, FortiAP-U321EV... Fortinet, Inc. \n", - "2 SBx908 GEN2, AT-x950-28XTQm, AT-x550-18XTQ, AT... Allied Telesis \n", - "3 Juniper Networks MX240, MX480, MX960 3D Univer... Juniper Networks, Inc. \n", - "4 Cisco Network Convergence System 1001 Cryptogr... Cisco Systems, Inc. \n", - "\n", - " standard status status_since first_seen last_seen \\\n", - "0 FIPS 140-2 MIPStatus.IN_REVIEW NaT 2020-10-22 2020-12-22 \n", - "1 FIPS 140-2 MIPStatus.IN_REVIEW NaT 2020-10-22 2021-04-28 \n", - "2 FIPS 140-2 MIPStatus.IN_REVIEW NaT 2020-10-22 2020-10-27 \n", - "3 FIPS 140-2 MIPStatus.IN_REVIEW NaT 2020-10-22 2021-02-10 \n", - "4 FIPS 140-2 MIPStatus.COORDINATION NaT 2020-10-22 2021-02-10 \n", - "\n", - " present seen_for_status seen_for \n", - "0 False NaN 61 \n", - "1 False NaN 188 \n", - "2 False NaN 5 \n", - "3 False NaN 111 \n", - "4 False NaN 111 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def mip_key(entry):\n", " return entry.module_name, entry.vendor_name, entry.standard, entry.status\n", @@ -560,141 +266,50 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "29679c73", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=mip_local_df.loc[mip_local_df.status == MIPStatus.REVIEW_PENDING], x=\"first_seen\", y=\"seen_for\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "771662ff", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=mip_local_df.loc[mip_local_df.status == MIPStatus.IN_REVIEW], x=\"first_seen\", y=\"seen_for\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "2b2ba494", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=mip_local_df.loc[mip_local_df.status == MIPStatus.COORDINATION], x=\"first_seen\", y=\"seen_for\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "883667b4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scatter = sns.relplot(kind=\"scatter\", data=mip_local_df.loc[mip_local_df.status == MIPStatus.FINALIZATION], x=\"first_seen\", y=\"seen_for\", hue=\"standard\")" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "9856e568", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MIPStatus.IN_REVIEW\n", - "All:\n", - "Average seen for 77.69056603773585\n", - "Average seen for (FIPS 140-2) 77.6923076923077\n", - "Average seen for (FIPS 140-3) 77.6734693877551\n", - "Only not present:\n", - "Average seen for 80.33265306122449\n", - "Average seen for (FIPS 140-2) 79.12608695652175\n", - "Average seen for (FIPS 140-3) 98.83333333333333\n", - "\n", - "MIPStatus.REVIEW_PENDING\n", - "All:\n", - "Average seen for 118.98618090452261\n", - "Average seen for (FIPS 140-2) 121.27191679049034\n", - "Average seen for (FIPS 140-3) 106.47967479674797\n", - "Only not present:\n", - "Average seen for 122.6840579710145\n", - "Average seen for (FIPS 140-2) 123.05008077544427\n", - "Average seen for (FIPS 140-3) 119.49295774647888\n", - "\n", - "MIPStatus.COORDINATION\n", - "All:\n", - "Average seen for 82.21221864951768\n", - "Average seen for (FIPS 140-2) 83.32062780269058\n", - "Average seen for (FIPS 140-3) 58.09756097560975\n", - "Only not present:\n", - "Average seen for 81.85060240963855\n", - "Average seen for (FIPS 140-2) 82.8583850931677\n", - "Average seen for (FIPS 140-3) 49.4\n", - "\n", - "MIPStatus.FINALIZATION\n", - "All:\n", - "Average seen for 5.648780487804878\n", - "Average seen for (FIPS 140-2) 5.648780487804878\n", - "Average seen for (FIPS 140-3) nan\n", - "Only not present:\n", - "Average seen for 5.666666666666667\n", - "Average seen for (FIPS 140-2) 5.666666666666667\n", - "Average seen for (FIPS 140-3) nan\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "for status in MIPStatus:\n", " print(status)\n", @@ -712,21 +327,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ccf7e2fc", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with sns.plotting_context(\"notebook\", font_scale=0.75):\n", " g = sns.FacetGrid(mip_local_df.loc[~mip_local_df.present], col=\"status\", hue=\"standard\", col_wrap=2, height=2, ylim=(0,300))\n", @@ -734,19 +338,11 @@ " g.set_titles(\"{col_name}\")\n", " plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2593c5cf", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -760,7 +356,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, diff --git a/notebooks/fips/temporal_trends.ipynb b/notebooks/fips/temporal_trends.ipynb index e74adbd9..10b28460 100644 --- a/notebooks/fips/temporal_trends.ipynb +++ b/notebooks/fips/temporal_trends.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "skip" @@ -34,21 +34,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading FIPS dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 239M/239M [00:48<00:00, 5.22MB/s]\n" - ] - } - ], + "outputs": [], "source": [ "dset = FIPSDataset.from_web_latest()\n", "df = dset.to_pandas()" @@ -56,312 +48,26 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
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cert_idnamestatusstandardtypelevelembodimentdate_validationdate_sunsetalgorithms...related_cvesweb_directly_referenced_byweb_indirectly_referenced_byweb_directly_referencingweb_indirectly_referencingst_directly_referenced_byst_indirectly_referenced_byst_directly_referencingst_indirectly_referencingyear_from
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fc95ce176603e9e1339Anonymous Key Technology (AKT) C++ Module (CPP...HistoricalFIPS140-2Software1.0Multi Chip Standalone2003-07-31NaT{AES algorithm #3194 created by None, None alg......NaNNaNNaNNaNNaNNaNNaNNaNNaN2003
158f3b1a355d5f642798Red Hat Enterprise Linux Kernel Crypto API Cry...HistoricalFIPS140-2Software Hybrid1.0Multi Chip Standalone2016-11-23NaT{None algorithm #3591 created by None, None al......{CVE-2007-6285, CVE-2007-2030, CVE-2011-3585, ...NaNNaN{2721, 2711}{2441, 2721, 2711}NaNNaN{2721, 2711}{2441, 2721, 2711}2016
47e03bf0b0b8b7382943TCT Crypto Engine CoreHistoricalFIPS140-2Hardware2.0Single Chip2017-06-23NaT{None algorithm 256 created by None, None algo......NaNNaNNaNNaNNaNNaNNaN{2614}{2614}2017
f6920d2ecbf720691385Red Hat Enterprise Linux 5 OpenSSH Client Cryp...HistoricalFIPS140-2Software1.0Multi Chip Standalone2010-08-12NaT{None algorithm 31 created by None, None algor......{CVE-2011-1593, CVE-2000-0701, CVE-2014-3566, ...NaNNaN{1320}{1320}NaNNaN{1320}{1320}2010
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5 rows × 23 columns

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" - ], - "text/plain": [ - " cert_id name \\\n", - "dgst \n", - "fc95ce176603e9e1 339 Anonymous Key Technology (AKT) C++ Module (CPP... \n", - "158f3b1a355d5f64 2798 Red Hat Enterprise Linux Kernel Crypto API Cry... \n", - "47e03bf0b0b8b738 2943 TCT Crypto Engine Core \n", - "f6920d2ecbf72069 1385 Red Hat Enterprise Linux 5 OpenSSH Client Cryp... \n", - "2cfc8ccbd7c0b176 294 nCipher 800 PCI and nCipher 1600 PCI \n", - "\n", - " status standard type level \\\n", - "dgst \n", - "fc95ce176603e9e1 Historical FIPS140-2 Software 1.0 \n", - "158f3b1a355d5f64 Historical FIPS140-2 Software Hybrid 1.0 \n", - "47e03bf0b0b8b738 Historical FIPS140-2 Hardware 2.0 \n", - "f6920d2ecbf72069 Historical FIPS140-2 Software 1.0 \n", - "2cfc8ccbd7c0b176 Historical FIPS140-2 Hardware 3.0 \n", - "\n", - " embodiment date_validation date_sunset \\\n", - "dgst \n", - "fc95ce176603e9e1 Multi Chip Standalone 2003-07-31 NaT \n", - "158f3b1a355d5f64 Multi Chip Standalone 2016-11-23 NaT \n", - "47e03bf0b0b8b738 Single Chip 2017-06-23 NaT \n", - "f6920d2ecbf72069 Multi Chip Standalone 2010-08-12 NaT \n", - "2cfc8ccbd7c0b176 Multi Chip Embedded 2003-03-07 NaT \n", - "\n", - " algorithms ... \\\n", - "dgst ... \n", - "fc95ce176603e9e1 {AES algorithm #3194 created by None, None alg... ... \n", - "158f3b1a355d5f64 {None algorithm #3591 created by None, None al... ... \n", - "47e03bf0b0b8b738 {None algorithm 256 created by None, None algo... ... \n", - "f6920d2ecbf72069 {None algorithm 31 created by None, None algor... ... \n", - "2cfc8ccbd7c0b176 {Triple-DES algorithm #109 created by None, AE... ... \n", - "\n", - " related_cves \\\n", - "dgst \n", - "fc95ce176603e9e1 NaN \n", - "158f3b1a355d5f64 {CVE-2007-6285, CVE-2007-2030, CVE-2011-3585, ... \n", - "47e03bf0b0b8b738 NaN \n", - "f6920d2ecbf72069 {CVE-2011-1593, CVE-2000-0701, CVE-2014-3566, ... \n", - "2cfc8ccbd7c0b176 NaN \n", - "\n", - " web_directly_referenced_by web_indirectly_referenced_by \\\n", - "dgst \n", - "fc95ce176603e9e1 NaN NaN \n", - "158f3b1a355d5f64 NaN NaN \n", - "47e03bf0b0b8b738 NaN NaN \n", - "f6920d2ecbf72069 NaN NaN \n", - "2cfc8ccbd7c0b176 NaN NaN \n", - "\n", - " web_directly_referencing web_indirectly_referencing \\\n", - "dgst \n", - "fc95ce176603e9e1 NaN NaN \n", - "158f3b1a355d5f64 {2721, 2711} {2441, 2721, 2711} \n", - "47e03bf0b0b8b738 NaN NaN \n", - "f6920d2ecbf72069 {1320} {1320} \n", - "2cfc8ccbd7c0b176 NaN NaN \n", - "\n", - " st_directly_referenced_by st_indirectly_referenced_by \\\n", - "dgst \n", - "fc95ce176603e9e1 NaN NaN \n", - "158f3b1a355d5f64 NaN NaN \n", - "47e03bf0b0b8b738 NaN NaN \n", - "f6920d2ecbf72069 NaN NaN \n", - "2cfc8ccbd7c0b176 NaN NaN \n", - "\n", - " st_directly_referencing st_indirectly_referencing year_from \n", - "dgst \n", - "fc95ce176603e9e1 NaN NaN 2003 \n", - "158f3b1a355d5f64 {2721, 2711} {2441, 2721, 2711} 2016 \n", - "47e03bf0b0b8b738 {2614} {2614} 2017 \n", - "f6920d2ecbf72069 {1320} {1320} 2010 \n", - "2cfc8ccbd7c0b176 NaN NaN 2003 \n", - "\n", - "[5 rows x 23 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "categories = df.loc[df.year_from < 2022].groupby([\"year_from\", \"type\"], as_index=False).size().copy()\n", "\n", @@ -377,24 +83,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "embodiments = df.loc[(df.embodiment.notnull()) & (df.year_from < 2022)].groupby([\"year_from\", \"embodiment\"], as_index=False, observed=True).size()\n", "\n", @@ -405,24 +100,13 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "standards = df.loc[(df.standard.notnull()) & (df.year_from < 2022)].groupby([\"year_from\", \"standard\"], as_index=False, observed=True).size()\n", "\n", @@ -433,25 +117,14 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sec_levels = df.loc[(df.level.notnull()) & (df.year_from < 2022)].groupby([\"year_from\", \"level\"], as_index=False).size()\n", "sec_levels.level = sec_levels.level.astype(int)\n", @@ -463,24 +136,13 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "avg_levels = df.loc[(df.year_from < 2022) & (df.level.notnull())].copy().groupby([\"year_from\"]).agg({'year_from':'size', 'level':'mean'}).rename(columns={\"year_from\": \"n_certs\"}).reset_index()\n", "avg_levels.year_from = avg_levels.year_from.astype(\"float\")\n", @@ -495,24 +157,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "avg_levels = df.loc[(df.year_from < 2022) & (df.level.notnull())].copy().groupby([\"year_from\", \"type\"]).agg({'year_from':'size', 'level':'mean'}).rename(columns={\"year_from\": \"n_certs\"}).reset_index()\n", "avg_levels.year_from = avg_levels.year_from.astype(\"float\")\n", @@ -530,45 +181,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df[\"validity_period\"] = (df.date_sunset - df.date_validation).dt.days / 365\n", "\n", @@ -595,27 +215,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# TODO: Resolve duplicity in crypto_scheme mac\n", - "st_keywords_df = dset.get_st_keywords_df().drop(columns=[\"crypto_scheme.MAC\"])\n", + "st_keywords_df = dset.get_keywords_df(\"keywords\").drop(columns=[\"crypto_scheme.MAC\"])\n", "st_keywords_df = st_keywords_df.rename(columns={x: x.split(\".\")[-1] for x in st_keywords_df.columns}).fillna(0).applymap(lambda x: x > 0)\n", "\n", "df_keywords = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\"]].copy()\n", @@ -652,65 +261,48 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "def algo_present(algorithms, algo):\n", - " for a in algorithms:\n", - " if a.algorithm_type == algo:\n", - " return True\n", - " return False\n", + "# TODO: This needs refactoring. Currently we don't hold FIPSAlgorithm objects, just strings\n", + "# def algo_present(algorithms, algo):\n", + "# for a in algorithms:\n", + "# if a.algorithm_type == algo:\n", + "# return True\n", + "# return False\n", "\n", - "algo_types = Counter()\n", - "for algos in df.algorithms:\n", - " for algo in algos:\n", - " if algo.algorithm_type:\n", - " algo_types[algo.algorithm_type] +=1\n", - "#print(algo_types)\n", - "df_algos = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\", \"algorithms\"]].copy()\n", - "for algo, count in algo_types.most_common(14):\n", - " df_algos[algo] = df_algos.algorithms.apply(algo_present, args=(algo,))\n", + "# algo_types = Counter()\n", + "# for algos in df.algorithms:\n", + "# for algo in algos:\n", + "# if algo.algorithm_type:\n", + "# algo_types[algo.algorithm_type] +=1\n", + "# #print(algo_types)\n", + "# df_algos = df.loc[:, [\"type\", \"level\", \"date_validation\", \"date_sunset\", \"year_from\", \"algorithms\"]].copy()\n", + "# for algo, count in algo_types.most_common(14):\n", + "# df_algos[algo] = df_algos.algorithms.apply(algo_present, args=(algo,))\n", " \n", - "crypto = df_algos.groupby(\"year_from\").sum()\n", - "crypto[\"n_certs\"] = df_algos.groupby(\"year_from\").size()\n", - "crypto.iloc[:,:-1] = crypto.iloc[:,:-1].div(crypto.n_certs, axis=0) * 100\n", - "crypto = crypto.drop(columns=[\"level\",\"n_certs\"]).reset_index().melt(id_vars=\"year_from\", var_name=\"keyword\", value_name=\"percentage\") # Bring to tidy form\n", + "# crypto = df_algos.groupby(\"year_from\").sum()\n", + "# crypto[\"n_certs\"] = df_algos.groupby(\"year_from\").size()\n", + "# crypto.iloc[:,:-1] = crypto.iloc[:,:-1].div(crypto.n_certs, axis=0) * 100\n", + "# crypto = crypto.drop(columns=[\"level\",\"n_certs\"]).reset_index().melt(id_vars=\"year_from\", var_name=\"keyword\", value_name=\"percentage\") # Bring to tidy form\n", "\n", - "line = sns.lineplot(data=crypto, x=\"year_from\", y=\"percentage\", hue=\"keyword\")\n", - "line.set(title=f\"Density of algorithm types over time\", xlabel=\"Year of certification\", ylabel=\"% of certs. containing algorithm type\")\n", - "line.yaxis.set_major_formatter(mtick.PercentFormatter())\n", - "line.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);" + "# line = sns.lineplot(data=crypto, x=\"year_from\", y=\"percentage\", hue=\"keyword\")\n", + "# line.set(title=f\"Density of algorithm types over time\", xlabel=\"Year of certification\", ylabel=\"% of certs. containing algorithm type\")\n", + "# line.yaxis.set_major_formatter(mtick.PercentFormatter())\n", + "# line.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -724,7 +316,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, From 2f21854eaaf71d95f7da34872dee8376ba290efd Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 15:42:06 +0100 Subject: [PATCH 51/81] move label studio interface layout file --- {examples => data}/label_studio_interface.txt | 0 data/readme.md | 3 +++ 2 files changed, 3 insertions(+) rename {examples => data}/label_studio_interface.txt (100%) diff --git a/examples/label_studio_interface.txt b/data/label_studio_interface.txt similarity index 100% rename from examples/label_studio_interface.txt rename to data/label_studio_interface.txt diff --git a/data/readme.md b/data/readme.md index 329ae649..9fcc4779 100644 --- a/data/readme.md +++ b/data/readme.md @@ -1,5 +1,8 @@ # Data +The file [label_studio_interface.txt](label_studio_interface.txt) contains XML-like specification of the labeling interface +for CPE matching. As such, it was used in the Label studio tool. + ## Certificate ID evaluation The directory `./cert_id_eval` contains data on a manual evaluation of certificate ID assignment. From 91b097353c2518432c03e63711b005a91de009e6 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 15:42:21 +0100 Subject: [PATCH 52/81] update readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 39db402d..1631422a 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,14 @@ ![](docs/_static/logo.svg) -A tool for data scraping and analysis of security certificates from Common Criteria and FIPS 140-2/3 frameworks. This project is developed by the [Centre for Research On Cryptography and Security](https://crocs.fi.muni.cz) at Faculty of Informatics, Masaryk University. +A tool for data scraping and analysis of security certificates from Common Criteria and FIPS 140-2/3 frameworks. This project is developed by the [Centre for Research On Cryptography and Security](https://crocs.fi.muni.cz) at Masaryk University, Czech Republic. [![Website](https://img.shields.io/website?down_color=red&down_message=offline&style=flat-square&up_color=SpringGreen&up_message=online&url=https%3A%2F%2Fseccerts.org)](https://seccerts.org) +[![Website](https://img.shields.io/website?down_color=red&down_message=offline&style=flat-square&up_color=SpringGreen&up_message=online&url=https%3A%2F%2Fseccerts.org/docs/index.html)](https://seccerts.org/docs/index.html) [![PyPI](https://img.shields.io/pypi/v/sec-certs?style=flat-square)](https://pypi.org/project/sec-certs/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sec-certs?label=Python%20versions&style=flat-square)](https://pypi.org/project/sec-certs/) [![GitHub Workflow Status](https://img.shields.io/github/workflow/status/crocs-muni/sec-certs/tests?style=flat-square)](https://github.com/crocs-muni/sec-certs/actions/workflows/tests.yml) [![GitHub Workflow Status](https://img.shields.io/github/workflow/status/crocs-muni/sec-certs/Docker%20Image%20CI?label=Docker%20build&style=flat-square)](https://hub.docker.com/repository/docker/seccerts/sec-certs) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/crocs-muni/sec-certs/dev?filepath=notebooks%2Fcpe_cve.ipynb) ## Installation From 4ddae8a7d572c4dcbc4d71049e4396a0ed4508e8 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:03:08 +0100 Subject: [PATCH 53/81] introduce pyupgrade --- .pre-commit-config.yaml | 23 ++-- CONTRIBUTING.md | 2 + requirements/dev_requirements.in | 3 +- requirements/dev_requirements.txt | 4 + sec_certs/config/configuration.py | 2 +- sec_certs/dataset/common_criteria.py | 64 +++++------ sec_certs/dataset/fips.py | 2 +- sec_certs/model/cpe_matching.py | 12 +- sec_certs/model/sar_transformer.py | 24 ++-- sec_certs/sample/common_criteria.py | 162 +++++++++++++-------------- sec_certs/sample/cve.py | 34 +++--- sec_certs/sample/fips.py | 134 +++++++++++----------- sec_certs/utils/extract.py | 24 ++-- sec_certs/utils/helpers.py | 4 +- sec_certs/utils/pandas.py | 50 ++++----- setup.py | 6 +- 16 files changed, 279 insertions(+), 271 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 60bccb72..278c9dc8 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,24 +1,29 @@ repos: + - repo: https://github.com/asottile/pyupgrade + rev: v3.2.3 + hooks: + - id: pyupgrade + args: ["--py38-plus"] - repo: https://github.com/psf/black rev: 22.6.0 hooks: - id: black - args: ['--check', '--target-version', 'py38'] + args: ["--check", "--target-version", "py38"] - repo: https://github.com/pycqa/isort rev: 5.10.1 hooks: - id: isort - args: ['--check-only'] + args: ["--check-only"] - repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v0.982' + rev: "v0.982" hooks: - id: mypy additional_dependencies: - - 'numpy' - - 'types-PyYAML' - - 'types-python-dateutil' - - 'types-requests' + - "numpy" + - "types-PyYAML" + - "types-python-dateutil" + - "types-requests" - repo: https://github.com/pycqa/flake8 - rev: '4.0.1' + rev: "4.0.1" hooks: - - id: flake8 + - id: flake8 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 11cd09f0..d95b2ef9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -49,6 +49,7 @@ All commits shall pass the lint pipeline of the following tools: - Black (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/main/pyproject.toml) for settings) - isort (see [pyproject.toml](https://github.com/crocs-muni/sec-certs/blob/main/pyproject.toml) for settings) - Flake8 (see [.flake8](https://github.com/crocs-muni/sec-certs/blob/main/.flake8) for settings) +- PyUpgrade These tools can be installed via [dev_requirements.txt](https://github.com/crocs-muni/sec-certs/blob/main/dev_requirements.txt) You can use [pre-commit](https://pre-commit.com/) tool register git hook that will evalute these checks prior to any commit and abort the commit for you. Note that the pre-commit is not meant to automatically fix the issues, just warn you. @@ -65,6 +66,7 @@ To ivoke the tools manually, you can, in the repository root, use: - Black: `black --check .` (without the flag to reformat) - isort: `isort --check-only .` (without the flag to actually fix the issue) - Flake8: `flake8 .` +- PyUpgrade: `pyupgrade --py38-plus 'find ./sec_certs/ -name "*.py" -type f'` ## Documentation diff --git a/requirements/dev_requirements.in b/requirements/dev_requirements.in index 7f6d9712..d7906029 100644 --- a/requirements/dev_requirements.in +++ b/requirements/dev_requirements.in @@ -15,4 +15,5 @@ sphinx myst-nb>=0.14 sphinx-book-theme sphinx-design -sphinx-copybutton \ No newline at end of file +sphinx-copybutton +pyupgrade diff --git a/requirements/dev_requirements.txt b/requirements/dev_requirements.txt index 3ea4c3af..4ad34ddb 100644 --- a/requirements/dev_requirements.txt +++ b/requirements/dev_requirements.txt @@ -210,6 +210,8 @@ python-dateutil==2.8.2 # via jupyter-client pytz==2022.1 # via babel +pyupgrade==3.2.3 + # via -r dev_requirements.in pyyaml==6.0 # via # jupyter-cache @@ -269,6 +271,8 @@ stack-data==0.2.0 # via ipython tabulate==0.8.9 # via jupyter-cache +tokenize-rt==5.0.0 + # via pyupgrade toml==0.10.2 # via pre-commit tomli==2.0.1 diff --git a/sec_certs/config/configuration.py b/sec_certs/config/configuration.py index 6a9a5880..cf96786f 100644 --- a/sec_certs/config/configuration.py +++ b/sec_certs/config/configuration.py @@ -6,7 +6,7 @@ import yaml -class Configuration(object): +class Configuration: def load(self, filepath: Union[str, Path]) -> None: with Path(filepath).open("r") as file: state = yaml.load(file, Loader=yaml.FullLoader) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 4269e68e..0a3b0e13 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -8,7 +8,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import ClassVar, Dict, Iterator, List, Optional, Set, Tuple, Union +from typing import ClassVar, Iterator import numpy as np import pandas as pd @@ -38,10 +38,10 @@ @dataclass class CCAuxillaryDatasets(AuxillaryDatasets): - cpe_dset: Optional[CPEDataset] = None - cve_dset: Optional[CVEDataset] = None - pp_dset: Optional[ProtectionProfileDataset] = None - mu_dset: Optional[CCDatasetMaintenanceUpdates] = None + cpe_dset: CPEDataset | None = None + cve_dset: CVEDataset | None = None + pp_dset: ProtectionProfileDataset | None = None + mu_dset: CCDatasetMaintenanceUpdates | None = None class CCDataset(Dataset[CommonCriteriaCert, CCAuxillaryDatasets], ComplexSerializableType): @@ -52,12 +52,12 @@ class CCDataset(Dataset[CommonCriteriaCert, CCAuxillaryDatasets], ComplexSeriali def __init__( self, - certs: Dict[str, CommonCriteriaCert] = dict(), - root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, - name: Optional[str] = None, + certs: dict[str, CommonCriteriaCert] = dict(), + root_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + name: str | None = None, description: str = None, - state: Optional[Dataset.DatasetInternalState] = None, - auxillary_datasets: Optional[CCAuxillaryDatasets] = None, + state: Dataset.DatasetInternalState | None = None, + auxillary_datasets: CCAuxillaryDatasets | None = None, ): self.certs = certs self._root_dir = Path(root_dir) @@ -178,7 +178,7 @@ def mu_dataset_path(self) -> Path: PP_CSV = {"cc_pp_active.csv": BASE_URL + "/pps/pps.csv", "cc_pp_archived.csv": BASE_URL + "/pps/pps-archived.csv"} @property - def active_html_tuples(self) -> List[Tuple[str, Path]]: + def active_html_tuples(self) -> list[tuple[str, Path]]: """ Returns List Tuple[str, Path] where first element is name of html file and second element is its Path. The files correspond to html files parsed from CC website that list all *active* certificates. @@ -186,7 +186,7 @@ def active_html_tuples(self) -> List[Tuple[str, Path]]: return [(x, self.web_dir / y) for y, x in self.HTML_PRODUCTS_URL.items() if "active" in y] @property - def archived_html_tuples(self) -> List[Tuple[str, Path]]: + def archived_html_tuples(self) -> list[tuple[str, Path]]: """ Returns List Tuple[str, Path] where first element is name of html file and second element is its Path. The files correspond to html files parsed from CC website that list all *archived* certificates. @@ -194,7 +194,7 @@ def archived_html_tuples(self) -> List[Tuple[str, Path]]: return [(x, self.web_dir / y) for y, x in self.HTML_PRODUCTS_URL.items() if "archived" in y] @property - def active_csv_tuples(self) -> List[Tuple[str, Path]]: + def active_csv_tuples(self) -> list[tuple[str, Path]]: """ Returns List Tuple[str, Path] where first element is name of csv file and second element is its Path. The files correspond to csv files downloaded from CC website that list all *active* certificates. @@ -202,7 +202,7 @@ def active_csv_tuples(self) -> List[Tuple[str, Path]]: return [(x, self.web_dir / y) for y, x in self.CSV_PRODUCTS_URL.items() if "active" in y] @property - def archived_csv_tuples(self) -> List[Tuple[str, Path]]: + def archived_csv_tuples(self) -> list[tuple[str, Path]]: """ Returns List Tuple[str, Path] where first element is name of csv file and second element is its Path. The files correspond to csv files downloaded from CC website that list all *archived* certificates. @@ -210,7 +210,7 @@ def archived_csv_tuples(self) -> List[Tuple[str, Path]]: return [(x, self.web_dir / y) for y, x in self.CSV_PRODUCTS_URL.items() if "archived" in y] @classmethod - def from_web_latest(cls) -> "CCDataset": + def from_web_latest(cls) -> CCDataset: """ Fetches the fresh snapshot of CCDataset from seccerts.org """ @@ -221,7 +221,7 @@ def _set_local_paths(self): for cert in self: cert.set_local_paths(self.reports_pdf_dir, self.targets_pdf_dir, self.reports_txt_dir, self.targets_txt_dir) - def _merge_certs(self, certs: Dict[str, "CommonCriteriaCert"], cert_source: Optional[str] = None) -> None: + def _merge_certs(self, certs: dict[str, CommonCriteriaCert], cert_source: str | None = None) -> None: """ Merges dictionary of certificates into the dataset. Assuming they all are CommonCriteria certificates """ @@ -287,7 +287,7 @@ def get_certs_from_web( self._set_local_paths() self.state.meta_sources_parsed = True - def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> Dict[str, "CommonCriteriaCert"]: + def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> dict[str, CommonCriteriaCert]: """ Creates dictionary of new certificates from csv sources. """ @@ -303,7 +303,7 @@ def _get_all_certs_from_csv(self, get_active: bool, get_archived: bool) -> Dict[ return new_certs @staticmethod - def _parse_single_csv(file: Path) -> Dict[str, "CommonCriteriaCert"]: + def _parse_single_csv(file: Path) -> dict[str, CommonCriteriaCert]: """ Using pandas, this parses a single CSV file. """ @@ -372,15 +372,13 @@ def _get_primary_key_str(row: Tag): df_main = df_main.drop_duplicates() profiles = { - x.dgst: set( - [ - ProtectionProfile(pp_name=y, pp_eal=None) - for y in sec_certs.utils.sanitization.sanitize_protection_profiles(x.protection_profiles) - ] - ) + x.dgst: { + ProtectionProfile(pp_name=y, pp_eal=None) + for y in sec_certs.utils.sanitization.sanitize_protection_profiles(x.protection_profiles) + } for x in df_base.itertuples() } - updates: Dict[str, Set] = {x.dgst: set() for x in df_base.itertuples()} + updates: dict[str, set] = {x.dgst: set() for x in df_base.itertuples()} for x in df_main.itertuples(): updates[x.dgst].add( CommonCriteriaCert.MaintenanceReport( @@ -412,7 +410,7 @@ def _get_primary_key_str(row: Tag): } return certs - def _get_all_certs_from_html(self, get_active: bool, get_archived: bool) -> Dict[str, "CommonCriteriaCert"]: + def _get_all_certs_from_html(self, get_active: bool, get_archived: bool) -> dict[str, CommonCriteriaCert]: """ Prepares dictionary of certificates from all html files. """ @@ -430,7 +428,7 @@ def _get_all_certs_from_html(self, get_active: bool, get_archived: bool) -> Dict return new_certs @staticmethod - def _parse_single_html(file: Path) -> Dict[str, "CommonCriteriaCert"]: + def _parse_single_html(file: Path) -> dict[str, CommonCriteriaCert]: """ Prepares a dictionary of certificates from a single html file. """ @@ -445,7 +443,7 @@ def _get_timestamp_from_footer(footer): def _parse_table( soup: BeautifulSoup, cert_status: str, table_id: str, category_string: str - ) -> Dict[str, "CommonCriteriaCert"]: + ) -> dict[str, CommonCriteriaCert]: tables = soup.find_all("table", id=table_id) if not len(tables) <= 1: @@ -789,7 +787,7 @@ def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMain self.mu_dataset_dir.mkdir(parents=True, exist_ok=True) if to_download or not self.mu_dataset_path.exists(): - maintained_certs: List[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] + maintained_certs: list[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] updates = list( itertools.chain.from_iterable( [CommonCriteriaMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs] @@ -820,11 +818,11 @@ class CCDatasetMaintenanceUpdates(CCDataset, ComplexSerializableType): # Quite difficult to achieve correct behaviour with MyPy here, opting for ignore def __init__( self, - certs: Dict[str, CommonCriteriaMaintenanceUpdate] = dict(), # type: ignore + certs: dict[str, CommonCriteriaMaintenanceUpdate] = dict(), # type: ignore root_dir: Path = constants.DUMMY_NONEXISTING_PATH, name: str = "dataset name", description: str = "dataset_description", - state: Optional[CCDataset.DatasetInternalState] = None, + state: CCDataset.DatasetInternalState | None = None, ): super().__init__(certs, root_dir, name, description, state) # type: ignore self.state.meta_sources_parsed = True @@ -855,7 +853,7 @@ def get_certs_from_web( raise NotImplementedError @classmethod - def from_json(cls, input_path: Union[str, Path]) -> "CCDatasetMaintenanceUpdates": + def from_json(cls, input_path: str | Path) -> CCDatasetMaintenanceUpdates: input_path = Path(input_path) with input_path.open("r") as handle: dset = json.load(handle, cls=CustomJSONDecoder) @@ -875,7 +873,7 @@ def to_pandas(self) -> pd.DataFrame: return df @classmethod - def from_web_latest(cls) -> "CCDatasetMaintenanceUpdates": + def from_web_latest(cls) -> CCDatasetMaintenanceUpdates: with tempfile.TemporaryDirectory() as tmp_dir: dset_path = Path(tmp_dir) / "cc_maintenances_latest_dataset.json" helpers.download_file(config.cc_maintenances_latest_snapshot, dset_path) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 09866055..2adca7d9 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -204,7 +204,7 @@ def _get_all_certs_from_html_sources(self) -> Set[FIPSCertificate]: ) def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: - with open(html_file, "r", encoding="utf-8") as handle: + with open(html_file, encoding="utf-8") as handle: html = BeautifulSoup(handle.read(), "html5lib") table = [x for x in html.find(id="searchResultsTable").tbody.contents if x != "\n"] diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index 40a7fa66..aff7dd30 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -122,9 +122,9 @@ def predict_single_cert( threshold = self.match_threshold if not relax_version else 100 final_matches_aux: List[Tuple[float, CPE]] = list(filter(lambda x: x[0] >= threshold, zip(ratings, candidates))) final_matches_aux = sorted(final_matches_aux, key=operator.itemgetter(0, 1), reverse=True) - final_matches: Optional[Set[str]] = set( - [x[1].uri for x in final_matches_aux[: self.n_max_matches] if x[1].uri is not None] - ) + final_matches: Optional[Set[str]] = { + x[1].uri for x in final_matches_aux[: self.n_max_matches] if x[1].uri is not None + } if not relax_title and not final_matches: final_matches = self.predict_single_cert( @@ -153,7 +153,7 @@ def filter_condition(regex: Pattern, cpe: CPE, min_value: int, soft: bool = True for update_regex in update_regexes: if matches := re.findall(update_regex, cert_title): min_value = min([int(re.findall(r"\d+", x)[0]) for x in matches]) - soft = False if any((re.search(update_regex, cpe.update + str(cpe.title)) for cpe in cpes)) else True + soft = False if any(re.search(update_regex, cpe.update + str(cpe.title)) for cpe in cpes) else True return [x for x in cpes if filter_condition(update_regex, x, min_value, soft)] return cpes @@ -163,7 +163,7 @@ def filter_condition(cpe: CPE, cert_platforms: Set[str]): if not cert_platforms and cpe.target_hw == "*": return True if cert_platforms and cpe.target_hw == "*": - return any((re.search(cert_rules.PLATFORM_REGEXES[x], str(cpe.title)) for x in cert_platforms)) + return any(re.search(cert_rules.PLATFORM_REGEXES[x], str(cpe.title)) for x in cert_platforms) if not cert_platforms and cpe.target_hw != "*": return False if cert_platforms and cpe.target_hw != "*": @@ -174,7 +174,7 @@ def filter_condition(cpe: CPE, cert_platforms: Set[str]): ] assert len(target_hw_platforms) <= 1 can_return_true = any( - (re.search(cert_rules.PLATFORM_REGEXES[x], cpe.target_hw + str(cpe.title)) for x in cert_platforms) + re.search(cert_rules.PLATFORM_REGEXES[x], cpe.target_hw + str(cpe.title)) for x in cert_platforms ) if not target_hw_platforms: return can_return_true diff --git a/sec_certs/model/sar_transformer.py b/sec_certs/model/sar_transformer.py index d87241b5..a5bcff57 100644 --- a/sec_certs/model/sar_transformer.py +++ b/sec_certs/model/sar_transformer.