Intrusion_detection_using_RNN_MULTICLASS.ipynb

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    "colab": {
      "name": "Intrusion-detection-using-RNN_MULTICLASS.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cp-qBpWH5lfT"
      },
      "source": [
        "# **A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks**\n",
        "\n",
        "\n",
        "\n",
        "> Multiclass-5 classes(4 Attack and Normal), KDDTest_21\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwauaClrCsgn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5291d59c-f9b6-485d-c46a-f081952eb5b9"
      },
      "source": [
        "# Linking Google drive to use preprocessed data \n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx_dyZdlSXMK"
      },
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sys\n",
        "import sklearn\n",
        "import io\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu0DCY-iCxlV"
      },
      "source": [
        "from tensorflow.keras.layers import Dense, Flatten, Dropout,BatchNormalization ,Activation\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from IPython.display import Image\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "from keras.layers import SimpleRNN"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Utaaqng3NOY0",
        "outputId": "a7799dda-99fc-4bc9-b53d-fe047755e12b"
      },
      "source": [
        "## add the columns' name and read the KDDTrain+ and KDDTest+ datasets\n",
        "col_names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
        "    \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
        "    \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
        "    \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
        "    \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
        "    \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
        "    \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
        "    \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
        "    \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
        "    \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"label\"]\n",
        "\n",
        "print(col_names)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'label']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_UFZjhKDErh"
      },
      "source": [
        "## Dataset\n",
        "The dataset covers the **KDDTrain** dataset as the training set and **KDDTest** and **KDDTest−21** datasets as the testing set, normal records and four different types of attack records."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfK2cMQNDMly",
        "outputId": "207ff55b-8dbd-424f-c14e-8eb897c96bbb"
      },
      "source": [
        "#Training set\n",
        "df = pd.read_csv(\"/content/NSL_KDD_Train.csv\",header=None, names = col_names)\n",
        "# Testing set\n",
        "df_test_21 = pd.read_csv(\"/content/NSL_KDD_Test_21.csv\", header=None, names = col_names)\n",
        "\n",
        "print('Dimensions of the Training set:',df.shape)\n",
        "print('Dimensions of the Test_21 set:',df_test_21.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of the Training set: (110368, 42)\n",
            "Dimensions of the Test_21 set: (11850, 42)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "_zKWRUDkWpiH",
        "outputId": "ef39f5ed-db0e-4825-e8d1-5f3bc2a24391"
      },
      "source": [
        "# review the features type\n",
        "df.head(5) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
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              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
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              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>label</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>150.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>tcp</td>\n",
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              "      <td>123</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.10</td>\n",
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              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
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              "      <td>255.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration protocol_type  ... dst_host_srv_rerror_rate    label\n",
              "0         0           tcp  ...                     0.00   normal\n",
              "1         0           udp  ...                     0.00   normal\n",
              "2         0           tcp  ...                     0.00  neptune\n",
              "3         0           tcp  ...                     0.01   normal\n",
              "4         0           tcp  ...                     0.00   normal\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "pCYANj6-DPaI",
        "outputId": "e45b0610-6f92-4944-d23c-605c9c73c354"
      },
      "source": [
        "df_test_21.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>num_file_creations</th>\n",
