Classification_of_Malware_with_PE_headers.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Classification of Malware with PE headers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNnKpSEYmlU/yfb3T1puduK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Azariagmt/jupyter-notebooks/blob/master/Classification_of_Malware_with_PE_headers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuQGHnw0Ksqw"
      },
      "source": [
        "load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsVHWuzSKmyY"
      },
      "source": [
        "import pandas as pd\n",
        "train_data = pd.read_csv(\"https://raw.githubusercontent.com/dphi-official/Datasets/master/clamp/train_set_label.csv\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "t2OWBqMJKvck",
        "outputId": "c73b04fa-1276-4e6b-bd9f-e322aaa6e0ec"
      },
      "source": [
        "print(train_data.shape)\n",
        "train_data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3888, 56)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>NumberOfRvaAndSizes</th>\n",
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              "      <td>2</td>\n",
              "      <td>1024</td>\n",
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              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   e_magic  e_cblp  e_cp  ...  LoaderFlags  NumberOfRvaAndSizes  class\n",
              "0    23117     144     3  ...            0                   16      1\n",
              "1    23117     144     3  ...            0                   16      1\n",
              "2    23117     144     3  ...            0                   16      0\n",
              "3    23117     144     3  ...            0                   16      0\n",
              "4    23117     144     3  ...            0                   16      0\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKCyrl5ZQe6b",
        "outputId": "35c105f4-8c30-4ad4-b92c-8a0992d1002e"
      },
      "source": [
        "train_data.dtypes"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "e_magic                          int64\n",
              "e_cblp                           int64\n",
              "e_cp                             int64\n",
              "e_crlc                           int64\n",
              "e_cparhdr                        int64\n",
              "e_minalloc                       int64\n",
              "e_maxalloc                       int64\n",
              "e_ss                             int64\n",
              "e_sp                             int64\n",
              "e_csum                           int64\n",
              "e_ip                             int64\n",
              "e_cs                             int64\n",
              "e_lfarlc                         int64\n",
              "e_ovno                           int64\n",
              "e_res                          float64\n",
              "e_oemid                          int64\n",
              "e_oeminfo                        int64\n",
              "e_res2                         float64\n",
              "e_lfanew                         int64\n",
              "Machine                          int64\n",
              "NumberOfSections                 int64\n",
              "CreationYear                     int64\n",
              "PointerToSymbolTable             int64\n",
              "NumberOfSymbols                  int64\n",
              "SizeOfOptionalHeader             int64\n",
              "Characteristics                  int64\n",
              "Magic                            int64\n",
              "MajorLinkerVersion               int64\n",
              "MinorLinkerVersion               int64\n",
              "SizeOfCode                       int64\n",
              "SizeOfInitializedData            int64\n",
              "SizeOfUninitializedData          int64\n",
              "AddressOfEntryPoint              int64\n",
              "BaseOfCode                       int64\n",
              "BaseOfData                       int64\n",
              "ImageBase                        int64\n",
              "SectionAlignment                 int64\n",
              "FileAlignment                    int64\n",
              "MajorOperatingSystemVersion      int64\n",
              "MinorOperatingSystemVersion      int64\n",
              "MajorImageVersion                int64\n",
              "MinorImageVersion                int64\n",
              "MajorSubsystemVersion            int64\n",
              "MinorSubsystemVersion            int64\n",
              "SizeOfImage                      int64\n",
              "SizeOfHeaders                    int64\n",
              "CheckSum                         int64\n",
              "Subsystem                        int64\n",
              "DllCharacteristics               int64\n",
              "SizeOfStackReserve               int64\n",
              "SizeOfStackCommit                int64\n",
              "SizeOfHeapReserve                int64\n",
              "SizeOfHeapCommit                 int64\n",
              "LoaderFlags                      int64\n",
              "NumberOfRvaAndSizes              int64\n",