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import Dict, Iterable, List, Optional, Set, Tuple, cast +from typing import Dict, Iterable, cast from sklearn.base import BaseEstimator, TransformerMixin @@ -27,7 +27,7 @@ def fit(self, certificates: Iterable[CommonCriteriaCert]) -> SARTransformer: """ return self - def transform(self, certificates: Iterable[CommonCriteriaCert]) -> List[Optional[Set[SAR]]]: + def transform(self, certificates: Iterable[CommonCriteriaCert]) -> list[set[SAR] | None]: """ Just a wrapper around transform_single_cert() called on an iterable of CommonCriteriaCert. @@ -36,7 +36,7 @@ def transform(self, certificates: Iterable[CommonCriteriaCert]) -> List[Optional """ return [self.transform_single_cert(cert) for cert in certificates] - def transform_single_cert(self, cert: CommonCriteriaCert) -> Optional[Set[SAR]]: + def transform_single_cert(self, cert: CommonCriteriaCert) -> set[SAR] | None: """ Given CommonCriteriaCert, will transform SAR keywords extracted from txt files into a set of SAR objects. Also handles extractin of correct SAR levels, duplicities and filtering. @@ -50,7 +50,7 @@ def transform_single_cert(self, cert: CommonCriteriaCert) -> Optional[Set[SAR]]: return self._resolve_candidate_conflicts(sec_level_candidates, st_candidates, report_candidates, cert.dgst) @staticmethod - def _collect_sar_candidates_from_all_sources(cert: CommonCriteriaCert) -> Tuple[Set[SAR], Set[SAR], Set[SAR]]: + def _collect_sar_candidates_from_all_sources(cert: CommonCriteriaCert) -> tuple[set[SAR], set[SAR], set[SAR]]: """ Parses SARs from three distinct sources and returns the results as a three tuple: - Security level from CSV scan @@ -67,13 +67,13 @@ def report_keywords_may_have_sars(sample: CommonCriteriaCert): sec_level_sars = SARTransformer._parse_sars_from_security_level_list(cert.security_level) if st_keywords_may_have_sars(cert): - st_dict: Dict = cast(Dict, cert.pdf_data.st_keywords) + st_dict: dict = cast(Dict, cert.pdf_data.st_keywords) st_sars = SARTransformer._parse_sar_dict(st_dict[SAR_DICT_KEY], cert.dgst) else: st_sars = set() if report_keywords_may_have_sars(cert): - report_dict: Dict = cast(Dict, cert.pdf_data.report_keywords) + report_dict: dict = cast(Dict, cert.pdf_data.report_keywords) report_sars = SARTransformer._parse_sar_dict(report_dict[SAR_DICT_KEY], cert.dgst) else: report_sars = set() @@ -82,9 +82,9 @@ def report_keywords_may_have_sars(sample: CommonCriteriaCert): @staticmethod def _resolve_candidate_conflicts( - sec_level_candidates: Set[SAR], st_candidates: Set[SAR], report_candidates: Set[SAR], cert_dgst: str - ) -> Optional[Set[SAR]]: - final_candidates: Dict[str, SAR] = {x.family: x for x in sec_level_candidates} + sec_level_candidates: set[SAR], st_candidates: set[SAR], report_candidates: set[SAR], cert_dgst: str + ) -> set[SAR] | None: + final_candidates: dict[str, SAR] = {x.family: x for x in sec_level_candidates} """ Given three parameters (SAR candidates from csv scan, ST and cert. report), builds final list of SARs in cert. This is done as follows: @@ -115,7 +115,7 @@ def _resolve_candidate_conflicts( return set(final_candidates.values()) if final_candidates else None @staticmethod - def _parse_sar_dict(dct: Dict[str, Dict[str, int]], dgst: str) -> Set[SAR]: + def _parse_sar_dict(dct: dict[str, dict[str, int]], dgst: str) -> set[SAR]: """ Accepts st_keywords or report_keywords dictionary. Will reconstruct SAR objects from it. Each SAR family can appear multiple times in the dictionary (due to conflicts) with different levels. Iterated item will replace @@ -128,7 +128,7 @@ def _parse_sar_dict(dct: Dict[str, Dict[str, int]], dgst: str) -> Set[SAR]: :param dgst: DIgest of the processed certificate. :return: _description_ """ - sars: Dict[str, Tuple[SAR, int]] = dict() + sars: dict[str, tuple[SAR, int]] = dict() for sar_class, class_matches in dct.items(): for sar_string, n_occurences in class_matches.items(): try: @@ -149,7 +149,7 @@ def _parse_sar_dict(dct: Dict[str, Dict[str, int]], dgst: str) -> Set[SAR]: return {x[0] for x in sars.values()} if sars else set() @staticmethod - def _parse_sars_from_security_level_list(lst: Iterable[str]) -> Set[SAR]: + def _parse_sars_from_security_level_list(lst: Iterable[str]) -> set[SAR]: sars = set() for element in lst: try: diff --git a/sec_certs/sample/common_criteria.py b/sec_certs/sample/common_criteria.py index 056c6c89..acd54178 100644 --- a/sec_certs/sample/common_criteria.py +++ b/sec_certs/sample/common_criteria.py @@ -7,7 +7,7 @@ from datetime import date, datetime from enum import Enum from pathlib import Path -from typing import Any, ClassVar, Dict, List, Optional, Set, Tuple, Union +from typing import Any, ClassVar from urllib.parse import unquote_plus, urlparse import numpy as np @@ -66,10 +66,10 @@ class MaintenanceReport(ComplexSerializableType): Object for holding maintenance reports. """ - maintenance_date: Optional[date] - maintenance_title: Optional[str] - maintenance_report_link: Optional[str] - maintenance_st_link: Optional[str] + maintenance_date: date | None + maintenance_title: str | None + maintenance_report_link: str | None + maintenance_st_link: str | None def __post_init__(self): super().__setattr__( @@ -84,7 +84,7 @@ def __post_init__(self): super().__setattr__("maintenance_date", sec_certs.utils.sanitization.sanitize_date(self.maintenance_date)) @classmethod - def from_dict(cls, dct: Dict) -> CommonCriteriaCert.MaintenanceReport: + def from_dict(cls, dct: dict) -> CommonCriteriaCert.MaintenanceReport: new_dct = dct.copy() new_dct["maintenance_date"] = ( date.fromisoformat(dct["maintenance_date"]) @@ -112,10 +112,10 @@ class InternalState(ComplexSerializableType): st_extract_ok: bool # Whether target extraction went OK report_extract_ok: bool # Whether report extraction went OK - st_pdf_hash: Optional[str] - report_pdf_hash: Optional[str] - st_txt_hash: Optional[str] - report_txt_hash: Optional[str] + st_pdf_hash: str | None + report_pdf_hash: str | None + st_txt_hash: str | None + report_txt_hash: str | None st_pdf_path: Path report_pdf_path: Path @@ -132,10 +132,10 @@ def __init__( report_convert_ok: bool = False, st_extract_ok: bool = False, report_extract_ok: bool = False, - st_pdf_hash: Optional[str] = None, - report_pdf_hash: Optional[str] = None, - st_txt_hash: Optional[str] = None, - report_txt_hash: Optional[str] = None, + st_pdf_hash: str | None = None, + report_pdf_hash: str | None = None, + st_txt_hash: str | None = None, + report_txt_hash: str | None = None, ): super().__init__() self.st_download_ok = st_download_ok @@ -152,7 +152,7 @@ def __init__( self.report_txt_hash = report_txt_hash @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return [ "st_download_ok", "report_download_ok", @@ -198,55 +198,55 @@ class PdfData(BasePdfData, ComplexSerializableType): Class that holds data extracted from pdf files. """ - report_metadata: Optional[Dict[str, Any]] = field(default=None) - st_metadata: Optional[Dict[str, Any]] = field(default=None) - report_frontpage: Optional[Dict[str, Dict[str, Any]]] = field(default=None) - st_frontpage: Optional[Dict[str, Dict[str, Any]]] = field(default=None) - report_keywords: Optional[Dict[str, Any]] = field(default=None) - st_keywords: Optional[Dict[str, Any]] = field(default=None) - report_filename: Optional[str] = field(default=None) - st_filename: Optional[str] = field(default=None) + report_metadata: dict[str, Any] | None = field(default=None) + st_metadata: dict[str, Any] | None = field(default=None) + report_frontpage: dict[str, dict[str, Any]] | None = field(default=None) + st_frontpage: dict[str, dict[str, Any]] | None = field(default=None) + report_keywords: dict[str, Any] | None = field(default=None) + st_keywords: dict[str, Any] | None = field(default=None) + report_filename: str | None = field(default=None) + st_filename: str | None = field(default=None) def __bool__(self) -> bool: return any([x is not None for x in vars(self)]) @property - def bsi_data(self) -> Optional[Dict[str, Any]]: + def bsi_data(self) -> dict[str, Any] | None: """ Returns frontpage data related to BSI-provided information """ return self.report_frontpage.get("bsi", None) if self.report_frontpage else None @property - def niap_data(self) -> Optional[Dict[str, Any]]: + def niap_data(self) -> dict[str, Any] | None: """ Returns frontpage data related to niap-provided information """ return self.report_frontpage.get("niap", None) if self.report_frontpage else None @property - def nscib_data(self) -> Optional[Dict[str, Any]]: + def nscib_data(self) -> dict[str, Any] | None: """ Returns frontpage data related to nscib-provided information """ return self.report_frontpage.get("nscib", None) if self.report_frontpage else None @property - def canada_data(self) -> Optional[Dict[str, Any]]: + def canada_data(self) -> dict[str, Any] | None: """ Returns frontpage data related to canada-provided information """ return self.report_frontpage.get("canada", None) if self.report_frontpage else None @property - def anssi_data(self) -> Optional[Dict[str, Any]]: + def anssi_data(self) -> dict[str, Any] | None: """ Returns frontpage data related to ANSSI-provided information """ return self.report_frontpage.get("anssi", None) if self.report_frontpage else None @property - def cert_lab(self) -> Optional[List[str]]: + def cert_lab(self) -> list[str] | None: """ Returns labs for which certificate data was parsed. """ @@ -258,26 +258,26 @@ def cert_lab(self) -> Optional[List[str]]: return labs if labs else None @property - def bsi_cert_id(self) -> Optional[str]: + def bsi_cert_id(self) -> str | None: return self.bsi_data.get("cert_id", None) if self.bsi_data else None @property - def niap_cert_id(self) -> Optional[str]: + def niap_cert_id(self) -> str | None: return self.niap_data.get("cert_id", None) if self.niap_data else None @property - def nscib_cert_id(self) -> Optional[str]: + def nscib_cert_id(self) -> str | None: return self.nscib_data.get("cert_id", None) if self.nscib_data else None @property - def canada_cert_id(self) -> Optional[str]: + def canada_cert_id(self) -> str | None: return self.canada_data.get("cert_id", None) if self.canada_data else None @property - def anssi_cert_id(self) -> Optional[str]: + def anssi_cert_id(self) -> str | None: return self.anssi_data.get("cert_id", None) if self.anssi_data else None - def frontpage_cert_id(self, scheme: str) -> Dict[str, float]: + def frontpage_cert_id(self, scheme: str) -> dict[str, float]: """ Get cert_id candidate from the frontpage of the report. """ @@ -292,7 +292,7 @@ def frontpage_cert_id(self, scheme: str) -> Dict[str, float]: return {candidate: 1.0} return {} - def filename_cert_id(self, scheme: str) -> Dict[str, float]: + def filename_cert_id(self, scheme: str) -> dict[str, float]: """ Get cert_id candidates from the matches in the report filename. """ @@ -314,7 +314,7 @@ def filename_cert_id(self, scheme: str) -> Dict[str, float]: # TODO count length in weight return results - def keywords_cert_id(self, scheme: str) -> Dict[str, float]: + def keywords_cert_id(self, scheme: str) -> dict[str, float]: """ Get cert_id candidates from the keywords matches in the report. """ @@ -336,7 +336,7 @@ def keywords_cert_id(self, scheme: str) -> Dict[str, float]: # TODO count length in weight return results - def metadata_cert_id(self, scheme: str) -> Dict[str, float]: + def metadata_cert_id(self, scheme: str) -> dict[str, float]: """ Get cert_id candidates from the report metadata. """ @@ -361,14 +361,14 @@ def metadata_cert_id(self, scheme: str) -> Dict[str, float]: # TODO count length in weight return results - def candidate_cert_ids(self, scheme: str) -> Dict[str, float]: + def candidate_cert_ids(self, scheme: str) -> dict[str, float]: frontpage_id = self.frontpage_cert_id(scheme) metadata_id = self.metadata_cert_id(scheme) filename_id = self.filename_cert_id(scheme) keywords_id = self.keywords_cert_id(scheme) # Join them and weigh them, each is normalized with weights from 0 to 1 (if anything is returned) - candidates: Dict[str, float] = defaultdict(lambda: 0.0) + candidates: dict[str, float] = defaultdict(lambda: 0.0) # TODO: Add heuristic based on ordering of ids (and extracted year + increment) # TODO: Add heuristic based on length for candidate, count in frontpage_id.items(): @@ -387,23 +387,23 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): Class for various heuristics related to CommonCriteriaCert """ - extracted_versions: Optional[Set[str]] = field(default=None) - cpe_matches: Optional[Set[str]] = field(default=None) - verified_cpe_matches: Optional[Set[str]] = field(default=None) - related_cves: Optional[Set[str]] = field(default=None) - cert_lab: Optional[List[str]] = field(default=None) - cert_id: Optional[str] = field(default=None) + extracted_versions: set[str] | None = field(default=None) + cpe_matches: set[str] | None = field(default=None) + verified_cpe_matches: set[str] | None = field(default=None) + related_cves: set[str] | None = field(default=None) + cert_lab: list[str] | None = field(default=None) + cert_id: str | None = field(default=None) st_references: References = field(default_factory=References) report_references: References = field(default_factory=References) - extracted_sars: Optional[Set[SAR]] = field(default=None) - direct_transitive_cves: Optional[Set[str]] = field(default=None) - indirect_transitive_cves: Optional[Set[str]] = field(default=None) + extracted_sars: set[SAR] | None = field(default=None) + direct_transitive_cves: set[str] | None = field(default=None) + indirect_transitive_cves: set[str] | None = field(default=None) @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return copy.deepcopy(super().serialized_attributes) - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "dgst", "cert_id", "name", @@ -437,20 +437,20 @@ def __init__( status: str, category: str, name: str, - manufacturer: Optional[str], + manufacturer: str | None, scheme: str, - security_level: Union[str, Set[str]], - not_valid_before: Optional[date], - not_valid_after: Optional[date], + security_level: str | set[str], + not_valid_before: date | None, + not_valid_after: date | None, report_link: str, st_link: str, - cert_link: Optional[str], - manufacturer_web: Optional[str], - protection_profiles: Optional[Set[ProtectionProfile]], - maintenance_updates: Optional[Set[MaintenanceReport]], - state: Optional[InternalState], - pdf_data: Optional[PdfData], - heuristics: Optional[Heuristics], + cert_link: str | None, + manufacturer_web: str | None, + protection_profiles: set[ProtectionProfile] | None, + maintenance_updates: set[MaintenanceReport] | None, + state: InternalState | None, + pdf_data: PdfData | None, + heuristics: Heuristics | None, ): super().__init__() @@ -486,7 +486,7 @@ def dgst(self) -> str: return helpers.get_first_16_bytes_sha256(self.category + self.name + self.report_link) @property - def eal(self) -> Optional[str]: + def eal(self) -> str | None: """ Returns EAL of certificate if it was extracted, None otherwise. """ @@ -502,7 +502,7 @@ def eal(self) -> Optional[str]: return None @property - def actual_sars(self) -> Optional[Set[SAR]]: + def actual_sars(self) -> set[SAR] | None: """ Computes actual SARs. First, SARs implied by EAL are computed. Then, these are augmented with heuristically extracted SARs :return Optional[Set[SAR]]: Set of actual SARs of a certificate, None if empty @@ -519,11 +519,11 @@ def actual_sars(self) -> Optional[Set[SAR]]: return set(sars.values()) if sars else None @property - def label_studio_title(self) -> Optional[str]: + def label_studio_title(self) -> str | None: return self.name @property - def pandas_tuple(self) -> Tuple: + def pandas_tuple(self) -> tuple: """ Returns tuple of attributes meant for pandas serialization """ @@ -560,7 +560,7 @@ def __str__(self) -> str: printed_manufacturer = self.manufacturer if self.manufacturer else "Unknown manufacturer" return str(printed_manufacturer) + " " + str(self.name) + " dgst: " + self.dgst - def merge(self, other: CommonCriteriaCert, other_source: Optional[str] = None) -> None: + def merge(self, other: CommonCriteriaCert, other_source: str | None = None) -> None: """ Merges with other CC sample. Assuming they come from different sources, e.g., csv and html. Assuming that html source has better protection profiles, they overwrite CSV info @@ -587,7 +587,7 @@ def merge(self, other: CommonCriteriaCert, other_source: Optional[str] = None) - ) @classmethod - def from_dict(cls, dct: Dict) -> CommonCriteriaCert: + def from_dict(cls, dct: dict) -> CommonCriteriaCert: """ Deserializes dictionary into `CommonCriteriaCert` """ @@ -611,7 +611,7 @@ def _html_row_get_name(cell: Tag) -> str: return list(cell.stripped_strings)[0] @staticmethod - def _html_row_get_manufacturer(cell: Tag) -> Optional[str]: + def _html_row_get_manufacturer(cell: Tag) -> str | None: if lst := list(cell.stripped_strings): return lst[0] else: @@ -626,7 +626,7 @@ def _html_row_get_security_level(cell: Tag) -> set: return set(cell.stripped_strings) @staticmethod - def _html_row_get_manufacturer_web(cell: Tag) -> Optional[str]: + def _html_row_get_manufacturer_web(cell: Tag) -> str | None: for link in cell.find_all("a"): if link is not None and link.get("title") == "Vendor's web site" and link.get("href") != "http://": return link.get("href") @@ -645,13 +645,13 @@ def _html_row_get_protection_profiles(cell: Tag) -> set: return protection_profiles @staticmethod - def _html_row_get_date(cell: Tag) -> Optional[date]: + def _html_row_get_date(cell: Tag) -> date | None: text = cell.get_text() extracted_date = datetime.strptime(text, "%Y-%m-%d").date() if text else None return extracted_date @staticmethod - def _html_row_get_report_st_links(cell: Tag) -> Tuple[str, str]: + def _html_row_get_report_st_links(cell: Tag) -> tuple[str, str]: links = cell.find_all("a") assert links[1].get("title").startswith("Certification Report") assert links[2].get("title").startswith("Security Target") @@ -662,12 +662,12 @@ def _html_row_get_report_st_links(cell: Tag) -> Tuple[str, str]: return report_link, security_target_link @staticmethod - def _html_row_get_cert_link(cell: Tag) -> Optional[str]: + def _html_row_get_cert_link(cell: Tag) -> str | None: links = cell.find_all("a") return CommonCriteriaCert.cc_url + links[0].get("href") if links else None @staticmethod - def _html_row_get_maintenance_div(cell: Tag) -> Optional[Tag]: + def _html_row_get_maintenance_div(cell: Tag) -> Tag | None: divs = cell.find_all("div") for d in divs: if d.find("div") and d.stripped_strings and list(d.stripped_strings)[0] == "Maintenance Report(s)": @@ -675,7 +675,7 @@ def _html_row_get_maintenance_div(cell: Tag) -> Optional[Tag]: return None @staticmethod - def _html_row_get_maintenance_updates(main_div: Tag) -> Set[CommonCriteriaCert.MaintenanceReport]: + def _html_row_get_maintenance_updates(main_div: Tag) -> set[CommonCriteriaCert.MaintenanceReport]: possible_updates = list(main_div.find_all("li")) maintenance_updates = set() for u in possible_updates: @@ -744,10 +744,10 @@ def from_html_row(cls, row: Tag, status: str, category: str) -> CommonCriteriaCe def set_local_paths( self, - report_pdf_dir: Optional[Union[str, Path]], - st_pdf_dir: Optional[Union[str, Path]], - report_txt_dir: Optional[Union[str, Path]], - st_txt_dir: Optional[Union[str, Path]], + report_pdf_dir: str | Path | None, + st_pdf_dir: str | Path | None, + report_txt_dir: str | Path | None, + st_txt_dir: str | Path | None, ) -> None: """ Sets paths to files given the requested directories @@ -774,7 +774,7 @@ def download_pdf_report(cert: CommonCriteriaCert) -> CommonCriteriaCert: :param CommonCriteriaCert cert: cert to download the pdf report for :return CommonCriteriaCert: returns the modified certificate with updated state """ - exit_code: Union[str, int] + exit_code: str | int if not cert.report_link: exit_code = "No link" else: @@ -797,7 +797,7 @@ def download_pdf_st(cert: CommonCriteriaCert) -> CommonCriteriaCert: :param CommonCriteriaCert cert: cert to download the pdf security target for :return CommonCriteriaCert: returns the modified certificate with updated state """ - exit_code: Union[str, int] + exit_code: str | int if not cert.st_link: exit_code = "No link" else: diff --git a/sec_certs/sample/cve.py b/sec_certs/sample/cve.py index 27d7501b..ca6e27af 100644 --- a/sec_certs/sample/cve.py +++ b/sec_certs/sample/cve.py @@ -3,7 +3,7 @@ import datetime import itertools from dataclasses import dataclass -from typing import Any, ClassVar, Dict, List, Optional, Set, Tuple +from typing import Any, ClassVar from dateutil.parser import isoparse @@ -24,7 +24,7 @@ class Impact(ComplexSerializableType): __slots__ = ["base_score", "severity", "exploitability_score", "impact_score"] @classmethod - def from_nist_dict(cls, dct: Dict[str, Any]) -> "CVE.Impact": + def from_nist_dict(cls, dct: dict[str, Any]) -> CVE.Impact: """ Will load Impact from dictionary defined at https://nvd.nist.gov/feeds/json/cve/1.1 """ @@ -47,14 +47,14 @@ def from_nist_dict(cls, dct: Dict[str, Any]) -> "CVE.Impact": raise ValueError("NIST Dict for CVE Impact badly formatted.") cve_id: str - vulnerable_cpes: List[CPE] + vulnerable_cpes: list[CPE] impact: Impact - published_date: Optional[datetime.datetime] - cwe_ids: Optional[Set[str]] + published_date: datetime.datetime | None + cwe_ids: set[str] | None __slots__ = ["cve_id", "vulnerable_cpes", "impact", "published_date", "cwe_ids"] - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "cve_id", "vulnerable_cpes", "base_score", @@ -66,7 +66,7 @@ def from_nist_dict(cls, dct: Dict[str, Any]) -> "CVE.Impact": ] def __init__( - self, cve_id: str, vulnerable_cpes: List[CPE], impact: Impact, published_date: str, cwe_ids: Optional[Set[str]] + self, cve_id: str, vulnerable_cpes: list[CPE], impact: Impact, published_date: str, cwe_ids: set[str] | None ): super().__init__() self.cve_id = cve_id @@ -106,7 +106,7 @@ def pandas_tuple(self): self.cwe_ids, ) - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: return { "cve_id": self.cve_id, "vulnerable_cpes": self.vulnerable_cpes, @@ -116,14 +116,14 @@ def to_dict(self) -> Dict[str, Any]: } @staticmethod - def _parse_nist_dict(lst: List) -> List[CPE]: - cpes: List[CPE] = [] + def _parse_nist_dict(lst: list) -> list[CPE]: + cpes: list[CPE] = [] for x in lst: if x["vulnerable"]: cpe_uri = x["cpe23Uri"] - version_start: Optional[Tuple[str, str]] - version_end: Optional[Tuple[str, str]] + version_start: tuple[str, str] | None + version_end: tuple[str, str] | None if "versionStartIncluding" in x and x["versionStartIncluding"]: version_start = ("including", x["versionStartIncluding"]) elif "versionStartExcluding" in x and x["versionStartExcluding"]: @@ -143,14 +143,14 @@ def _parse_nist_dict(lst: List) -> List[CPE]: return cpes @classmethod - def from_nist_dict(cls, dct: Dict) -> CVE: + def from_nist_dict(cls, dct: dict) -> CVE: """ Will load CVE from dictionary defined at https://nvd.nist.gov/feeds/json/cve/1.1 """ - def get_vulnerable_cpes_from_nist_dict(dct: Dict) -> List[CPE]: - def get_vulnerable_cpes_from_node(node: Dict) -> List[CPE]: - cpes: List[CPE] = [] + def get_vulnerable_cpes_from_nist_dict(dct: dict) -> list[CPE]: + def get_vulnerable_cpes_from_node(node: dict) -> list[CPE]: + cpes: list[CPE] = [] if node["operator"] == "AND": return cpes @@ -182,6 +182,6 @@ def get_vulnerable_cpes_from_node(node: Dict) -> List[CPE]: return cls(cve_id, vulnerable_cpes, impact, published_date, cwe_ids) @staticmethod - def parse_cwe_data(dct: Dict) -> Optional[Set[str]]: + def parse_cwe_data(dct: dict) -> set[str] | None: descriptions = dct["cve"]["problemtype"]["problemtype_data"][0]["description"] return {x["value"] for x in descriptions} if descriptions else None diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 0e9e13dd..70f6d9ab 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -5,7 +5,7 @@ from dataclasses import dataclass, field from datetime import date from pathlib import Path -from typing import Any, Callable, ClassVar, Dict, Final, List, Literal, Optional, Set, Tuple +from typing import Any, Callable, ClassVar, Final, Literal import dateutil import numpy as np @@ -35,7 +35,7 @@ class FIPSHTMLParser: def __init__(self, soup: BeautifulSoup): self._soup = soup - def get_web_data_and_algorithms(self) -> Tuple[Set[str], FIPSCertificate.WebData]: + def get_web_data_and_algorithms(self) -> tuple[set[str], FIPSCertificate.WebData]: divs = self._soup.find_all("div", class_="panel panel-default") details_div, vendor_div, related_files_div, validation_history_div = divs details_dict = self._build_details_dict(details_div) @@ -56,7 +56,7 @@ def get_web_data_and_algorithms(self) -> Tuple[Set[str], FIPSCertificate.WebData **{**details_dict, **vendor_dict, **related_files_dict, **validation_history_dict} ) - def _build_details_dict(self, details_div: Tag) -> Dict[str, Any]: + def _build_details_dict(self, details_div: Tag) -> dict[str, Any]: def parse_single_detail_entry(key, entry): normalized_key = DETAILS_KEY_NORMALIZATION_DICT[key] normalization_func = DETAILS_KEY_TO_NORMALIZATION_FUNCTION.get(normalized_key, None) @@ -71,7 +71,7 @@ def parse_single_detail_entry(key, entry): ) entries = [(FIPSHTMLParser.normalize_string(key.text), entry) for key, entry in entries] entries = [parse_single_detail_entry(*x) for x in entries if x[0] in DETAILS_KEY_NORMALIZATION_DICT.keys()] - entries = dict((x, y) for x, y in entries) + entries = {x: y for x, y in entries} if "caveat" in entries: entries["mentioned_certs"] = FIPSHTMLParser.get_mentioned_certs_from_caveat(entries["caveat"]) @@ -87,21 +87,21 @@ def parse_single_detail_entry(key, entry): return entries @staticmethod - def _build_vendor_dict(vendor_div: Tag) -> Dict[str, Any]: + def _build_vendor_dict(vendor_div: Tag) -> dict[str, Any]: if not (link := vendor_div.find("a")): return {"vendor_url": None, "vendor": list(vendor_div.find("div", "panel-body").children)[0].strip()} else: return {"vendor_url": link.get("href"), "vendor": link.text.strip()} @staticmethod - def _build_related_files_dict(related_files_div: Tag) -> Dict[str, Any]: + def _build_related_files_dict(related_files_div: Tag) -> dict[str, Any]: if cert_link := [x for x in related_files_div.find_all("a") if "Certificate" in x.text]: return {"certificate_pdf_url": constants.FIPS_BASE_URL + cert_link[0].get("href")} else: return {"certificate_pdf_url": None} @staticmethod - def _build_validation_history_dict(validation_history_div: Tag) -> Dict[str, Any]: + def _build_validation_history_dict(validation_history_div: Tag) -> dict[str, Any]: def parse_row(row): validation_date, validation_type, lab = row.find_all("td") return FIPSCertificate.ValidationHistoryEntry( @@ -109,12 +109,12 @@ def parse_row(row): ) rows = validation_history_div.find("tbody").find_all("tr") - history: Optional[List[FIPSCertificate.ValidationHistoryEntry]] = [parse_row(x) for x in rows] if rows else None + history: list[FIPSCertificate.ValidationHistoryEntry] | None = [parse_row(x) for x in rows] if rows else None return {"validation_history": history} @staticmethod - def get_mentioned_certs_from_caveat(caveat: str) -> Dict[str, int]: - ids_found: Dict[str, int] = {} + def get_mentioned_certs_from_caveat(caveat: str) -> dict[str, int]: + ids_found: dict[str, int] = {} r_key = r"(?P\w+)?\s?(?:#\s?|Cert\.?(?!.\s)\s?|Certificate\s?)+(?P\d+)" for m in re.finditer(r_key, caveat): if m.group("word") and m.group("word").lower() in {"rsa", "shs", "dsa", "pkcs", "aes"}: @@ -126,13 +126,13 @@ def get_mentioned_certs_from_caveat(caveat: str) -> Dict[str, int]: return ids_found @staticmethod - def get_algs_from_description(description: str) -> Set[str]: + def get_algs_from_description(description: str) -> set[str]: return {m.group() for m in re.finditer(FIPS_ALGS_IN_TABLE, description)} @staticmethod - def parse_algorithms(algorithms_div: Tag) -> Dict[str, Set[str]]: + def parse_algorithms(algorithms_div: Tag) -> dict[str, set[str]]: rows = algorithms_div.find("tbody").find_all("tr") - dct: Dict[str, Set[str]] = dict() + dct: dict[str, set[str]] = dict() for row in rows: cells = row.find_all("td") dct[cells[0].text] = {m.group() for m in re.finditer(FIPS_ALGS_IN_TABLE, cells[1].text)} @@ -143,12 +143,12 @@ def normalize_string(string: str) -> str: return " ".join(string.split()) @staticmethod - def parse_tested_configurations(tested_configurations: Tag) -> Optional[List[str]]: + def parse_tested_configurations(tested_configurations: Tag) -> list[str] | None: configurations = [y.text for y in tested_configurations.find_all("li")] return configurations if not configurations == ["N/A"] else None -DETAILS_KEY_NORMALIZATION_DICT: Final[Dict[str, str]] = { +DETAILS_KEY_NORMALIZATION_DICT: Final[dict[str, str]] = { "Module Name": "module_name", "Standard": "standard", "Status": "status", @@ -171,7 +171,7 @@ def parse_tested_configurations(tested_configurations: Tag) -> Optional[List[str "Product URL": "product_url", } -DETAILS_KEY_TO_NORMALIZATION_FUNCTION: Dict[str, Callable] = { +DETAILS_KEY_TO_NORMALIZATION_FUNCTION: dict[str, Callable] = { "date_sunset": lambda x: dateutil.parser.parse(x.text).date(), "algorithms": getattr(FIPSHTMLParser, "parse_algorithms"), "tested_conf": getattr(FIPSHTMLParser, "parse_tested_configurations"), @@ -193,7 +193,7 @@ class FIPSCertificate( the certificate can handle itself. `FIPSDataset` class then instrument this functionality. """ - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "dgst", "cert_id", "name", @@ -234,8 +234,8 @@ class InternalState(ComplexSerializableType): module_extract_ok: bool policy_extract_ok: bool - policy_pdf_hash: Optional[str] - policy_txt_hash: Optional[str] + policy_pdf_hash: str | None + policy_txt_hash: str | None policy_pdf_path: Path policy_txt_path: Path @@ -249,8 +249,8 @@ def __init__( policy_convert_ok: bool = False, module_extract_ok: bool = False, policy_extract_ok: bool = False, - policy_pdf_hash: Optional[str] = None, - policy_txt_hash: Optional[str] = None, + policy_pdf_hash: str | None = None, + policy_txt_hash: str | None = None, ): self.module_download_ok = module_download_ok self.policy_download_ok = policy_download_ok @@ -262,7 +262,7 @@ def __init__( self.policy_txt_hash = policy_txt_hash @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return [ "module_download_ok", "policy_download_ok", @@ -309,7 +309,7 @@ class ValidationHistoryEntry(ComplexSerializableType): lab: str @classmethod - def from_dict(cls, dct: Dict) -> FIPSCertificate.ValidationHistoryEntry: + def from_dict(cls, dct: dict) -> FIPSCertificate.ValidationHistoryEntry: new_dct = dct.copy() new_dct["date"] = dateutil.parser.parse(dct["date"]).date() return cls(**new_dct) @@ -320,28 +320,28 @@ class WebData(ComplexSerializableType): Data structure for data obtained from scanning certificate webpage at NIST.gov """ - module_name: Optional[str] = field(default=None) - validation_history: Optional[List[FIPSCertificate.ValidationHistoryEntry]] = field(default=None) - vendor_url: Optional[str] = field(default=None) - vendor: Optional[str] = field(default=None) - certificate_pdf_url: Optional[str] = field(default=None) - module_type: Optional[str] = field(default=None) - standard: Optional[str] = field(default=None) - status: Optional[Literal["active", "historical", "revoked"]] = field(default=None) - level: Optional[Literal[1, 2, 3, 4]] = field(default=None) - caveat: Optional[str] = field(default=None) - exceptions: Optional[List[str]] = field(default=None) - embodiment: Optional[str] = field(default=None) - description: Optional[str] = field(default=None) - tested_conf: Optional[List[str]] = field(default=None) - hw_versions: Optional[str] = field(default=None) - fw_versions: Optional[str] = field(default=None) - sw_versions: Optional[str] = field(default=None) - mentioned_certs: Optional[Dict[str, int]] = field(default=None) # Cert_id: n_occurences - historical_reason: Optional[str] = field(default=None) - date_sunset: Optional[date] = field(default=None) - revoked_reason: Optional[str] = field(default=None) - revoked_link: Optional[str] = field(default=None) + module_name: str | None = field(default=None) + validation_history: list[FIPSCertificate.ValidationHistoryEntry] | None = field(default=None) + vendor_url: str | None = field(default=None) + vendor: str | None = field(default=None) + certificate_pdf_url: str | None = field(default=None) + module_type: str | None = field(default=None) + standard: str | None = field(default=None) + status: Literal["active", "historical", "revoked"] | None = field(default=None) + level: Literal[1, 2, 3, 4] | None = field(default=None) + caveat: str | None = field(default=None) + exceptions: list[str] | None = field(default=None) + embodiment: str | None = field(default=None) + description: str | None = field(default=None) + tested_conf: list[str] | None = field(default=None) + hw_versions: str | None = field(default=None) + fw_versions: str | None = field(default=None) + sw_versions: str | None = field(default=None) + mentioned_certs: dict[str, int] | None = field(default=None) # Cert_id: n_occurences + historical_reason: str | None = field(default=None) + date_sunset: date | None = field(default=None) + revoked_reason: str | None = field(default=None) + revoked_link: str | None = field(default=None) # Those below are left unused at the moment # product_url: Optional[str] = field(default=None) @@ -359,7 +359,7 @@ def __str__(self) -> str: return repr(self) @classmethod - def from_dict(cls, dct: Dict) -> FIPSCertificate.WebData: + def from_dict(cls, dct: dict) -> FIPSCertificate.WebData: new_dct = dct.copy() if new_dct["date_sunset"]: new_dct["date_sunset"] = dateutil.parser.parse(new_dct["date_sunset"]).date() @@ -371,11 +371,11 @@ class PdfData(BasePdfData, ComplexSerializableType): Data structure that holds data obtained from scanning pdf files (or their converted txt documents). """ - keywords: Dict = field(default_factory=dict) - policy_metadata: Dict[str, Any] = field(default_factory=dict) + keywords: dict = field(default_factory=dict) + policy_metadata: dict[str, Any] = field(default_factory=dict) @property - def certlike_algorithm_numbers(self) -> Set[str]: + def certlike_algorithm_numbers(self) -> set[str]: """Returns numbers of certificates from keywords["fips_certlike"]["Certlike"]""" if self.keywords and "fips_certlike" in self.keywords: fips_certlike = self.keywords["fips_certlike"].get("Certlike", dict()) @@ -390,20 +390,20 @@ class Heuristics(BaseHeuristics, ComplexSerializableType): Data structure that holds data obtained by processing the certificate and applying various heuristics. """ - algorithms: Set[str] = field(default_factory=set) - extracted_versions: Set[str] = field(default_factory=set) - cpe_matches: Optional[Set[str]] = field(default=None) - verified_cpe_matches: Optional[Set[CPE]] = field(default=None) - related_cves: Optional[Set[str]] = field(default=None) - policy_prunned_references: Set[str] = field(default_factory=set) - module_prunned_references: Set[str] = field(default_factory=set) + algorithms: set[str] = field(default_factory=set) + extracted_versions: set[str] = field(default_factory=set) + cpe_matches: set[str] | None = field(default=None) + verified_cpe_matches: set[CPE] | None = field(default=None) + related_cves: set[str] | None = field(default=None) + policy_prunned_references: set[str] = field(default_factory=set) + module_prunned_references: set[str] = field(default_factory=set) policy_processed_references: References = field(default_factory=References) module_processed_references: References = field(default_factory=References) - direct_transitive_cves: Optional[Set[str]] = field(default=None) - indirect_transitive_cves: Optional[Set[str]] = field(default=None) + direct_transitive_cves: set[str] | None = field(default=None) + indirect_transitive_cves: set[str] | None = field(default=None) @property - def algorithm_numbers(self) -> Set[str]: + def algorithm_numbers(self) -> set[str]: """Returns numbers of algorithms""" def alg_to_number(alg: str) -> str: @@ -419,7 +419,7 @@ def dgst(self) -> str: return fips_dgst(self.cert_id) @property - def manufacturer(self) -> Optional[str]: # type: ignore + def manufacturer(self) -> str | None: # type: ignore return self.web_data.vendor @property @@ -431,7 +431,7 @@ def policy_pdf_url(self) -> str: return constants.FIPS_SP_URL.format(self.cert_id) @property - def name(self) -> Optional[str]: # type: ignore + def name(self) -> str | None: # type: ignore return self.web_data.module_name @property @@ -453,10 +453,10 @@ def label_studio_title(self) -> str: def __init__( self, cert_id: str, - web_data: Optional[FIPSCertificate.WebData] = None, - pdf_data: Optional[FIPSCertificate.PdfData] = None, - heuristics: Optional[FIPSCertificate.Heuristics] = None, - state: Optional[InternalState] = None, + web_data: FIPSCertificate.WebData | None = None, + pdf_data: FIPSCertificate.PdfData | None = None, + heuristics: FIPSCertificate.Heuristics | None = None, + state: InternalState | None = None, ): super().