android_malware_detection.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/SantoshKumarP1412/Network_Fundamentals/blob/main/android_malware_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "We changed some of parameters like \"no of features selection\" and in algorithm in parameters line \"estimator or no of states\"."
      ],
      "metadata": {
        "id": "-5GO195WFbhn"
      },
      "id": "-5GO195WFbhn"
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "384beb2e"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "\n",
        "\n",
        "\n"
      ],
      "id": "384beb2e"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "h6FvVSMhtHJn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c6d8c42-02d0-43ad-baf9-af4de518d077"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "h6FvVSMhtHJn"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d01fc6f"
      },
      "source": [
        "# Looking at the data"
      ],
      "id": "3d01fc6f"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "25d186c1"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/feature_vectors_syscallsbinders_frequency_5_Cat.csv')\n",
        "\n",
        "# Labels\n",
        "  # Adware: 1,253-------1\n",
        "# Banking: 2,100------2\n",
        "# SMS malware: 3,904--3\n",
        "# Riskware: 2,546-----4\n",
        "# Benign: 1,795-------5"
      ],
      "id": "25d186c1"
    },
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          "metadata": {},
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-781351de-2cee-4214-b890-10f486498943 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df"
      ],
      "id": "-KWmhVMgv3xK"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ad2cd097",
        "outputId": "6fbd41f5-b819-4836-865e-60b88f696836"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11598, 471)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.shape"
      ],
      "id": "ad2cd097"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ec33bc45",
        "outputId": "82a4aa12-b5d6-4a9c-e433-64107b8aa7f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "label_counts = df['Class'].value_counts()\n",
        "\n",
        "labels = label_counts.index.tolist()\n",
        "counts = label_counts.tolist()\n",
        "\n",
        "plt.bar(labels, counts)\n",
        "plt.xlabel('Malware')\n",
        "plt.ylabel('Counts')\n",
        "plt.title('Malware Distribution in Dataset')\n",
        "plt.show()"
      ],
      "id": "ec33bc45"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e5ef434",
        "outputId": "c22bd518-6909-4a75-cdf9-95efef17cd41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ACCESS_PERSONAL_INFO___', 'ALTER_PHONE_STATE___', 'ANTI_DEBUG_____',\n",
              "       'CREATE_FOLDER_____', 'CREATE_PROCESS`_____', 'CREATE_THREAD_____',\n",
              "       'DEVICE_ACCESS_____', 'EXECUTE_____', 'FS_ACCESS____',\n",
              "       'FS_ACCESS()____',\n",
              "       ...\n",
              "       'utimes', 'vfork', 'vibrate', 'vibratePattern', 'wait4',\n",
              "       'watchRotation', 'windowGainedFocus', 'write', 'writev', 'Class'],\n",
              "      dtype='object', length=471)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# features\n",
        "df.columns\n"
      ],
      "id": "7e5ef434"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c59392e3",
        "outputId": "1d3134c8-848a-4255-ac24-247c300cda9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df[\"Class\"].unique()"
      ],
      "id": "c59392e3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59e64411"
      },
      "source": [
        "# Data Preprocessing"
      ],
      "id": "59e64411"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9b511dc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "40117778-1fc1-4c35-e9a7-35ffdf8b38b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [157 169 171 191 275 315 320 352 436 456] are constant.\n",
            "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
            "  f = msb / msw\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CREATE_FOLDER_____  CREATE_PROCESS`_____  CREATE_THREAD_____  \\\n",
              "6227                   2                     0                  10   \n",
              "3009                   0                     0                   8   \n",
              "994                    0                     0                  18   \n",
              "2778                   3                     1                  14   \n",
              "8345                   0                     0                   8   \n",
              "\n",
              "      EXECUTE_____  FS_ACCESS____  FS_ACCESS()____  FS_ACCESS(CREATE)____  \\\n",
              "6227             0             16                0                      2   \n",
              "3009             0              4                0                      1   \n",