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              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>tcp</td>\n",
              "      <td>telnet</td>\n",
              "      <td>SF</td>\n",
              "      <td>118</td>\n",
              "      <td>2425</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.3</td>\n",
              "      <td>guess_passwd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>private</td>\n",
              "      <td>SF</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255</td>\n",
              "      <td>254</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>snmpguess</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>telnet</td>\n",
              "      <td>S3</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255</td>\n",
              "      <td>79</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>processtable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>private</td>\n",
              "      <td>SF</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>511</td>\n",
              "      <td>511</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>SH</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>nmap</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration protocol_type  ... dst_host_srv_rerror_rate         label\n",
              "0        13           tcp  ...                      0.3  guess_passwd\n",
              "1         0           udp  ...                      0.0     snmpguess\n",
              "2         0           tcp  ...                      0.0  processtable\n",
              "3         0           udp  ...                      0.0        normal\n",
              "4         0           tcp  ...                      0.0          nmap\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IP9h5dhPDUW1",
        "outputId": "dcc31ea5-9bc2-407f-9098-b8a5dbe3ce7b"
      },
      "source": [
        "print(\"Training dim: \",df.shape)\n",
        "print(\"Testing dim: \",df_test_21.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training dim:  (110368, 42)\n",
            "Testing dim:  (11850, 42)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAa8_nBFWwxf",
        "outputId": "1962a89b-ad8f-40f8-a03c-015ed6603f7f"
      },
      "source": [
        "print('Label distribution in the Training set:')\n",
        "print(\"****************************************\")\n",
        "print(df['label'].value_counts())\n",
        "print('\\n')\n",
        "print('Label distribution in the Test set:')\n",
        "print(\"**************************************\")\n",
        "print(df_test_21['label'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label distribution in the Training set:\n",
            "****************************************\n",
            "normal             58940\n",
            "neptune            36161\n",
            "satan               3193\n",
            "ipsweep             3167\n",
            "portsweep           2566\n",
            "smurf               2320\n",
            "nmap                1293\n",
            "back                 838\n",
            "teardrop             788\n",
            "warezclient          782\n",
            "pod                  168\n",
            "guess_passwd          42\n",
            "buffer_overflow       26\n",
            "warezmaster           18\n",
            "land                  15\n",
            "imap                  11\n",
            "rootkit               10\n",
            "multihop               7\n",
            "loadmodule             7\n",
            "ftp_write              7\n",
            "perl                   3\n",
            "phf                    3\n",
            "spy                    2\n",
            "Name: label, dtype: int64\n",
            "\n",
            "\n",
            "Label distribution in the Test set:\n",
            "**************************************\n",
            "normal             2152\n",
            "neptune            1579\n",
            "guess_passwd       1231\n",
            "mscan               996\n",
            "warezmaster         944\n",
            "apache2             737\n",
            "satan               727\n",
            "processtable        685\n",
            "smurf               627\n",
            "back                359\n",
            "snmpguess           331\n",
            "saint               309\n",
            "mailbomb            293\n",
            "snmpgetattack       178\n",
            "portsweep           156\n",
            "ipsweep             141\n",
            "httptunnel          133\n",
            "nmap                 73\n",
            "pod                  41\n",
            "buffer_overflow      20\n",
            "multihop             18\n",
            "named                17\n",
            "ps                   15\n",
            "sendmail             14\n",
            "rootkit              13\n",