__init__() @@ -467,7 +467,7 @@ def __init__( self.state: FIPSCertificate.InternalState = state if state else FIPSCertificate.InternalState() @property - def pandas_tuple(self) -> Tuple: + def pandas_tuple(self) -> tuple: return ( self.dgst, self.cert_id, @@ -611,7 +611,7 @@ def compute_heuristics_version(self) -> None: versions_for_extraction += f" {self.web_data.fw_versions}" self.heuristics.extracted_versions = helpers.compute_heuristics_version(versions_for_extraction) - def _prune_reference_ids_variable(self, attribute_to_prune: Set[str]) -> Set[str]: + def _prune_reference_ids_variable(self, attribute_to_prune: set[str]) -> set[str]: """ Prunnes cert_ids from variable "attribute_to_prune", return result. Steps: 0. Consider only ids != self.cert_id diff --git a/sec_certs/utils/extract.py b/sec_certs/utils/extract.py index 400fbe3f..09460fb7 100644 --- a/sec_certs/utils/extract.py +++ b/sec_certs/utils/extract.py @@ -6,7 +6,7 @@ from collections import Counter from enum import Enum from pathlib import Path -from typing import Any, Iterator, List, Optional, Tuple, Union +from typing import Any, Iterator import numpy as np @@ -354,7 +354,7 @@ def search_only_headers_bsi(filepath: Path): # noqa: C901 for m in re.finditer(rule_and_sep, whole_text): # check if previous rules had at least one match if constants.TAG_CERT_ID not in items_found.keys(): - logger.error("ERROR: front page not found for file: {}".format(filepath)) + logger.error(f"ERROR: front page not found for file: {filepath}") match_groups = m.groups() ref_protection_profiles = match_groups[0] @@ -585,7 +585,7 @@ def search_only_headers_canada(filepath: Path): # noqa: C901 return constants.RETURNCODE_OK, items_found -def search_files(folder: Union[str, Path]) -> Iterator[str]: +def search_files(folder: str | Path) -> Iterator[str]: for root, _, files in os.walk(str(folder)): yield from [os.path.join(root, x) for x in files] @@ -648,7 +648,7 @@ def walk(obj, depth): return walk(dct, 0) -def extract_keywords(filepath: Path, search_rules) -> Optional[dict[str, dict[str, int]]]: +def extract_keywords(filepath: Path, search_rules) -> dict[str, dict[str, int]] | None: """ Extract keywords from filepath using the search rules. @@ -697,8 +697,8 @@ def normalize_match_string(match: str) -> str: def load_text_file( - file_name: Union[str, Path], limit_max_lines: int = -1, line_separator: str = LINE_SEPARATOR -) -> Tuple[str, str, bool]: + file_name: str | Path, limit_max_lines: int = -1, line_separator: str = LINE_SEPARATOR +) -> tuple[str, str, bool]: """ Load the text contents of a file at `file_name`, upto `limit_max_lines` of lines, replace newlines in the text with `line_separator`. @@ -747,17 +747,17 @@ def load_text_file( def load_cert_html_file(file_name: str) -> str: - with open(file_name, "r", errors=FILE_ERRORS_STRATEGY) as f: + with open(file_name, errors=FILE_ERRORS_STRATEGY) as f: try: return f.read() except UnicodeDecodeError: logger.warning("UnicodeDecodeError, opening as utf8") - with open(file_name, "r", encoding="utf8", errors=FILE_ERRORS_STRATEGY) as f2: + with open(file_name, encoding="utf8", errors=FILE_ERRORS_STRATEGY) as f2: try: return f2.read() except UnicodeDecodeError: - logger.error("Failed to read file {}".format(file_name)) + logger.error(f"Failed to read file {file_name}") return "" @@ -772,7 +772,7 @@ def rules_get_subset(desired_path: str) -> dict: return dct -def extract_key_paths(dct: dict, current_path: str) -> List[str]: +def extract_key_paths(dct: dict, current_path: str) -> list[str]: """ Given subset of cc_rules dictionary, will compute full paths to all leafs in the dictionaries, s.t. the final value of each path is a list of regex @@ -787,7 +787,7 @@ def extract_key_paths(dct: dict, current_path: str) -> List[str]: return paths -def get_sum_of_values_from_dict_path(dct: Optional[dict], path: str, default: float = np.nan) -> float: +def get_sum_of_values_from_dict_path(dct: dict | None, path: str, default: float = np.nan) -> float: """ Given dictionary and path, will compute sum of occurences of values in the inner-most layer of that path. If the key is missing from dict, return default value. @@ -806,7 +806,7 @@ def get_sum_of_values_from_dict_path(dct: Optional[dict], path: str, default: fl return sum(res.values()) -def get_sums_for_rules_subset(dct: Optional[dict], path: str) -> dict[str, float]: +def get_sums_for_rules_subset(dct: dict | None, path: str) -> dict[str, float]: """ Given path to search in cc_rules (e.g., "symmetric_crypto"), will get the finest resolution and count occurences of the keys in the diff --git a/sec_certs/utils/helpers.py b/sec_certs/utils/helpers.py index d4c668c5..3e4c6943 100644 --- a/sec_certs/utils/helpers.py +++ b/sec_certs/utils/helpers.py @@ -207,7 +207,7 @@ def warn_if_missing_poppler() -> None: logger.warning( "Attempting to run pipeline with pdf->txt conversion, but poppler-cpp dependency was not found." ) - except EnvironmentError: + except OSError: logger.warning("Attempting to find poppler-cpp, but pkg-config was not found.") @@ -220,7 +220,7 @@ def warn_if_missing_tesseract() -> None: logger.warning( "Attempting to run pipeline with pdf->txt conversion, that requires tesseract, but tesseract was not found." ) - except EnvironmentError: + except OSError: logger.warning("Attempting to find tesseract, but pkg-config was not found.") diff --git a/sec_certs/utils/pandas.py b/sec_certs/utils/pandas.py index 27caed3a..97068e77 100644 --- a/sec_certs/utils/pandas.py +++ b/sec_certs/utils/pandas.py @@ -9,7 +9,7 @@ from dataclasses import dataclass from pathlib import Path from shutil import copyfile -from typing import Any, Dict, Final, List, Optional, Set, Tuple, Union +from typing import Any, Final import numpy as np import pandas as pd @@ -30,19 +30,19 @@ class SecondarySFPCluster: children: frozenset[int] @classmethod - def from_xml_id(cls, xml_categories: List[ET.Element], cwe_id: int): + def from_xml_id(cls, xml_categories: list[ET.Element], cwe_id: int): cat = cls.find_correct_category(xml_categories, cwe_id) name = cat.attrib["Name"] members = cat.find("{http://cwe.mitre.org/cwe-6}Relationships") assert members is not None member_ids = frozenset( - (int(x.attrib["CWE_ID"]) for x in members if x.tag == "{http://cwe.mitre.org/cwe-6}Has_Member") + int(x.attrib["CWE_ID"]) for x in members if x.tag == "{http://cwe.mitre.org/cwe-6}Has_Member" ) return cls(name, member_ids) @staticmethod - def find_correct_category(xml_categories: List[ET.Element], cwe_id: int) -> ET.Element: + def find_correct_category(xml_categories: list[ET.Element], cwe_id: int) -> ET.Element: for cat in xml_categories: if cat.attrib["ID"] == str(cwe_id): return cat @@ -56,7 +56,7 @@ class PrimarySFPCluster: cwe_ids: frozenset[int] @classmethod - def from_xml(cls, xml_categories: List[ET.Element], primary_cluster_element: ET.Element): + def from_xml(cls, xml_categories: list[ET.Element], primary_cluster_element: ET.Element): name = primary_cluster_element.attrib["Name"].split("SFP Primary Cluster: ")[1] members = primary_cluster_element.find("{http://cwe.mitre.org/cwe-6}Relationships") @@ -83,7 +83,7 @@ def __init__(self, primary_clusters: frozenset[PrimarySFPCluster]): self.primary_clusters = primary_clusters @classmethod - def from_xml(cls, xml_filepath: Union[str, Path]): + def from_xml(cls, xml_filepath: str | Path): tree = ET.parse(xml_filepath) category_tag = tree.getroot().find("{http://cwe.mitre.org/cwe-6}Categories") @@ -93,14 +93,12 @@ def from_xml(cls, xml_filepath: Union[str, Path]): # The XML contains two weird primary clusters not specified in https://samate.nist.gov/BF/Enlightenment/SFP.html. # After manual inspection, we skip those primary_clusters = frozenset( - ( - PrimarySFPCluster.from_xml(categories, x) - for x in categories - if ( - "SFP Primary Cluster" in x.attrib["Name"] - and x.attrib["Name"] != "SFP Primary Cluster: Failure to Release Memory" - and x.attrib["Name"] != "SFP Primary Cluster: Faulty Resource Release" - ) + PrimarySFPCluster.from_xml(categories, x) + for x in categories + if ( + "SFP Primary Cluster" in x.attrib["Name"] + and x.attrib["Name"] != "SFP Primary Cluster: Failure to Release Memory" + and x.attrib["Name"] != "SFP Primary Cluster: Faulty Resource Release" ) ) @@ -117,7 +115,7 @@ def from_web(cls): return cls.from_xml(Path(tmp_dir) / cls.XML_FILENAME) - def search_cwe(self, cwe_id: int) -> Tuple[Optional[str], Optional[str]]: + def search_cwe(self, cwe_id: int) -> tuple[str | None, str | None]: for primary in self.primary_clusters: for secondary in primary.secondary_clusters: if cwe_id in secondary.children: @@ -127,7 +125,7 @@ def search_cwe(self, cwe_id: int) -> Tuple[Optional[str], Optional[str]]: return None, None -def discover_sar_families(ser: pd.Series) -> List[str]: +def discover_sar_families(ser: pd.Series) -> list[str]: """ Returns a list of all SAR families that occur in the pandas Series, where each entry is a set of SAR objects. """ @@ -138,7 +136,7 @@ def discover_sar_families(ser: pd.Series) -> List[str]: return list(families) -def get_sar_level_from_set(sars: Set[SAR], sar_family: str) -> Optional[int]: +def get_sar_level_from_set(sars: set[SAR], sar_family: str) -> int | None: """ Given a set of SARs and a family name, will return level of the seeked SAR from the set. """ @@ -151,8 +149,8 @@ def get_sar_level_from_set(sars: Set[SAR], sar_family: str) -> Optional[int]: def compute_cve_correlations( df: pd.DataFrame, exclude_vuln_free_certs: bool = False, - sar_families: Optional[List[str]] = None, - output_path: Optional[Union[str, Path]] = None, + sar_families: list[str] | None = None, + output_path: str | Path | None = None, filter_nans: bool = True, ) -> pd.DataFrame: """ @@ -252,11 +250,11 @@ def filter_to_cves_within_validity_period(cc_df: pd.DataFrame, cve_dset: CVEData """ def filter_cves( - cve_dset: CVEDataset, cves: Set[str], not_valid_before: pd.Timestamp, not_valid_after: pd.Timestamp - ) -> Union[Set[str], float]: + cve_dset: CVEDataset, cves: set[str], not_valid_before: pd.Timestamp, not_valid_after: pd.Timestamp + ) -> set[str] | float: # Mypy is complaining, but the Optional date is resolved at the beginning of the and condition - result: Set[str] = { + result: set[str] = { x for x in cves if cve_dset[x].published_date @@ -313,7 +311,7 @@ def expand_df_with_cve_cols(df: pd.DataFrame, cve_dset: CVEDataset) -> pd.DataFr def prepare_cwe_df( cc_df: pd.DataFrame, cve_dset: CVEDataset, fine_grained: bool = False -) -> Tuple[pd.DataFrame, pd.DataFrame]: +) -> tuple[pd.DataFrame, pd.DataFrame]: """ This function does the following: 1. Filter CC DF to columns relevant for CWE examination (eal, related_cves, category) @@ -458,7 +456,7 @@ def get_coarse_grained_cwes(fine_grained_df: pd.DataFrame, cwe_df: pd.DataFrame) def get_top_n_cwes( - df: pd.DataFrame, cwe_df: pd.DataFrame, category: Optional[str] = None, eal: Optional[str] = None, n_cwes: int = 10 + df: pd.DataFrame, cwe_df: pd.DataFrame, category: str | None = None, eal: str | None = None, n_cwes: int = 10 ) -> pd.DataFrame: """Fetches top-n CWEs, overall, per category, or per EAL""" top_n = df.copy() @@ -495,7 +493,7 @@ def compute_maintenances_that_come_after_vulns(df: pd.DataFrame) -> pd.DataFrame def move_fixing_mu_to_directory( - df_fixed: pd.DataFrame, main_df: pd.DataFrame, outdir: Union[str, Path], inpath: Union[str, Path] + df_fixed: pd.DataFrame, main_df: pd.DataFrame, outdir: str | Path, inpath: str | Path ) -> pd.DataFrame: """ Localizes reports of maintenance updates that should fix some vulnerability and copies them into a directory. @@ -523,7 +521,7 @@ def move_fixing_mu_to_directory( def plot_dataframe_graph( - data: Dict, + data: dict, label: str, file_name: str, density: bool = False, diff --git a/setup.py b/setup.py index f532da9f..1db53172 100644 --- a/setup.py +++ b/setup.py @@ -22,10 +22,10 @@ "Intended Audience :: Science/Research", ], python_requires=">=3.8", - install_requires=open("requirements/requirements.in", "r").read().splitlines(), + install_requires=open("requirements/requirements.in").read().splitlines(), extras_require={ - "dev": open("requirements/dev_requirements.in", "r").read().splitlines(), - "test": open("requirements/test_requirements.in", "r").read().splitlines(), + "dev": open("requirements/dev_requirements.in").read().splitlines(), + "test": open("requirements/test_requirements.in").read().splitlines(), }, include_package_data=True, package_data={"sec_certs": ["settings.yaml", "settings-schema.json", "rules.yaml"]}, From 6d665522f33c873f7729ea97307e86ef3e052ffe Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:09:00 +0100 Subject: [PATCH 54/81] bump scipy, dependabot errors on it --- requirements/requirements.in | 2 +- requirements/requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.in b/requirements/requirements.in index 77192041..88723ee0 100644 --- a/requirements/requirements.in +++ b/requirements/requirements.in @@ -26,5 +26,5 @@ en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_ pkgconfig seaborn pySankeyBeta -scipy +scipy>=1.9.0 networkx \ No newline at end of file diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 53c0dbe0..d6ab5b82 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -273,7 +273,7 @@ requests==2.27.1 # spacy scikit-learn==1.0.2 # via -r requirements.in -scipy==1.8.0 +scipy==1.9.3 # via # -r requirements.in # scikit-learn From 40206cddb3f629030655a877f73c326d574f508c Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:13:48 +0100 Subject: [PATCH 55/81] bump pillow lib --- requirements/requirements.in | 2 +- requirements/requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.in b/requirements/requirements.in index 88723ee0..8b66cc67 100644 --- a/requirements/requirements.in +++ b/requirements/requirements.in @@ -9,7 +9,7 @@ numpy pandas pdftotext pikepdf -Pillow>=9.1.1 +Pillow>=9.2.0 PyPDF2 python-dateutil PyYAML diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d6ab5b82..33871c7c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -207,7 +207,7 @@ pickleshare==0.7.5 # via ipython pikepdf==5.1.1 # via -r requirements.in -pillow==9.1.1 +pillow==9.3.0 # via # -r requirements.in # matplotlib From 25dcec98a57fcc043a858b6fcba1d22ea2ef613c Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:17:43 +0100 Subject: [PATCH 56/81] bump Github action versions --- .github/workflows/docs.yml | 2 +- .github/workflows/draft_release.yml | 2 +- .github/workflows/lint.yml | 10 +++++----- .github/workflows/release.yml | 2 +- .github/workflows/tests.yml | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 97eb8827..66c153b1 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -11,7 +11,7 @@ jobs: - uses: actions/checkout@v3 with: fetch-depth: 0 - - uses: actions/setup-python@v2 + - uses: actions/setup-python@v4 with: python-version: 3.8 - name: Install external dependencies diff --git a/.github/workflows/draft_release.yml b/.github/workflows/draft_release.yml index be7618a7..55c6a38a 100644 --- a/.github/workflows/draft_release.yml +++ b/.github/workflows/draft_release.yml @@ -11,7 +11,7 @@ jobs: if: (github.repository == 'crocs-muni/sec-certs') && (github.ref == 'refs/heads/main') steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v3 - name: Release uses: softprops/action-gh-release@v1 with: diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index c908adb6..d2479c55 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -6,9 +6,9 @@ jobs: run-mypy: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: "3.9" - name: Install external dependencies @@ -22,13 +22,13 @@ jobs: black: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: psf/black@stable isort: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 with: python-version: 3.8 - uses: isort/isort-action@master diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 0682582a..7f3338f0 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -13,7 +13,7 @@ jobs: with: fetch-depth: 0 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: "3.8" - name: Install build dependencies diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 06861ed0..558ade11 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -17,9 +17,9 @@ jobs: sudo add-apt-repository ppa:hlprasu/poppler sudo apt-get update sudo apt-get install -y build-essential libpoppler-cpp-dev pkg-config python3-dev - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: "3.8" - name: Install python dependencies From ca0c4e2229ba5bd1e457d57decd3a1ef3cbe05a4 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:24:03 +0100 Subject: [PATCH 57/81] convert examples to notebooks --- examples/cc_cpe_labeling.py | 52 -------------------- examples/fips_cpe_labeling.py | 41 ---------------- examples/fips_iut_demo.py | 30 ------------ examples/fips_mip_demo.py | 30 ------------ examples/readme.md | 80 ------------------------------- notebooks/examples/fips_iut.ipynb | 45 +++++++++++++++++ notebooks/examples/fips_mip.ipynb | 54 +++++++++++++++++++++ 7 files changed, 99 insertions(+), 233 deletions(-) delete mode 100644 examples/cc_cpe_labeling.py delete mode 100644 examples/fips_cpe_labeling.py delete mode 100755 examples/fips_iut_demo.py delete mode 100755 examples/fips_mip_demo.py delete mode 100644 examples/readme.md create mode 100644 notebooks/examples/fips_iut.ipynb create mode 100644 notebooks/examples/fips_mip.ipynb diff --git a/examples/cc_cpe_labeling.py b/examples/cc_cpe_labeling.py deleted file mode 100644 index 63c030fd..00000000 --- a/examples/cc_cpe_labeling.py +++ /dev/null @@ -1,52 +0,0 @@ -import logging -from datetime import datetime -from pathlib import Path - -from sec_certs.config.configuration import config -from sec_certs.dataset import CCDataset -from sec_certs.model.evaluation import evaluate, get_validation_dgsts - -logger = logging.getLogger(__name__) - - -def main(): - file_handler = logging.FileHandler(config.log_filepath) - stream_handler = logging.StreamHandler() - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - stream_handler.setFormatter(formatter) - logging.basicConfig(level=logging.INFO, handlers=[file_handler, stream_handler]) - start = datetime.now() - - # Fetch dataset metadata from CC Website, don't download nor parse PDFS - dset = CCDataset({}, Path("./my_debug_dataset"), "cc_full_dataset", "Full CC dataset") - dset.get_certs_from_web(to_download=True) - - # Automatically match CPEs and CVEs - dset.compute_cpe_heuristics() - dset.compute_related_cves() - - # Load dataset of ground truth CPE labels - dset.load_label_studio_labels(Path(__file__).parent.parent / "data/manual_cpe_labels/cc.json") - - # Limit dataset only to validation part - validation_dgsts = get_validation_dgsts( - Path(__file__).parent.parent / "data/validation_test_split/cc/validation.json" - ) - validation_certs = [x for x in dset if x.dgst in validation_dgsts] - - # Evaluate CPE matching performance metrics (on validation set) and dump classification report into json - y_valid = [(x.heuristics.verified_cpe_matches) for x in validation_certs] - assert dset.auxillary_datasets.cpe_dset - evaluate( - validation_certs, y_valid, "./my_debug_dataset/classification_report.json", dset.auxillary_datasets.cpe_dset - ) - - logger.info(f"{dset.json_path} should now contain fully labeled dataset.") - - end = datetime.now() - logger.info(f"The computation took {(end-start)} seconds.") - - -if __name__ == "__main__": - main() diff --git a/examples/fips_cpe_labeling.py b/examples/fips_cpe_labeling.py deleted file mode 100644 index d81c4abe..00000000 --- a/examples/fips_cpe_labeling.py +++ /dev/null @@ -1,41 +0,0 @@ -import logging -from datetime import datetime -from pathlib import Path - -from sec_certs.dataset import FIPSDataset - -logger = logging.getLogger(__name__) - - -def main(): - logging.basicConfig(level=logging.INFO) - start = datetime.now() - - # dset: FIPSDataset = FIPSDataset({}, Path('./my_debug_dataset'), 'sample_dataset', 'sample dataset description') - # dset.get_certs_from_web(no_download_algorithms=True) - - dset = FIPSDataset({}, Path("../fips_dataset"), "sample_dataset", "sample dataset description") - dset.get_certs_from_web() - # # Label CPE and compute related vulnerabilities - # dset.compute_cpe_heuristics() - # dset.compute_related_cves() - # - # # Load dataset of ground truth CPE labels - # dset.load_label_studio_labels(Path(__file__).parent.parent / 'data/manual_cpe_labels/fips.json') - # - # # Limit dataset only to validation part - # validation_dgsts = get_validation_dgsts(Path(__file__).parent.parent / 'data/validation_test_split/fips/validation.json') - # validation_certs = [x for x in dset if x.dgst in validation_dgsts] - # - # # Evaluate CPE matching performance metrics (on validation set) and dump classification report into json - # y_valid = [x.heuristics.verified_cpe_matches for x in validation_certs] - # evaluate(validation_certs, y_valid, './my_debug_dataset/classification_report.json') - - logger.info(f"{dset.json_path} should now contain fully labeled dataset.") - - end = datetime.now() - logger.info(f"The computation took {(end - start)} seconds.") - - -if __name__ == "__main__": - main() diff --git a/examples/fips_iut_demo.py b/examples/fips_iut_demo.py deleted file mode 100755 index 2d193e1f..00000000 --- a/examples/fips_iut_demo.py +++ /dev/null @@ -1,30 +0,0 @@ -#!/usr/bin/env python3 - -import click - -from sec_certs.dataset import IUTDataset - - -@click.command() -@click.argument("directory", type=click.Path(exists=True, file_okay=False)) -@click.argument("output", type=click.Path(dir_okay=False, writable=True)) -def main(directory, output): - """ - Parse FIPS 'Implementation Under Test' pages. - - \b - To use, download pages from the URL: - https://csrc.nist.gov/Projects/cryptographic-module-validation-program/modules-in-process/IUT-List - into a directory `d` and name them `fips_iut_.html`. - - \b - Then run: - in_process.py fips-iut d output.json - to obtain the parsed output in `output.json`. - """ - dataset = IUTDataset.from_dumps(directory) - dataset.to_json(output) - - -if __name__ == "__main__": - main() diff --git a/examples/fips_mip_demo.py b/examples/fips_mip_demo.py deleted file mode 100755 index e29ad16b..00000000 --- a/examples/fips_mip_demo.py +++ /dev/null @@ -1,30 +0,0 @@ -#!/usr/bin/env python3 - -import click - -from sec_certs.dataset import MIPDataset - - -@click.command() -@click.argument("directory", type=click.Path(exists=True, file_okay=False)) -@click.argument("output", type=click.Path(dir_okay=False, writable=True)) -def main(directory, output): - """ - Parse FIPS 'Modules In Process' pages. - - \b - To use, download pages from the URL: - https://csrc.nist.gov/Projects/cryptographic-module-validation-program/modules-in-process/Modules-In-Process-List - into a directory `d` and name them `fips_mip_.html`. - - \b - Then run: - in_process.py fips-mip d output.json - to obtain the parsed output in `output.json`. - """ - dataset = MIPDataset.from_dumps(directory) - dataset.to_json(output) - - -if __name__ == "__main__": - main() diff --git a/examples/readme.md b/examples/readme.md deleted file mode 100644 index 7687dbf7..00000000 --- a/examples/readme.md +++ /dev/null @@ -1,80 +0,0 @@ -## New CommonCriteria API - -The file [cc_oop_demo.py](cc_oop_demo.py) contains a rough overview of public methods available on `CCDataset`. The chain in which the methods are run roughly corresponds to running `cc-certs all` with CLI. - - -## Manual CPE labeling - -The tool contains a fuzzy procedure that attempts to map [CPE names](https://nvd.nist.gov/products/cpe) to CC certificates. Result is a list of potentially promising matchings that should be manually evaluated by an analyst to obtain ground truth labeling. There are two ways of how one can manually evaluate the suggested matches: - -1. Exporting the matches into [label studio](https://labelstud.io/) format, label them there and import the acquired ground truth back into dataset -2. Do the same thing using command line prompts (unrecommended, further unmaintained) - -We proceed with description of both methods. - -### Labeling in Label studio - -[Label studio](https://labelstud.io/) is a web UI for labeling of datasets. To label CPE dataset, one must do the following: - -1. Export the candidate matches into json that can be imported into label studio - -```python -dset = CCDataset.from_json('path/to/your/dataset.json') -dset.to_label_studio('./label_studio_input.json') -``` - -2. Label the instances in the label studio, choose the labeling interface according to example in [label_studio_interface.txt](label_studio_interface.txt) and export them using [JSON-MIN](https://labelstud.io/guide/export.html#JSON-MIN) option into a file, say `./label_studio_output.json`. -3. Import them back into your dataset and save the updated version of the dataset - -```python -dset = CCDataset.from_json('path/to/your/dataset.json') -dset.load_label_studio_labels('./label_studio_output.json', update_json=True) -``` - -:tada you should now have your dataset labeled. - -### Manual labeling - -The analyst should run the file [cc_cpe_labeling.py](cc_cpe_labeling.py) - -```python -dset = CCDataset({}, Path('./my_debug_datset'), 'cc_full_dataset', 'Full CC dataset') -dset.get_certs_from_web(to_download=True, update_json=True) -dset._compute_heuristics() -dset.manually_verify_cpe_matches() -``` - -For each of the certificates, the user is then prompted for an expert knowledge, see example below: - -``` -[0/1516] Vendor: NetIQ Corporation, Name: NetIQ Identity Manager 4.7 - - [0]: netiq NetIQ Sentinel CPE-URI: cpe:2.3:a:netiq:sentinel:-:*:*:*:*:*:*:* - - [1]: netiq NetIQ Sentinel Agent Manager CPE-URI: cpe:2.3:a:netiq:sentinel_agent_manager:-:*:*:*:*:*:*:* - - [A]: All are fitting - - [X]: No fitting match -Select fitting CPE matches (split with comma if choosing more): -``` - -Here, one should type `X` (case insensitive) and press enter, since all guesses are false positives. In different case - -``` -[1/1516] Vendor: NetIQ, Incorporated, Name: NetIQ Access Manager 4.5 - - [0]: netiq NetIQ Access Manager 4.5 CPE-URI: cpe:2.3:a:netiq:access_manager:4.5:-:*:*:*:*:*:* - - [1]: netiq NetIQ Access Manager 4.5 Service Pack 1 CPE-URI: cpe:2.3:a:netiq:access_manager:4.5:sp1:*:*:*:*:*:* - - [2]: netiq NetIQ Access Manager 4.5 Hotfix 1 CPE-URI: cpe:2.3:a:netiq:access_manager:4.5:hotfix1:*:*:*:*:*:* - - [A]: All are fitting - - [X]: No fitting match -``` - -one may answer with `0,1,2` or simply `A` as all CPEs may be releated to the certificate. - -The progress of the expert is periodically saved. Currently, there's no way to gracefully exit the process, just do keyboard interrupt if you want to stop. The json will be updated and next time you will get prompted only for the unlabeled certificates. - -We strongly suggest you try the process with `dset.manually_verify_cpe_matches(update_json=False)` to experiment with correct inputs/outputs. While you will get prompted again if the input is recognized incorrect, the `update_json=False` will not store the results so you can experiment with the tool without loosing your results or creating bad labels. - -Would you want to exit in the middle and return back after a while, just exit with CTRL+C. You can then label the rest of the unlabeled certificates with - -```python -dset = CCDataset.from_json('./my_debug_datset/cc_full_dataset.json') -dset.manually_verify_cpe_matches() -``` diff --git a/notebooks/examples/fips_iut.ipynb b/notebooks/examples/fips_iut.ipynb new file mode 100644 index 00000000..77138282 --- /dev/null +++ b/notebooks/examples/fips_iut.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parse FIPS 'Implementation Under Test' pages.\n", + "\n", + "To use, download pages from the URL: https://csrc.nist.gov/Projects/cryptographic-module-validation-program/modules-in-process/IUT-List\n", + "into a `directory` and name them `fips_iut_.html`.\n", + "\n", + "Then run the cells below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sec_certs.dataset import IUTDataset\n", + "from pathlib import Path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "directory = Path(\"/path/to/directory/with/fips_iut_.html\")\n", + "dataset = IUTDataset.from_dumps(directory)\n", + "dataset.to_json(\"output.json\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/examples/fips_mip.ipynb b/notebooks/examples/fips_mip.ipynb new file mode 100644 index 00000000..7955a32f --- /dev/null +++ b/notebooks/examples/fips_mip.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parse FIPS 'Modules In Process' pages.\n", + "\n", + "To use, download pages from the URL: https://csrc.nist.gov/Projects/cryptographic-module-validation-program/modules-in-process/Modules-In-Process-List into a `directory` and name them `fips_mip_.html`.\n", + "\n", + "Then run the following cells" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sec_certs.dataset import MIPDataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "directory = \"/path/to/directory/with/fips_mip_.html\"\n", + "dataset = MIPDataset.from_dumps(directory)\n", + "dataset.to_json(\"output.json\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 86a62cb7e212bfd8f956abcdeffaf9614f27891b Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Fri, 2 Dec 2022 16:51:48 +0100 Subject: [PATCH 58/81] fips normalize embodiment string --- sec_certs/sample/fips.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 70f6d9ab..0d8be343 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -147,6 +147,17 @@ def parse_tested_configurations(tested_configurations: Tag) -> list[str] | None: configurations = [y.text for y in tested_configurations.find_all("li")] return configurations if not configurations == ["N/A"] else None + @staticmethod + def normalize_embodiment(embodiment_element: Tag) -> str: + text = FIPSHTMLParser.normalize_string(embodiment_element.text) + embodiment_normalization_dict = { + "Multi-chip embedded": "Multi-Chip Embedded", + "Multi-chip Standalone": "Multi-Chip Stand Alone", + "Multi-chip standalone": "Multi-Chip Stand Alone", + "Single-chip": "Single Chip", + } + return embodiment_normalization_dict.get(text, text) + DETAILS_KEY_NORMALIZATION_DICT: Final[dict[str, str]] = { "Module Name": "module_name", @@ -178,6 +189,7 @@ def parse_tested_configurations(tested_configurations: Tag) -> list[str] | None: "exceptions": lambda x: [y.text for y in x.find_all("li")], "status": lambda x: FIPSHTMLParser.normalize_string(x.text).lower(), "level": lambda x: int(FIPSHTMLParser.normalize_string(x.text)), + "embodiment": getattr(FIPSHTMLParser, "normalize_embodiment"), } From 6ce7007ab5b5f721abf6d8659ef4bd54e4f61590 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 15:35:22 +0100 Subject: [PATCH 59/81] unify from __future__ import annotations --- .pre-commit-config.yaml | 2 + requirements/dev_requirements.in | 1 + sec_certs/cert_rules.py | 8 ++- sec_certs/cli.py | 28 ++++----- sec_certs/config/configuration.py | 8 ++- sec_certs/dataset/cpe.py | 34 +++++------ sec_certs/dataset/cve.py | 30 +++++----- sec_certs/dataset/dataset.py | 44 +++++++------- sec_certs/dataset/fips.py | 30 +++++----- sec_certs/dataset/fips_algorithm.py | 22 +++---- sec_certs/dataset/fips_iut.py | 14 +++-- sec_certs/dataset/fips_mip.py | 14 +++-- sec_certs/dataset/protection_profile.py | 13 +++-- sec_certs/model/cpe_matching.py | 58 ++++++++++--------- sec_certs/model/evaluation.py | 11 ++-- sec_certs/model/reference_finder.py | 12 ++-- .../model/transitive_vulnerability_finder.py | 12 ++-- sec_certs/sample/cc_certificate_id.py | 5 +- sec_certs/sample/cc_maintenance_update.py | 20 ++++--- sec_certs/sample/certificate.py | 28 ++++----- sec_certs/sample/cpe.py | 26 +++++---- sec_certs/sample/fips_algorithm.py | 8 ++- sec_certs/sample/fips_iut.py | 34 ++++++----- sec_certs/sample/fips_mip.py | 26 +++++---- sec_certs/sample/protection_profile.py | 16 ++--- sec_certs/serialization/json.py | 16 ++--- sec_certs/utils/helpers.py | 28 ++++----- sec_certs/utils/parallel_processing.py | 12 ++-- sec_certs/utils/pdf.py | 12 ++-- sec_certs/utils/sanitization.py | 11 ++-- sec_certs/utils/tables.py | 11 ++-- tests/cc/test_cc_analysis.py | 15 ++--- tests/fips/test_fips_algorithm_dataset.py | 8 ++- tests/fips/test_fips_analysis.py | 19 +++--- tests/test_cpe.py | 6 +- tests/test_cve.py | 14 +++-- 36 files changed, 359 insertions(+), 297 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 278c9dc8..7e4da57e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -27,3 +27,5 @@ repos: rev: "4.0.1" hooks: - id: flake8 + additional_dependencies: + - "flake8-future-annotations" diff --git a/requirements/dev_requirements.in b/requirements/dev_requirements.in index d7906029..5071c08e 100644 --- a/requirements/dev_requirements.in +++ b/requirements/dev_requirements.in @@ -17,3 +17,4 @@ sphinx-book-theme sphinx-design sphinx-copybutton pyupgrade +flake8-future-annotations diff --git a/sec_certs/cert_rules.py b/sec_certs/cert_rules.py index 44e0e911..145566f7 100644 --- a/sec_certs/cert_rules.py +++ b/sec_certs/cert_rules.py @@ -1,11 +1,13 @@ +from __future__ import annotations + import re from pathlib import Path -from typing import Dict, Final, List, Set, Tuple +from typing import Final import yaml # This ignores ACM and AMA SARs that are present in CC version 2 -SARS_IMPLIED_FROM_EAL: Dict[str, Set[Tuple[str, int]]] = { +SARS_IMPLIED_FROM_EAL: dict[str, set[tuple[str, int]]] = { "EAL1": { ("ADV_FSP", 1), ("AGD_OPE", 1), @@ -231,7 +233,7 @@ def _process(obj): fips_rules[rule_group] = _process(rules[rule_group]) -PANDAS_KEYWORDS_CATEGORIES: Final[List[str]] = [ +PANDAS_KEYWORDS_CATEGORIES: Final[list[str]] = [ "symmetric_crypto", "asymmetric_crypto", "pq_crypto", diff --git a/sec_certs/cli.py b/sec_certs/cli.py index 6ae117a0..fd665057 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -1,10 +1,12 @@ #!