              "994              0             16                0                      1   \n",
              "2778             1             26                1                      3   \n",
              "8345             0             45                0                      5   \n",
              "\n",
              "      FS_ACCESS(CREATE__APPEND)__  FS_ACCESS(CREATE__READ)__  \\\n",
              "6227                            0                          0   \n",
              "3009                            0                          0   \n",
              "994                             0                          0   \n",
              "2778                            0                          0   \n",
              "8345                            0                          1   \n",
              "\n",
              "      FS_ACCESS(CREATE__READ__WRITE)  ...  socketpair  startInput  \\\n",
              "6227                               0  ...           0           1   \n",
              "3009                               0  ...           0           0   \n",
              "994                                0  ...           0           0   \n",
              "2778                               0  ...           0           0   \n",
              "8345                               0  ...           0           0   \n",
              "\n",
              "      startWatchingRoutes  stat64  statfs64  sysinfo  ugetrlimit  uname  \\\n",
              "6227                    0      31         0        0           0      1   \n",
              "3009                    0      11         0        0           0      1   \n",
              "994                     0      38         0        0           0      1   \n",
              "2778                    0      41         1        0           1      1   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "X = df.drop(columns=['Class'])  # Features\n",
        "y = df['Class']  # Target\n",
        "\n",
        "# Split the data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,test_size=0.2, random_state=42)\n",
        "\n",
        "#ANOVA-based feature selection\n",
        "num_features_to_select = 160\n",
        "selector = SelectKBest(score_func=f_classif, k=num_features_to_select)\n",
        "X_train_selected = selector.fit_transform(X_train, y_train)\n",
        "X_test_selected = selector.transform(X_test)\n",
        "\n",
        "# Get the indices of selected features\n",
        "selected_feature_indices = selector.get_support(indices=True)\n",
        "\n",
        "# Convert selected features to a DataFrame\n",
        "X_train = X_train.iloc[:, selected_feature_indices]\n",
        "X_test = X_test.iloc[:, selected_feature_indices]\n",
        "X_train.head()\n"
      ],
      "id": "9b511dc6"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0d8a8607",
        "outputId": "e6aad9fe-d1f5-4e0a-f880-985bc2e6969e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2320, 160)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "X_test.shape"
      ],
      "id": "0d8a8607"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ee4e725a"
      },
      "outputs": [],
      "source": [
        "#Standardize Data Removing Irrelevant Values\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "id": "ee4e725a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acfd3904"
      },
      "source": [
        "# Done with preprocessing. Now we will run our data through different ML models and store the evaluation metrics."
      ],
      "id": "acfd3904"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "335fa186"
      },
      "source": [
        "# Random Forest"
      ],
      "id": "335fa186"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "70bdf229",
        "outputId": "2f66de88-b655-430c-bb78-911fc81945b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classifier Accuracy: 0.9457\n",
            "Random Forest Classifier Precision: 0.9465\n",
            "Random Forest Classifier Recall: 0.9457\n",
            "Random Forest Classifier F1-Score: 0.9456\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#here best 700 estimation can give better accuracy\n",
        "\n",
        "# Random Forest Classifier Accuracy: 0.9435\n",
        "# Random Forest Classifier Precision: 0.9445\n",
        "# Random Forest Classifier Recall: 0.9435\n",
        "# Random Forest Classifier F1-Score: 0.9435\n",
        "\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "\n",
        "\n",
        "rfModel = RandomForestClassifier(n_estimators=300, random_state=42)\n",
        "rfModel.fit(X_train, y_train)\n",
        "\n",
        "# Predict labels on the test set\n",
        "y_pred = rfModel.predict(X_test)\n",
        "\n",
        "# Calculate Random Forest classifier metrics\n",
        "rf_accuracy = accuracy_score(y_test, y_pred)\n",