            "xterm                13\n",
            "teardrop             12\n",
            "xlock                 9\n",
            "land                  7\n",
            "xsnoop                4\n",
            "ftp_write             3\n",
            "udpstorm              2\n",
            "loadmodule            2\n",
            "sqlattack             2\n",
            "phf                   2\n",
            "perl                  2\n",
            "worm                  2\n",
            "imap                  1\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "IZX9nOWDDmBM",
        "outputId": "59ec8eee-45f5-4907-aa0f-9c9f3e055e4d"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "\n",
        "df['label'].value_counts().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6e760d2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFUPOfTWW7jF"
      },
      "source": [
        "# **Step 1: Data preprocessing:**\n",
        "\n",
        "One-Hot-Encoding is used to convert all categorical properties to binary properties. One-Hot-Endcoding requirement, the input to this transformer must be an integer matrix expressing values taken with categorical (discrete) properties. The output will be a sparse matrix in which each column corresponds to a possible value. It is assumed that the input properties have values in the range [0, n_values]. Therefore, to convert each category to a number, properties must first be converted with LabelEncoder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TyLnY-XCXVnk",
        "outputId": "5f98d0d9-7fab-40a8-c71c-6d62adb054d1"
      },
      "source": [
        "# columns are categorical, not yet binary: protocol_type (column 2), service (column 3), flag (column 4).\n",
        "print('Training set:')\n",
        "for col_name in df.columns:\n",
        "    if df[col_name].dtypes == 'object' :\n",
        "        unique_cat = len(df[col_name].unique())\n",
        "        print(\"Feature '{col_name}' has {unique_cat} categories\".format(col_name=col_name, unique_cat=unique_cat))\n",
        "\n",
        "print()\n",
        "print('Distribution of categories in service:')\n",
        "print(df['service'].value_counts().sort_values(ascending=False).head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set:\n",
            "Feature 'protocol_type' has 3 categories\n",
            "Feature 'service' has 70 categories\n",
            "Feature 'flag' has 11 categories\n",
            "Feature 'label' has 24 categories\n",
            "\n",
            "Distribution of categories in service:\n",
            "http        35267\n",
            "private     19211\n",
            "domain_u     7955\n",
            "smtp         6403\n",
            "ftp_data     6021\n",
            "Name: service, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhKXNXlwXb5G",
        "outputId": "a23ed2f4-6f57-4483-b721-38c2ed1b6db8"
      },
      "source": [
        "# Test set\n",
        "print('Test set:')\n",
        "for col_name in df_test_21.columns:\n",
        "    if df_test_21[col_name].dtypes == 'object' :\n",
        "        unique_cat = len(df_test_21[col_name].unique())\n",
        "        print(\"Feature '{col_name}' has {unique_cat} categories\".format(col_name=col_name, unique_cat=unique_cat))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set:\n",
            "Feature 'protocol_type' has 3 categories\n",
            "Feature 'service' has 62 categories\n",
            "Feature 'flag' has 11 categories\n",
            "Feature 'label' has 38 categories\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZKviPW0XnzU"
      },
      "source": [
        "**LabelEncoder**\n",
        "\n",
        "**Insert categorical features into a 2D numpy array**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYmvn2qWXxs2"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
        "categorical_columns=['protocol_type', 'service', 'flag']\n",
        "\n",
        "df_categorical_values = df[categorical_columns]\n",
        "# for Test\n",
        "testdf_categorical_values = df_test_21[categorical_columns]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNVa7mN9X1qV",
        "outputId": "e818a622-eff0-41e3-ee98-da33bb8c8f4b"
      },
      "source": [
        "# protocol type\n",
        "unique_protocol=sorted(df.protocol_type.unique())\n",
        "string1 = 'Protocol_type_'\n",
        "unique_protocol2=[string1 + x for x in unique_protocol]\n",
        "print(unique_protocol2)\n",
        "\n",
        "# service\n",
        "unique_service=sorted(df.service.unique())\n",
        "string2 = 'service_'\n",
        "unique_service2=[string2 + x for x in unique_service]\n",
        "print(unique_service2)\n",
        "\n",
        "\n",
        "# flag\n",
        "unique_flag=sorted(df.flag.unique())\n",
        "string3 = 'flag_'\n",
        "unique_flag2=[string3 + x for x in unique_flag]\n",
        "print(unique_flag2)\n",
        "\n",