/usr/bin/env python3 +from __future__ import annotations + import logging import sys from dataclasses import dataclass, field from datetime import datetime from pathlib import Path -from typing import Callable, List, Optional, Type, Union +from typing import Callable import click @@ -20,11 +22,11 @@ class ProcessingStep: name: str processing_function_name: str - preconditions: List[str] = field(default_factory=list) - precondition_error_msg: Optional[str] = field(default=None) - pre_callback_func: Optional[Callable] = field(default=None) + preconditions: list[str] = field(default_factory=list) + precondition_error_msg: str | None = field(default=None) + pre_callback_func: Callable | None = field(default=None) - def run(self, dset: Union[CCDataset, FIPSDataset]) -> None: + def run(self, dset: CCDataset | FIPSDataset) -> None: for condition in self.preconditions: if not getattr(dset.state, condition): err_msg = ( @@ -47,12 +49,12 @@ def warn_missing_libs(): def build_or_load_dataset( framework: str, - inputpath: Optional[Path], + inputpath: Path | None, to_build: bool, outputpath: Path = constants.DUMMY_NONEXISTING_PATH, -) -> Union[CCDataset, FIPSDataset]: - constructor: Union[Type[CCDataset], Type[FIPSDataset]] = CCDataset if framework == "cc" else FIPSDataset - dset: Union[CCDataset, FIPSDataset] +) -> CCDataset | FIPSDataset: + constructor: type[CCDataset] | type[FIPSDataset] = CCDataset if framework == "cc" else FIPSDataset + dset: CCDataset | FIPSDataset if to_build: if inputpath: @@ -122,10 +124,10 @@ def build_or_load_dataset( @click.option("-s", "--silent", is_flag=True, help="If set, will not print to stdout") def main( framework: str, - actions: List[str], + actions: list[str], outputpath: Path, - configpath: Optional[str], - inputpath: Optional[Path], + configpath: str | None, + inputpath: Path | None, silent: bool, ): file_handler = logging.FileHandler(config.log_filepath) @@ -133,7 +135,7 @@ def main( formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") file_handler.setFormatter(formatter) stream_handler.setFormatter(formatter) - handlers: List[logging.StreamHandler] = [file_handler] if silent else [file_handler, stream_handler] + handlers: list[logging.StreamHandler] = [file_handler] if silent else [file_handler, stream_handler] logging.basicConfig(level=logging.INFO, handlers=handlers) start = datetime.now() diff --git a/sec_certs/config/configuration.py b/sec_certs/config/configuration.py index cf96786f..43cfe698 100644 --- a/sec_certs/config/configuration.py +++ b/sec_certs/config/configuration.py @@ -1,13 +1,15 @@ +from __future__ import annotations + import json from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import jsonschema import yaml class Configuration: - def load(self, filepath: Union[str, Path]) -> None: + def load(self, filepath: str | Path) -> None: with Path(filepath).open("r") as file: state = yaml.load(file, Loader=yaml.FullLoader) @@ -30,7 +32,7 @@ def __getattribute__(self, key: str) -> Any: return res["value"] return object.__getattribute__(self, key) - def get_desription(self, key: str) -> Optional[str]: + def get_desription(self, key: str) -> str | None: res = object.__getattribute__(self, key) if isinstance(res, dict) and "description" in res: return res["description"] diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index e66fb7ec..e1e16c60 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import copy import itertools import logging @@ -5,7 +7,7 @@ import xml.etree.ElementTree as ET import zipfile from pathlib import Path -from typing import ClassVar, Dict, Iterator, List, Set, Tuple, Union, cast +from typing import ClassVar, Iterator, cast import pandas as pd @@ -30,17 +32,17 @@ class CPEDataset(ComplexSerializableType): def __init__( self, was_enhanced_with_vuln_cpes: bool, - cpes: Dict[str, CPE], - json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, + cpes: dict[str, CPE], + json_path: str | Path = constants.DUMMY_NONEXISTING_PATH, ): self.was_enhanced_with_vuln_cpes = was_enhanced_with_vuln_cpes self.cpes = cpes self._json_path = Path(json_path) - self.vendor_to_versions: Dict[str, Set[str]] = dict() - self.vendor_version_to_cpe: Dict[Tuple[str, str], Set[CPE]] = dict() - self.title_to_cpes: Dict[str, Set[CPE]] = dict() - self.vendors: Set[str] = set() + self.vendor_to_versions: dict[str, set[str]] = dict() + self.vendor_version_to_cpe: dict[tuple[str, str], set[CPE]] = dict() + self.title_to_cpes: dict[str, set[CPE]] = dict() + self.vendors: set[str] = set() self.build_lookup_dicts() @@ -49,7 +51,7 @@ def json_path(self) -> Path: return self._json_path @json_path.setter - def json_path(self, new_json_path: Union[str, Path]) -> None: + def json_path(self, new_json_path: str | Path) -> None: self._json_path = Path(new_json_path) self.to_json() @@ -74,7 +76,7 @@ def __eq__(self, other: object) -> bool: return isinstance(other, CPEDataset) and self.cpes == other.cpes @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return ["was_enhanced_with_vuln_cpes", "cpes"] def build_lookup_dicts(self) -> None: @@ -100,7 +102,7 @@ def build_lookup_dicts(self) -> None: self.title_to_cpes[cpe.title].add(cpe) @classmethod - def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH) -> "CPEDataset": + def from_web(cls, json_path: str | Path = constants.DUMMY_NONEXISTING_PATH) -> CPEDataset: """ Creates CPEDataset from NIST resources published on-line @@ -118,9 +120,7 @@ def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH return cls._from_xml(xml_path, json_path) @classmethod - def _from_xml( - cls, xml_path: Union[str, Path], json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH - ) -> "CPEDataset": + def _from_xml(cls, xml_path: str | Path, json_path: str | Path = constants.DUMMY_NONEXISTING_PATH) -> CPEDataset: logger.info("Loading CPE dataset from XML.") root = ET.parse(xml_path).getroot() dct = {} @@ -144,7 +144,7 @@ def _from_xml( return cls(False, dct, json_path) @classmethod - def from_json(cls, input_path: Union[str, Path]) -> "CPEDataset": + def from_json(cls, input_path: str | Path) -> CPEDataset: """ Loads dataset from json @@ -166,7 +166,7 @@ def to_pandas(self) -> pd.DataFrame: return df @serialize - def enhance_with_cpes_from_cve_dataset(self, cve_dset: Union[CVEDataset, str, Path]) -> None: + def enhance_with_cpes_from_cve_dataset(self, cve_dset: CVEDataset | str | Path) -> None: """ Some CPEs are present only in the CVEDataset and are missing from the CPE Dataset. This method goes through the provided CVEDataset and enriches self with CPEs from @@ -177,8 +177,8 @@ def enhance_with_cpes_from_cve_dataset(self, cve_dset: Union[CVEDataset, str, Pa def _adding_condition( considered_cpe: CPE, - vndr_item_lookup: Set[Tuple[str, str]], - vndr_item_version_lookup: Set[Tuple[str, str, str]], + vndr_item_lookup: set[tuple[str, str]], + vndr_item_version_lookup: set[tuple[str, str, str]], ) -> bool: if ( considered_cpe.version == constants.CPE_VERSION_NA diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index 35952678..f00e3a93 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import datetime import glob import itertools @@ -7,7 +9,7 @@ import tempfile import zipfile from pathlib import Path -from typing import ClassVar, Dict, List, Optional, Set, Union +from typing import ClassVar import numpy as np import pandas as pd @@ -28,22 +30,22 @@ class CVEDataset(ComplexSerializableType): CVE_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cve/1.1/nvdcve-1.1-" CPE_MATCH_FEED_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cpematch/1.0/nvdcpematch-1.0.json.zip" - def __init__(self, cves: Dict[str, CVE], json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH): + def __init__(self, cves: dict[str, CVE], json_path: str | Path = constants.DUMMY_NONEXISTING_PATH): self.cves = cves self._json_path = Path(json_path) - self.cpe_to_cve_ids_lookup: Dict[str, Set[str]] = dict() + self.cpe_to_cve_ids_lookup: dict[str, set[str]] = dict() @property def json_path(self) -> Path: return self._json_path @json_path.setter - def json_path(self, new_json_path: Union[str, Path]) -> None: + def json_path(self, new_json_path: str | Path) -> None: self._json_path = Path(new_json_path) self.to_json() @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return ["cves"] def __iter__(self): @@ -61,7 +63,7 @@ def __len__(self) -> int: def __eq__(self, other: object): return isinstance(other, CVEDataset) and self.cves == other.cves - def build_lookup_dict(self, use_nist_mapping: bool = True, nist_matching_filepath: Optional[Path] = None): + def build_lookup_dict(self, use_nist_mapping: bool = True, nist_matching_filepath: Path | None = None): """ Builds look-up dictionary CPE -> Set[CVE] Developer's note: There are 3 CPEs that are present in the cpe matching feed, but are badly processed by CVE @@ -113,7 +115,7 @@ def download_cves(cls, output_path_str: str, start_year: int, end_year: int): zip_handle.extractall(output_path) @classmethod - def from_nist_json(cls, input_path: str) -> "CVEDataset": + def from_nist_json(cls, input_path: str) -> CVEDataset: with Path(input_path).open("r") as handle: data = json.load(handle) cves = [CVE.from_nist_dict(x) for x in data["CVE_Items"]] @@ -124,7 +126,7 @@ def from_web( cls, start_year: int = 2002, end_year: int = datetime.datetime.now().year, - json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, + json_path: str | Path = constants.DUMMY_NONEXISTING_PATH, ): logger.info("Building CVE dataset from nist.gov website.") with tempfile.TemporaryDirectory() as tmp_dir: @@ -146,16 +148,16 @@ def from_web( return cls(all_cves, json_path) @classmethod - def from_json(cls, input_path: Union[str, Path]): + def from_json(cls, input_path: str | Path): with Path(input_path).open("r") as handle: dset = json.load(handle, cls=CustomJSONDecoder) dset._json_path = input_path return dset - def get_cve_ids_for_cpe_uri(self, cpe_uri: str) -> Optional[Set[str]]: + def get_cve_ids_for_cpe_uri(self, cpe_uri: str) -> set[str] | None: return self.cpe_to_cve_ids_lookup.get(cpe_uri, None) - def filter_related_cpes(self, relevant_cpes: Set[CPE]): + def filter_related_cpes(self, relevant_cpes: set[CPE]): """ Since each of the CVEs is related to many CPEs, the dataset size explodes (serialized). For certificates, only CPEs within sample dataset are relevant. This function modifies all CVE elements. Specifically, it @@ -182,12 +184,12 @@ def to_pandas(self) -> pd.DataFrame: df.cwe_ids = df.cwe_ids.map(lambda x: x if x else np.nan) return df.set_index("cve_id") - def get_nist_cpe_matching_dict(self, input_filepath: Optional[Path]) -> Dict[CPE, List[CPE]]: + def get_nist_cpe_matching_dict(self, input_filepath: Path | None) -> dict[CPE, list[CPE]]: """ Computes dictionary that maps complex CPEs to list of simple CPEs. """ - def parse_key_cpe(field: Dict) -> CPE: + def parse_key_cpe(field: dict) -> CPE: start_version = None if "versionStartIncluding" in field: start_version = ("including", field["versionStartIncluding"]) @@ -202,7 +204,7 @@ def parse_key_cpe(field: Dict) -> CPE: return cached_cpe(field["cpe23Uri"], start_version=start_version, end_version=end_version) - def parse_values_cpe(field: Dict) -> List[CPE]: + def parse_values_cpe(field: dict) -> list[CPE]: return [cached_cpe(x["cpe23Uri"]) for x in field["cpe_name"]] logger.debug("Attempting to get NIST mapping file.") diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 2589d6aa..c5899c88 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import copy import itertools import json @@ -9,7 +11,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Dict, Generic, Iterator, List, Optional, Pattern, Set, Type, TypeVar, Union, cast +from typing import Any, Generic, Iterator, Pattern, TypeVar, cast import pandas as pd @@ -29,8 +31,8 @@ @dataclass class AuxillaryDatasets: - cpe_dset: Optional[CPEDataset] = None - cve_dset: Optional[CVEDataset] = None + cpe_dset: CPEDataset | None = None + cve_dset: CVEDataset | None = None CertSubType = TypeVar("CertSubType", bound=Certificate) @@ -52,12 +54,12 @@ def __bool__(self): def __init__( self, - certs: Dict[str, CertSubType] = dict(), - root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, - name: Optional[str] = None, + certs: dict[str, CertSubType] = dict(), + root_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + name: str | None = None, description: str = None, - state: Optional[DatasetInternalState] = None, - auxillary_datasets: Optional[AuxillaryDatasetsSubType] = None, + state: DatasetInternalState | None = None, + auxillary_datasets: AuxillaryDatasetsSubType | None = None, ): self.certs = certs self._root_dir = Path(root_dir) @@ -81,7 +83,7 @@ def root_dir(self) -> Path: return self._root_dir @root_dir.setter - def root_dir(self, new_dir: Union[str, Path]) -> None: + def root_dir(self, new_dir: str | Path) -> None: old_dset = copy.deepcopy(self) self._root_dir = Path(new_dir) self.root_dir.mkdir(exist_ok=True) @@ -151,13 +153,13 @@ def __str__(self) -> str: return str(type(self).__name__) + ":" + self.name + ", " + str(len(self)) + " certificates" @classmethod - def from_web(cls: Type[DatasetSubType], url: str, progress_bar_desc: str, filename: str) -> DatasetSubType: + def from_web(cls: type[DatasetSubType], url: str, progress_bar_desc: str, filename: str) -> DatasetSubType: with tempfile.TemporaryDirectory() as tmp_dir: dset_path = Path(tmp_dir) / filename helpers.download_file(url, dset_path, show_progress_bar=True, progress_bar_desc=progress_bar_desc) return cls.from_json(dset_path) - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: return { "state": self.state, "timestamp": self.timestamp, @@ -169,7 +171,7 @@ def to_dict(self) -> Dict[str, Any]: } @classmethod - def from_dict(cls: Type[DatasetSubType], dct: Dict) -> DatasetSubType: + def from_dict(cls: type[DatasetSubType], dct: dict) -> DatasetSubType: certs = {x.dgst: x for x in dct["certs"]} dset = cls(certs, name=dct["name"], description=dct["description"], state=dct["state"]) if len(dset) != (claimed := dct["n_certs"]): @@ -179,7 +181,7 @@ def from_dict(cls: Type[DatasetSubType], dct: Dict) -> DatasetSubType: return dset @classmethod - def from_json(cls: Type[DatasetSubType], input_path: Union[str, Path]) -> DatasetSubType: + def from_json(cls: type[DatasetSubType], input_path: str | Path) -> DatasetSubType: dset = cast("DatasetSubType", ComplexSerializableType.from_json(input_path)) dset._root_dir = Path(input_path).parent.absolute() dset._set_local_paths() @@ -198,7 +200,7 @@ def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> No except FileNotFoundError as e: logger.error(f"Attempted to copy dataset from {old_dset.root_dir}, but it's not there ({e}).") - def _get_certs_by_name(self, name: str) -> Set[CertSubType]: + def _get_certs_by_name(self, name: str) -> set[CertSubType]: """ Returns list of certificates that match given name. """ @@ -320,7 +322,7 @@ def _prepare_cve_dataset( @serialize def compute_cpe_heuristics(self, download_fresh_cpes: bool = False) -> CPEClassifier: RELEASE_CANDIDATE_REGEX: Pattern = re.compile(r"rc\d{0,2}$", re.IGNORECASE) - WINDOWS_WEAK_CPES: Set[CPE] = { + WINDOWS_WEAK_CPES: set[CPE] = { CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", "Microsoft Windows on X64", None, None), CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x86:*", "Microsoft Windows on X86", None, None), } @@ -374,7 +376,7 @@ def filter_condition(cpe: CPE) -> bool: return clf - def to_label_studio_json(self, output_path: Union[str, Path]) -> None: + def to_label_studio_json(self, output_path: str | Path) -> None: cpe_dset = self._prepare_cpe_dataset() lst = [] @@ -390,12 +392,12 @@ def to_label_studio_json(self, output_path: Union[str, Path]) -> None: json.dump(lst, handle, indent=4) @serialize - def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: + def load_label_studio_labels(self, input_path: str | Path) -> set[str]: with Path(input_path).open("r") as handle: data = json.load(handle) cpe_dset = self._prepare_cpe_dataset() - labeled_cert_digests: Set[str] = set() + labeled_cert_digests: set[str] = set() logger.info("Translating label studio matches into their CPE representations and assigning to certificates.") for annotation in tqdm(data, desc="Translating label studio matches"): @@ -404,7 +406,7 @@ def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: } if "verified_cpe_match" not in annotation: - incorrect_keys: Set[str] = set() + incorrect_keys: set[str] = set() elif isinstance(annotation["verified_cpe_match"], str): incorrect_keys = {annotation["verified_cpe_match"]} else: @@ -413,7 +415,7 @@ def load_label_studio_labels(self, input_path: Union[str, Path]) -> Set[str]: incorrect_keys = {x.lstrip("$") for x in incorrect_keys} predicted_annotations = {annotation[x] for x in cpe_candidate_keys - incorrect_keys} - cpes: Set[CPE] = set() + cpes: set[CPE] = set() for x in predicted_annotations: if x not in cpe_dset.title_to_cpes: logger.error(f"{x} not in dataset") @@ -502,7 +504,7 @@ def get_keywords_df(self, var: str) -> pd.DataFrame: data = [dict({"dgst": x.dgst}, **x.pdf_data.get_keywords_df_data(var)) for x in self] return pd.DataFrame(data).set_index("dgst") - def update_with_certs(self, certs: List[CertSubType]) -> None: + def update_with_certs(self, certs: list[CertSubType]) -> None: """ Enriches the dataset with `certs` :param List[CommonCriteriaCert] certs: new certs to include into the dataset. diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 2adca7d9..efe07bfe 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import datetime import itertools import logging import shutil from pathlib import Path -from typing import Dict, Final, Optional, Set, Union +from typing import Final import numpy as np import pandas as pd @@ -27,9 +29,9 @@ class FIPSAuxillaryDatasets(AuxillaryDatasets): - cpe_dset: Optional[CPEDataset] = None - cve_dset: Optional[CVEDataset] = None - algorithm_dset: Optional[FIPSAlgorithmDataset] = None + cpe_dset: CPEDataset | None = None + cve_dset: CVEDataset | None = None + algorithm_dset: FIPSAlgorithmDataset | None = None class FIPSDataset(Dataset[FIPSCertificate, FIPSAuxillaryDatasets], ComplexSerializableType): @@ -39,12 +41,12 @@ class FIPSDataset(Dataset[FIPSCertificate, FIPSAuxillaryDatasets], ComplexSerial def __init__( self, - certs: Dict[str, FIPSCertificate] = dict(), - root_dir: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH, - name: Optional[str] = None, + certs: dict[str, FIPSCertificate] = dict(), + root_dir: str | Path = constants.DUMMY_NONEXISTING_PATH, + name: str | None = None, description: str = None, - state: Optional[Dataset.DatasetInternalState] = None, - auxillary_datasets: Optional[FIPSAuxillaryDatasets] = None, + state: Dataset.DatasetInternalState | None = None, + auxillary_datasets: FIPSAuxillaryDatasets | None = None, ): self.certs = certs self._root_dir = Path(root_dir) @@ -57,7 +59,7 @@ def __init__( auxillary_datasets if auxillary_datasets else FIPSAuxillaryDatasets() ) - LIST_OF_CERTS_HTML: Final[Dict[str, str]] = { + LIST_OF_CERTS_HTML: Final[dict[str, str]] = { "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, "fips_modules_historical.html": constants.FIPS_HISTORICAL_MODULES_URL, "fips_modules_revoked.html": constants.FIPS_REVOKED_MODULES_URL, @@ -196,19 +198,19 @@ def _download_html_resources(self) -> None: html_paths = [self.web_dir / x for x in FIPSDataset.LIST_OF_CERTS_HTML.keys()] helpers.download_parallel(html_urls, html_paths) - def _get_all_certs_from_html_sources(self) -> Set[FIPSCertificate]: + def _get_all_certs_from_html_sources(self) -> set[FIPSCertificate]: return set( itertools.chain.from_iterable( [self._get_certificates_from_html(self.web_dir / x) for x in self.LIST_OF_CERTS_HTML.keys()] ) ) - def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: + def _get_certificates_from_html(self, html_file: Path) -> set[FIPSCertificate]: with open(html_file, encoding="utf-8") as handle: html = BeautifulSoup(handle.read(), "html5lib") table = [x for x in html.find(id="searchResultsTable").tbody.contents if x != "\n"] - cert_ids: Set[str] = set() + cert_ids: set[str] = set() for entry in table: if isinstance(entry, NavigableString): @@ -220,7 +222,7 @@ def _get_certificates_from_html(self, html_file: Path) -> Set[FIPSCertificate]: return {FIPSCertificate(cert_id) for cert_id in cert_ids} @classmethod - def from_web_latest(cls) -> "FIPSDataset": + def from_web_latest(cls) -> FIPSDataset: """ Fetches the fresh snapshot of FIPSDataset from mirror. """ diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index 9edb5427..ee70db60 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import itertools import logging import re from pathlib import Path from tempfile import TemporaryDirectory -from typing import Dict, Iterator, List, Set, Union, cast +from typing import Iterator, cast import pandas as pd from bs4 import BeautifulSoup @@ -18,16 +20,16 @@ class FIPSAlgorithmDataset(ComplexSerializableType): def __init__( - self, algs: Dict[str, FIPSAlgorithm] = dict(), json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH + self, algs: dict[str, FIPSAlgorithm] = dict(), json_path: str | Path = constants.DUMMY_NONEXISTING_PATH ): self.algs = algs self._json_path = Path(json_path) - self.alg_number_to_algs: Dict[str, Set[FIPSAlgorithm]] = dict() + self.alg_number_to_algs: dict[str, set[FIPSAlgorithm]] = dict() self._build_lookup_dicts() @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return ["algs"] @property @@ -35,7 +37,7 @@ def json_path(self) -> Path: return self._json_path @json_path.setter - def json_path(self, new_json_path: Union[str, Path]) -> None: + def json_path(self, new_json_path: str | Path) -> None: self._json_path = Path(new_json_path) self.to_json() @@ -60,7 +62,7 @@ def __eq__(self, other: object) -> bool: return isinstance(other, FIPSAlgorithmDataset) and self.algs == other.algs @classmethod - def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH) -> "FIPSAlgorithmDataset": + def from_web(cls, json_path: str | Path = constants.DUMMY_NONEXISTING_PATH) -> FIPSAlgorithmDataset: with TemporaryDirectory() as tmp_dir: htmls = FIPSAlgorithmDataset.download_alg_list_htmls(Path(tmp_dir)) algs = set( @@ -69,7 +71,7 @@ def from_web(cls, json_path: Union[str, Path] = constants.DUMMY_NONEXISTING_PATH return cls({x.dgst: x for x in algs}, json_path) @staticmethod - def download_alg_list_htmls(output_dir: Path) -> List[Path]: + def download_alg_list_htmls(output_dir: Path) -> list[Path]: first_page_path = output_dir / "page1.html" ITEMS_PER_PAGE = "ipp=250" @@ -104,7 +106,7 @@ def get_number_of_html_pages(html_path: Path) -> int: return int(soup.select("span[data-total-pages]")[0].attrs["data-total-pages"]) @staticmethod - def parse_algorithms_from_html(html_path: Path) -> Set[FIPSAlgorithm]: + def parse_algorithms_from_html(html_path: Path) -> set[FIPSAlgorithm]: df = pd.read_html(html_path)[0] df["alg_type"] = df["Validation Number"].map(lambda x: re.sub(r"[0-9\s]", "", x)) df["alg_number"] = df["Validation Number"].map(lambda x: re.sub(r"[^0-9]", "", x)) @@ -117,7 +119,7 @@ def parse_algorithms_from_html(html_path: Path) -> Set[FIPSAlgorithm]: return set(df["alg"]) @classmethod - def from_json(cls, input_path: Union[str, Path]) -> "FIPSAlgorithmDataset": + def from_json(cls, input_path: str | Path) -> FIPSAlgorithmDataset: dset = cast("FIPSAlgorithmDataset", ComplexSerializableType.from_json(input_path)) dset._json_path = Path(input_path) return dset @@ -134,5 +136,5 @@ def _build_lookup_dicts(self) -> None: else: self.alg_number_to_algs[alg.alg_number].add(alg) - def get_algorithms_by_id(self, alg_number: str) -> Set[FIPSAlgorithm]: + def get_algorithms_by_id(self, alg_number: str) -> set[FIPSAlgorithm]: return self.alg_number_to_algs.get(alg_number, set()) diff --git a/sec_certs/dataset/fips_iut.py b/sec_certs/dataset/fips_iut.py index 9604c63d..f4221e91 100644 --- a/sec_certs/dataset/fips_iut.py +++ b/sec_certs/dataset/fips_iut.py @@ -1,7 +1,9 @@ +from __future__ import annotations + from dataclasses import dataclass from pathlib import Path from tempfile import NamedTemporaryFile -from typing import Dict, Iterator, List, Mapping, Union +from typing import Iterator, Mapping import requests @@ -14,7 +16,7 @@ @dataclass class IUTDataset(ComplexSerializableType): - snapshots: List[IUTSnapshot] + snapshots: list[IUTSnapshot] def __iter__(self) -> Iterator[IUTSnapshot]: yield from self.snapshots @@ -26,7 +28,7 @@ def __len__(self) -> int: return len(self.snapshots) @classmethod - def from_dumps(cls, dump_path: Union[str, Path]) -> "IUTDataset": + def from_dumps(cls, dump_path: str | Path) -> IUTDataset: directory = Path(dump_path) fnames = list(directory.glob("*")) snapshots = [] @@ -37,15 +39,15 @@ def from_dumps(cls, dump_path: Union[str, Path]) -> "IUTDataset": logger.error(e) return cls(snapshots) - def to_dict(self) -> Dict[str, List[IUTSnapshot]]: + def to_dict(self) -> dict[str, list[IUTSnapshot]]: return {"snapshots": list(self.snapshots)} @classmethod - def from_dict(cls, dct: Mapping) -> "IUTDataset": + def from_dict(cls, dct: Mapping) -> IUTDataset: return cls(dct["snapshots"]) @classmethod - def from_web_latest(cls) -> "IUTDataset": + def from_web_latest(cls) -> IUTDataset: """ Get the IUTDataset from seccerts.org """ diff --git a/sec_certs/dataset/fips_mip.py b/sec_certs/dataset/fips_mip.py index 8927ee8a..c913879a 100644 --- a/sec_certs/dataset/fips_mip.py +++ b/sec_certs/dataset/fips_mip.py @@ -1,7 +1,9 @@ +from __future__ import annotations + from dataclasses import dataclass from pathlib import Path from tempfile import NamedTemporaryFile -from typing import Dict, Iterator, List, Mapping, Union +from typing import Iterator, Mapping import requests @@ -14,7 +16,7 @@ @dataclass class MIPDataset(ComplexSerializableType): - snapshots: List[MIPSnapshot] + snapshots: list[MIPSnapshot] def __iter__(self) -> Iterator[MIPSnapshot]: yield from self.snapshots @@ -26,7 +28,7 @@ def __len__(self) -> int: return len(self.snapshots) @classmethod - def from_dumps(cls, dump_path: Union[str, Path]) -> "MIPDataset": + def from_dumps(cls, dump_path: str | Path) -> MIPDataset: directory = Path(dump_path) fnames = list(directory.glob("*")) snapshots = [] @@ -37,15 +39,15 @@ def from_dumps(cls, dump_path: Union[str, Path]) -> "MIPDataset": logger.error(e) return cls(snapshots) - def to_dict(self) -> Dict[str, List[MIPSnapshot]]: + def to_dict(self) -> dict[str, list[MIPSnapshot]]: return {"snapshots": list(self.snapshots)} @classmethod - def from_dict(cls, dct: Mapping) -> "MIPDataset": + def from_dict(cls, dct: Mapping) -> MIPDataset: return cls(dct["snapshots"]) @classmethod - def from_web_latest(cls) -> "MIPDataset": + def from_web_latest(cls) -> MIPDataset: """ Get the MIPDataset from seccerts.org """ diff --git a/sec_certs/dataset/protection_profile.py b/sec_certs/dataset/protection_profile.py index 58ea2044..d8320a04 100644 --- a/sec_certs/dataset/protection_profile.py +++ b/sec_certs/dataset/protection_profile.py @@ -1,9 +1,10 @@ +from __future__ import annotations + import json import logging import tempfile from dataclasses import dataclass from pathlib import Path -from typing import Dict, Optional, Tuple, Union import sec_certs.utils.helpers as helpers from sec_certs.config.configuration import config @@ -14,15 +15,15 @@ @dataclass class ProtectionProfileDataset: - pps: Dict[Tuple[str, Optional[str]], ProtectionProfile] + pps: dict[tuple[str, str | None], ProtectionProfile] def __iter__(self): yield from self.pps.values() - def __getitem__(self, item: Tuple[str, Optional[str]]) -> ProtectionProfile: + def __getitem__(self, item: tuple[str, str | None]) -> ProtectionProfile: return self.pps.__getitem__(item) - def __setitem__(self, key: Tuple[str, Optional[str]], value: ProtectionProfile): + def __setitem__(self, key: tuple[str, str | None], value: ProtectionProfile): self.pps.__setitem__(key, value) def __contains__(self, key): @@ -32,7 +33,7 @@ def __len__(self) -> int: return len(self.pps) @classmethod - def from_json(cls, json_path: Union[str, Path]): + def from_json(cls, json_path: str | Path): with Path(json_path).open("r") as handle: data = json.load(handle) pps = [ProtectionProfile.from_old_api_dict(x) for x in data.values()] @@ -46,7 +47,7 @@ def from_json(cls, json_path: Union[str, Path]): return cls(dct) @classmethod - def from_web(cls, store_dataset_path: Optional[Path] = None): + def from_web(cls, store_dataset_path: Path | None = None): logger.info(f"Downloading static PP dataset from: {config.pp_latest_snapshot}") if not store_dataset_path: diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index aff7dd30..39179a7d 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -1,8 +1,10 @@ +from __future__ import annotations + import itertools import logging import operator import re -from typing import Dict, List, Optional, Pattern, Set, Tuple +from typing import Pattern import spacy from rapidfuzz import fuzz @@ -22,16 +24,16 @@ class CPEClassifier(BaseEstimator): Fit method is called on list of CPEs and build two look-up dictionaries, see description of attributes. """ - vendor_to_versions_: Dict[str, Set[str]] # Key: CPE vendor, Value: versions of all CPE records of that vendor - vendor_version_to_cpe_: Dict[Tuple[str, str], Set[CPE]] # Key: (CPE vendor, version), Value: CPEs (vendor, version) - vendors_: Set[str] + vendor_to_versions_: dict[str, set[str]] # Key: CPE vendor, Value: versions of all CPE records of that vendor + vendor_version_to_cpe_: dict[tuple[str, str], set[CPE]] # Key: (CPE vendor, version), Value: CPEs (vendor, version) + vendors_: set[str] def __init__(self, match_threshold: int = 80, n_max_matches: int = 10, spacy_model_to_use: str = "en_core_web_sm"): self.match_threshold = match_threshold self.n_max_matches = n_max_matches self.nlp = spacy.load(spacy_model_to_use, disable=["parser", "ner"]) - def fit(self, X: List[CPE], y: Optional[List[str]] = None) -> "CPEClassifier": + def fit(self, X: list[CPE], y: list[str] | None = None) -> CPEClassifier: """ Just creates look-up structures from provided list of CPEs @@ -43,7 +45,7 @@ def fit(self, X: List[CPE], y: Optional[List[str]] = None) -> "CPEClassifier": return self @staticmethod - def _filter_short_cpes(cpes: List[CPE]) -> List[CPE]: + def _filter_short_cpes(cpes: list[CPE]) -> list[CPE]: """ Short CPE items are super easy to match with 100% rank, but they are hardly informative. This method discards them. @@ -52,7 +54,7 @@ def _filter_short_cpes(cpes: List[CPE]) -> List[CPE]: """ return list(filter(lambda x: x.item_name is not None and len(x.item_name) > 3, cpes)) - def _build_lookup_structures(self, X: List[CPE]) -> None: + def _build_lookup_structures(self, X: list[CPE]) -> None: """ Builds several look-up dictionaries for fast matching. - vendor_to_version_: each vendor is mapped to set of versions that appear in combination with vendor in CPE dataset @@ -73,7 +75,7 @@ def _build_lookup_structures(self, X: List[CPE]) -> None: else: self.vendor_version_to_cpe_[(cpe.vendor, cpe.version)].add(cpe) - def predict(self, X: List[Tuple[str, str, str]]) -> List[Optional[Set[str]]]: + def predict(self, X: list[tuple[str, str, str]]) -> list[set[str] | None]: """ Will predict CPE uris for List of Tuples (vendor, product name, identified versions in product name) @@ -84,12 +86,12 @@ def predict(self, X: List[Tuple[str, str, str]]) -> List[Optional[Set[str]]]: def predict_single_cert( self, - vendor: Optional[str], + vendor: str | None, product_name: str, - versions: Set[str], + versions: set[str], relax_version: bool = False, relax_title: bool = False, - ) -> Optional[Set[str]]: + ) -> set[str] | None: """ Predict List of CPE uris for triplet (vendor, product_name, list_of_versions). The prediction is made as follows: 1. Sanitize vendor name, lemmatize product name. @@ -120,9 +122,9 @@ def predict_single_cert( for cpe in candidates ] threshold = self.match_threshold if not relax_version else 100 - final_matches_aux: List[Tuple[float, CPE]] = list(filter(lambda x: x[0] >= threshold, zip(ratings, candidates))) + final_matches_aux: list[tuple[float, CPE]] = list(filter(lambda x: x[0] >= threshold, zip(ratings, candidates))) final_matches_aux = sorted(final_matches_aux, key=operator.itemgetter(0, 1), reverse=True) - final_matches: Optional[Set[str]] = { + final_matches: set[str] | None = { x[1].uri for x in final_matches_aux[: self.n_max_matches] if x[1].uri is not None } @@ -138,7 +140,7 @@ def predict_single_cert( return final_matches if final_matches else None - def _filter_candidates_by_update(self, cpes: List[CPE], cert_title: str) -> List[CPE]: + def _filter_candidates_by_update(self, cpes: list[CPE], cert_title: str) -> list[CPE]: """ Update means `service pack` or `release`. """ @@ -158,8 +160,8 @@ def filter_condition(regex: Pattern, cpe: CPE, min_value: int, soft: bool = True return cpes - def _filter_candidates_by_platform(self, cpes: List[CPE], cert_title: str) -> List[CPE]: - def filter_condition(cpe: CPE, cert_platforms: Set[str]): + def _filter_candidates_by_platform(self, cpes: list[CPE], cert_title: str) -> list[CPE]: + def filter_condition(cpe: CPE, cert_platforms: set[str]): if not cert_platforms and cpe.target_hw == "*": return True if cert_platforms and cpe.target_hw == "*": @@ -190,8 +192,8 @@ def _compute_best_match( self, cpe: CPE, product_name: str, - candidate_vendors: Optional[Set[str]], - versions: Set[str], + candidate_vendors: set[str] | None, + versions: set[str], relax_title: bool = False, ) -> float: """ @@ -267,20 +269,20 @@ def _discard_trademark_symbols(string: str) -> str: return string.replace("®", "").replace("â„¢", "") @staticmethod - def _strip_manufacturer_and_version(string: str, manufacturers: Optional[Set[str]], versions: Set[str]) -> str: + def _strip_manufacturer_and_version(string: str, manufacturers: set[str] | None, versions: set[str]) -> str: to_strip = versions | manufacturers if manufacturers else versions for x in to_strip: string = string.lower().replace(CPEClassifier._replace_special_chars_with_space(x.lower()), " ").strip() return string @staticmethod - def _standardize_version_in_cert_name(string: str, detected_versions: Set[str]) -> str: + def _standardize_version_in_cert_name(string: str, detected_versions: set[str]) -> str: for ver in detected_versions: version_regex = r"(" + r"(\bversion)\s*" + ver + r"+) | (\bv\s*" + ver + r"+)" string = re.sub(version_regex, " " + ver + " ", string, flags=re.IGNORECASE) return string - def _process_manufacturer(self, manufacturer: str, result: Set) -> Set[str]: + def _process_manufacturer(self, manufacturer: str, result: set) -> set[str]: tokenized = manufacturer.split() if tokenized[0] in self.vendors_: result.add(tokenized[0]) @@ -303,14 +305,14 @@ def _process_manufacturer(self, manufacturer: str, result: Set) -> Set[str]: return set(result) if result else set() - def _get_candidate_list_of_vendors(self, manufacturer: Optional[str]) -> Set[str]: + def _get_candidate_list_of_vendors(self, manufacturer: str | None) -> set[str]: """ Given manufacturer name, this method will find list of plausible vendors from CPE dataset that are likely related. :param Optional[str] manufacturer: manufacturer :return Set[str]: List of related manufacturers, None if nothing relevant is found. """ - result: Set[str] = set() + result: set[str] = set() if not manufacturer: return result @@ -330,8 +332,8 @@ def _get_candidate_list_of_vendors(self, manufacturer: Optional[str]) -> Set[str return self._process_manufacturer(manufacturer, result) def _get_candidate_vendor_version_pairs( - self, cert_candidate_cpe_vendors: Set[str], cert_candidate_versions: Set[str] - ) -> Optional[List[Tuple[str, str]]]: + self, cert_candidate_cpe_vendors: set[str], cert_candidate_versions: set[str] + ) -> list[tuple[str, str]] | None: """ Given parameters, will return Pairs (cpe_vendor, cpe_version) that are relevant to a given sample @@ -341,7 +343,7 @@ def _get_candidate_vendor_version_pairs( :return Optional[List[Tuple[str, str]]]: List of tuples (cpe_vendor, cpe_version) that can be