        "rf_precision = precision_score(y_test, y_pred,average='weighted')\n",
        "rf_recall = recall_score(y_test, y_pred,average='weighted')\n",
        "rf_f1 = f1_score(y_test, y_pred,average='weighted')\n",
        "\n",
        "print(f\"Random Forest Classifier Accuracy: {rf_accuracy:.4f}\")\n",
        "print(f\"Random Forest Classifier Precision: {rf_precision:.4f}\")\n",
        "print(f\"Random Forest Classifier Recall: {rf_recall:.4f}\")\n",
        "print(f\"Random Forest Classifier F1-Score: {rf_f1:.4f}\")\n",
        "\n",
        "conf = confusion_matrix(y_pred, y_test)\n",
        "sns.heatmap(conf , cmap='YlGnBu', fmt='', xticklabels=['Adware' ,'Banking' ,'SMS malware', 'Riskware','Benign'], yticklabels=['Adware' ,'Banking' ,'SMS malware', 'Riskware','Benign'], annot=True)"
      ],
      "id": "70bdf229"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81cf2cb7"
      },
      "source": [
        "# SVM"
      ],
      "id": "81cf2cb7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b5fc822a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "0abae8c9-b31d-4a30-a703-c41ddb010a15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Classifier Accuracy: 0.8461\n",
            "SVM Classifier Precision: 0.8467\n",
            "SVM Classifier Recall: 0.8461\n",
            "SVM Classifier F1-Score: 0.8445\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#SVM Classifier Accuracy: 0.8496\n",
        "#SVM Classifier Precision: 0.8486\n",
        "#SVM Classifier Recall: 0.8496\n",
        "#SVM Classifier F1-Score: 0.8473\n",
        "\n",
        "\n",
        "SVmodel = SVC(kernel='linear', C=1.0, random_state=16) #C=1.0, random_state=16\n",
        "SVmodel.fit(X_train, y_train)\n",
        "\n",
        "# Predict labels on the test set\n",
        "y_pred = SVmodel.predict(X_test)\n",
        "\n",
        "\n",
        "# Calculate SVM classifier metrics\n",
        "svm_accuracy = accuracy_score(y_test, y_pred)\n",
        "svm_precision = precision_score(y_test, y_pred,average='weighted')\n",
        "svm_recall = recall_score(y_test, y_pred,average='weighted')\n",
        "svm_f1 = f1_score(y_test, y_pred,average='weighted')\n",
        "\n",
        "print(f\"SVM Classifier Accuracy: {svm_accuracy:.4f}\")\n",
        "print(f\"SVM Classifier Precision: {svm_precision:.4f}\")\n",
        "print(f\"SVM Classifier Recall: {svm_recall:.4f}\")\n",
        "print(f\"SVM Classifier F1-Score: {svm_f1:.4f}\")\n",
        "\n",
        "conf = confusion_matrix(y_pred, y_test)\n",
        "sns.heatmap(conf , cmap='YlGnBu', fmt='', xticklabels=['Adware' ,'Banking' ,'SMS malware', 'Riskware','Benign'], yticklabels=['Adware' ,'Banking' ,'SMS malware', 'Riskware','Benign'], annot=True)"
      ],
      "id": "b5fc822a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "df0d853b"
      },
      "outputs": [],
      "source": [
        "#Combine result visulaization\n",
        "\n"
      ],
      "id": "df0d853b"
    },
    {
      "cell_type": "code",
      "source": [
        "#classifiers and their colors\n",
        "classifiers = ['SVM', 'Random Forest']\n",
        "colors = ['blue', 'orange']\n",
        "\n",
        "accuracy_scores = [ svm_accuracy, rf_accuracy]\n",
        "precision_scores = [ svm_precision, rf_precision]\n",
        "recall_scores = [ svm_recall, rf_recall]\n",
        "f1_scores = [ svm_f1, rf_f1]\n",
        "\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
        "\n",
        "# Define the data for each metric\n",
        "data = [accuracy_scores, precision_scores, recall_scores, f1_scores]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "x = np.arange(len(metrics))\n",
        "\n",
        "# Set the width of each bar\n",
        "width = 0.10\n",
        "\n",
        "#each classifier and plot bars\n",
        "for i, classifier in enumerate(classifiers):\n",
        "    ax.bar(x + i*width, [d[i] for d in data], width, label=classifier, color=colors[i])\n",
        "\n",
        "#labels, title, and legend\n",
        "ax.set_xlabel('Metrics')\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Performance Metrics Comparison for Different Classifiers')\n",
        "ax.set_xticks(x + 2.5*width)\n",
        "\n",
        "\n",
        "\n",
        "ax.set_xticklabels(metrics)\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RI9o9g2a3Xgr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "c5b19e03-9338-403d-ae94-0590f14cf93d"
      },
      "id": "RI9o9g2a3Xgr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#classifiers and metrics\n",
        "classifiers = ['SVM', 'Random Forest']\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n",
        "\n",
        "# metric scores\n",
        "accuracy_scores = [svm_accuracy, rf_accuracy]\n",
        "precision_scores = [ svm_precision, rf_precision]\n",
        "recall_scores = [ svm_recall, rf_recall]\n",
        "f1_scores = [svm_f1, rf_f1]\n",