        "\n",
        "# put together\n",
        "dumcols=unique_protocol2 + unique_service2 + unique_flag2\n",
        "\n",
        "\n",
        "#do it for test set\n",
        "unique_service_test=sorted(df_test_21.service.unique())\n",
        "unique_service2_test=[string2 + x for x in unique_service_test]\n",
        "testdumcols=unique_protocol2 + unique_service2_test + unique_flag2\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Protocol_type_icmp', 'Protocol_type_tcp', 'Protocol_type_udp']\n",
            "['service_IRC', 'service_X11', 'service_Z39_50', 'service_aol', 'service_auth', 'service_bgp', 'service_courier', 'service_csnet_ns', 'service_ctf', 'service_daytime', 'service_discard', 'service_domain', 'service_domain_u', 'service_echo', 'service_eco_i', 'service_ecr_i', 'service_efs', 'service_exec', 'service_finger', 'service_ftp', 'service_ftp_data', 'service_gopher', 'service_harvest', 'service_hostnames', 'service_http', 'service_http_2784', 'service_http_443', 'service_http_8001', 'service_imap4', 'service_iso_tsap', 'service_klogin', 'service_kshell', 'service_ldap', 'service_link', 'service_login', 'service_mtp', 'service_name', 'service_netbios_dgm', 'service_netbios_ns', 'service_netbios_ssn', 'service_netstat', 'service_nnsp', 'service_nntp', 'service_ntp_u', 'service_other', 'service_pm_dump', 'service_pop_2', 'service_pop_3', 'service_printer', 'service_private', 'service_red_i', 'service_remote_job', 'service_rje', 'service_shell', 'service_smtp', 'service_sql_net', 'service_ssh', 'service_sunrpc', 'service_supdup', 'service_systat', 'service_telnet', 'service_tftp_u', 'service_tim_i', 'service_time', 'service_urh_i', 'service_urp_i', 'service_uucp', 'service_uucp_path', 'service_vmnet', 'service_whois']\n",
            "['flag_OTH', 'flag_REJ', 'flag_RSTO', 'flag_RSTOS0', 'flag_RSTR', 'flag_S0', 'flag_S1', 'flag_S2', 'flag_S3', 'flag_SF', 'flag_SH']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nf-QhZyfX9Jd"
      },
      "source": [
        "**Transform categorical features into numbers using LabelEncoder()**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ta0CsfRUX5Tg",
        "outputId": "0ff45d18-b0ee-4efc-9997-c6ff4921a57a"
      },
      "source": [
        "df_categorical_values_enc=df_categorical_values.apply(LabelEncoder().fit_transform)\n",
        "print(df_categorical_values.head())\n",
        "print('--------------------')\n",
        "print(df_categorical_values_enc.head())\n",
        "# test set\n",
        "testdf_categorical_values_enc=testdf_categorical_values.apply(LabelEncoder().fit_transform)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  protocol_type   service flag\n",
            "0           tcp  ftp_data   SF\n",
            "1           udp     other   SF\n",
            "2           tcp   private   S0\n",
            "3           tcp      http   SF\n",
            "4           tcp      http   SF\n",
            "--------------------\n",
            "   protocol_type  service  flag\n",
            "0              1       20     9\n",
            "1              2       44     9\n",
            "2              1       49     5\n",
            "3              1       24     9\n",
            "4              1       24     9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "BaN-8KdNErU_",
        "outputId": "c3614c87-0c33-4fee-ba4f-6bfeb0558a83"
      },
      "source": [
        "corr = df_categorical_values_enc.corr()\n",
        "plt.figure(figsize=(8,6))\n",
        "sb.heatmap(corr)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iq_pnkScYDOi"
      },
      "source": [
        "**One-Hot-Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVBytj5dYGDu"
      },
      "source": [
        "enc = OneHotEncoder(categories='auto')\n",
        "df_categorical_values_encenc = enc.fit_transform(df_categorical_values_enc)\n",
        "df_cat_data = pd.DataFrame(df_categorical_values_encenc.toarray(),columns=dumcols)\n",
        "# test set\n",
        "testdf_categorical_values_encenc = enc.fit_transform(testdf_categorical_values_enc)\n",
        "testdf_cat_data = pd.DataFrame(testdf_categorical_values_encenc.toarray(),columns=testdumcols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7D-ZvKY6bS-r"
      },
      "source": [
        "Missing columns in test set are added \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxTdPirkbW-V",
        "outputId": "acc1b7ce-6b3c-458c-f1dc-3f421c6d7dfb"
      },
      "source": [
        "trainservice=df['service'].tolist()\n",
        "testservice= df_test_21['service'].tolist()\n",
        "difference=list(set(trainservice) - set(testservice))\n",
        "string = 'service_'\n",
        "difference=[string + x for x in difference]\n",
        "\n",
        "for col in difference:\n",
        "    testdf_cat_data[col] = 0\n",
        "\n",
        "print(df_cat_data.shape)    \n",
        "print(testdf_cat_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(110368, 84)\n",