used in the lookup table to search the CPE dataset. """ - def is_cpe_version_among_cert_versions(cpe_version: Optional[str], cert_versions: Set[str]) -> bool: + def is_cpe_version_among_cert_versions(cpe_version: str | None, cert_versions: set[str]) -> bool: def simple_startswith(seeked_version: str, checked_string: str) -> bool: if seeked_version == checked_string: return True @@ -372,7 +374,7 @@ def simple_startswith(seeked_version: str, checked_string: str) -> bool: if not cert_candidate_cpe_vendors: return None - candidate_vendor_version_pairs: List[Tuple[str, str]] = [] + candidate_vendor_version_pairs: list[tuple[str, str]] = [] for vendor in cert_candidate_cpe_vendors: viable_cpe_versions = self.vendor_to_versions_.get(vendor, set()) matched_cpe_versions = [ @@ -381,7 +383,7 @@ def simple_startswith(seeked_version: str, checked_string: str) -> bool: candidate_vendor_version_pairs.extend([(vendor, x) for x in matched_cpe_versions]) return candidate_vendor_version_pairs - def _get_candidate_cpe_matches(self, candidate_vendors: Set[str], candidate_versions: Set[str]) -> List[CPE]: + def _get_candidate_cpe_matches(self, candidate_vendors: set[str], candidate_versions: set[str]) -> list[CPE]: """ Given List of candidate vendors and candidate versions found in certificate, candidate CPE matches are found diff --git a/sec_certs/model/evaluation.py b/sec_certs/model/evaluation.py index e29aa3cd..dd5eaffd 100644 --- a/sec_certs/model/evaluation.py +++ b/sec_certs/model/evaluation.py @@ -1,7 +1,8 @@ +from __future__ import annotations + import json import logging from pathlib import Path -from typing import List, Optional, Set, Union import numpy as np @@ -14,7 +15,7 @@ logger = logging.getLogger(__name__) -def get_validation_dgsts(filepath: Union[str, Path]) -> Set[str]: +def get_validation_dgsts(filepath: str | Path) -> set[str]: with Path(filepath).open("r") as handle: return set(json.load(handle)) @@ -32,9 +33,9 @@ def compute_precision(y: np.ndarray, y_pred: np.ndarray, **kwargs) -> float: def evaluate( - x_valid: List[Union[CommonCriteriaCert, FIPSCertificate]], - y_valid: List[Optional[Set[str]]], - outpath: Optional[Union[Path, str]], + x_valid: list[CommonCriteriaCert | FIPSCertificate], + y_valid: list[set[str] | None], + outpath: Path | str | None, cpe_dset: CPEDataset, ) -> None: y_pred = [x.heuristics.cpe_matches for x in x_valid] diff --git a/sec_certs/model/reference_finder.py b/sec_certs/model/reference_finder.py index 9a94b7e2..117a8b9d 100644 --- a/sec_certs/model/reference_finder.py +++ b/sec_certs/model/reference_finder.py @@ -1,4 +1,6 @@ -from typing import Callable, Dict, List, Optional, Set, Tuple, TypeVar, Union +from __future__ import annotations + +from typing import Callable, Dict, List, Optional, Set, TypeVar from sec_certs.sample.certificate import Certificate, References @@ -76,7 +78,7 @@ def _compute_indirect_references(self, referenced_by: ReferencedByDirect) -> Ref def _build_referenced_by( self, certificates: Certificates, ref_lookup_func: ReferenceLookupFunc - ) -> Tuple[ReferencedByDirect, ReferencedByIndirect]: + ) -> tuple[ReferencedByDirect, ReferencedByIndirect]: referenced_by: ReferencedByDirect = {} for this_cert_id, cert_digests in self.id_mapping.items(): @@ -102,8 +104,8 @@ def _build_referenced_by( return referenced_by, referenced_by_indirect def _get_reverse_references( - self, cert_id: str, references: Union[ReferencedByDirect, ReferencedByIndirect] - ) -> Optional[Set[str]]: + self, cert_id: str, references: ReferencedByDirect | ReferencedByIndirect + ) -> set[str] | None: result = set() for other_id in references: @@ -203,7 +205,7 @@ def wrap(res): wrap(self.references[dgst].get("indirectly_referencing", None)), ) - def predict(self, dgst_list: List[str], keep_unknowns: bool = True) -> Dict[str, References]: + def predict(self, dgst_list: list[str], keep_unknowns: bool = True) -> dict[str, References]: """ Get the references for a list of certificate digests. diff --git a/sec_certs/model/transitive_vulnerability_finder.py b/sec_certs/model/transitive_vulnerability_finder.py index 17fcad84..1d4c8243 100644 --- a/sec_certs/model/transitive_vulnerability_finder.py +++ b/sec_certs/model/transitive_vulnerability_finder.py @@ -1,8 +1,10 @@ +from __future__ import annotations + import logging from collections import Counter from dataclasses import dataclass, field from enum import Enum -from typing import Callable, Dict, List, Optional, Set, TypeVar +from typing import Callable, Dict, Optional, Set, TypeVar from sec_certs.sample.certificate import Certificate, References from sec_certs.serialization.json import ComplexSerializableType @@ -19,8 +21,8 @@ class ReferenceType(Enum): @dataclass class TransitiveCVEs(ComplexSerializableType): - direct_transitive_cves: Optional[Set[str]] = field(default=None) - indirect_transitive_cves: Optional[Set[str]] = field(default=None) + direct_transitive_cves: set[str] | None = field(default=None) + indirect_transitive_cves: set[str] | None = field(default=None) Certificates = Dict[str, CertSubType] @@ -50,7 +52,7 @@ def _fill_dataset_cert_ids_counter(self) -> None: def _get_cert_transitive_cves( self, cert: CertSubType, reference_type: ReferenceType, ref_func: ReferenceLookupFunc - ) -> Optional[Set[str]]: + ) -> set[str] | None: references = ( ref_func(cert).directly_referenced_by @@ -129,7 +131,7 @@ def predict_single_cert(self, dgst: str) -> TransitiveCVEs: self.vulnerabilities[dgst][ReferenceType.INDIRECT.value], ) - def predict(self, dgst_list: List[str]) -> Dict[str, TransitiveCVEs]: + def predict(self, dgst_list: list[str]) -> dict[str, TransitiveCVEs]: """ Method returns vulnerabilities for a list of certificate digests diff --git a/sec_certs/sample/cc_certificate_id.py b/sec_certs/sample/cc_certificate_id.py index 20731383..428ca73b 100644 --- a/sec_certs/sample/cc_certificate_id.py +++ b/sec_certs/sample/cc_certificate_id.py @@ -1,6 +1,7 @@ +from __future__ import annotations + import re from dataclasses import dataclass -from typing import List, Tuple @dataclass(eq=True, frozen=True) @@ -26,7 +27,7 @@ def _canonical_fr(self) -> str: return new_cert_id def _canonical_de(self) -> str: - def extract_parts(bsi_parts: List[str]) -> Tuple: + def extract_parts(bsi_parts: list[str]) -> tuple: cert_num = None cert_version = None cert_year = None diff --git a/sec_certs/sample/cc_maintenance_update.py b/sec_certs/sample/cc_maintenance_update.py index 1ff28fb8..abe3b176 100644 --- a/sec_certs/sample/cc_maintenance_update.py +++ b/sec_certs/sample/cc_maintenance_update.py @@ -1,6 +1,8 @@ +from __future__ import annotations + import logging from datetime import date -from typing import ClassVar, Dict, List, Optional, Tuple +from typing import ClassVar import sec_certs.utils.helpers as helpers from sec_certs.sample.common_criteria import CommonCriteriaCert @@ -10,7 +12,7 @@ class CommonCriteriaMaintenanceUpdate(CommonCriteriaCert, ComplexSerializableType): - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "dgst", "name", "report_link", @@ -24,9 +26,9 @@ def __init__( name: str, report_link: str, st_link: str, - state: Optional[CommonCriteriaCert.InternalState], - pdf_data: Optional[CommonCriteriaCert.PdfData], - heuristics: Optional[CommonCriteriaCert.Heuristics], + state: CommonCriteriaCert.InternalState | None, + pdf_data: CommonCriteriaCert.PdfData | None, + heuristics: CommonCriteriaCert.Heuristics | None, related_cert_digest: str, maintenance_date: date, ): @@ -53,7 +55,7 @@ def __init__( self.maintenance_date = maintenance_date @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return ["dgst"] + list(self.__class__.__init__.__code__.co_varnames)[1:] @property @@ -63,16 +65,16 @@ def dgst(self) -> str: return "cert_" + self.related_cert_digest + "_update_" + helpers.get_first_16_bytes_sha256(self.name) @property - def pandas_tuple(self) -> Tuple: + def pandas_tuple(self) -> tuple: return tuple([getattr(self, x) for x in CommonCriteriaMaintenanceUpdate.pandas_columns]) @classmethod - def from_dict(cls, dct: Dict) -> "CommonCriteriaMaintenanceUpdate": + def from_dict(cls, dct: dict) -> CommonCriteriaMaintenanceUpdate: dct.pop("dgst") return cls(*(tuple(dct.values()))) @classmethod - def get_updates_from_cc_cert(cls, cert: CommonCriteriaCert) -> List["CommonCriteriaMaintenanceUpdate"]: + def get_updates_from_cc_cert(cls, cert: CommonCriteriaCert) -> list[CommonCriteriaMaintenanceUpdate]: if cert.maintenance_updates is None: raise RuntimeError("Dataset was probably not built correctly - this should not be happening.") diff --git a/sec_certs/sample/certificate.py b/sec_certs/sample/certificate.py index bfc63e56..8d5936b5 100644 --- a/sec_certs/sample/certificate.py +++ b/sec_certs/sample/certificate.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import copy import json import logging @@ -5,7 +7,7 @@ from collections import ChainMap from dataclasses import dataclass, field from pathlib import Path -from typing import Any, Dict, Generic, Optional, Set, Type, TypeVar, Union +from typing import Any, Generic, TypeVar import sec_certs.utils.extract from sec_certs.cert_rules import PANDAS_KEYWORDS_CATEGORIES @@ -20,19 +22,19 @@ @dataclass class References(ComplexSerializableType): - directly_referenced_by: Optional[Set[str]] = field(default=None) - indirectly_referenced_by: Optional[Set[str]] = field(default=None) - directly_referencing: Optional[Set[str]] = field(default=None) - indirectly_referencing: Optional[Set[str]] = field(default=None) + directly_referenced_by: set[str] | None = field(default=None) + indirectly_referenced_by: set[str] | None = field(default=None) + directly_referencing: set[str] | None = field(default=None) + indirectly_referencing: set[str] | None = field(default=None) class Heuristics: - cpe_matches: Optional[Set[str]] - related_cves: Optional[Set[str]] + cpe_matches: set[str] | None + related_cves: set[str] | None class PdfData: - def get_keywords_df_data(self, var: str) -> Dict[str, float]: + def get_keywords_df_data(self, var: str) -> dict[str, float]: data_dct = getattr(self, var) return dict( ChainMap( @@ -45,8 +47,8 @@ def get_keywords_df_data(self, var: str) -> Dict[str, float]: class Certificate(Generic[T, H, P], ABC, ComplexSerializableType): - manufacturer: Optional[str] - name: Optional[str] + manufacturer: str | None + name: str | None pdf_data: P heuristics: H @@ -77,19 +79,19 @@ def __eq__(self, other: object) -> bool: def __hash__(self) -> int: return hash(self.dgst) - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: return { **{"dgst": self.dgst}, **{key: val for key, val in copy.deepcopy(self.__dict__).items() if key in self.serialized_attributes}, } @classmethod - def from_dict(cls: Type[T], dct: Dict) -> T: + def from_dict(cls: type[T], dct: dict) -> T: dct.pop("dgst") return cls(**dct) @classmethod - def from_json(cls: Type[T], input_path: Union[Path, str]) -> T: + def from_json(cls: type[T], input_path: Path | str) -> T: with Path(input_path).open("r") as handle: return json.load(handle, cls=CustomJSONDecoder) diff --git a/sec_certs/sample/cpe.py b/sec_certs/sample/cpe.py index c03b8552..a7532f7f 100644 --- a/sec_certs/sample/cpe.py +++ b/sec_certs/sample/cpe.py @@ -1,6 +1,8 @@ +from __future__ import annotations + from dataclasses import dataclass from functools import lru_cache -from typing import Any, ClassVar, Dict, List, Optional, Tuple +from typing import Any, ClassVar from sec_certs import constants from sec_certs.serialization.json import ComplexSerializableType @@ -14,13 +16,13 @@ class CPE(PandasSerializableType, ComplexSerializableType): version: str vendor: str item_name: str - title: Optional[str] - start_version: Optional[Tuple[str, str]] - end_version: Optional[Tuple[str, str]] + title: str | None + start_version: tuple[str, str] | None + end_version: tuple[str, str] | None __slots__ = ["uri", "version", "vendor", "item_name", "title", "start_version", "end_version"] - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "uri", "vendor", "item_name", @@ -31,9 +33,9 @@ class CPE(PandasSerializableType, ComplexSerializableType): def __init__( self, uri: str, - title: Optional[str] = None, - start_version: Optional[Tuple[str, str]] = None, - end_version: Optional[Tuple[str, str]] = None, + title: str | None = None, + start_version: tuple[str, str] | None = None, + end_version: tuple[str, str] | None = None, ): super().__init__() self.uri = uri @@ -46,7 +48,7 @@ def __init__( self.start_version = start_version self.end_version = end_version - def __lt__(self, other: "CPE") -> bool: + def __lt__(self, other: CPE) -> bool: return self.uri < other.uri @staticmethod @@ -59,7 +61,7 @@ def normalize_version(version: str) -> str: return version @classmethod - def from_dict(cls, dct: Dict[str, Any]) -> "CPE": + def from_dict(cls, dct: dict[str, Any]) -> CPE: if isinstance(dct["start_version"], list): dct["start_version"] = tuple(dct["start_version"]) if isinstance(dct["end_version"], list): @@ -67,7 +69,7 @@ def from_dict(cls, dct: Dict[str, Any]) -> "CPE": return super().from_dict(dct) @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: return ["uri", "title", "start_version", "end_version"] @property @@ -83,7 +85,7 @@ def target_hw(self) -> str: return " ".join(self.uri.split(":")[10].split("_")) @property - def pandas_tuple(self) -> Tuple: + def pandas_tuple(self) -> tuple: return self.uri, self.vendor, self.item_name, self.version, self.title def __hash__(self) -> int: diff --git a/sec_certs/sample/fips_algorithm.py b/sec_certs/sample/fips_algorithm.py index 83ac839b..16f19e64 100644 --- a/sec_certs/sample/fips_algorithm.py +++ b/sec_certs/sample/fips_algorithm.py @@ -1,6 +1,8 @@ +from __future__ import annotations + from dataclasses import dataclass from datetime import date -from typing import ClassVar, List, Tuple +from typing import ClassVar from sec_certs import constants from sec_certs.serialization.json import ComplexSerializableType @@ -19,7 +21,7 @@ class FIPSAlgorithm(PandasSerializableType, ComplexSerializableType): implementation_name: str validation_date: date - pandas_columns: ClassVar[List[str]] = [ + pandas_columns: ClassVar[list[str]] = [ "dgst", "alg_number", "algorithm_type", @@ -29,7 +31,7 @@ class FIPSAlgorithm(PandasSerializableType, ComplexSerializableType): ] @property - def pandas_tuple(self) -> Tuple: + def pandas_tuple(self) -> tuple: return ( self.dgst, self.alg_number, diff --git a/sec_certs/sample/fips_iut.py b/sec_certs/sample/fips_iut.py index 95f5a863..cb521ee4 100644 --- a/sec_certs/sample/fips_iut.py +++ b/sec_certs/sample/fips_iut.py @@ -1,8 +1,10 @@ +from __future__ import annotations + from dataclasses import dataclass from datetime import date, datetime from pathlib import Path from tempfile import NamedTemporaryFile -from typing import Dict, Iterator, List, Mapping, Optional, Set, Union +from typing import Iterator, Mapping import requests from bs4 import BeautifulSoup, Tag @@ -20,11 +22,11 @@ class IUTEntry(ComplexSerializableType): standard: str iut_date: date - def to_dict(self) -> Dict[str, str]: + def to_dict(self) -> dict[str, str]: return {**self.__dict__, "iut_date": self.iut_date.isoformat()} @classmethod - def from_dict(cls, dct: Mapping) -> "IUTEntry": + def from_dict(cls, dct: Mapping) -> IUTEntry: return cls( dct["module_name"], dct["vendor_name"], @@ -35,12 +37,12 @@ def from_dict(cls, dct: Mapping) -> "IUTEntry": @dataclass class IUTSnapshot(ComplexSerializableType): - entries: Set[IUTEntry] + entries: set[IUTEntry] timestamp: datetime last_updated: date - displayed: Optional[int] - not_displayed: Optional[int] - total: Optional[int] + displayed: int | None + not_displayed: int | None + total: int | None def __len__(self) -> int: return len(self.entries) @@ -48,7 +50,7 @@ def __len__(self) -> int: def __iter__(self) -> Iterator[IUTEntry]: yield from self.entries - def to_dict(self) -> Dict[str, Union[Optional[int], List[IUTEntry], str]]: + def to_dict(self) -> dict[str, int | None | list[IUTEntry] | str]: return { "entries": list(self.entries), "timestamp": self.timestamp.isoformat(), @@ -59,7 +61,7 @@ def to_dict(self) -> Dict[str, Union[Optional[int], List[IUTEntry], str]]: } @classmethod - def from_dict(cls, dct: Mapping) -> "IUTSnapshot": + def from_dict(cls, dct: Mapping) -> IUTSnapshot: return cls( set(dct["entries"]), datetime.fromisoformat(dct["timestamp"]), @@ -70,7 +72,7 @@ def from_dict(cls, dct: Mapping) -> "IUTSnapshot": ) @classmethod - def from_page(cls, content: bytes, snapshot_date: datetime) -> "IUTSnapshot": + def from_page(cls, content: bytes, snapshot_date: datetime) -> IUTSnapshot: """ Get an IUT snapshot from a HTML dump of the FIPS website. """ @@ -103,9 +105,9 @@ def from_page(cls, content: bytes, snapshot_date: datetime) -> "IUTSnapshot": # Parse footer footer = soup.find(id="IUTFooter") - displayed: Optional[int] - not_displayed: Optional[int] - total: Optional[int] + displayed: int | None + not_displayed: int | None + total: int | None if footer: footer_lines = footer.find_all("tr") @@ -125,7 +127,7 @@ def from_page(cls, content: bytes, snapshot_date: datetime) -> "IUTSnapshot": ) @classmethod - def from_dump(cls, dump_path: Union[str, Path], snapshot_date: Optional[datetime] = None) -> "IUTSnapshot": + def from_dump(cls, dump_path: str | Path, snapshot_date: datetime | None = None) -> IUTSnapshot: """ Get an IUT snapshot from a HTML file dump of the FIPS website. """ @@ -140,7 +142,7 @@ def from_dump(cls, dump_path: Union[str, Path], snapshot_date: Optional[datetime return cls.from_page(content, snapshot_date) @classmethod - def from_web(cls) -> "IUTSnapshot": + def from_web(cls) -> IUTSnapshot: """ Get an IUT snapshot from the FIPS website right now. """ @@ -152,7 +154,7 @@ def from_web(cls) -> "IUTSnapshot": return cls.from_page(iut_resp.content, snapshot_date) @classmethod - def from_web_latest(cls) -> "IUTSnapshot": + def from_web_latest(cls) -> IUTSnapshot: """ Get a IUT snapshot from seccerts.org. """ diff --git a/sec_certs/sample/fips_mip.py b/sec_certs/sample/fips_mip.py index 4d6cd380..6918d2aa 100644 --- a/sec_certs/sample/fips_mip.py +++ b/sec_certs/sample/fips_mip.py @@ -1,10 +1,12 @@ +from __future__ import annotations + import logging from dataclasses import dataclass from datetime import date, datetime from enum import Enum from pathlib import Path from tempfile import NamedTemporaryFile -from typing import Dict, Iterator, List, Mapping, Optional, Set, Union +from typing import Iterator, Mapping import requests from bs4 import BeautifulSoup, Tag @@ -30,10 +32,10 @@ class MIPEntry(ComplexSerializableType): module_name: str vendor_name: str standard: str - status: Optional[MIPStatus] - status_since: Optional[date] + status: MIPStatus | None + status_since: date | None - def to_dict(self) -> Dict[str, Union[str, Optional[MIPStatus], Optional[date]]]: + def to_dict(self) -> dict[str, str | MIPStatus | None | date | None]: return { **self.__dict__, "status": self.status.value if self.status else None, @@ -41,7 +43,7 @@ def to_dict(self) -> Dict[str, Union[str, Optional[MIPStatus], Optional[date]]]: } @classmethod - def from_dict(cls, dct: Mapping) -> "MIPEntry": + def from_dict(cls, dct: Mapping) -> MIPEntry: return cls( dct["module_name"], dct["vendor_name"], @@ -53,7 +55,7 @@ def from_dict(cls, dct: Mapping) -> "MIPEntry": @dataclass class MIPSnapshot(ComplexSerializableType): - entries: Set[MIPEntry] + entries: set[MIPEntry] timestamp: datetime last_updated: date displayed: int @@ -66,7 +68,7 @@ def __len__(self) -> int: def __iter__(self) -> Iterator[MIPEntry]: yield from self.entries - def to_dict(self) -> Dict[str, Union[int, str, List[MIPEntry]]]: + def to_dict(self) -> dict[str, int | str | list[MIPEntry]]: return { "entries": list(self.entries), "timestamp": self.timestamp.isoformat(), @@ -77,7 +79,7 @@ def to_dict(self) -> Dict[str, Union[int, str, List[MIPEntry]]]: } @classmethod - def from_dict(cls, dct: Mapping) -> "MIPSnapshot": + def from_dict(cls, dct: Mapping) -> MIPSnapshot: return cls( set(dct["entries"]), datetime.fromisoformat(dct["timestamp"]), @@ -158,7 +160,7 @@ def _extract_entries(cls, lines, snapshot_date): return entries @classmethod - def from_page(cls, content: bytes, snapshot_date: datetime) -> "MIPSnapshot": + def from_page(cls, content: bytes, snapshot_date: datetime) -> MIPSnapshot: """ Get a MIP snapshot from a HTML dump of the FIPS website. """ @@ -201,7 +203,7 @@ def from_page(cls, content: bytes, snapshot_date: datetime) -> "MIPSnapshot": ) @classmethod - def from_dump(cls, dump_path: Union[str, Path], snapshot_date: Optional[datetime] = None) -> "MIPSnapshot": + def from_dump(cls, dump_path: str | Path, snapshot_date: datetime | None = None) -> MIPSnapshot: """ Get a MIP snapshot from a HTML file dump of the FIPS website. """ @@ -216,7 +218,7 @@ def from_dump(cls, dump_path: Union[str, Path], snapshot_date: Optional[datetime return cls.from_page(content, snapshot_date) @classmethod - def from_web(cls) -> "MIPSnapshot": + def from_web(cls) -> MIPSnapshot: """ Get a MIP snapshot from the FIPS website right now. """ @@ -228,7 +230,7 @@ def from_web(cls) -> "MIPSnapshot": return cls.from_page(mip_resp.content, snapshot_date) @classmethod - def from_web_latest(cls) -> "MIPSnapshot": + def from_web_latest(cls) -> MIPSnapshot: """ Get a MIP snapshot from seccerts.org. """ diff --git a/sec_certs/sample/protection_profile.py b/sec_certs/sample/protection_profile.py index c789254a..b7c2ec34 100644 --- a/sec_certs/sample/protection_profile.py +++ b/sec_certs/sample/protection_profile.py @@ -1,7 +1,9 @@ +from __future__ import annotations + import copy import logging from dataclasses import dataclass -from typing import Any, Dict, FrozenSet, Optional +from typing import Any import sec_certs.utils.sanitization as sanitization from sec_certs.serialization.json import ComplexSerializableType @@ -16,22 +18,22 @@ class ProtectionProfile(ComplexSerializableType): """ pp_name: str - pp_eal: Optional[str] - pp_link: Optional[str] = None - pp_ids: Optional[FrozenSet[str]] = None + pp_eal: str | None + pp_link: str | None = None + pp_ids: frozenset[str] | None = None def __post_init__(self): super().__setattr__("pp_name", sanitization.sanitize_string(self.pp_name)) super().__setattr__("pp_link", sanitization.sanitize_link(self.pp_link)) @classmethod - def from_dict(cls, dct: Dict[str, Any]) -> "ProtectionProfile": + def from_dict(cls, dct: dict[str, Any]) -> ProtectionProfile: new_dct = copy.deepcopy(dct) new_dct["pp_ids"] = frozenset(new_dct["pp_ids"]) if new_dct["pp_ids"] else None return cls(*tuple(new_dct.values())) @classmethod - def from_old_api_dict(cls, dct: Dict[str, Any]) -> "ProtectionProfile": + def from_old_api_dict(cls, dct: dict[str, Any]) -> ProtectionProfile: pp_name = sanitization.sanitize_string(dct["csv_scan"]["cc_pp_name"]) pp_link = sanitization.sanitize_link(dct["csv_scan"]["link_pp_document"]) pp_ids = frozenset(dct["processed"]["cc_pp_csvid"]) if dct["processed"]["cc_pp_csvid"] else None @@ -49,5 +51,5 @@ def __eq__(self, other: object) -> bool: return False return self.pp_name == other.pp_name and self.pp_link == other.pp_link - def __lt__(self, other: "ProtectionProfile") -> bool: + def __lt__(self, other: ProtectionProfile) -> bool: return self.pp_name < other.pp_name diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py index 792ab47c..003954ee 100644 --- a/sec_certs/serialization/json.py +++ b/sec_certs/serialization/json.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import copy import json from datetime import date from functools import wraps from pathlib import Path -from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TypeVar, Union +from typing import Any, Callable, TypeVar from sec_certs import constants @@ -11,7 +13,7 @@ class ComplexSerializableType: - __slots__: Tuple[str] + __slots__: tuple[str] def __init__(self, *args, **kwargs): pass @@ -19,12 +21,12 @@ def __init__(self, *args, **kwargs): # Ideally, the serialized_fields would be an class variable referencing itself, but that it virtually impossible # to achieve without using metaclasses. Not to complicate the code, we choose instance variable. @property - def serialized_attributes(self) -> List[str]: + def serialized_attributes(self) -> list[str]: if hasattr(self, "__slots__") and self.__slots__: return list(self.__slots__) return list(self.__dict__.keys()) - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: if hasattr(self, "__slots__") and self.__slots__: return { key: copy.deepcopy(getattr(self, key)) for key in self.__slots__ if key in self.serialized_attributes @@ -32,13 +34,13 @@ def to_dict(self) -> Dict[str, Any]: return {key: val for key, val in copy.deepcopy(self.__dict__).items() if key in self.serialized_attributes} @classmethod - def from_dict(cls: Type[T], dct: Dict) -> T: + def from_dict(cls: type[T], dct: dict) -> T: try: return cls(**dct) except TypeError as e: raise TypeError(f"Dict: {dct} on {cls.__mro__}") from e - def to_json(self, output_path: Optional[Union[str, Path]] = None) -> None: + def to_json(self, output_path: str | Path | None = None) -> None: if not output_path and (not hasattr(self, "json_path") or not self.json_path): # type: ignore raise ValueError( f"The object {self} of type {self.__class__} does not have json_path attribute set but to_json() was called without an argument." @@ -56,7 +58,7 @@ def to_json(self, output_path: Optional[Union[str, Path]] = None) -> None: json.dump(self, handle, indent=4, cls=CustomJSONEncoder, ensure_ascii=False) @classmethod - def from_json(cls: Type[T], input_path: Union[str, Path]) -> T: + def from_json(cls: type[T], input_path: str | Path) -> T: input_path = Path(input_path) with input_path.open("r") as handle: obj = json.load(handle, cls=CustomJSONDecoder) diff --git a/sec_certs/utils/helpers.py b/sec_certs/utils/helpers.py index 3e4c6943..302f4e6a 100644 --- a/sec_certs/utils/helpers.py +++ b/sec_certs/utils/helpers.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import hashlib import logging import re @@ -6,7 +8,7 @@ from datetime import datetime from functools import partial from pathlib import Path -from typing import Any, Collection, Dict, List, Optional, Set, Union +from typing import Any, Collection import numpy as np import pkgconfig @@ -21,8 +23,8 @@ def download_file( - url: str, output: Path, delay: float = 0, show_progress_bar: bool = False, progress_bar_desc: Optional[str] = None -) -> Union[str, int]: + url: str, output: Path, delay: float = 0, show_progress_bar: bool = False, progress_bar_desc: str | None = None +) -> str | int: try: time.sleep(delay) # See https://github.com/psf/requests/issues/3953 for header justification @@ -60,8 +62,8 @@ def download_file( def download_parallel( - urls: Collection[str], paths: Collection[Path], progress_bar_desc: Optional[str] = None -) -> List[int]: + urls: Collection[str], paths: Collection[Path], progress_bar_desc: str | None = None +) -> list[int]: exit_codes = parallel_processing.process_parallel( download_file, list(zip(urls, paths)), config.n_threads, unpack=True, progress_bar_desc=progress_bar_desc ) @@ -75,7 +77,7 @@ def download_parallel( return exit_codes -def fips_dgst(cert_id: Union[int, str]) -> str: +def fips_dgst(cert_id: int | str) -> str: return get_first_16_bytes_sha256(str(cert_id)) @@ -83,7 +85,7 @@ def get_first_16_bytes_sha256(string: str) -> str: return hashlib.sha256(string.encode("utf-8")).hexdigest()[:16] -def get_sha256_filepath(filepath: Union[str, Path]) -> str: +def get_sha256_filepath(filepath: str | Path) -> str: hash_sha256 = hashlib.sha256() with Path(filepath).open("rb") as f: for chunk in iter(lambda: f.read(4096), b""): @@ -100,7 +102,7 @@ def to_utc(timestamp: datetime) -> datetime: return timestamp -def is_in_dict(target_dict: Dict, path: str) -> bool: +def is_in_dict(target_dict: dict, path: str) -> bool: current_level = target_dict for item in path: if item not in current_level: @@ -110,7 +112,7 @@ def is_in_dict(target_dict: Dict, path: str) -> bool: return True -def compute_heuristics_version(cert_name: str) -> Set[str]: +def compute_heuristics_version(cert_name: str) -> set[str]: """ Will extract possible versions from the name of sample """ @@ -141,11 +143,11 @@ def compute_heuristics_version(cert_name: str) -> Set[str]: return {x.group() for x in matched if x is not None} -def tokenize_dataset(dset: List[str], keywords: Set[str]) -> np.ndarray: +def tokenize_dataset(dset: list[str], keywords: set[str]) -> np.ndarray: return np.array([tokenize(x, keywords) for x in dset]) -def tokenize(string: str, keywords: Set[str]) -> str: +def tokenize(string: str, keywords: set[str]) -> str: return " ".join([x for x in string.split() if x.lower() in keywords]) @@ -162,7 +164,7 @@ def normalize_fips_vendor(string: str) -> str: # Credit: https://stackoverflow.com/questions/18092354/ -def split_unescape(s: str, delim: str, escape: str = "\\", unescape: bool = True) -> List[str]: +def split_unescape(s: str, delim: str, escape: str = "\\", unescape: bool = True) -> list[str]: """ >>> split_unescape('foo,bar', ',') ['foo', 'bar'] @@ -224,7 +226,7 @@ def warn_if_missing_tesseract() -> None: logger.warning("Attempting to find tesseract, but pkg-config was not found.") -def choose_lowest_eal(eals: Optional[Set[str]]) -> Optional[str]: +def choose_lowest_eal(eals: set[str] | None) -> str | None: """ Given a set of EAL strings, chooses the lowest one. """ diff --git a/sec_certs/utils/parallel_processing.py b/sec_certs/utils/parallel_processing.py index f3526511..50806a67 100644 --- a/sec_certs/utils/parallel_processing.py +++ b/sec_certs/utils/parallel_processing.py @@ -1,6 +1,8 @@ +from __future__ import annotations + import time from multiprocessing.pool import ThreadPool -from typing import Any, Callable, Iterable, List, Optional, Union +from typing import Any, Callable, Iterable from billiard.pool import Pool @@ -11,14 +13,14 @@ def process_parallel( func: Callable, items: Iterable, max_workers: int, - callback: Optional[Callable] = None, + callback: Callable | None = None, use_threading: bool = True, progress_bar: bool = True, unpack: bool = False, - progress_bar_desc: Optional[str] = None, -) -> List[Any]: + progress_bar_desc: str | None = None, +) -> list[Any]: - pool: Union[Pool, ThreadPool] = ThreadPool(max_workers) if use_threading else Pool(max_workers) + pool: Pool | ThreadPool = ThreadPool(max_workers) if use_threading else Pool(max_workers) results = ( [pool.apply_async(func, (*i,), callback=callback) for i in items] if unpack diff --git a/sec_certs/utils/pdf.py b/sec_certs/utils/pdf.py index 8f0e6abf..1d04a697 100644 --- a/sec_certs/utils/pdf.py +++ b/sec_certs/utils/pdf.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import glob import logging import subprocess @@ -5,7 +7,7 @@ from functools import reduce from pathlib import Path from tempfile import TemporaryDirectory -from typing import Any, Dict, Optional, Tuple +from typing import Any import pdftotext import pikepdf @@ -65,7 +67,7 @@ def ocr_pdf_file(pdf_path: Path) -> str: return contents -def convert_pdf_file(pdf_path: Path, txt_path: Path) -> Tuple[bool, bool]: +def convert_pdf_file(pdf_path: Path, txt_path: Path) -> tuple[bool, bool]: """ Convert a PDF tile to text and save it on the `txt_path`. @@ -101,7 +103,7 @@ def convert_pdf_file(pdf_path: Path, txt_path: Path) -> Tuple[bool, bool]: return ocr, ok -def parse_pdf_date(dateval: Optional[bytes]) -> Optional[datetime]: +def parse_pdf_date(dateval: bytes | None) -> datetime | None: """ Parse PDF metadata date format: @@ -142,7 +144,7 @@ def parse_pdf_date(dateval: Optional[bytes]) -> Optional[datetime]: return None -def extract_pdf_metadata(filepath: Path) -> Tuple[str, Optional[Dict[str, Any]]]: # noqa: C901 +def extract_pdf_metadata(filepath: Path) -> tuple[str, dict[str, Any] | None]: # noqa: C901 """ Extract PDF metadata, such as the number of pages, author, title, etc. @@ -179,7 +181,7 @@ def resolve_indirect(val, bound=10): else: return val - metadata: Dict[str, Any] = dict() + metadata: dict[str, Any] = dict() try: metadata["pdf_file_size_bytes"] = filepath.stat().st_size diff --git a/sec_certs/utils/sanitization.py b/sec_certs/utils/sanitization.py index cc456d05..2f9cd046 100644 --- a/sec_certs/utils/sanitization.py +++ b/sec_certs/utils/sanitization.py @@ -1,7 +1,8 @@ +from __future__ import annotations + import html import logging from datetime import date -from typing import Optional, Set, Union import numpy as np import pandas as pd @@ -10,19 +11,19 @@ logger = logging.getLogger(__name__) -def sanitize_navigable_string(string: Optional[Union[NavigableString, str]]) -> Optional[str]: +def sanitize_navigable_string(string: NavigableString | str | None) -> str | None: if not string: return None return str(string).strip().replace("\xad", "").replace("\xa0", "") -def sanitize_link(record: Optional[str]) -> Optional[str]: +def sanitize_link(record: str | None) -> str | None: if not record: return None return record.replace(":443", "").replace(" ", "%20").replace("http://", "https://") -def sanitize_date(record: Union[pd.Timestamp, date, np.datetime64]) -> Union[date, None]: +def sanitize_date(record: pd.Timestamp | date | np.datetime64) -> date | None: if pd.isnull(record): return None elif isinstance(record, pd.Timestamp): @@ -38,7 +39,7 @@ def sanitize_string(record: str) -> str: return " ".join(string.split()) -def sanitize_security_levels(record: Union[str, Set[str]]) -> Set[str]: +def sanitize_security_levels(record: str | set[str]) -> set[str]: if isinstance(record, str): record = set(record.split(",")) return record - {"Basic", "ND-PP", "PP\xa0Compliant", "None", "Medium"} diff --git a/sec_certs/utils/tables.py b/sec_certs/utils/tables.py index 431c8346..29def971 100644 --- a/sec_certs/utils/tables.py +++ b/sec_certs/utils/tables.py @@ -1,14 +1,15 @@ +from __future__ import annotations + import logging import re from pathlib import Path -from typing import Set from sec_certs.cert_rules import FIPS_LIST_OF_TABLES logger = logging.getLogger(__name__) -def parse_list_of_tables(txt: str) -> Set[int]: +def parse_list_of_tables(txt: str) -> set[int]: """ Parses list of tables in policy txt, returns page numbers of tables that mention algorithms """ @@ -16,7 +17,7 @@ def parse_list_of_tables(txt: str) -> Set[int]: return {int(m.group("page_num")) for m in rr.finditer(txt)} -def get_table_rich_page_numbers_from_footer(file_text: str) -> Set[int]: +def get_table_rich_page_numbers_from_footer(file_text: str) -> set[int]: """ Parses page numbers of policy txt pages that may contain tables with algorithm data """ @@ -39,7 +40,7 @@ def get_table_rich_page_numbers_from_footer(file_text: str) -> Set[int]: return pages -def find_pages_with_tables(txt_filepath: Path) -> Set[int]: +def find_pages_with_tables(txt_filepath: Path) -> set[int]: """ Identifies pages in txt file that may contain tables. Return their page numbers. """ @@ -56,6 +57,6 @@ def find_pages_with_tables(txt_filepath: Path) -> Set[int]: return result if result else set() -def get_algs_from_table(dataframe_text: str) -> Set[str]: +def get_algs_from_table(dataframe_text: str) -> set[str]: reg = r"(?:#?