              "class                            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQ6MhhCeQKOT"
      },
      "source": [
        "Split the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDK67ha5KxaY"
      },
      "source": [
        "X = train_data.drop(columns=['e_res', 'e_res2', 'class'])\n",
        "y = train_data['class']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=101)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eO8IMethQMB2"
      },
      "source": [
        "Train a logistic regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVPcLyBALWIL",
        "outputId": "1047e501-7592-4456-c57f-6d09dacda4e3"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "reg = 0.01\n",
        "\n",
        "# train a logistic regression model on the training set\n",
        "model = LogisticRegression(C=1/reg, solver=\"liblinear\").fit(X_train, y_train)\n",
        "print (model)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LogisticRegression(C=100.0, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHeNTjb3Rf-0"
      },
      "source": [
        "Make predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5fs457YQm-f"
      },
      "source": [
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynQmRQdoRiZa",
        "outputId": "5d7d2f6c-cc8e-4ad6-c0c2-072d1f25f89e"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.82      0.87       582\n",
            "           1       0.84      0.95      0.89       585\n",
            "\n",
            "    accuracy                           0.88      1167\n",
            "   macro avg       0.89      0.88      0.88      1167\n",
            "weighted avg       0.89      0.88      0.88      1167\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdegLDMNRut8",
        "outputId": "8a1e9371-c7f8-49a4-e75a-86c8703c7cad"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(y_test, predictions))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[475 107]\n",
            " [ 30 555]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "b8bcfTRsSKa9",
        "outputId": "7a19efc7-2282-4501-d02a-8f78d227357d"
      },
      "source": [
        "y_scores = model.predict_proba(X_test)\n",
        "print(y_scores)\n",
        "\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# calculate ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
        "\n",
        "# plot ROC curve\n",
        "fig = plt.figure(figsize=(6, 6))\n",
        "# Plot the diagonal 50% line\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "# Plot the FPR and TPR achieved by our model\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.19310752e-01 8.80689248e-01]\n",
            " [8.06461293e-01 1.93538707e-01]\n",
            " [9.99979537e-01 2.04630907e-05]\n",
            " ...\n",
            " [1.00000000e+00 5.77702295e-12]\n",
            " [1.04731378e-03 9.98952686e-01]\n",
            " [8.99465466e-01 1.00534534e-01]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_agjqtZpUFmw"
      },
      "source": [
        "Try a different algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xja1-c6aUIbl",
        "outputId": "3838fb2f-07a8-4b8d-bc35-52140976d547"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isnRYkTTUi2a"
      },
      "source": [
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WCe1b2mUa2O",
        "outputId": "50927dec-3b9f-4585-bc6e-52a505af5129"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.97      0.98       582\n",
            "           1       0.97      0.99      0.98       585\n",
            "\n",
            "    accuracy                           0.98      1167\n",
            "   macro avg       0.98      0.98      0.98      1167\n",
            "weighted avg       0.98      0.98      0.98      1167\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSHLfheyUwrr",
        "outputId": "f6c72176-3f9d-4132-ce99-7afec7a97d29"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(y_test, predictions))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[564  18]\n",
            " [  5 580]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "XnJZAjC7U08A",
        "outputId": "91a8b4ca-2ac6-4403-e078-2b4450b3d828"
      },
      "source": [
        "y_scores = model.predict_proba(X_test)\n",
        "print(y_scores)\n",
        "\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# calculate ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
        "\n",
        "# plot ROC curve\n",
        "fig = plt.figure(figsize=(6, 6))\n",
        "# Plot the diagonal 50% line\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "# Plot the FPR and TPR achieved by our model\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.   1.  ]\n",
            " [1.   0.  ]\n",
            " [0.98 0.02]\n",
            " ...\n",
            " [1.   0.  ]\n",
            " [0.   1.  ]\n",
            " [1.   0.  ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD8tA3uBTnJD"
      },
      "source": [
        "Save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AfNN0boScc8"
      },
      "source": [
        "import joblib\n",
        "filename = '/models/malware_classification_model.pkl'\n",
        "joblib.dump(model, filename)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}