Poseidon is a python-based application that leverages software defined networks (SDN) to acquire and then feed network traffic to a number of machine learning techniques. The machine learning algorithms classify and predict the type of device.

using deep learning to classify the encrypted network traffic

Machine learning plugins for network traffic

Self-attentive deep learning method for online traffic classification and its interpretability (CN21 & NetAI20)

基于IDS 2018数据集使用LightGBM和XGBoost实现DDoS流量分类 sFlow RT&Mininet流量采集与分类 杭电综合项目实践

🛜→🖼️ Replication of the model set forth in "FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition" by Tal Shapira and Yuval Shavitt

Network traffic classifier based on Apache Spark and MLlib

Network traffic Simulation & Classification system

An ML-based classification method to classify the traffic and intrusions in a network which improves the overall performance of the network system.

🐳📡🐶 Generate network communication data for target tasks in diverse network conditions.

Code resource (R) for the paper: https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4181

Supporting page for the manuscript titled, "Incremental Federated Learning for Traffic Flow Classification in Heterogeneous Data Scenarios."

Code Repo for Leveraging Federated Learning and XAI for Private and Lightweight Edge Training In Network Traffic Classification

This was the classification of Network Traffic using 5 features for 10 apps. We created this app using Python and Streamlit and then it was hosted by Heroku

A machine learning project to detect cyberattacks in IoT healthcare networks. Utilizes PCA for dimensionality reduction, data visualization for insights, and ANN for classification. Features a FastAPI backend and Streamlit UI for inference with labeled and unlabeled datasets.

This is a code repository for a paper with title "Adversarial Attack and Defence of Federated Learning-Based Network Traffic Classification in Edge Computing Environment"

Supporting page for the manuscript titled, "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."