\s?|(?:Cert)\.?[^. ]*?\s?)(?:[CcAa]\s)?(?P[CcAa]? ?\d+)" return {m.group() for m in re.finditer(reg, dataframe_text)} diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 399859f5..55c5553a 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -1,6 +1,7 @@ +from __future__ import annotations + import shutil from pathlib import Path -from typing import Dict, Set import pytest @@ -30,7 +31,7 @@ def cpe_single_sign_on() -> CPE: @pytest.fixture(scope="module") -def cpes(cpe_single_sign_on: CPE) -> Set[CPE]: +def cpes(cpe_single_sign_on: CPE) -> set[CPE]: return { cpe_single_sign_on, CPE( @@ -49,12 +50,12 @@ def cpes(cpe_single_sign_on: CPE) -> Set[CPE]: @pytest.fixture(scope="module") -def cpe_dset(cpes: Set[CPE]) -> CPEDataset: +def cpe_dset(cpes: set[CPE]) -> CPEDataset: return CPEDataset(False, {x.uri: x for x in cpes}) @pytest.fixture(scope="module") -def cves(cpe_single_sign_on) -> Set[CVE]: +def cves(cpe_single_sign_on) -> set[CVE]: return { CVE( "CVE-2017-1732", @@ -74,7 +75,7 @@ def cves(cpe_single_sign_on) -> Set[CVE]: @pytest.fixture(scope="module") -def cve_dset(cves: Set[CVE]) -> CVEDataset: +def cve_dset(cves: set[CVE]) -> CVEDataset: cve_dset = CVEDataset({x.cve_id: x for x in cves}) cve_dset.build_lookup_dict(use_nist_mapping=False) return cve_dset @@ -113,7 +114,7 @@ def test_match_cpe(cpe_single_sign_on: CPE, random_certificate: CommonCriteriaCe def test_find_related_cves( - cc_dset: CCDataset, cpe_single_sign_on: CPE, cves: Set[CVE], random_certificate: CommonCriteriaCert + cc_dset: CCDataset, cpe_single_sign_on: CPE, cves: set[CVE], random_certificate: CommonCriteriaCert ): random_certificate.heuristics.cpe_matches = {cpe_single_sign_on.uri} cc_dset.compute_related_cves() @@ -156,7 +157,7 @@ def test_cert_id_heuristics(random_certificate: CommonCriteriaCert): def test_keywords_heuristics(random_certificate: CommonCriteriaCert): assert random_certificate.pdf_data.st_keywords - extracted_keywords: Dict = random_certificate.pdf_data.st_keywords + extracted_keywords: dict = random_certificate.pdf_data.st_keywords assert "cc_security_level" in extracted_keywords assert extracted_keywords["cc_security_level"]["EAL"]["EAL3"] == 1 diff --git a/tests/fips/test_fips_algorithm_dataset.py b/tests/fips/test_fips_algorithm_dataset.py index 9f1d0008..c42347d4 100644 --- a/tests/fips/test_fips_algorithm_dataset.py +++ b/tests/fips/test_fips_algorithm_dataset.py @@ -1,5 +1,7 @@ +from __future__ import annotations + from pathlib import Path -from typing import Any, Dict +from typing import Any import pytest @@ -30,7 +32,7 @@ def alg_dset(alg_dset_path: Path) -> FIPSAlgorithmDataset: @pytest.fixture(scope="module") -def alg_dict() -> Dict[str, Any]: +def alg_dict() -> dict[str, Any]: return { "alg_number": "2902", "algorithm_type": "AES", @@ -45,7 +47,7 @@ def test_alg_dset_lookup_dict(alg_dset: FIPSAlgorithmDataset): assert alg_dset.alg_number_to_algs["2902"] == {alg} -def test_alg_from_to_dict(alg_dict: Dict[str, Any]): +def test_alg_from_to_dict(alg_dict: dict[str, Any]): alg = FIPSAlgorithm.from_dict(alg_dict) ret = alg.to_dict() assert alg_dict == ret diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py index b4d5106b..cdd1d46b 100644 --- a/tests/fips/test_fips_analysis.py +++ b/tests/fips/test_fips_analysis.py @@ -1,5 +1,6 @@ +from __future__ import annotations + from pathlib import Path -from typing import Optional, Set import pytest @@ -117,7 +118,7 @@ def processed_dataset( ("3197", {"3195", "3096", "3196", "3644", "3651"}), ], ) -def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): +def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str]): crt = processed_dataset[input_dgst] if not crt.state.module_extract_ok: pytest.xfail(reason="Data from module not extracted") @@ -125,7 +126,7 @@ def test_html_modules_directly_referencing(processed_dataset: FIPSDataset, input @pytest.mark.parametrize("input_dgst, expected_refs", [("3095", {"3093", "3094", "3096"}), ("3093", {"3090", "3091"})]) -def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): +def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str]): crt = processed_dataset[input_dgst] if not crt.state.policy_extract_ok: pytest.xfail(reason="Data from policy not extracted") @@ -145,7 +146,7 @@ def test_pdf_policies_directly_referencing(processed_dataset: FIPSDataset, input ("3095", {"3090", "3091", "3093", "3094", "3096"}), ], ) -def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): +def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str]): crt = processed_dataset[input_dgst] if not crt.state.module_extract_ok: pytest.xfail(reason="Data from module not extracted") @@ -156,7 +157,7 @@ def test_html_modules_indirectly_referencing(processed_dataset: FIPSDataset, inp "input_dgst, expected_refs", [("3095", {"3090", "3091", "3093", "3094", "3096"}), ("3093", {"3090", "3091"})], ) -def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Set[str]): +def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str]): crt = processed_dataset[input_dgst] if not crt.state.policy_extract_ok: pytest.xfail(reason="Data from policy not extracted") @@ -165,7 +166,7 @@ def test_pdf_policies_indirectly_referencing(processed_dataset: FIPSDataset, inp @pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) def test_html_modules_directly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str] | None ): crt = processed_dataset[input_dgst] if not crt.state.module_extract_ok: @@ -175,7 +176,7 @@ def test_html_modules_directly_referenced_by( @pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) def test_pdf_policies_directly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str] | None ): crt = processed_dataset[input_dgst] if not crt.state.policy_extract_ok: @@ -185,7 +186,7 @@ def test_pdf_policies_directly_referenced_by( @pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) def test_html_modules_indirectly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str] | None ): crt = processed_dataset[input_dgst] if not crt.state.module_extract_ok: @@ -195,7 +196,7 @@ def test_html_modules_indirectly_referenced_by( @pytest.mark.parametrize("input_dgst, expected_refs", [("3095", None), ("3093", {"3095"})]) def test_pdf_policies_indirectly_referenced_by( - processed_dataset: FIPSDataset, input_dgst: str, expected_refs: Optional[Set[str]] + processed_dataset: FIPSDataset, input_dgst: str, expected_refs: set[str] | None ): crt = processed_dataset[input_dgst] if not crt.state.policy_extract_ok: diff --git a/tests/test_cpe.py b/tests/test_cpe.py index 240a8b29..2c09a393 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -1,5 +1,7 @@ +from __future__ import annotations + from pathlib import Path -from typing import Any, Dict +from typing import Any import pytest @@ -20,7 +22,7 @@ def cpe_dset(cpe_dset_path: Path) -> CPEDataset: @pytest.fixture(scope="module") -def cpe_dict() -> Dict[str, Any]: +def cpe_dict() -> dict[str, Any]: return { "uri": "cpe:2.3:o:freebsd:freebsd:1.0:*:*:*:*:*:*:*", "title": None, diff --git a/tests/test_cve.py b/tests/test_cve.py index a436a5e8..61740030 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -1,5 +1,7 @@ +from __future__ import annotations + from pathlib import Path -from typing import Any, Dict, List +from typing import Any import pytest @@ -25,14 +27,14 @@ def cve_dataset_path() -> Path: @pytest.fixture(scope="module") -def cve_dset(cves: List[CVE]) -> CVEDataset: +def cve_dset(cves: list[CVE]) -> CVEDataset: cve_dset = CVEDataset({x.cve_id: x for x in cves}) cve_dset.build_lookup_dict(use_nist_mapping=False) return cve_dset @pytest.fixture(scope="module") -def cve_dict() -> Dict[str, Any]: +def cve_dict() -> dict[str, Any]: return { "cve_id": "CVE-1999-0001", "vulnerable_cpes": [ @@ -56,7 +58,7 @@ def cve_dict() -> Dict[str, Any]: @pytest.fixture(scope="module") -def cves() -> List[CVE]: +def cves() -> list[CVE]: cpe_single_sign_on = CPE( "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*", "IBM Security Access Manager For Enterprise Single Sign-On 8.2.2", @@ -80,7 +82,7 @@ def cves() -> List[CVE]: ] -def test_cve_dset_lookup_dicts(cves: List[CVE], cve_dset: CVEDataset): +def test_cve_dset_lookup_dicts(cves: list[CVE], cve_dset: CVEDataset): alt_lookup = {x: set(y) for x, y in cve_dset.cpe_to_cve_ids_lookup.items()} assert alt_lookup == { "cpe:2.3:a:ibm:security_access_manager_for_enterprise_single_sign-on:8.2.2:*:*:*:*:*:*:*": { @@ -94,7 +96,7 @@ def test_cve_dset_from_json(cve_dataset_path: Path, cve_dset: CVEDataset): assert dset == cve_dset -def test_cve_from_to_dict(cve_dict: Dict[str, Any]): +def test_cve_from_to_dict(cve_dict: dict[str, Any]): cve = CVE.from_dict(cve_dict) ret = cve.to_dict() assert ret == cve_dict From 4e62ae1fc3f030b8e83f612fb9652bd636a5d671 Mon Sep 17 00:00:00 2001 From: adamjanovsky Date: Mon, 5 Dec 2022 16:12:14 +0100 Subject: [PATCH 60/81] Update sec_certs/dataset/common_criteria.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ján JanÄár --- sec_certs/dataset/common_criteria.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 0a3b0e13..0f50a8a6 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -688,7 +688,7 @@ def _compute_cert_labs(self) -> None: cert.compute_heuristics_cert_lab() def _compute_normalized_cert_ids(self) -> None: - logger.info("omputing heuristics: Deriving information about sample ids from pdf scan.") + logger.info("Computing heuristics: Deriving information about certificate ids from artifacts.") for cert in self: cert.compute_heuristics_cert_id() From 1a3502a5361c7e3932b21b4b3fe71f064da983ad Mon Sep 17 00:00:00 2001 From: adamjanovsky Date: Mon, 5 Dec 2022 16:17:29 +0100 Subject: [PATCH 61/81] Update sec_certs/dataset/fips.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ján JanÄár --- sec_certs/dataset/fips.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index efe07bfe..7daf5201 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -226,7 +226,7 @@ def from_web_latest(cls) -> FIPSDataset: """ Fetches the fresh snapshot of FIPSDataset from mirror. """ - return cls.from_web(config.cc_latest_snapshot, "Downloading FIPS Dataset", "fips_latest_dataset.json") + return cls.from_web(config.fips_latest_snapshot, "Downloading FIPS Dataset", "fips_latest_dataset.json") def _set_local_paths(self) -> None: cert: FIPSCertificate From 8f7a14bb905b7d00570b2535f6faa5a19cd0875b Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 16:25:15 +0100 Subject: [PATCH 62/81] entry guard --- sec_certs/__main__.py | 5 +- sec_certs/cli.py | 134 ++++++++++++++++++++++-------------------- 2 files changed, 73 insertions(+), 66 deletions(-) diff --git a/sec_certs/__main__.py b/sec_certs/__main__.py index 4bf567a3..1d699b85 100644 --- a/sec_certs/__main__.py +++ b/sec_certs/__main__.py @@ -1,3 +1,6 @@ +import sys + from sec_certs.cli import main -main() +if __name__ == "__main__": + sys.exit(main()) diff --git a/sec_certs/cli.py b/sec_certs/cli.py index fd665057..9044b05c 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -130,71 +130,75 @@ def main( inputpath: Path | None, silent: bool, ): - file_handler = logging.FileHandler(config.log_filepath) - stream_handler = logging.StreamHandler(sys.stderr) - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - stream_handler.setFormatter(formatter) - handlers: list[logging.StreamHandler] = [file_handler] if silent else [file_handler, stream_handler] - logging.basicConfig(level=logging.INFO, handlers=handlers) - start = datetime.now() - - if configpath: - try: - config.load(Path(configpath)) - except FileNotFoundError: - print("Error: Bad path to configuration file") - sys.exit(1) - except ValueError as e: - print(f"Error: Bad format of configuration file: {e}") - - actions_set = ( - {"build", "process-aux-dsets", "download", "convert", "analyze", "maintenances"} - if "all" in actions - else set(actions) - ) - - dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) - aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" - analysis_pre_callback = None - - steps = [ - ProcessingStep( - "process-aux-dsets", - "process_auxillary_datasets", - preconditions=["meta_sources_parsed"], - precondition_error_msg=f"Error: You want to process the auxillary datasets: {aux_dsets_to_handle} , but the data from cert. framework website was not parsed. You must use 'build' action first.", - pre_callback_func=None, - ), - ProcessingStep( - "download", - "download_all_artifacts", - preconditions=["meta_sources_parsed"], - precondition_error_msg="Error: You want to download all artifacts, but the data from the cert. framework website was not parsed. You must use 'build' action first.", - pre_callback_func=None, - ), - ProcessingStep( - "convert", - "convert_all_pdfs", - preconditions=["pdfs_downloaded"], - precondition_error_msg="Error: You want to convert pdfs -> txt, but the pdfs were not downloaded. You must use 'download' action first.", - pre_callback_func=warn_missing_libs, - ), - ProcessingStep( - "analyze", - "analyze_certificates", - preconditions=["pdfs_converted", "auxillary_datasets_processed"], - precondition_error_msg="Error: You want to process txt documents of certificates, but pdfs were not converted. You must use 'convert' action first.", - pre_callback_func=analysis_pre_callback, - ), - ] - - processing_step: ProcessingStep - for processing_step in [x for x in steps if x in actions_set]: - processing_step.run(dset) - - end = datetime.now() - logger.info(f"The computation took {(end-start)} seconds.") + try: + file_handler = logging.FileHandler(config.log_filepath) + stream_handler = logging.StreamHandler(sys.stderr) + formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + file_handler.setFormatter(formatter) + stream_handler.setFormatter(formatter) + handlers: list[logging.StreamHandler] = [file_handler] if silent else [file_handler, stream_handler] + logging.basicConfig(level=logging.INFO, handlers=handlers) + start = datetime.now() + + if configpath: + try: + config.load(Path(configpath)) + except FileNotFoundError: + print("Error: Bad path to configuration file") + sys.exit(1) + except ValueError as e: + print(f"Error: Bad format of configuration file: {e}") + + actions_set = ( + {"build", "process-aux-dsets", "download", "convert", "analyze", "maintenances"} + if "all" in actions + else set(actions) + ) + + dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) + aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" + analysis_pre_callback = None + + steps = [ + ProcessingStep( + "process-aux-dsets", + "process_auxillary_datasets", + preconditions=["meta_sources_parsed"], + precondition_error_msg=f"Error: You want to process the auxillary datasets: {aux_dsets_to_handle} , but the data from cert. framework website was not parsed. You must use 'build' action first.", + pre_callback_func=None, + ), + ProcessingStep( + "download", + "download_all_artifacts", + preconditions=["meta_sources_parsed"], + precondition_error_msg="Error: You want to download all artifacts, but the data from the cert. framework website was not parsed. You must use 'build' action first.", + pre_callback_func=None, + ), + ProcessingStep( + "convert", + "convert_all_pdfs", + preconditions=["pdfs_downloaded"], + precondition_error_msg="Error: You want to convert pdfs -> txt, but the pdfs were not downloaded. You must use 'download' action first.", + pre_callback_func=warn_missing_libs, + ), + ProcessingStep( + "analyze", + "analyze_certificates", + preconditions=["pdfs_converted", "auxillary_datasets_processed"], + precondition_error_msg="Error: You want to process txt documents of certificates, but pdfs were not converted. You must use 'convert' action first.", + pre_callback_func=analysis_pre_callback, + ), + ] + + processing_step: ProcessingStep + for processing_step in [x for x in steps if x in actions_set]: + processing_step.run(dset) + + end = datetime.now() + logger.info(f"The computation took {(end-start)} seconds.") + except Exception: + return 1 + return 0 if __name__ == "__main__": From 4085c61d62e3e9d307ee8f05f3409d3cd10188ad Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 16:29:33 +0100 Subject: [PATCH 63/81] revive tests settings --- tests/data/settings_tests.yml | 59 +++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 tests/data/settings_tests.yml diff --git a/tests/data/settings_tests.yml b/tests/data/settings_tests.yml new file mode 100644 index 00000000..cf5a29b7 --- /dev/null +++ b/tests/data/settings_tests.yml @@ -0,0 +1,59 @@ +--- +log_filepath: + description: Path to the file, relative to working directory, where the log will be stored + value: ./cert_processing_log.txt +always_false_positive_fips_cert_id_threshold: + description: + During validation we don't connect certificates with number lower than + _this_ to connections due to these numbers being typically false positives + value: 40 +year_difference_between_validations: + description: + During validation we don't connect certificates with validation dates + difference higher than _this_ + value: 7 +n_threads: + description: How many threads to use for parallel computations + value: 8 +cpe_matching_threshold: + description: Level of required string similarity between CPE and certificate name on CC CPE matching, 0-100. Lower values yield more false negatives, higher values more false positives + value: 92 +cpe_n_max_matches: + description: Maximum number of candidate CPE items that may be related to given certificate, >0 + value: 99 +cc_latest_snapshot: + description: URL from where to fetch the latest snapshot of fully processed CC dataset + value: https://seccerts.org/cc/dataset.json +cc_maintenances_latest_snapshot: + description: URL from where to fetch the latest snapshot of CC maintenance updates + value: https://seccerts.org/cc/maintenance_updates.json +pp_latest_snapshot: + description: URL from where to fetch the latest snapshot of the PP dataset + value: https://seccerts.org/static/pp.json +ignore_first_page: + description: During keyword search, first page usually contains addresses - ignore it. + value: true +cert_threshold: + description: Used with --higher-precision-results. Determines the amount of mismatched algorithms to be considered faulty. + value: 5 +fips_latest_snapshot: + description: URL for the latest snapshot of FIPS dataset + value: https://seccerts.org/fips/dataset.json +fips_iut_dataset: + description: URL for the dataset of FIPS IUT data + value: https://seccerts.org/fips/iut/dataset.json +fips_iut_latest_snapshot: + description: URL for the latest snapshot of FIPS IUT data + value: https://seccerts.org/fips/iut/latest.json +fips_mip_dataset: + description: URL for the dataset of FIPS MIP data + value: https://seccerts.org/fips/mip/dataset.json +fips_mip_latest_snapshot: + description: URL for the latest snapshot of FIPS MIP data + value: https://seccerts.org/fips/mip/latest.json +minimal_token_length: + description: Minimal length of a string that will be considered as a token during keyword extraction in CVE matching + value: 3 +enable_progress_bars: + description: Whether to enable pretty-printed progress bars while processing. + value: False From b37eaafaaa4a511ac758e56f1ecba08640340530 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 17:16:14 +0100 Subject: [PATCH 64/81] fix here, fix there --- sec_certs/cli.py | 4 ++-- sec_certs/constants.py | 2 ++ sec_certs/dataset/common_criteria.py | 11 +++++++---- sec_certs/dataset/dataset.py | 9 ++++----- sec_certs/dataset/fips.py | 4 ++-- sec_certs/sample/fips.py | 6 ++---- tests/cc/test_cc_analysis.py | 2 +- tests/fips/test_fips_analysis.py | 2 +- 8 files changed, 21 insertions(+), 19 deletions(-) diff --git a/sec_certs/cli.py b/sec_certs/cli.py index 9044b05c..70e3a953 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -103,7 +103,7 @@ def build_or_load_dataset( "--output", type=click.Path(file_okay=False, dir_okay=True, writable=True, readable=True, resolve_path=True), help="Path where the output of the experiment will be stored. May overwrite existing content.", - default=Path("./cc_dset/"), + default=Path("./dataset/"), show_default=True, ) @click.option( @@ -121,7 +121,7 @@ def build_or_load_dataset( type=click.Path(file_okay=True, dir_okay=False, writable=True, readable=True), help="If set, the actions will be performed on a CC dataset loaded from JSON from the input path.", ) -@click.option("-s", "--silent", is_flag=True, help="If set, will not print to stdout") +@click.option("-q", "--quiet", is_flag=True, help="If set, will not print to stdout") def main( framework: str, actions: list[str], diff --git a/sec_certs/constants.py b/sec_certs/constants.py index 261cfbfe..f71bb3c3 100644 --- a/sec_certs/constants.py +++ b/sec_certs/constants.py @@ -18,6 +18,8 @@ CPE_VERSION_NA = "-" +RELEASE_CANDIDATE_REGEX: re.Pattern = re.compile(r"rc\d{0,2}$", re.IGNORECASE) + FIPS_BASE_URL = "https://csrc.nist.gov" FIPS_CMVP_URL = FIPS_BASE_URL + "/projects/cryptographic-module-validation-program" FIPS_CAVP_URL = FIPS_BASE_URL + "/projects/Cryptographic-Algorithm-Validation-Program" diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 0f50a8a6..9ac20889 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -260,7 +260,6 @@ def get_certs_from_web( """ Downloads CSV and HTML files that hold lists of certificates from common criteria website. Parses these files and constructs CommonCriteriaCert objects, fills the dataset with those. - Also downloads protection profiles snapshot :param bool to_download: If CSV and HTML files shall be downloaded (or existing files utilized), defaults to True :param bool keep_metadata: If CSV and HTML files shall be kept on disk after download, defaults to True @@ -675,7 +674,7 @@ def _extract_pdf_keywords(self) -> None: self._extract_report_keywords() self._extract_targets_keywords() - def _extract_data(self) -> None: + def extract_data(self) -> None: logger.info("Extracting various data from certification artifacts") self._extract_pdf_metadata() self._extract_pdf_frontpage() @@ -751,7 +750,9 @@ def process_auxillary_datasets(self, download_fresh: bool = False) -> None: self.auxillary_datasets.mu_dset = self.process_maintenance_updates(to_download=download_fresh) @serialize - def process_protection_profiles(self, to_download: bool = True, keep_metadata: bool = True) -> None: + def process_protection_profiles( + self, to_download: bool = True, keep_metadata: bool = True + ) -> ProtectionProfileDataset: """ Downloads new snapshot of dataset with processed protection profiles (if it doesn't exist) and links PPs with certificates within self. Assigns PPs to all certificates @@ -777,6 +778,8 @@ def process_protection_profiles(self, to_download: bool = True, keep_metadata: b if not keep_metadata: self.pp_dataset_path.unlink() + return pp_dataset + def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMaintenanceUpdates: """ Downloads or loads from json a dataset of maintenance updates. Runs analysis on that dataset if it's not completed. @@ -804,7 +807,7 @@ def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMain if not update_dset.state.pdfs_converted: update_dset.convert_all_pdfs() if not update_dset.state.certs_analyzed: - update_dset._extract_data() + update_dset.extract_data() return update_dset diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index c5899c88..2d09df0f 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -11,7 +11,7 @@ from dataclasses import dataclass from datetime import datetime from pathlib import Path -from typing import Any, Generic, Iterator, Pattern, TypeVar, cast +from typing import Any, Generic, Iterator, TypeVar, cast import pandas as pd @@ -268,11 +268,11 @@ def analyze_certificates(self) -> None: self.state.certs_analyzed = True def _analyze_certificates_body(self) -> None: - self._extract_data() + self.extract_data() self._compute_heuristics() @abstractmethod - def _extract_data(self) -> None: + def extract_data(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") def _compute_heuristics(self) -> None: @@ -321,7 +321,6 @@ def _prepare_cve_dataset( @serialize def compute_cpe_heuristics(self, download_fresh_cpes: bool = False) -> CPEClassifier: - RELEASE_CANDIDATE_REGEX: Pattern = re.compile(r"rc\d{0,2}$", re.IGNORECASE) WINDOWS_WEAK_CPES: set[CPE] = { CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", "Microsoft Windows on X64", None, None), CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x86:*", "Microsoft Windows on X86", None, None), @@ -344,7 +343,7 @@ def filter_condition(cpe: CPE) -> bool: and not any(char.isdigit() for char in cpe.item_name) ): return False - elif re.match(RELEASE_CANDIDATE_REGEX, cpe.update): + elif re.match(constants.RELEASE_CANDIDATE_REGEX, cpe.update): return False elif cpe in WINDOWS_WEAK_CPES: return False diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 7daf5201..f7228f17 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -112,7 +112,7 @@ def _extract_data_from_html_modules(self) -> None: self.update_with_certs(processed_certs) @serialize - def _extract_data(self) -> None: + def extract_data(self) -> None: logger.info("Extracting various data from certification artifacts.") for cert in self: cert.state.policy_extract_ok = True @@ -244,7 +244,7 @@ def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = Tru if to_download: self._download_html_resources() - logger.info("Adding empty FIPS certificates into FIPSDataset.") + logger.info("Adding unprocessed FIPS certificates into FIPSDataset.") self.certs = {x.dgst: x for x in self._get_all_certs_from_html_sources()} logger.info(f"The dataset now contains {len(self)} certificates.") diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index 0d8be343..b9d0c302 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -44,13 +44,11 @@ def get_web_data_and_algorithms(self) -> tuple[set[str], FIPSCertificate.WebData related_files_dict = self._build_related_files_dict(related_files_div) validation_history_dict = self._build_validation_history_dict(validation_history_div) + algorithms = set() if "algorithms" in details_dict: algorithms_data = details_dict.pop("algorithms") - algorithms = set() for category, alg_ids in algorithms_data.items(): algorithms |= {category + x for x in alg_ids} - else: - algorithms = set() return algorithms, FIPSCertificate.WebData( **{**details_dict, **vendor_dict, **related_files_dict, **validation_history_dict} @@ -603,7 +601,7 @@ def prune_referenced_cert_ids(self) -> None: self.heuristics.module_prunned_references = self._prune_reference_ids_variable(html_module_ids) if self.pdf_data.keywords: - pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict().keys())) + pdf_policy_ids = set(self.pdf_data.keywords["fips_cert_id"].get("Cert", dict()).keys()) pdf_policy_ids = {"".join([y for y in x if y.isdigit()]) for x in pdf_policy_ids} else: pdf_policy_ids = set() diff --git a/tests/cc/test_cc_analysis.py b/tests/cc/test_cc_analysis.py index 55c5553a..98318789 100644 --- a/tests/cc/test_cc_analysis.py +++ b/tests/cc/test_cc_analysis.py @@ -88,7 +88,7 @@ def cc_dset(data_dir: Path, cve_dset: CVEDataset, tmp_path_factory) -> CCDataset cc_dset = CCDataset.from_json(tmp_dir / "vulnerable_dataset.json") cc_dset.process_protection_profiles() - cc_dset._extract_data() + cc_dset.extract_data() cc_dset.auxillary_datasets.cve_dset = cve_dset cc_dset._compute_heuristics() return cc_dset diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py index cdd1d46b..29efc0dc 100644 --- a/tests/fips/test_fips_analysis.py +++ b/tests/fips/test_fips_analysis.py @@ -98,7 +98,7 @@ def processed_dataset( toy_static_dataset.download_all_artifacts() toy_static_dataset.convert_all_pdfs() - toy_static_dataset._extract_data() + toy_static_dataset.extract_data() toy_static_dataset._compute_references(keep_unknowns=True) toy_static_dataset.auxillary_datasets.cpe_dset = cpe_dataset From 6c0238334c3d5c83eae308dfc44eeb056f4c070d Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 17:23:12 +0100 Subject: [PATCH 65/81] rename dataset of maintenance updates --- sec_certs/cli.py | 1 + sec_certs/dataset/common_criteria.py | 4 ++-- tests/cc/test_cc_maintenance_updates.py | 6 +++--- .../{Maintenance updates.json => maintenance_updates.json} | 2 +- 4 files changed, 7 insertions(+), 6 deletions(-) rename tests/data/cc/dataset/auxillary_datasets/maintenances/{Maintenance updates.json => maintenance_updates.json} (99%) diff --git a/sec_certs/cli.py b/sec_certs/cli.py index 70e3a953..c410c0cb 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -157,6 +157,7 @@ def main( dset = build_or_load_dataset(framework, inputpath, "build" in actions_set, outputpath) aux_dsets_to_handle = "PP, Maintenance updates" if framework == "cc" else "Algorithms" + aux_dsets_to_handle += "CPE, CVE" analysis_pre_callback = None steps = [ diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 9ac20889..256d0218 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -157,7 +157,7 @@ def mu_dataset_path(self) -> Path: """ Returns json that holds the datase of maintenance updates """ - return self.mu_dataset_dir / "Maintenance updates.json" + return self.mu_dataset_dir / "maintenance_updates.json" BASE_URL: ClassVar[str] = "https://www.commoncriteriaportal.org" @@ -797,7 +797,7 @@ def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMain ) ) update_dset = CCDatasetMaintenanceUpdates( - {x.dgst: x for x in updates}, root_dir=self.mu_dataset_dir, name="Maintenance updates" + {x.dgst: x for x in updates}, root_dir=self.mu_dataset_dir, name="maintenance_updates" ) else: update_dset = CCDatasetMaintenanceUpdates.from_json(self.mu_dataset_path) diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index 456ed523..ac91667c 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -21,7 +21,7 @@ def cc_dset(data_dir: Path) -> CCDataset: @pytest.fixture def mu_dset(data_dir: Path, tmp_path_factory) -> CCDatasetMaintenanceUpdates: tmp_dir = tmp_path_factory.mktemp("mu_dset") - dset = CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/Maintenance updates.json") + dset = CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/maintenance_updates.json") dset.root_dir = tmp_dir return dset @@ -58,7 +58,7 @@ def test_dataset_to_json(mu_dset: CCDatasetMaintenanceUpdates, data_dir: Path, t with (tmp_path / "dset.json").open("r") as handle: data = json.load(handle) - with (data_dir / "auxillary_datasets/maintenances/Maintenance updates.json").open("r") as handle: + with (data_dir / "auxillary_datasets/maintenances/maintenance_updates.json").open("r") as handle: template_data = json.load(handle) del template_data["timestamp"] @@ -68,7 +68,7 @@ def test_dataset_to_json(mu_dset: CCDatasetMaintenanceUpdates, data_dir: Path, t def test_dataset_from_json(mu_dset: CCDatasetMaintenanceUpdates, data_dir: Path): assert mu_dset == CCDatasetMaintenanceUpdates.from_json( - data_dir / "auxillary_datasets/maintenances/Maintenance updates.json" + data_dir / "auxillary_datasets/maintenances/maintenance_updates.json" ) diff --git a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json b/tests/data/cc/dataset/auxillary_datasets/maintenances/maintenance_updates.json similarity index 99% rename from tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json rename to tests/data/cc/dataset/auxillary_datasets/maintenances/maintenance_updates.json index cab918a9..a2e3f7df 100644 --- a/tests/data/cc/dataset/auxillary_datasets/maintenances/Maintenance updates.json +++ b/tests/data/cc/dataset/auxillary_datasets/maintenances/maintenance_updates.json @@ -10,7 +10,7 @@ }, "timestamp": "2022-11-10 13:44:35.171285", "sha256_digest": "not implemented", - "name": "Maintenance updates", + "name": "maintenance_updates", "description": "dataset_description", "n_certs": 1, "certs": [ From 8ac389a335a73bb694238c25b0bc14f1b7ae9dec Mon Sep 17 00:00:00 2001 From: adamjanovsky Date: Mon, 5 Dec 2022 17:27:33 +0100 Subject: [PATCH 66/81] Update sec_certs/dataset/common_criteria.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ján JanÄár --- sec_certs/dataset/common_criteria.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 256d0218..5ecc72f8 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -710,7 +710,7 @@ def _compute_heuristics(self) -> None: self._compute_sars() def _compute_sars(self) -> None: - logger.info("omputing heuristics: Computing SARs") + logger.info("Computing heuristics: Computing SARs") transformer = SARTransformer().fit(self.certs.values()) for cert in self: cert.heuristics.extracted_sars = transformer.transform_single_cert(cert) From bc5a5320d862dfbe07bf61327af5f38eb434f600 Mon Sep 17 00:00:00 2001 From: adamjanovsky Date: Mon, 5 Dec 2022 17:29:09 +0100 Subject: [PATCH 67/81] Update sec_certs/model/cpe_matching.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ján JanÄár --- sec_certs/model/cpe_matching.