            "(11850, 84)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Vsl_YcrcSd8"
      },
      "source": [
        "New numeric columns are added to main dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOXgIdAZcVAF",
        "outputId": "69f6000b-61af-48eb-cfb9-4e4af4ccadde"
      },
      "source": [
        "newdf=df.join(df_cat_data)\n",
        "newdf.drop('flag', axis=1, inplace=True)\n",
        "newdf.drop('protocol_type', axis=1, inplace=True)\n",
        "newdf.drop('service', axis=1, inplace=True)\n",
        "\n",
        "# test data\n",
        "newdf_test=df_test_21.join(testdf_cat_data)\n",
        "newdf_test.drop('flag', axis=1, inplace=True)\n",
        "newdf_test.drop('protocol_type', axis=1, inplace=True)\n",
        "newdf_test.drop('service', axis=1, inplace=True)\n",
        "\n",
        "print(newdf.shape)\n",
        "print(newdf_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(110368, 123)\n",
            "(11850, 123)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRfv_qU7Ezdk"
      },
      "source": [
        "**Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__tRm4sRaVnm",
        "outputId": "6068bfda-5263-454a-aac6-8f8e687aa9d1"
      },
      "source": [
        "# step1: apply the logarithmic scaling method for scaling to obtain the ranges of `duration[0,4.77]', `src_bytes[0,9.11]' and `dst_bytes[0,9.11]\n",
        "newdf['log2_value1'] = np.log2(newdf['duration'])\n",
        "newdf['log2_value2'] = np.log(newdf['src_bytes'])\n",
        "newdf['log2_value3'] = np.log(newdf['dst_bytes'])\n",
        "newdf=newdf.drop(['log2_value3','log2_value2','log2_value1'], axis=1)\n",
        "\n",
        "\n",
        "# testing set\n",
        "\n",
        "newdf_test['log2_value1'] = np.log2(newdf_test['duration'])\n",
        "newdf_test['log2_value2'] = np.log(newdf_test['src_bytes'])\n",
        "newdf_test['log2_value3'] = np.log(newdf_test['dst_bytes'])\n",
        "newdf_test=newdf_test.drop(['log2_value3','log2_value2','log2_value1'], axis=1)\n",
        "\n",
        "print(newdf)\n",
        "print(newdf_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        duration  src_bytes  dst_bytes  ...  flag_S3  flag_SF  flag_SH\n",
            "0              0        491          0  ...      0.0      1.0      0.0\n",
            "1              0        146          0  ...      0.0      1.0      0.0\n",
            "2              0          0          0  ...      0.0      0.0      0.0\n",
            "3              0        232       8153  ...      0.0      1.0      0.0\n",
            "4              0        199        420  ...      0.0      1.0      0.0\n",
            "...          ...        ...        ...  ...      ...      ...      ...\n",
            "110363         0          0          0  ...      0.0      0.0      0.0\n",
            "110364         0        332       6700  ...      0.0      1.0      0.0\n",
            "110365         0          0          0  ...      0.0      0.0      0.0\n",
            "110366         0          0          0  ...      0.0      0.0      0.0\n",
            "110367         0          0          0  ...      0.0      0.0      0.0\n",
            "\n",
            "[110368 rows x 123 columns]\n",
            "       duration  src_bytes  ...  service_aol  service_http_2784\n",
            "0            13        118  ...            0                  0\n",
            "1             0         44  ...            0                  0\n",
            "2             0          0  ...            0                  0\n",
            "3             0         53  ...            0                  0\n",
            "4             0          0  ...            0                  0\n",
            "...         ...        ...  ...          ...                ...\n",
            "11845         0         43  ...            0                  0\n",
            "11846         0        336  ...            0                  0\n",
            "11847         1          0  ...            0                  0\n",
            "11848         0          0  ...            0                  0\n",
            "11849         0         41  ...            0                  0\n",
            "\n",
            "[11850 rows x 123 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTLFmtWrFJN_"
      },
      "source": [
        "# Further steps:\n",
        "**for binary classification**\n",
        "- Normal - 0\n",
        "- Anomaly - 1,2,3,4\n",
        "\n",
        "**for multiclass classification**\n",
        "proceed with the classes of 0,1,2,3,4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZsTN_Xoca_b"
      },
      "source": [
        "The dataset was divided into separate datasets for each attack category. Attack tags have been renamed for each. 0 = Normal, 1 = DoS, 2 = Probe, 3 = R2L, 4 = U2R. In the new datasets, the tag column has been replaced with the new values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "706-Qqhycc_-"