        "\n",
        "#dictionary to store metric\n",
        "metric_scores = {\n",
        "    'Accuracy': accuracy_scores,\n",
        "    'Precision': precision_scores,\n",
        "    'Recall': recall_scores,\n",
        "    'F1-Score': f1_scores\n",
        "}\n",
        "\n",
        "#DataFrame from the dictionary\n",
        "df = pd.DataFrame(metric_scores, index=classifiers)\n",
        "\n",
        "#heatmap\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.set(font_scale=1.2)\n",
        "sns.heatmap(df, annot=True, cmap='YlGnBu', fmt='.4f')\n",
        "plt.title(\"Model Evaluation Metrics Heatmap\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "pkHEZrAX3yJa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "e462c179-e641-4106-c740-c8af4613f800"
      },
      "id": "pkHEZrAX3yJa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "86EYxjSipI86",
        "outputId": "06e1d926-95ac-4a1f-e795-b39aedf2a046"
      },
      "id": "86EYxjSipI86",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Accuracy  Precision    Recall  F1-Score\n",
              "SVM            0.846121   0.846713  0.846121  0.844464\n",
              "Random Forest  0.945690   0.946535  0.945690  0.945625"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96d9b09f-dc88-4a3a-94fe-2f817f0c3048\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>0.846121</td>\n",
              "      <td>0.846713</td>\n",
              "      <td>0.846121</td>\n",
              "      <td>0.844464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest</th>\n",
              "      <td>0.945690</td>\n",
              "      <td>0.946535</td>\n",
              "      <td>0.945690</td>\n",
              "      <td>0.945625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96d9b09f-dc88-4a3a-94fe-2f817f0c3048')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96d9b09f-dc88-4a3a-94fe-2f817f0c3048 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-96d9b09f-dc88-4a3a-94fe-2f817f0c3048');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ede1aed1-3190-4d91-a38f-a93e88e90b9f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ede1aed1-3190-4d91-a38f-a93e88e90b9f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ede1aed1-3190-4d91-a38f-a93e88e90b9f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LSTM"
      ],
      "metadata": {
        "id": "xIaNXzokZhXA"
      },
      "id": "xIaNXzokZhXA"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Encode string labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)\n",
        "\n",
        "# Print data types of selected columns\n",
        "print(X.dtypes)\n",
        "\n",
        "# Convert all columns to numeric\n",
        "X = X.apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "# Scale numerical features\n",
        "scaler = StandardScaler()\n",
        "# Print the shape of X before scaling\n",
        "print(\"Shape of X before scaling:\", X.shape)\n",
        "\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Print the shape of X after scaling\n",
        "print(\"Shape of X after scaling:\", X_scaled.shape)\n",
        "\n",
        "# Convert X_scaled to a 3D array suitable for LSTM\n",
        "X_lstm = X_scaled.reshape((X_scaled.shape[0], 1, X_scaled.shape[1]))\n",
        "\n",
        "# Split into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_lstm, y_encoded, train_size=0.70, random_state=2)\n",
        "\n",
        "# One-hot encode the target variable\n",
        "num_classes = len(np.unique(y_encoded))\n",
        "y_train_one_hot = to_categorical(y_train, num_classes=num_classes)\n",
        "y_test_one_hot = to_categorical(y_test, num_classes=num_classes)\n",
        "\n",
        "# Adjust the model output layer\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(num_classes, activation='softmax'))  # Adjust to the number of classes\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Start measuring execution time\n",
        "start_time = time.time()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train_one_hot, epochs=10, batch_size=32, validation_data=(X_test, y_test_one_hot))\n",
        "# Stop measuring execution time\n",
        "end_time = time.time()\n",
        "\n",
        "# Obtain predictions on the test set\n",
        "y_pred_probs = model.predict(X_test)\n",
        "y_pred = np.argmax(y_pred_probs, axis=1)  # Convert probabilities to class labels\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred,average='weighted')\n",
        "recall = recall_score(y_test, y_pred,average='weighted')\n",
        "f1 = f1_score(y_test, y_pred,average='weighted')\n",
        "\n",
        "# Calculate and print the execution time\n",
        "execution_time = end_time - start_time\n",
        "print(f'Training and evaluation took {execution_time:.2f} seconds')\n",
        "\n",
        "print(f\"LSTM Classifier Accuracy: {accuracy:.4f}\")\n",