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index 39179a7d..8bbead82 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -155,7 +155,7 @@ def filter_condition(regex: Pattern, cpe: CPE, min_value: int, soft: bool = True for update_regex in update_regexes: if matches := re.findall(update_regex, cert_title): min_value = min([int(re.findall(r"\d+", x)[0]) for x in matches]) - soft = False if any(re.search(update_regex, cpe.update + str(cpe.title)) for cpe in cpes) else True + soft = not any(re.search(update_regex, cpe.update + str(cpe.title)) for cpe in cpes) return [x for x in cpes if filter_condition(update_regex, x, min_value, soft)] return cpes From 4712279eccd19d427ed4a5e10963fbe6f5054486 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 17:36:31 +0100 Subject: [PATCH 68/81] chain.from_iterable() now working with generator expessions --- notebooks/cc/vulnerabilities.ipynb | 2 +- notebooks/fips/vulnerabilities.ipynb | 23 ++++++++++++++--------- sec_certs/dataset/common_criteria.py | 2 +- sec_certs/dataset/cpe.py | 2 +- sec_certs/dataset/cve.py | 2 +- sec_certs/dataset/dataset.py | 4 ++-- sec_certs/dataset/fips.py | 2 +- sec_certs/dataset/fips_algorithm.py | 4 +--- sec_certs/model/cpe_matching.py | 8 +++----- sec_certs/sample/cve.py | 4 +--- sec_certs/sample/fips.py | 2 +- 11 files changed, 27 insertions(+), 28 deletions(-) diff --git a/notebooks/cc/vulnerabilities.ipynb b/notebooks/cc/vulnerabilities.ipynb index 3b7ed226..4c9836ba 100644 --- a/notebooks/cc/vulnerabilities.ipynb +++ b/notebooks/cc/vulnerabilities.ipynb @@ -109,7 +109,7 @@ " df = pd.concat([df, main_dates], axis=\"columns\")\n", "\n", "# Expand DataFrame with CVEs that affect some certificate\n", - "cves = list(itertools.chain.from_iterable([x.heuristics.related_cves for x in dset if x.heuristics.related_cves]))\n", + "cves = list(itertools.chain.from_iterable(x.heuristics.related_cves for x in dset if x.heuristics.related_cves))\n", "cve_dict = {x: cve_dset[x] for x in cves}\n", "cve_dset.cves = cve_dict # Limit cve_dset to CVEs relevant to some certificate\n", "df = expand_df_with_cve_cols(df, cve_dset)\n", diff --git a/notebooks/fips/vulnerabilities.ipynb b/notebooks/fips/vulnerabilities.ipynb index a226ca61..d00cccde 100644 --- a/notebooks/fips/vulnerabilities.ipynb +++ b/notebooks/fips/vulnerabilities.ipynb @@ -51,7 +51,7 @@ "outputs": [], "source": [ "df = dset.to_pandas()\n", - "cves = list(itertools.chain.from_iterable([x.heuristics.related_cves for x in dset if x.heuristics.related_cves]))\n", + "cves = list(itertools.chain.from_iterable(x.heuristics.related_cves for x in dset if x.heuristics.related_cves))\n", "cve_dict = {x: cve_dset[x] for x in cves}\n", "cve_dset.cves = cve_dict # Limit cve_dset to CVEs relevant to some certificate\n", "df = expand_df_with_cve_cols(df, cve_dset)\n", @@ -395,7 +395,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -425,7 +425,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -513,7 +513,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -568,7 +568,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", "name": "python3" }, @@ -582,7 +582,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.13" + }, + "vscode": { + "interpreter": { + "hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4" + } } }, "nbformat": 4, diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 5ecc72f8..f8cfd552 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -793,7 +793,7 @@ def process_maintenance_updates(self, to_download: bool = True) -> CCDatasetMain maintained_certs: list[CommonCriteriaCert] = [x for x in self if x.maintenance_updates] updates = list( itertools.chain.from_iterable( - [CommonCriteriaMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs] + CommonCriteriaMaintenanceUpdate.get_updates_from_cc_cert(x) for x in maintained_certs ) ) update_dset = CCDatasetMaintenanceUpdates( diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index e1e16c60..b1dcaea1 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -198,7 +198,7 @@ def _adding_condition( if not isinstance(cve_dset, CVEDataset): raise RuntimeError("Conversion of CVE dataset did not work.") - all_cpes_in_cve_dset = set(itertools.chain.from_iterable([cve.vulnerable_cpes for cve in cve_dset])) + all_cpes_in_cve_dset = set(itertools.chain.from_iterable(cve.vulnerable_cpes for cve in cve_dset)) old_len = len(self.cpes) diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index f00e3a93..60c22568 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -86,7 +86,7 @@ def build_lookup_dict(self, use_nist_mapping: bool = True, nist_matching_filepat # See note above, we use matching_dict.get(cpe, []) instead of matching_dict[cpe] as would be expected if use_nist_mapping: vulnerable_configurations = list( - itertools.chain.from_iterable([matching_dict.get(cpe, []) for cpe in cve.vulnerable_cpes]) + itertools.chain.from_iterable(matching_dict.get(cpe, []) for cpe in cve.vulnerable_cpes) ) else: vulnerable_configurations = cve.vulnerable_cpes diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 2d09df0f..1277f8f7 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -473,7 +473,7 @@ def compute_related_cves( ) return - relevant_cpes = set(itertools.chain.from_iterable([x.heuristics.cpe_matches for x in cpe_rich_certs])) + relevant_cpes = set(itertools.chain.from_iterable(x.heuristics.cpe_matches for x in cpe_rich_certs)) self.auxillary_datasets.cve_dset.filter_related_cpes(relevant_cpes) cert: Certificate @@ -485,7 +485,7 @@ def compute_related_cves( related_cves = list(filter(lambda x: x is not None, related_cves)) if related_cves: cert.heuristics.related_cves = set( - itertools.chain.from_iterable([x for x in related_cves if x is not None]) + itertools.chain.from_iterable(x for x in related_cves if x is not None) ) else: cert.heuristics.related_cves = None diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index f7228f17..bfe48ea9 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -201,7 +201,7 @@ def _download_html_resources(self) -> None: def _get_all_certs_from_html_sources(self) -> set[FIPSCertificate]: return set( itertools.chain.from_iterable( - [self._get_certificates_from_html(self.web_dir / x) for x in self.LIST_OF_CERTS_HTML.keys()] + self._get_certificates_from_html(self.web_dir / x) for x in self.LIST_OF_CERTS_HTML.keys() ) ) diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index ee70db60..19fafb06 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -65,9 +65,7 @@ def __eq__(self, other: object) -> bool: def from_web(cls, json_path: str | Path = constants.DUMMY_NONEXISTING_PATH) -> FIPSAlgorithmDataset: with TemporaryDirectory() as tmp_dir: htmls = FIPSAlgorithmDataset.download_alg_list_htmls(Path(tmp_dir)) - algs = set( - itertools.chain.from_iterable([FIPSAlgorithmDataset.parse_algorithms_from_html(x) for x in htmls]) - ) + algs = set(itertools.chain.from_iterable(FIPSAlgorithmDataset.parse_algorithms_from_html(x) for x in htmls)) return cls({x.dgst: x for x in algs}, json_path) @staticmethod diff --git a/sec_certs/model/cpe_matching.py b/sec_certs/model/cpe_matching.py index 8bbead82..0febea5d 100644 --- a/sec_certs/model/cpe_matching.py +++ b/sec_certs/model/cpe_matching.py @@ -320,10 +320,10 @@ def _get_candidate_list_of_vendors(self, manufacturer: str | None) -> set[str]: if splits: vendor_tokens = set( - itertools.chain.from_iterable([[x.strip() for x in manufacturer.split(s)] for s in splits]) + itertools.chain.from_iterable([x.strip() for x in manufacturer.split(s)] for s in splits) ) result_aux = [self._get_candidate_list_of_vendors(x) for x in vendor_tokens] - result_used = set(set(itertools.chain.from_iterable([x for x in result_aux if x]))) + result_used = set(set(itertools.chain.from_iterable(x for x in result_aux if x))) return result_used if result_used else set() if manufacturer in self.vendors_: @@ -393,9 +393,7 @@ def _get_candidate_cpe_matches(self, candidate_vendors: set[str], candidate_vers """ candidate_vendor_version_pairs = self._get_candidate_vendor_version_pairs(candidate_vendors, candidate_versions) return ( - list( - itertools.chain.from_iterable([self.vendor_version_to_cpe_[x] for x in candidate_vendor_version_pairs]) - ) + list(itertools.chain.from_iterable(self.vendor_version_to_cpe_[x] for x in candidate_vendor_version_pairs)) if candidate_vendor_version_pairs else [] ) diff --git a/sec_certs/sample/cve.py b/sec_certs/sample/cve.py index ca6e27af..ec024d35 100644 --- a/sec_certs/sample/cve.py +++ b/sec_certs/sample/cve.py @@ -168,9 +168,7 @@ def get_vulnerable_cpes_from_node(node: dict) -> list[CPE]: return cpes return list( - itertools.chain.from_iterable( - [get_vulnerable_cpes_from_node(x) for x in dct["configurations"]["nodes"]] - ) + itertools.chain.from_iterable(get_vulnerable_cpes_from_node(x) for x in dct["configurations"]["nodes"]) ) cve_id = dct["cve"]["CVE_data_meta"]["ID"] diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index b9d0c302..ca8e1a06 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -584,7 +584,7 @@ def get_algorithms_from_policy_tables(cert: FIPSCertificate): try: tabular_data = read_pdf(cert.state.policy_pdf_path, pages=list(table_rich_page_numbers), silent=True) cert.heuristics.algorithms |= set( - itertools.chain.from_iterable([tables.get_algs_from_table(df.to_string()) for df in tabular_data]) + itertools.chain.from_iterable(tables.get_algs_from_table(df.to_string()) for df in tabular_data) ) except Exception as e: logger.warning(f"Error when parsing tables from {cert.dgst}: {e}") From f14dfe3900baa0a87a51f42188646f409b9beeb9 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Mon, 5 Dec 2022 17:39:41 +0100 Subject: [PATCH 69/81] fix getitem on fips dataset --- sec_certs/dataset/fips.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index bfe48ea9..4d3f0bfd 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -90,10 +90,10 @@ def algorithm_dataset_path(self) -> Path: return self.algorithms_dir / "algorithms.json" def __getitem__(self, item: str) -> FIPSCertificate: - if len(item) < 5: - return super().__getitem__(fips_dgst(item)) - else: + try: return super().__getitem__(item) + except KeyError: + return super().__getitem__(fips_dgst(item)) def _extract_data_from_html_modules(self) -> None: """ From a1ec9861d9eadfa678a6fd73d6cc88c5d39f569e Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 6 Dec 2022 18:05:28 +0100 Subject: [PATCH 70/81] test config global fixture --- tests/conftest.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 tests/conftest.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..b00c1bbe --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,12 @@ +from pathlib import Path + +import pytest + +import tests.data +from sec_certs.config.configuration import config + + +@pytest.fixture(scope="session", autouse=True) +def load_test_config(): + pth = Path(tests.data.__path__[0]) / "settings_tests.yml" + config.load(pth) From 577300e4c4f841cd0caed1821749007729611d32 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Tue, 6 Dec 2022 20:35:56 +0100 Subject: [PATCH 71/81] add pyupgrade into linter pipeline --- .github/workflows/lint.yml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index d2479c55..9b261049 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -34,6 +34,20 @@ jobs: - uses: isort/isort-action@master with: requirementsFiles: "requirements/requirements.txt requirements/dev_requirements.txt" + pyupgrade: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Setup python + uses: actions/setup-python@v4 + with: + python-version: "3.8" + - name: Install python dependencies + run: | + pip install -r requirements/requirements.txt + pip install -r requirements/dev_requirements.txt + - name: Run pyupgrade + run: pre-commit run pyupgrade --all-files flake8-lint: runs-on: ubuntu-latest name: Flake8 @@ -46,3 +60,5 @@ jobs: python-version: "3.8" - name: flake8 Lint uses: py-actions/flake8@v2 + with: + plugins: "flake8-future-annotations" From ed8813e58f41d18ff0d3862e0799b55509767101 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 7 Dec 2022 14:27:11 +0100 Subject: [PATCH 72/81] reimplement dataset serialization constraints --- sec_certs/cli.py | 2 +- sec_certs/dataset/common_criteria.py | 13 +++-- sec_certs/dataset/cpe.py | 28 ++-------- sec_certs/dataset/cve.py | 25 ++------- sec_certs/dataset/dataset.py | 64 +++++++++++++++-------- sec_certs/dataset/fips.py | 19 +++---- sec_certs/dataset/fips_algorithm.py | 22 ++------ sec_certs/dataset/fips_iut.py | 9 +++- sec_certs/dataset/fips_mip.py | 9 +++- sec_certs/dataset/json_path_dataset.py | 46 ++++++++++++++++ sec_certs/dataset/protection_profile.py | 35 +++++++++++++ sec_certs/serialization/json.py | 25 ++++++--- tests/cc/test_cc_dataset.py | 4 +- tests/cc/test_cc_maintenance_updates.py | 2 +- tests/fips/test_fips_algorithm_dataset.py | 3 +- tests/fips/test_fips_analysis.py | 2 +- tests/fips/test_fips_dataset.py | 2 +- tests/test_cpe.py | 3 +- tests/test_cve.py | 3 +- 19 files changed, 198 insertions(+), 118 deletions(-) create mode 100644 sec_certs/dataset/json_path_dataset.py diff --git a/sec_certs/cli.py b/sec_certs/cli.py index c410c0cb..541f3eeb 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -75,7 +75,7 @@ def build_or_load_dataset( print( "Warning: you provided both input and output paths. The dataset from input path will get copied to output path." ) - dset.root_dir = outputpath + dset.copy_dataset(outputpath) else: print( "Error: If you do not use 'build' action, you must provide --input parameter to point to an existing dataset." diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index f8cfd552..82793fea 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -60,7 +60,6 @@ def __init__( auxillary_datasets: CCAuxillaryDatasets | None = None, ): self.certs = certs - self._root_dir = Path(root_dir) self.timestamp = datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" @@ -71,6 +70,8 @@ def __init__( auxillary_datasets if auxillary_datasets else CCAuxillaryDatasets() ) + self.root_dir = Path(root_dir) + def to_pandas(self) -> pd.DataFrame: """ Return self serialized into pandas DataFrame @@ -218,8 +219,16 @@ def from_web_latest(cls) -> CCDataset: def _set_local_paths(self): super()._set_local_paths() + + if self.auxillary_datasets.pp_dset: + self.auxillary_datasets.pp_dset.json_path = self.pp_dataset_path + + if self.auxillary_datasets.mu_dset: + self.auxillary_datasets.mu_dset.root_dir = self.mu_dataset_dir + for cert in self: cert.set_local_paths(self.reports_pdf_dir, self.targets_pdf_dir, self.reports_txt_dir, self.targets_txt_dir) + # TODO: This forgets to set local paths for other auxillary datasets def _merge_certs(self, certs: dict[str, CommonCriteriaCert], cert_source: str | None = None) -> None: """ @@ -703,7 +712,6 @@ def _compute_transitive_vulnerabilities(self): self.certs[dgst].heuristics.indirect_transitive_cves = transitive_cve.indirect_transitive_cves def _compute_heuristics(self) -> None: - logger.info("Computing various heuristics on certificates.") self._compute_normalized_cert_ids() super()._compute_heuristics() self._compute_cert_labs() @@ -829,7 +837,6 @@ def __init__( ): super().__init__(certs, root_dir, name, description, state) # type: ignore self.state.meta_sources_parsed = True - self._set_local_paths() @property def certs_dir(self) -> Path: diff --git a/sec_certs/dataset/cpe.py b/sec_certs/dataset/cpe.py index b1dcaea1..927ce674 100644 --- a/sec_certs/dataset/cpe.py +++ b/sec_certs/dataset/cpe.py @@ -7,12 +7,13 @@ import xml.etree.ElementTree as ET import zipfile from pathlib import Path -from typing import ClassVar, Iterator, cast +from typing import ClassVar, Iterator import pandas as pd from sec_certs import constants from sec_certs.dataset.cve import CVEDataset +from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample.cpe import CPE, cached_cpe from sec_certs.serialization.json import ComplexSerializableType, serialize from sec_certs.utils import helpers @@ -21,7 +22,7 @@ logger = logging.getLogger(__name__) -class CPEDataset(ComplexSerializableType): +class CPEDataset(JSONPathDataset, ComplexSerializableType): """ Dataset of CPE records. Includes look-up dictionaries for fast search. """ @@ -37,7 +38,7 @@ def __init__( ): self.was_enhanced_with_vuln_cpes = was_enhanced_with_vuln_cpes self.cpes = cpes - self._json_path = Path(json_path) + self.json_path = Path(json_path) self.vendor_to_versions: dict[str, set[str]] = dict() self.vendor_version_to_cpe: dict[tuple[str, str], set[CPE]] = dict() @@ -46,15 +47,6 @@ def __init__( self.build_lookup_dicts() - @property - def json_path(self) -> Path: - return self._json_path - - @json_path.setter - def json_path(self, new_json_path: str | Path) -> None: - self._json_path = Path(new_json_path) - self.to_json() - def __iter__(self) -> Iterator[CPE]: yield from self.cpes.values() @@ -143,18 +135,6 @@ def _from_xml(cls, xml_path: str | Path, json_path: str | Path = constants.DUMMY return cls(False, dct, json_path) - @classmethod - def from_json(cls, input_path: str | Path) -> CPEDataset: - """ - Loads dataset from json - - :param Union[str, Path] input_path: Path to the serialized json dataset - :return CPEDataset: the resulting dataset. - """ - dset = cast("CPEDataset", ComplexSerializableType.from_json(input_path)) - dset._json_path = Path(input_path) - return dset - def to_pandas(self) -> pd.DataFrame: """ Turns the dataset into pandas DataFrame. Each CPE record forms a row. diff --git a/sec_certs/dataset/cve.py b/sec_certs/dataset/cve.py index 60c22568..b65c81c6 100644 --- a/sec_certs/dataset/cve.py +++ b/sec_certs/dataset/cve.py @@ -17,33 +17,25 @@ import sec_certs.constants as constants import sec_certs.utils.helpers as helpers from sec_certs.config.configuration import config +from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample.cpe import CPE, cached_cpe from sec_certs.sample.cve import CVE -from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder +from sec_certs.serialization.json import ComplexSerializableType from sec_certs.utils.parallel_processing import process_parallel from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) -class CVEDataset(ComplexSerializableType): +class CVEDataset(JSONPathDataset, ComplexSerializableType): CVE_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cve/1.1/nvdcve-1.1-" CPE_MATCH_FEED_URL: ClassVar[str] = "https://nvd.nist.gov/feeds/json/cpematch/1.0/nvdcpematch-1.0.json.zip" def __init__(self, cves: dict[str, CVE], json_path: str | Path = constants.DUMMY_NONEXISTING_PATH): self.cves = cves - self._json_path = Path(json_path) + self.json_path = Path(json_path) self.cpe_to_cve_ids_lookup: dict[str, set[str]] = dict() - @property - def json_path(self) -> Path: - return self._json_path - - @json_path.setter - def json_path(self, new_json_path: str | Path) -> None: - self._json_path = Path(new_json_path) - self.to_json() - @property def serialized_attributes(self) -> list[str]: return ["cves"] @@ -107,7 +99,7 @@ def download_cves(cls, output_path_str: str, start_year: int, end_year: int): logger.info(f"Identified {len(urls)} CVE files to fetch from nist.gov. Downloading them into {output_path}") with tempfile.TemporaryDirectory() as tmp_dir: outpaths = [Path(tmp_dir) / Path(x).name.rstrip(".zip") for x in urls] - responses = helpers.download_parallel(urls, outpaths) + responses = helpers.download_parallel(urls, outpaths, "Downloading CVEs resources from NVD") for o, r in zip(outpaths, responses): if r == constants.RESPONSE_OK: @@ -147,13 +139,6 @@ def from_web( return cls(all_cves, json_path) - @classmethod - def from_json(cls, input_path: str | Path): - with Path(input_path).open("r") as handle: - dset = json.load(handle, cls=CustomJSONDecoder) - dset._json_path = input_path - return dset - def get_cve_ids_for_cpe_uri(self, cpe_uri: str) -> set[str] | None: return self.cpe_to_cve_ids_lookup.get(cpe_uri, None) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index 1277f8f7..c9ac8fcc 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -1,6 +1,5 @@ from __future__ import annotations -import copy import itertools import json import logging @@ -23,7 +22,7 @@ from sec_certs.model.cpe_matching import CPEClassifier from sec_certs.sample.certificate import Certificate from sec_certs.sample.cpe import CPE -from sec_certs.serialization.json import ComplexSerializableType, serialize +from sec_certs.serialization.json import ComplexSerializableType, get_class_fullname, serialize from sec_certs.utils.tqdm import tqdm logger = logging.getLogger(__name__) @@ -62,14 +61,10 @@ def __init__( auxillary_datasets: AuxillaryDatasetsSubType | None = None, ): self.certs = certs - self._root_dir = Path(root_dir) - - if not self._root_dir and self._root_dir != constants.DUMMY_NONEXISTING_PATH: - self._root_dir.mkdir(parents=True) self.timestamp = datetime.now() self.sha256_digest = "not implemented" - self.name = name if name else type(self).__name__ + " dataset" + self.name = name if name else type(self).__name__.lower() + "_dataset" self.description = description if description else "No description provided" self.state = state if state else self.DatasetInternalState() @@ -78,21 +73,20 @@ def __init__( else: self.auxillary_datasets = auxillary_datasets + self.root_dir = Path(root_dir) + @property def root_dir(self) -> Path: return self._root_dir @root_dir.setter - def root_dir(self, new_dir: str | Path) -> None: - old_dset = copy.deepcopy(self) - self._root_dir = Path(new_dir) - self.root_dir.mkdir(exist_ok=True) - self._set_local_paths() + def root_dir(self: DatasetSubType, new_dir: str | Path) -> None: + new_dir = Path(new_dir) + if new_dir.is_file(): + raise ValueError(f"Root dir of {get_class_fullname(self)} cannot be a file.") - if old_dset.root_dir != self.root_dir: - logger.info(f"Changing root dir of partially processed dataset. All contents will get copied to {new_dir}") - self._copy_dataset_contents(old_dset) - self.to_json() + self._root_dir = new_dir + self._set_local_paths() @property def web_dir(self) -> Path: @@ -157,7 +151,9 @@ def from_web(cls: type[DatasetSubType], url: str, progress_bar_desc: str, filena with tempfile.TemporaryDirectory() as tmp_dir: dset_path = Path(tmp_dir) / filename helpers.download_file(url, dset_path, show_progress_bar=True, progress_bar_desc=progress_bar_desc) - return cls.from_json(dset_path) + dset = cls.from_json(dset_path) + dset.root_dir = constants.DUMMY_NONEXISTING_PATH + return dset def to_dict(self) -> dict[str, Any]: return { @@ -193,12 +189,32 @@ def _set_local_paths(self) -> None: if self.auxillary_datasets.cve_dset: self.auxillary_datasets.cve_dset.json_path = self.cve_dataset_path - # Workaround from https://peps.python.org/pep-0673/ applied. - def _copy_dataset_contents(self: DatasetSubType, old_dset: DatasetSubType) -> None: - try: - shutil.copytree(old_dset.root_dir, self.root_dir, dirs_exist_ok=True) - except FileNotFoundError as e: - logger.error(f"Attempted to copy dataset from {old_dset.root_dir}, but it's not there ({e}).") + def move_dataset(self, new_root_dir: str | Path) -> None: + """ + Moves all dataset files to `new_root_dir` and adjusts all paths internally. Deletes the artifacts from the original location. + :param str | Path new_root_dir: path to directory where the new dataset shall be stored. + """ + new_root_dir = Path(new_root_dir) + if new_root_dir.is_file(): + raise ValueError("New root dir must be a directory, not an existing file.") + new_root_dir.mkdir(parents=True, exist_ok=True) + + shutil.copytree(str(self.root_dir), str(new_root_dir), dirs_exist_ok=True) + shutil.rmtree(self.root_dir) + self.root_dir = new_root_dir + + def copy_dataset(self, new_root_dir: str | Path) -> None: + """ + Copies all dataset files to `new_root_dir` and adjusts all paths internally. Keeps the artifacts from the original location. + :param str | Path new_root_dir: path to directory where the new dataset shall be stored. + """ + new_root_dir = Path(new_root_dir) + if new_root_dir.is_file(): + raise ValueError("New root dir must be a directory, not an existing file.") + new_root_dir.mkdir(parents=True, exist_ok=True) + + shutil.copytree(str(self.root_dir), str(new_root_dir), dirs_exist_ok=True) + self.root_dir = new_root_dir def _get_certs_by_name(self, name: str) -> set[CertSubType]: """ @@ -210,9 +226,11 @@ def _get_certs_by_name(self, name: str) -> set[CertSubType]: def get_certs_from_web(self) -> None: raise NotImplementedError("Not meant to be implemented by the base class.") + @serialize @abstractmethod def process_auxillary_datasets(self, download_fresh: bool = False) -> None: logger.info("Processing auxillary datasets.") + self.auxillary_datasets_dir.mkdir(parents=True, exist_ok=True) self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) self.auxillary_datasets.cve_dset = self._prepare_cve_dataset(download_fresh_cves=download_fresh) self.state.auxillary_datasets_processed = True diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index 4d3f0bfd..a10ceba0 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -49,7 +49,6 @@ def __init__( auxillary_datasets: FIPSAuxillaryDatasets | None = None, ): self.certs = certs - self._root_dir = Path(root_dir) self.timestamp = datetime.datetime.now() self.sha256_digest = "not implemented" self.name = name if name else type(self).__name__ + " dataset" @@ -59,6 +58,8 @@ def __init__( auxillary_datasets if auxillary_datasets else FIPSAuxillaryDatasets() ) + self.root_dir = Path(root_dir) + LIST_OF_CERTS_HTML: Final[dict[str, str]] = { "fips_modules_active.html": constants.FIPS_ACTIVE_MODULES_URL, "fips_modules_historical.html": constants.FIPS_HISTORICAL_MODULES_URL, @@ -81,13 +82,9 @@ def policies_txt_dir(self) -> Path: def module_dir(self) -> Path: return self.certs_dir / "modules" - @property - def algorithms_dir(self) -> Path: - return self.auxillary_datasets_dir / "algorithms" - @property def algorithm_dataset_path(self) -> Path: - return self.algorithms_dir / "algorithms.json" + return self.auxillary_datasets_dir / "algorithms.json" def __getitem__(self, item: str) -> FIPSCertificate: try: @@ -229,14 +226,14 @@ def from_web_latest(cls) -> FIPSDataset: return cls.from_web(config.fips_latest_snapshot, "Downloading FIPS Dataset", "fips_latest_dataset.json") def _set_local_paths(self) -> None: - cert: FIPSCertificate - for cert in self.certs.values(): - cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.module_dir) - super()._set_local_paths() if self.auxillary_datasets.algorithm_dset: self.auxillary_datasets.algorithm_dset.json_path = self.algorithm_dataset_path + cert: FIPSCertificate + for cert in self.certs.values(): + cert.set_local_paths(self.policies_pdf_dir, self.policies_txt_dir, self.module_dir) + @serialize def get_certs_from_web(self, to_download: bool = True, keep_metadata: bool = True) -> None: self.web_dir.mkdir(parents=True, exist_ok=True) @@ -261,8 +258,6 @@ def process_auxillary_datasets(self, download_fresh: bool = False) -> None: def _prepare_algorithm_dataset(self, download_fresh_algs: bool = False) -> FIPSAlgorithmDataset: logger.info("Preparing FIPSAlgorithm dataset.") - self.algorithms_dir.mkdir(parents=True, exist_ok=True) - if not self.algorithm_dataset_path.exists() or download_fresh_algs: alg_dset = FIPSAlgorithmDataset.from_web(self.algorithm_dataset_path) alg_dset.to_json() diff --git a/sec_certs/dataset/fips_algorithm.py b/sec_certs/dataset/fips_algorithm.py index 19fafb06..c48cff07 100644 --- a/sec_certs/dataset/fips_algorithm.py +++ b/sec_certs/dataset/fips_algorithm.py @@ -5,12 +5,13 @@ import re from pathlib import Path from tempfile import TemporaryDirectory -from typing import Iterator, cast +from typing import Iterator import pandas as pd from bs4 import BeautifulSoup from sec_certs import constants +from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample import FIPSAlgorithm from sec_certs.serialization.json import ComplexSerializableType from sec_certs.utils import helpers @@ -18,12 +19,12 @@ logger = logging.getLogger(__name__) -class FIPSAlgorithmDataset(ComplexSerializableType): +class FIPSAlgorithmDataset(JSONPathDataset, ComplexSerializableType): def __init__( self, algs: dict[str, FIPSAlgorithm] = dict(), json_path: str | Path = constants.DUMMY_NONEXISTING_PATH ): self.algs = algs - self._json_path = Path(json_path) + self.json_path = Path(json_path) self.alg_number_to_algs: dict[str, set[FIPSAlgorithm]] = dict() self._build_lookup_dicts() @@ -32,15 +33,6 @@ def __init__( def serialized_attributes(self) -> list[str]: return ["algs"] - @property - def json_path(self) -> Path: - return self._json_path - - @json_path.setter - def json_path(self, new_json_path: str | Path) -> None: - self._json_path = Path(new_json_path) - self.to_json() - def __iter__(self) -> Iterator[FIPSAlgorithm]: yield from self.algs.values() @@ -116,12 +108,6 @@ def parse_algorithms_from_html(html_path: Path) -> set[FIPSAlgorithm]: ) return set(df["alg"]) - @classmethod - def from_json(cls, input_path: str | Path) -> FIPSAlgorithmDataset: - dset = cast("FIPSAlgorithmDataset", ComplexSerializableType.from_json(input_path)) - dset._json_path = Path(input_path) - return dset - def to_pandas(self) -> pd.DataFrame: df = pd.DataFrame([x.pandas_tuple for x in self], columns=FIPSAlgorithm.pandas_columns) df = df.set_index("dgst") diff --git a/sec_certs/dataset/fips_iut.py b/sec_certs/dataset/fips_iut.py index f4221e91..ce0f2f76 100644 --- a/sec_certs/dataset/fips_iut.py +++ b/sec_certs/dataset/fips_iut.py @@ -7,16 +7,23 @@ import requests +from sec_certs import constants from sec_certs.config.configuration import config from sec_certs.dataset.dataset import logger +from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample.fips_iut import IUTSnapshot from sec_certs.serialization.json import ComplexSerializableType from sec_certs.utils.tqdm import tqdm @dataclass -class IUTDataset(ComplexSerializableType): +class IUTDataset(JSONPathDataset, ComplexSerializableType): snapshots: list[IUTSnapshot] + _json_path: Path + + def __init__(self, snapshots: list[IUTSnapshot], json_path: str | Path = constants.DUMMY_NONEXISTING_PATH): + self.snapshots = snapshots + self.json_path = Path(json_path) def __iter__(self) -> Iterator[IUTSnapshot]: yield from self.snapshots diff --git a/sec_certs/dataset/fips_mip.py b/sec_certs/dataset/fips_mip.py index c913879a..05ca5854 100644 --- a/sec_certs/dataset/fips_mip.py +++ b/sec_certs/dataset/fips_mip.py @@ -7,16 +7,23 @@ import requests +from sec_certs import constants from sec_certs.config.configuration import config from sec_certs.dataset.dataset import logger +from sec_certs.dataset.json_path_dataset import JSONPathDataset from sec_certs.sample.fips_mip import MIPSnapshot from sec_certs.serialization.json import ComplexSerializableType from sec_certs.utils.tqdm import tqdm @dataclass -class MIPDataset(ComplexSerializableType): +class MIPDataset(JSONPathDataset, ComplexSerializableType): snapshots: list[MIPSnapshot] + _json_path: Path + + def __init__(self, snapshots: list[MIPSnapshot], json_path: str | Path = constants.DUMMY_NONEXISTING_PATH): + self.snapshots = snapshots + self.json_path = Path(json_path) def __iter__(self) -> Iterator[MIPSnapshot]: yield from self.snapshots diff --git a/sec_certs/dataset/json_path_dataset.py b/sec_certs/dataset/json_path_dataset.py new file mode 100644 index 00000000..6eb4d968 --- /dev/null +++ b/sec_certs/dataset/json_path_dataset.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import json +import logging +import shutil +from abc import ABC +from pathlib import Path + +from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder, get_class_fullname + +logger = logging.getLogger(__name__) + + +class JSONPathDataset(ComplexSerializableType, ABC): + _json_path: Path + + @property + def json_path(self) -> Path: + return self._json_path + + @json_path.setter + def json_path(self, new_path: str | Path) -> None: + new_path = Path(new_path) + if new_path.is_dir(): + raise ValueError(f"Json path of {get_class_fullname(self)} cannot be a directory.") + + self._json_path = new_path + + def move_dataset(self, new_json_path: str | Path) -> None: + logger.info(f"Moving {get_class_fullname(self)} dataset to {new_json_path}") + new_json_path = Path(new_json_path) + new_json_path.parent.mkdir(parents=True, exist_ok=True) + + if self.json_path.exists(): + shutil.move(str(self.json_path), str(new_json_path)) + self.json_path = new_json_path + else: + self.json_path = new_json_path + self.to_json() + + @classmethod + def from_json(cls, input_path: str | Path): + with Path(input_path).open("r") as handle: + dset = json.load(handle, cls=CustomJSONDecoder) + dset.json_path = Path(input_path) + return dset diff --git a/sec_certs/dataset/protection_profile.py b/sec_certs/dataset/protection_profile.py index d8320a04..edfb6850 100644 --- a/sec_certs/dataset/protection_profile.py +++ b/sec_certs/dataset/protection_profile.py @@ -2,13 +2,16 @@ import json import logging +import shutil import tempfile from dataclasses import dataclass from pathlib import Path import sec_certs.utils.helpers as helpers +from sec_certs import constants from sec_certs.config.configuration import config from sec_certs.sample.protection_profile import ProtectionProfile +from sec_certs.serialization.json import get_class_fullname logger = logging.getLogger(__name__) @@ -16,6 +19,38 @@ @dataclass class ProtectionProfileDataset: pps: dict[tuple[str, str | None], ProtectionProfile] + _json_path: Path + + def __init__( + self, + pps: dict[tuple[str, str | None], ProtectionProfile], + json_path: str | Path = constants.DUMMY_NONEXISTING_PATH, + ) -> None: + self.pps = pps + self.json_path = Path(json_path) + + @property + def json_path(self): + return self._json_path + + @json_path.setter + def json_path(self, new_path: str | Path): + new_path = Path(new_path) + if new_path.is_dir(): + raise ValueError(f"Json path of {get_class_fullname(self)} cannot be a directory.") + + self._json_path = new_path + + def move_dataset(self, new_json_path: str | Path) -> None: + logger.info(f"Moving {get_class_fullname(self)} dataset to {new_json_path}") + new_json_path = Path(new_json_path) + new_json_path.parent.mkdir(parents=True, exist_ok=True) + + if not self.json_path.exists(): + raise ValueError("Cannot move the PPDataset if the json path does not exist.") + + shutil.move(str(self.json_path), str(new_json_path)) + self.json_path = new_json_path def __iter__(self): yield from self.pps.values() diff --git a/sec_certs/serialization/json.py b/sec_certs/serialization/json.py index 003954ee..69bbc2ff 100644 --- a/sec_certs/serialization/json.py +++ b/sec_certs/serialization/json.py @@ -12,6 +12,10 @@ T = TypeVar("T", bound="ComplexSerializableType") +class SerializationError(Exception): + pass + + class ComplexSerializableType: __slots__: tuple[str] @@ -42,16 +46,18 @@ def from_dict(cls: type[T], dct: dict) -> T: def to_json(self, output_path: str | Path | None = None) -> None: if not output_path and (not hasattr(self, "json_path") or not self.json_path): # type: ignore - raise ValueError( + raise SerializationError( f"The object {self} of type {self.__class__} does not have json_path attribute set but to_json() was called without an argument." ) if not output_path: output_path = self.json_path # type: ignore if self.json_path == constants.DUMMY_NONEXISTING_PATH: # type: ignore - raise ValueError(f"json_path variable for {type(self)} was not yet set.") + raise SerializationError(f"json_path attribute for '{get_class_fullname(self)}' was not yet set.") + if hasattr(self, "root_dir") and self.root_dir == constants.DUMMY_NONEXISTING_PATH: # type: ignore + raise SerializationError(f"root_dir attribute for '{get_class_fullname(self)}' was not yet set.") if Path(output_path).is_dir(): # type: ignore - raise ValueError("output path for json cannot be directory.") + raise SerializationError("output path for json cannot be directory.") # false positive MyPy warning, cannot be None with Path(output_path).open("w") as handle: # type: ignore @@ -74,6 +80,11 @@ def inner_func(*args, **kwargs): "@serialize decorator is to be used only on instance methods of ComplexSerializableType child classes." ) + if hasattr(args[0], "_root_dir") and args[0]._root_dir == constants.DUMMY_NONEXISTING_PATH: + raise SerializationError( + "The invoked method requires dataset serialization. Cannot serialize without root_dir set. You can set it with obj.root_dir = ..." + ) + update_json = kwargs.pop("update_json", True) result = func(*args, **kwargs) if update_json: @@ -83,7 +94,7 @@ def inner_func(*args, **kwargs): return inner_func -def _class_fullname(obj: Any) -> str: +def get_class_fullname(obj: Any) -> str: if isinstance(obj, type): klass = obj else: @@ -97,7 +108,7 @@ def _class_fullname(obj: Any) -> str: class CustomJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, ComplexSerializableType): - return {**{"_type": _class_fullname(obj)}, **obj.to_dict()} + return {**{"_type": get_class_fullname(obj)}, **obj.to_dict()} if isinstance(obj, dict): return obj if isinstance(obj, set): @@ -120,7 +131,7 @@ class CustomJSONDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) - self.serializable_complex_types = {_class_fullname(x): x for x in ComplexSerializableType.__subclasses__()} + self.serializable_complex_types = {get_class_fullname(x): x for x in ComplexSerializableType.__subclasses__()} def object_hook(self, obj): if "_type" in obj and obj["_type"] == "Set": @@ -129,6 +140,6 @@ def object_hook(self, obj): complex_type = obj.pop("_type") return self.serializable_complex_types[complex_type].from_dict(obj) elif "_type" in obj: - print(obj) + raise SerializationError(f"JSONDecoder doesn't know how to handle {obj}") return obj diff --git a/tests/cc/test_cc_dataset.py b/tests/cc/test_cc_dataset.py index fae19282..8247b4cb 100644 --- a/tests/cc/test_cc_dataset.py +++ b/tests/cc/test_cc_dataset.py @@ -59,7 +59,7 @@ def test_download_and_convert_pdfs(toy_dataset: CCDataset, data_dir: Path): } with TemporaryDirectory() as td: - toy_dataset.root_dir = Path(td) + toy_dataset.copy_dataset(td) toy_dataset.download_all_artifacts() if not ( @@ -148,7 +148,7 @@ def test_build_dataset(data_dir: Path, crt: CommonCriteriaCert, toy_dataset: CCD def test_process_pp_dataset(toy_dataset: CCDataset): with TemporaryDirectory() as tmp_dir: - toy_dataset.root_dir = tmp_dir # type: ignore + toy_dataset.copy_dataset(tmp_dir) toy_dataset.process_protection_profiles() assert toy_dataset.pp_dataset_path.exists() assert toy_dataset.pp_dataset_path.stat().st_size > constants.MIN_CC_PP_DATASET_SIZE diff --git a/tests/cc/test_cc_maintenance_updates.py b/tests/cc/test_cc_maintenance_updates.py index ac91667c..c96b426e 100644 --- a/tests/cc/test_cc_maintenance_updates.py +++ b/tests/cc/test_cc_maintenance_updates.py @@ -22,7 +22,7 @@ def cc_dset(data_dir: Path) -> CCDataset: def mu_dset(data_dir: Path, tmp_path_factory) -> CCDatasetMaintenanceUpdates: tmp_dir = tmp_path_factory.mktemp("mu_dset") dset = CCDatasetMaintenanceUpdates.from_json(data_dir / "auxillary_datasets/maintenances/maintenance_updates.json") - dset.root_dir = tmp_dir + dset.copy_dataset(tmp_dir) return dset diff --git a/tests/fips/test_fips_algorithm_dataset.py b/tests/fips/test_fips_algorithm_dataset.py index c42347d4..88016f4f 100644 --- a/tests/fips/test_fips_algorithm_dataset.py +++ b/tests/fips/test_fips_algorithm_dataset.py @@ -8,6 +8,7 @@ import tests.data.fips.dataset from sec_certs.dataset.fips_algorithm import FIPSAlgorithmDataset from sec_certs.sample.fips_algorithm import FIPSAlgorithm +from sec_certs.serialization.json import SerializationError @pytest.mark.xfail(reason="May fail due to errors with NIST server.") @@ -64,5 +65,5 @@ def test_to_pandas(alg_dset: FIPSAlgorithmDataset): def test_serialization_missing_path(): dummy_dset = FIPSAlgorithmDataset() - with pytest.raises(ValueError): + with pytest.raises(SerializationError): dummy_dset.to_json() diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py index 29efc0dc..a1bcc69f 100644 --- a/tests/fips/test_fips_analysis.py +++ b/tests/fips/test_fips_analysis.py @@ -86,7 +86,7 @@ def processed_dataset( toy_static_dataset: FIPSDataset, cpe_dataset: CPEDataset, cve_dataset: CVEDataset, tmp_path_factory ) -> FIPSDataset: tmp_dir = tmp_path_factory.mktemp("cc_dset") - toy_static_dataset.root_dir = tmp_dir + toy_static_dataset.copy_dataset(tmp_dir) tested_certs = { toy_static_dataset["3095"], diff --git a/tests/fips/test_fips_dataset.py b/tests/fips/test_fips_dataset.py index 7e8a6e4d..b5f09aa9 100644 --- a/tests/fips/test_fips_dataset.py +++ b/tests/fips/test_fips_dataset.py @@ -84,7 +84,7 @@ def test_download_and_convert_artifacts(toy_dataset: FIPSDataset, data_dir: Path crt = toy_dataset["184097a88a9b4ad9"] toy_dataset.certs = {crt.dgst: crt} with TemporaryDirectory() as tmp_dir: - toy_dataset.root_dir = Path(tmp_dir) + toy_dataset.copy_dataset(tmp_dir) toy_dataset.download_all_artifacts() if not crt.state.policy_download_ok or crt.state.module_download_ok: diff --git a/tests/test_cpe.py b/tests/test_cpe.py index 2c09a393..1d1e1624 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -9,6 +9,7 @@ from sec_certs import constants from sec_certs.dataset import CPEDataset from sec_certs.sample import CPE +from sec_certs.serialization.json import SerializationError @pytest.fixture(scope="module") @@ -134,5 +135,5 @@ def test_to_pandas(cpe_dset: CPEDataset): def test_serialization_missing_path(): dummy_dset = CPEDataset(False, dict()) - with pytest.raises(ValueError): + with pytest.raises(SerializationError): dummy_dset.to_json() diff --git a/tests/test_cve.py b/tests/test_cve.py index 61740030..cd098d7b 100644 --- a/tests/test_cve.py +++ b/tests/test_cve.py @@ -9,6 +9,7 @@ from sec_certs.dataset import CVEDataset from sec_certs.sample import CVE from sec_certs.sample.cpe import CPE +from sec_certs.serialization.json import SerializationError @pytest.mark.slow @@ -113,5 +114,5 @@ def test_to_pandas(cve_dset: CVEDataset): def test_serialization_missing_path(): dummy_dset = CVEDataset({}) - with pytest.raises(ValueError): + with pytest.raises(SerializationError): dummy_dset.to_json() From 29dd48c4dc824ce849de1b3f449a930fd2206428 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 7 Dec 2022 14:39:34 +0100 Subject: [PATCH 73/81] delete pp dataset json --- .../auxillary_datasets/pp_dataset.json | 76952 ---------------- 1 file changed, 76952 deletions(-) delete mode 100644 tests/data/cc/analysis/auxillary_datasets/pp_dataset.json diff --git a/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json b/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json deleted file mode 100644 index 1cd1969f..00000000 --- a/tests/data/cc/analysis/auxillary_datasets/pp_dataset.json +++ /dev/null @@ -1,76952 +0,0 @@ -{ - "20081013_BeveiligingsprofielBCTV1.0.pdf__178": { - "csv_scan": { - "cc_archived_date": "02/15/2010", - "cc_category": "Other Devices and Systems", - "cc_certification_date": "12/02/2008", - "cc_pp_name": "Beveilingsprofiel Boordcomputer Taxi", - "cc_pp_version": "1.0", - "cc_security_level": "EAL3", - "cert_status": "archived", - "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/20081013_BeveiligingsprofielBCTV1.0.pdf", - "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/Certification Report NSCIB-PP-08-10381-CR.pdf", - "maintainance_updates": [], - "scheme": "NL" - }, - "pp_analysis": { - "separate_profiles": [] - }, - "processed": { - "cc_pp_csvid": [], - "cert_id": "" - } - }, - "20110221143918.pdf__93": { - "csv_scan": { - "cc_archived_date": "", - "cc_category": "ICs, Smart Cards and Smart Card-Related Devices and Systems", - "cc_certification_date": "05/06/2010", - "cc_pp_name": "ePassport Protection Profile V2.1, Version 2.1", - "cc_pp_version": "2.1", - "cc_security_level": "EAL4+", - "cert_status": "active", - "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/20110221143918.pdf", - "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/20110221144244.pdf", - "maintainance_updates": [], - "scheme": "KR" - }, - "frontpage_scan": [ - { - "cc_security_level": "EAL4+ (ADV_IMP.2", - "cc_version": "V3.1r3", - "developer": "KISA(Korea Internet & Security Agency", - "keywords": "ePassport, COS, MRTD, ICAO", - "match_rules": [ - "PP Reference.+?Title : (?P.+)?2 Protection Profile Version : (?P.+)?3 Evaluation Criteria : .+?4 Common Criteria Version : (?P.+)?5 Evaluation Assurance Level : (?P.+?)?, (?P.+?)?6 Developer ?: (?P.+)?7 Certification Body : .+?8 Certification Number : (?P.+)?9 Validation Result : (?P.+)?10 Keywords : (?P.+)?1\\.2 TOE Overview" - ], - "pp_date": "ATE_DPT.2, AVA_VAN.4", - "pp_general_status": "This Protection Profile is certified to be compatible with Common Criteria", - "pp_id": "KECS-PP-0163a-2009, June 10, 2010", - "pp_registrator_simplified": "KECS", - "pp_title": "ePassport Protection Profile", - "pp_version_number": "V2.1" - } - ], - "keywords_scan": { - "block_cipher_modes": { - "CBC": { - "CBC": { - "count": 3 - } - } - }, - "rules_IC_data_groups": { - "EF\\.COM": { - "EF.COM": { - "count": 6 - } - }, - "EF\\.DG[1-9][0-6]?": { - "EF.DG1": { - "count": 2 - }, - "EF.DG13": { - "count": 2 - }, - "EF.DG14": { - "count": 1 - }, - "EF.DG15": { - "count": 1 - }, - "EF.DG16": { - "count": 3 - }, - "EF.DG2": { - "count": 2 - }, - "EF.DG3": { - "count": 2 - }, - "EF.DG4": { - "count": 2 - } - }, - "EF\\.SOD": { - "EF.SOD": { - "count": 3 - } - } - }, - "rules_cert_id": {}, - "rules_certification_process": {}, - "rules_cplc": {}, - "rules_crypto_algs": { - "DES": { - "DES": { - "count": 8 - } - }, - "Diffie-Hellman": { - "Diffie-Hellman": { - "count": 2 - } - }, - "ECDSA": { - "ECDSA": { - "count": 2 - } - } - }, - "rules_crypto_engines": {}, - "rules_crypto_libs": {}, - "rules_defenses": { - "DPA": { - 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RSA Generated by ERCA; inserted in card by manufacturer at the end of the manufacturing phase Out of scope for this PP Not applicable Card non- volatile memory VU.PK (conditional, possibly multiple) VU": { - "count": 1 - }, - "e 21. 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RSA Generated by ERCA; inserted in card by manufacturer at the end of the manufacturing phase Out of scope for this PP Not applicable Card non- volatile memory VU.PK (conditional, possibly multiple": { - "count": 1 - }, - ".C as part of the EUR.Link.C; obtained by card during mutual authentication towards such a VU. 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"count": 4 - }, - "FCS_RNG.1": { - "count": 25 - } - }, - "FDP_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FDP_ACC.1": { - "count": 10 - }, - "FDP_ACC.2": { - "count": 14 - }, - "FDP_ACF.1": { - "count": 15 - }, - "FDP_DAU.1": { - "count": 4 - }, - "FDP_ETC.1": { - "count": 3 - }, - "FDP_ETC.2": { - "count": 3 - }, - "FDP_IFC.1": { - "count": 8 - }, - "FDP_ITC.1": { - "count": 30 - }, - "FDP_ITC.2": { - "count": 32 - }, - "FDP_RIP.1": { - "count": 5 - }, - "FDP_SDI.2": { - "count": 6 - } - }, - "FIA_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FIA_AFL.1": { - "count": 8 - }, - "FIA_ATD.1": { - "count": 7 - }, - "FIA_SOS.2": { - "count": 1 - }, - "FIA_UAU.1": { - "count": 11 - }, - "FIA_UAU.3": { - "count": 4 - }, - "FIA_UAU.4": { - "count": 4 - }, - "FIA_UID.1": { - "count": 7 - }, - "FIA_UID.2": { - "count": 7 - }, - "FIA_USB.1": { - "count": 4 - } - }, - "FMT_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FMT_MSA.3": { - "count": 5 - } - }, - "FPR_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FPR_UNO.1": { - "count": 5 - } - }, - "FPT_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FPT_EMS": { - "count": 6 - }, - "FPT_EMS.1": { - "count": 7 - }, - "FPT_FLS.1": { - "count": 6 - }, - "FPT_ITC.1": { - "count": 1 - }, - "FPT_PHP.3": { - "count": 8 - }, - "FPT_TDC.1": { - "count": 8 - }, - "FPT_TST.1": { - "count": 6 - } - }, - "FTP_[A-Z][A-Z][A-Z](?:\\.[0-9]|)": { - "FTP_ITC.1": { - "count": 9 - }, - "FTP_TRP.1": { - "count": 2 - } - } - }, - "rules_security_level": { - "EAL[ ]*[0-9+]+?": { - "EAL4": { - "count": 15 - } - }, - "EAL[ ]*[0-9] augmented+?": { - "EAL4 augmented": { - "count": 5 - } - } - }, - "rules_standard_id": { - "FIPS ?(?:PUB )?[0-9]+-[0-9]+?": { - "FIPS PUB 46-3": { - "count": 2 - } - }, - "ISO/IEC[ ]*[0-9]+": { - "ISO/IEC 10373": { - "count": 1 - }, - "ISO/IEC 7810": { - "count": 1 - }, - "ISO/IEC 7816": { - "count": 1 - } - }, - "ISO/IEC[ ]*[0-9]+[-]*[0-9]*": { - "ISO/IEC 10373": { - "count": 1 - }, - "ISO/IEC 7810": { - "count": 1 - }, - "ISO/IEC 7816": { - "count": 1 - } - } - }, - "rules_technical_reports": {}, - 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ECC Generated by ERCA; inserted in EGF by manufacturer at the end of the manufacturing phase Out of scope for this PP Not applicable EGF non- volatile memory EUR.PK previous (conditional; only present if": { - "count": 1 - }, - "of EUR.C as part of the EUR.Link.C; obtained by EGF during mutual authentication towards such a VU Out of scope for this PP Not applicable EGF general non-volatile memory Page 45 of 55 \fCommon Criteria": { - "count": 1 - } - }, - "[\\.\\(].{0,100}?.[oO]ut of [sS]cope..{0,100}?[\\.\\)]": { - ". 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"count": 1 - }, - "ble 23 ECC Generated by MSCA ; obtained by VU in MSCA_Card certificate during mutual authentication Out of scope for this PP Not applicable VU non-volatile memory (conditional, possibly multiple) MSCA_VU- EGF.PK": { - "count": 1 - }, - "ble 23. ECC Generated by ERCA; inserted in VU by manufacturer at the end of the manufacturing phase Out of scope for this PP Not applicable VU non-volatile memory \fCommon Criteria Protection Profile Digital": { - "count": 1 - }, - "ble 23. ECC Generated by MSCA ; obtained by VU in MSCA_VU-EGF certificate during coupling to an EGF Out of scope for this PP Not applicable VU non-volatile memory (conditional, possibly multiple) Table 21 \u2013": { - "count": 1 - }, - "er received from VU manufacturer; inserted by VU manufacturer at the end of the manufacturing phase Out of scope for this PP Made unavailable when the VU has reached end of life VU non- volatile memory 34 Note": { - "count": 1 - }, - "er received from VU manufacturer; inserted by VU manufacturer at the end of the manufacturing phase Out of scope for this PP Made unavailable when the VU has reached end of life VU non- volatile memory Table 22 -": { - "count": 1 - }, - "phase. Note: as explained in [5] Annex 1C, Appendix 11, section 12.2, a VU contains only one KM-VU. 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"cert_status": "active", - "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/pp0100b_pdf.pdf", - "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/pp0100a_pdf.pdf", - "maintainance_updates": [], - "scheme": "DE" - }, - "frontpage_scan": [ - { - "cc_security_level": "EAL4 augmented with ALC_DVS.2 and AVA_VAN.5", - "cc_version": "3.1 release 5", - "keywords": "Embedded UICC, Consumer devices, Remote provisioning", - "match_rules": [ - "Reference Title: (?P.+)?Author: (?P.+)?Editor: (?P.+)?Reference: .+?Version: (?P.+)?CC Version: (?P.+)?Assurance Level: (?P.+)?General Status: (?P.+)?Registration: (?P.+)?Keywords: (?P.+)?8\\.2 Components statement", - "1\\.1 Protection Profile identification Title: (?P.+)?Author: (?P.+)?Editor: (?P.+)?Reference: .+?Version: (?P.+)?CC Version: (?P.+)?Assurance Level: (?P.+)?General Status: (?P.+)?Registration: (?P.+)?Keywords: (?P.+)?1\\.2 TOE overview" - ], - "pp_authors": "GSMA", - "pp_editor": "Trusted Labs", - "pp_general_status": "Complete", - "pp_id": "BSI-CC-PP-0100", - "pp_registrator_simplified": "BSI", - "pp_title": "LPAe Configuration for Embedded UICC for Consumer Devices Protection Profile", - "pp_version_number": "1.0 05-June-2018" - } - ], - "keywords_scan": { - "block_cipher_modes": { - "CBC": { - "CBC": { - "count": 1 - } - } - }, - "rules_IC_data_groups": {}, - "rules_cert_id": {}, - "rules_certification_process": { - ".{0,100}[oO]ut of [sS]cope.{0,100}": { - "S10b ES9+ ES8+ New interface compared to SGP.02 Existing interface in SGP.02 Production interfaces (out of scope) Existing Role New Role Enhanced Role Enhanced interface compared to SGP.02 End User interfaces ": { - "count": 1 - }, - "Seum ESeu ESop New interface compared to SGP.02 Existing interface in SGP.02 Production interfaces (out of scope) Existing Role New Role Enhanced Role Enhanced interface compared to SGP.02 End User interfaces ": { - "count": 1 - }, - "out of scope) ES6 Interface tunneled within other interfaces LDSd ES10a SM-DS with references to SGP.02 ES12": { - 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"pp0101V2b_pdf.pdf__45": { - "csv_scan": { - "cc_archived_date": "08/04/2030", - "cc_category": "ICs, Smart Cards and Smart Card-Related Devices and Systems", - "cc_certification_date": "08/04/2020", - "cc_pp_name": "Java Card System \u2013 Closed Configuration Protection Profile Version 3.1, June 2020", - "cc_pp_version": "3.1", - "cc_security_level": "EAL4+", - "cert_status": "active", - "link_pp_document": "https://www.commoncriteriaportal.org/files/ppfiles/pp0101V2b_pdf.pdf", - "link_pp_report": "https://www.commoncriteriaportal.org/files/ppfiles/pp0101V2a_pdf.pdf", - "maintainance_updates": [], - "scheme": "DE" - }, - "frontpage_scan": [ - { - "cc_security_level": "EAL 4 augmented with AVA_VAN.5 \u201cAdvanced methodical vulnerability analysis\u201d and ALC_DVS.2 \u201cSufficiency of security measures\u201d. 1.2", - "cc_version": "3.1 revision 5", - "match_rules": [ - "IDENTIFICATION.+?Title: (?P.+)?Version: (?P.+)?Publication date: (?P.+)?Certified by: (?P.+)?Sponsor: (?P.+)?Review Committee: (?P.+)?This Protection Profile is conformant to the Common Criteria version (?P.+)?The minimum assurance level for this Protection Profile is (?P.+)?PROTECTION PROFILE PRESENTATION" - 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"cc_pp_csvid": [], - "cert_id": "" - } - } -} \ No newline at end of file From 52bddcef979677f9f076ed6d43431a8ba75fd564 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Wed, 7 Dec 2022 17:09:22 +0100 Subject: [PATCH 74/81] update docs --- docs/api/dataset.md | 8 +++++- docs/api/model.md | 8 +++--- sec_certs/dataset/common_criteria.py | 5 +++- sec_certs/dataset/dataset.py | 41 ++++++++++++++++++++++++++++ sec_certs/sample/fips.py | 14 +++++++++- 5 files changed, 69 insertions(+), 7 deletions(-) diff --git a/docs/api/dataset.md b/docs/api/dataset.md index 168fa12a..17ec943c 100644 --- a/docs/api/dataset.md +++ b/docs/api/dataset.md @@ -5,7 +5,7 @@ :no-members: ``` -This documentation doesn't provide full API reference for all members of `dataset` package. Instead, it concentrattes on the Dataset that are immediately exposed to the users. Namely, we focus on `CCDataset` and `FIPSDataset`. +This documentation doesn't provide full API reference for all members of `dataset` package. Instead, it concentrates on the Dataset that are immediately exposed to the users. Namely, we focus on `CCDataset`, `FIPSDataset` and their abstract base class `Dataset`. ```{tip} The examples related to this package can be found at [common criteria notebook](./../notebooks/examples/common_criteria.ipynb) and [fips notebook](./../notebooks/examples/fips.ipynb). @@ -13,6 +13,12 @@ The examples related to this package can be found at [common criteria notebook]( ## CCDataset +```{eval-rst} +.. currentmodule:: sec_certs.dataset +.. autoclass:: Dataset + :members: +``` + ```{eval-rst} .. currentmodule:: sec_certs.dataset .. autoclass:: CCDataset diff --git a/docs/api/model.md b/docs/api/model.md index 4e7836d2..329e92cf 100644 --- a/docs/api/model.md +++ b/docs/api/model.md @@ -31,18 +31,18 @@ However, come CVEs are missed due to omitted vulnerable configurations in [CVEDa :members: ``` -## DependencyFinder +## ReferenceFinder ```{eval-rst} .. currentmodule:: sec_certs.model -.. autoclass:: DependencyFinder +.. autoclass:: ReferenceFinder :members: ``` -## DependencyVulnerabilityFinder +## TransitiveVulnerabilityFinder ```{eval-rst} .. currentmodule:: sec_certs.model -.. autoclass:: DependencyVulnerabilityFinder +.. autoclass:: TransitiveVulnerabilityFinder :members: ``` \ No newline at end of file diff --git a/sec_certs/dataset/common_criteria.py b/sec_certs/dataset/common_criteria.py index 82793fea..b4a7a21f 100644 --- a/sec_certs/dataset/common_criteria.py +++ b/sec_certs/dataset/common_criteria.py @@ -523,7 +523,6 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: self._download_targets(fresh) def _download_reports(self, fresh: bool = True) -> None: - self.reports_pdf_dir.mkdir(parents=True, exist_ok=True) certs_to_process = [x for x in self if x.state.report_is_ok_to_download(fresh) and x.report_link] @@ -753,6 +752,10 @@ def func(cert): setattr(self.certs[dgst].heuristics, dep_attr, finder.predict_single_cert(dgst, keep_unknowns=False)) def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + """ + Processes all auxillary datasets needed during computation. On top of base-class processing, + CC handles protection profiles and maintenance updates. + """ super().process_auxillary_datasets(download_fresh) self.auxillary_datasets.pp_dset = self.process_protection_profiles(to_download=download_fresh) self.auxillary_datasets.mu_dset = self.process_maintenance_updates(to_download=download_fresh) diff --git a/sec_certs/dataset/dataset.py b/sec_certs/dataset/dataset.py index c9ac8fcc..49a6b340 100644 --- a/sec_certs/dataset/dataset.py +++ b/sec_certs/dataset/dataset.py @@ -40,6 +40,11 @@ class AuxillaryDatasets: class Dataset(Generic[CertSubType, AuxillaryDatasetsSubType], ComplexSerializableType, ABC): + """ + Base class for dataset of certificates from CC and FIPS 140 schemes. Layouts public + functions, the processing pipeline and common operations on the dataset and certs. + """ + @dataclass class DatasetInternalState(ComplexSerializableType): meta_sources_parsed: bool = False @@ -77,10 +82,17 @@ def __init__( @property def root_dir(self) -> Path: + """ + Directory that will hold the serialized dataset files. + """ return self._root_dir @root_dir.setter def root_dir(self: DatasetSubType, new_dir: str | Path) -> None: + """ + This setter will only set the root dir and all internal paths so that they point + to the new root dir. No data is being moved around. + """ new_dir = Path(new_dir) if new_dir.is_file(): raise ValueError(f"Root dir of {get_class_fullname(self)} cannot be a file.") @@ -90,10 +102,16 @@ def root_dir(self: DatasetSubType, new_dir: str | Path) -> None: @property def web_dir(self) -> Path: + """ + Path to certification-artifacts posted on web. + """ return self.root_dir / "web" @property def auxillary_datasets_dir(self) -> Path: + """ + Path to directory with auxillary datasets. + """ return self.root_dir / "auxillary_datasets" @property @@ -148,6 +166,9 @@ def __str__(self) -> str: @classmethod def from_web(cls: type[DatasetSubType], url: str, progress_bar_desc: str, filename: str) -> DatasetSubType: + """ + Fetches a fully processed dataset instance from static site that hosts it. + """ with tempfile.TemporaryDirectory() as tmp_dir: dset_path = Path(tmp_dir) / filename helpers.download_file(url, dset_path, show_progress_bar=True, progress_bar_desc=progress_bar_desc) @@ -229,6 +250,9 @@ def get_certs_from_web(self) -> None: @serialize @abstractmethod def process_auxillary_datasets(self, download_fresh: bool = False) -> None: + """ + Processes all auxillary datasets (CPE, CVE, ...) that are required during computation. + """ logger.info("Processing auxillary datasets.") self.auxillary_datasets_dir.mkdir(parents=True, exist_ok=True) self.auxillary_datasets.cpe_dset = self._prepare_cpe_dataset(download_fresh) @@ -237,6 +261,9 @@ def process_auxillary_datasets(self, download_fresh: bool = False) -> None: @serialize def download_all_artifacts(self, fresh: bool = True) -> None: + """ + Downloads all artifacts related to certification in the given scheme. + """ if not self.state.meta_sources_parsed: logger.error("Attempting to download pdfs while not having csv/html meta-sources parsed. Returning.") return @@ -254,6 +281,9 @@ def _download_all_artifacts_body(self, fresh: bool = True) -> None: @serialize def convert_all_pdfs(self, fresh: bool = True) -> None: + """ + Converts all pdf artifacts to txt, given the certification scheme. + """ if not self.state.artifacts_downloaded: logger.error("Attempting to convert pdfs while not having the artifacts downloaded. Returning.") return @@ -271,6 +301,11 @@ def _convert_all_pdfs_body(self, fresh: bool = True) -> None: @serialize def analyze_certificates(self) -> None: + """ + Does two things: + - Extracts data from certificates (keywords, etc.) + - Computes various heuristics on the certificates. + """ if not self.state.pdfs_converted: logger.info( "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning." @@ -339,6 +374,9 @@ def _prepare_cve_dataset( @serialize def compute_cpe_heuristics(self, download_fresh_cpes: bool = False) -> CPEClassifier: + """ + Computes matching CPEs for the certificates. + """ WINDOWS_WEAK_CPES: set[CPE] = { CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x64:*", "Microsoft Windows on X64", None, None), CPE("cpe:2.3:o:microsoft:windows:-:*:*:*:*:*:x86:*", "Microsoft Windows on X86", None, None), @@ -475,6 +513,9 @@ def compute_related_cves( download_fresh_cves: bool = False, use_nist_cpe_matching_dict: bool = True, ) -> None: + """ + Computes CVEs for the certificates, given their CPE matches. + """ logger.info("Retrieving related CVEs to verified CPE matches") if download_fresh_cves or not self.auxillary_datasets.cve_dset: self.auxillary_datasets.cve_dset = self._prepare_cve_dataset( diff --git a/sec_certs/sample/fips.py b/sec_certs/sample/fips.py index ca8e1a06..770b21b0 100644 --- a/sec_certs/sample/fips.py +++ b/sec_certs/sample/fips.py @@ -539,6 +539,9 @@ def download_policy(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: + """ + Converts policy pdf -> txt + """ ocr_done, ok_result = sec_certs.utils.pdf.convert_pdf_file( cert.state.policy_pdf_path, cert.state.policy_txt_path ) @@ -558,7 +561,9 @@ def convert_policy_pdf(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: - """Extract the PDF metadata from the security policy. Staticmethod to allow for parametrization.""" + """ + Extract the PDF metadata from the security policy. + """ _, metadata = sec_certs.utils.pdf.extract_pdf_metadata(cert.state.policy_pdf_path) if metadata: @@ -570,6 +575,9 @@ def extract_policy_pdf_metadata(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def extract_policy_pdf_keywords(cert: FIPSCertificate) -> FIPSCertificate: + """ + Extract keywords from policy document + """ keywords = sec_certs.utils.extract.extract_keywords(cert.state.policy_txt_path, fips_rules) if not keywords: cert.state.policy_extract_ok = False @@ -579,6 +587,10 @@ def extract_policy_pdf_keywords(cert: FIPSCertificate) -> FIPSCertificate: @staticmethod def get_algorithms_from_policy_tables(cert: FIPSCertificate): + """ + Retrieves IDs of algorithms from tables inside security policy pdfs. + External library is used to handle this. + """ if table_rich_page_numbers := tables.find_pages_with_tables(cert.state.policy_txt_path): pdf.repair_pdf(cert.state.policy_pdf_path) try: From 30ef160f94b0ef044cd147a2b1f20dbab0aef16b Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 09:53:41 +0100 Subject: [PATCH 75/81] attempt to fix pipelines --- .github/workflows/lint.yml | 2 ++ .github/workflows/tests.yml | 10 ++-------- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 9b261049..d62b62b4 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -42,6 +42,8 @@ jobs: uses: actions/setup-python@v4 with: python-version: "3.8" + - name: Install external dependencies + run: sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python3-dev -y - name: Install python dependencies run: | pip install -r requirements/requirements.txt diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 558ade11..99c6b6fb 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -9,14 +9,8 @@ jobs: run-test: runs-on: ubuntu-latest steps: - - name: Install Poppler 21.06 - run: | - sudo apt-get update - sudo apt-get install -y software-properties-common - sudo apt-get update - sudo add-apt-repository ppa:hlprasu/poppler - sudo apt-get update - sudo apt-get install -y build-essential libpoppler-cpp-dev pkg-config python3-dev + - name: Install Poppler + run: sudo apt-get install -y build-essential libpoppler-cpp-dev pkg-config python3-dev - uses: actions/checkout@v3 - name: Setup python uses: actions/setup-python@v4 From 0bcda6bcdc6f86aab07c6be892785adfe397b236 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 10:01:17 +0100 Subject: [PATCH 76/81] don't download spacy model test pipeline --- .github/workflows/tests.yml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 99c6b6fb..b56f3620 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -17,11 +17,8 @@ jobs: with: python-version: "3.8" - name: Install python dependencies - run: pip install -r requirements/requirements.txt - - name: Download spacy language model - run: python -m spacy download en_core_web_sm - - name: Install pytest and package run: | + pip install -r requirements/requirements.txt pip install -r requirements/test_requirements.txt - name: Run tests run: pytest --cov=sec_certs tests From 7710ef845798cc608b5c84ca10d4c9f52ca70f83 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 10:14:27 +0100 Subject: [PATCH 77/81] test pipeline ubuntu 20.04 --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index b56f3620..6ab7ac18 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -7,7 +7,7 @@ env: jobs: run-test: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - name: Install Poppler run: sudo apt-get install -y build-essential libpoppler-cpp-dev pkg-config python3-dev From 7af69ca81bc52618945a267dbbf324cb52adad4f Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 10:39:34 +0100 Subject: [PATCH 78/81] disable CPE from web test --- tests/test_cpe.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_cpe.py b/tests/test_cpe.py index 1d1e1624..26d0cbf3 100644 --- a/tests/test_cpe.py +++ b/tests/test_cpe.py @@ -35,6 +35,7 @@ def cpe_dict() -> dict[str, Any]: @pytest.mark.slow @pytest.mark.monitor_test @pytest.mark.xfail(reason="May fail due to errors with NIST server.") +@pytest.mark.skip(reason="Too much memory consumed.") def test_cpe_dset_from_web(tmp_path: Path): dset = CPEDataset.from_web(tmp_path) assert dset is not None From be7f6d723368fcf2c7e5b4caf1e91de2a111531d Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 10:51:22 +0100 Subject: [PATCH 79/81] try ubuntu 22.04 test runner --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6ab7ac18..d8f5d5f5 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -7,7 +7,7 @@ env: jobs: run-test: - runs-on: ubuntu-20.04 + runs-on: ubuntu-22.04 steps: - name: Install Poppler run: sudo apt-get install -y build-essential libpoppler-cpp-dev pkg-config python3-dev From 7d59063d33806baa2be9289594fcc9bc7e54411f Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 12:38:02 +0100 Subject: [PATCH 80/81] cli print -> click.echo() --- sec_certs/cli.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/sec_certs/cli.py b/sec_certs/cli.py index 541f3eeb..a25b55ee 100644 --- a/sec_certs/cli.py +++ b/sec_certs/cli.py @@ -17,6 +17,9 @@ logger = logging.getLogger(__name__) +EXIT_CODE_NOK: int = 1 +EXIT_CODE_OK: int = 0 + @dataclass class ProcessingStep: @@ -34,8 +37,8 @@ def run(self, dset: CCDataset | FIPSDataset) -> None: if self.precondition_error_msg else f"Error, precondition to run {self.name} not met, exiting." ) - print(err_msg) - sys.exit(1) + click.echo(err_msg, err=True) + sys.exit(EXIT_CODE_NOK) if self.pre_callback_func: self.pre_callback_func() @@ -77,10 +80,11 @@ def build_or_load_dataset( ) dset.copy_dataset(outputpath) else: - print( - "Error: If you do not use 'build' action, you must provide --input parameter to point to an existing dataset." + click.echo( + "Error: If you do not use 'build' action, you must provide --input parameter to point to an existing dataset.", + err=True, ) - sys.exit(1) + sys.exit(EXIT_CODE_NOK) return dset @@ -144,10 +148,11 @@ def main( try: config.load(Path(configpath)) except FileNotFoundError: - print("Error: Bad path to configuration file") - sys.exit(1) + click.echo("Error: Bad path to configuration file", err=True) + sys.exit(EXIT_CODE_NOK) except ValueError as e: - print(f"Error: Bad format of configuration file: {e}") + click.echo(f"Error: Bad format of configuration file: {e}", err=True) + sys.exit(EXIT_CODE_NOK) actions_set = ( {"build", "process-aux-dsets", "download", "convert", "analyze", "maintenances"} @@ -198,8 +203,8 @@ def main( end = datetime.now() logger.info(f"The computation took {(end-start)} seconds.") except Exception: - return 1 - return 0 + return EXIT_CODE_NOK + return EXIT_CODE_OK if __name__ == "__main__": From 4574a3d9dde1c93773743899ca721bf10a90c280 Mon Sep 17 00:00:00 2001 From: Adam Janovsky Date: Thu, 8 Dec 2022 13:27:44 +0100 Subject: [PATCH 81/81] FIPSCertificate no longer hashable --- sec_certs/dataset/fips.py | 8 ++++---- sec_certs/sample/certificate.py | 12 +----------- tests/fips/test_fips_analysis.py | 4 ++-- 3 files changed, 7 insertions(+), 17 deletions(-) diff --git a/sec_certs/dataset/fips.py b/sec_certs/dataset/fips.py index a10ceba0..bdf220c8 100644 --- a/sec_certs/dataset/fips.py +++ b/sec_certs/dataset/fips.py @@ -195,14 +195,14 @@ def _download_html_resources(self) -> None: html_paths = [self.web_dir / x for x in FIPSDataset.LIST_OF_CERTS_HTML.keys()] helpers.download_parallel(html_urls, html_paths) - def _get_all_certs_from_html_sources(self) -> set[FIPSCertificate]: - return set( + def _get_all_certs_from_html_sources(self) -> list[FIPSCertificate]: + return list( itertools.chain.from_iterable( self._get_certificates_from_html(self.web_dir / x) for x in self.LIST_OF_CERTS_HTML.keys() ) ) - def _get_certificates_from_html(self, html_file: Path) -> set[FIPSCertificate]: + def _get_certificates_from_html(self, html_file: Path) -> list[FIPSCertificate]: with open(html_file, encoding="utf-8") as handle: html = BeautifulSoup(handle.read(), "html5lib") @@ -216,7 +216,7 @@ def _get_certificates_from_html(self, html_file: Path) -> set[FIPSCertificate]: if cert_id not in cert_ids: cert_ids.add(cert_id) - return {FIPSCertificate(cert_id) for cert_id in cert_ids} + return [FIPSCertificate(cert_id) for cert_id in cert_ids] @classmethod def from_web_latest(cls) -> FIPSDataset: diff --git a/sec_certs/sample/certificate.py b/sec_certs/sample/certificate.py index 8d5936b5..bb49c0df 100644 --- a/sec_certs/sample/certificate.py +++ b/sec_certs/sample/certificate.py @@ -1,17 +1,15 @@ from __future__ import annotations import copy -import json import logging from abc import ABC, abstractmethod from collections import ChainMap from dataclasses import dataclass, field -from pathlib import Path from typing import Any, Generic, TypeVar import sec_certs.utils.extract from sec_certs.cert_rules import PANDAS_KEYWORDS_CATEGORIES -from sec_certs.serialization.json import ComplexSerializableType, CustomJSONDecoder +from sec_certs.serialization.json import ComplexSerializableType logger = logging.getLogger(__name__) @@ -76,9 +74,6 @@ def __eq__(self, other: object) -> bool: return False return self.dgst == other.dgst - def __hash__(self) -> int: - return hash(self.dgst) - def to_dict(self) -> dict[str, Any]: return { **{"dgst": self.dgst}, @@ -90,11 +85,6 @@ def from_dict(cls: type[T], dct: dict) -> T: dct.pop("dgst") return cls(**dct) - @classmethod - def from_json(cls: type[T], input_path: Path | str) -> T: - with Path(input_path).open("r") as handle: - return json.load(handle, cls=CustomJSONDecoder) - @abstractmethod def compute_heuristics_version(self) -> None: raise NotImplementedError("Not meant to be implemented") diff --git a/tests/fips/test_fips_analysis.py b/tests/fips/test_fips_analysis.py index a1bcc69f..b7b5d89f 100644 --- a/tests/fips/test_fips_analysis.py +++ b/tests/fips/test_fips_analysis.py @@ -88,12 +88,12 @@ def processed_dataset( tmp_dir = tmp_path_factory.mktemp("cc_dset") toy_static_dataset.copy_dataset(tmp_dir) - tested_certs = { + tested_certs = [ toy_static_dataset["3095"], toy_static_dataset["3093"], toy_static_dataset["3197"], toy_static_dataset["2441"], - } + ] toy_static_dataset.certs = {x.dgst: x for x in tested_certs} toy_static_dataset.download_all_artifacts()

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