        "print(f\"LSTM Classifier Precision: {precision:.4f}\")\n",
        "print(f\"LSTM Classifier Recall: {recall:.4f}\")\n",
        "print(f\"LSTM Classifier F1-Score: {f1:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjg4vvfiXkdF",
        "outputId": "665845d9-7aef-458a-f116-7cecdf544b89"
      },
      "id": "gjg4vvfiXkdF",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCESS_PERSONAL_INFO___    int64\n",
            "ALTER_PHONE_STATE___       int64\n",
            "ANTI_DEBUG_____            int64\n",
            "CREATE_FOLDER_____         int64\n",
            "CREATE_PROCESS`_____       int64\n",
            "                           ...  \n",
            "wait4                      int64\n",
            "watchRotation              int64\n",
            "windowGainedFocus          int64\n",
            "write                      int64\n",
            "writev                     int64\n",
            "Length: 470, dtype: object\n",
            "Shape of X before scaling: (11598, 470)\n",
            "Shape of X after scaling: (11598, 470)\n",
            "Epoch 1/10\n",
            "254/254 [==============================] - 6s 10ms/step - loss: 0.9400 - accuracy: 0.6700 - val_loss: 0.6796 - val_accuracy: 0.7716\n",
            "Epoch 2/10\n",
            "254/254 [==============================] - 2s 6ms/step - loss: 0.5843 - accuracy: 0.8147 - val_loss: 0.5526 - val_accuracy: 0.8161\n",
            "Epoch 3/10\n",
            "254/254 [==============================] - 2s 6ms/step - loss: 0.4818 - accuracy: 0.8511 - val_loss: 0.4884 - val_accuracy: 0.8437\n",
            "Epoch 4/10\n",
            "254/254 [==============================] - 2s 6ms/step - loss: 0.4243 - accuracy: 0.8700 - val_loss: 0.4584 - val_accuracy: 0.8546\n",
            "Epoch 5/10\n",
            "254/254 [==============================] - 2s 6ms/step - loss: 0.3887 - accuracy: 0.8798 - val_loss: 0.4279 - val_accuracy: 0.8750\n",
            "Epoch 6/10\n",
            "254/254 [==============================] - 2s 7ms/step - loss: 0.3573 - accuracy: 0.8912 - val_loss: 0.4096 - val_accuracy: 0.8802\n",
            "Epoch 7/10\n",
            "254/254 [==============================] - 2s 10ms/step - loss: 0.3299 - accuracy: 0.8987 - val_loss: 0.3952 - val_accuracy: 0.8753\n",
            "Epoch 8/10\n",
            "254/254 [==============================] - 3s 10ms/step - loss: 0.3172 - accuracy: 0.9021 - val_loss: 0.3913 - val_accuracy: 0.8868\n",
            "Epoch 9/10\n",
            "254/254 [==============================] - 2s 9ms/step - loss: 0.2945 - accuracy: 0.9092 - val_loss: 0.3758 - val_accuracy: 0.8839\n",
            "Epoch 10/10\n",
            "254/254 [==============================] - 2s 8ms/step - loss: 0.2794 - accuracy: 0.9157 - val_loss: 0.3743 - val_accuracy: 0.8897\n",
            "109/109 [==============================] - 1s 2ms/step\n",
            "Training and evaluation took 44.14 seconds\n",
            "LSTM Classifier Accuracy: 0.8897\n",
            "LSTM Classifier Precision: 0.8891\n",
            "LSTM Classifier Recall: 0.8897\n",
            "LSTM Classifier F1-Score: 0.8885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#classifiers and their colors\n",
        "classifiers = ['LSTM', 'Random Forest']\n",
        "colors = ['blue', 'orange']\n",
        "\n",
        "accuracy_scores = [ accuracy, rf_accuracy]\n",
        "precision_scores = [ precision, rf_precision]\n",
        "recall_scores = [ recall, rf_recall]\n",
        "f1_scores = [ f1, rf_f1]\n",
        "\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
        "\n",
        "# Define the data for each metric\n",
        "data = [accuracy_scores, precision_scores, recall_scores, f1_scores]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "x = np.arange(len(metrics))\n",
        "\n",
        "# Set the width of each bar\n",
        "width = 0.10\n",
        "\n",
        "#each classifier and plot bars\n",
        "for i, classifier in enumerate(classifiers):\n",
        "    ax.bar(x + i*width, [d[i] for d in data], width, label=classifier, color=colors[i])\n",
        "\n",
        "#labels, title, and legend\n",
        "ax.set_xlabel('Metrics')\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Performance Metrics Comparison for Different Classifiers')\n",
        "ax.set_xticks(x + 2.5*width)\n",
        "\n",
        "\n",
        "\n",
        "ax.set_xticklabels(metrics)\n",
        "ax.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7WIQi_ZpZIZK",
        "outputId": "9794dd93-afcd-4c7d-8153-2bf4a692a007"
      },
      "id": "7WIQi_ZpZIZK",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 43.062473,
      "end_time": "2023-09-05T20:17:21.829273",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2023-09-05T20:16:38.766800",
      "version": "2.4.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}