      },
      "source": [
        "labeldf=newdf['label']\n",
        "labeldf_test=newdf_test['label']\n",
        "\n",
        "\n",
        "# change the label column\n",
        "newlabeldf=labeldf.replace({ 'normal' : 0, 'neptune' : 1 ,'back': 1, 'land': 1, 'pod': 1, 'smurf': 1, 'teardrop': 1,'mailbomb': 1, 'apache2': 1, 'processtable': 1, 'udpstorm': 1, 'worm': 1,\n",
        "                           'ipsweep' : 2,'nmap' : 2,'portsweep' : 2,'satan' : 2,'mscan' : 2,'saint' : 2\n",
        "                           ,'ftp_write': 3,'guess_passwd': 3,'imap': 3,'multihop': 3,'phf': 3,'spy': 3,'warezclient': 3,'warezmaster': 3,'sendmail': 3,'named': 3,'snmpgetattack': 3,'snmpguess': 3,'xlock': 3,'xsnoop': 3,'httptunnel': 3,\n",
        "                           'buffer_overflow': 4,'loadmodule': 4,'perl': 4,'rootkit': 4,'ps': 4,'sqlattack': 4,'xterm': 4})\n",
        "newlabeldf_test=labeldf_test.replace({ 'normal' : 0, 'neptune' : 1 ,'back': 1, 'land': 1, 'pod': 1, 'smurf': 1, 'teardrop': 1,'mailbomb': 1, 'apache2': 1, 'processtable': 1, 'udpstorm': 1, 'worm': 1,\n",
        "                           'ipsweep' : 2,'nmap' : 2,'portsweep' : 2,'satan' : 2,'mscan' : 2,'saint' : 2\n",
        "                           ,'ftp_write': 3,'guess_passwd': 3,'imap': 3,'multihop': 3,'phf': 3,'spy': 3,'warezclient': 3,'warezmaster': 3,'sendmail': 3,'named': 3,'snmpgetattack': 3,'snmpguess': 3,'xlock': 3,'xsnoop': 3,'httptunnel': 3,\n",
        "                            'buffer_overflow': 4,'loadmodule': 4,'perl': 4,'rootkit': 4,'ps': 4,'sqlattack': 4,'xterm': 4})\n",
        "\n",
        "\n",
        "\n",
        "#put the new label column back\n",
        "newdf['label'] = newlabeldf\n",
        "newdf_test['label'] = newlabeldf_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "7sPBcZdZESJW",
        "outputId": "ee489d5b-798d-4167-8f78-557fd5e91055"
      },
      "source": [
        "newdf.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
              "      <th>srv_diff_host_rate</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>label</th>\n",
              "      <th>Protocol_type_icmp</th>\n",
              "      <th>...</th>\n",
              "      <th>service_nnsp</th>\n",
              "      <th>service_nntp</th>\n",
              "      <th>service_ntp_u</th>\n",
              "      <th>service_other</th>\n",
              "      <th>service_pm_dump</th>\n",
              "      <th>service_pop_2</th>\n",
              "      <th>service_pop_3</th>\n",
              "      <th>service_printer</th>\n",
              "      <th>service_private</th>\n",
              "      <th>service_red_i</th>\n",
              "      <th>service_remote_job</th>\n",
              "      <th>service_rje</th>\n",
              "      <th>service_shell</th>\n",
              "      <th>service_smtp</th>\n",
              "      <th>service_sql_net</th>\n",
              "      <th>service_ssh</th>\n",
              "      <th>service_sunrpc</th>\n",
              "      <th>service_supdup</th>\n",
              "      <th>service_systat</th>\n",
              "      <th>service_telnet</th>\n",
              "      <th>service_tftp_u</th>\n",
              "      <th>service_tim_i</th>\n",
              "      <th>service_time</th>\n",
              "      <th>service_urh_i</th>\n",
              "      <th>service_urp_i</th>\n",
              "      <th>service_uucp</th>\n",
              "      <th>service_uucp_path</th>\n",
              "      <th>service_vmnet</th>\n",
              "      <th>service_whois</th>\n",
              "      <th>flag_OTH</th>\n",
              "      <th>flag_REJ</th>\n",
              "      <th>flag_RSTO</th>\n",
              "      <th>flag_RSTOS0</th>\n",
              "      <th>flag_RSTR</th>\n",
              "      <th>flag_S0</th>\n",
              "      <th>flag_S1</th>\n",
              "      <th>flag_S2</th>\n",
              "      <th>flag_S3</th>\n",
              "      <th>flag_SF</th>\n",
              "      <th>flag_SH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>25.0</td>\n",
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              "    <tr>\n",
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              "      <td>123</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>255.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 123 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration  src_bytes  dst_bytes  land  ...  flag_S2  flag_S3  flag_SF  flag_SH\n",
              "0         0        491          0     0  ...      0.0      0.0      1.0      0.0\n",
              "1         0        146          0     0  ...      0.0      0.0      1.0      0.0\n",
              "2         0          0          0     0  ...      0.0      0.0      0.0      0.0\n",
              "3         0        232       8153     0  ...      0.0      0.0      1.0      0.0\n",
              "4         0        199        420     0  ...      0.0      0.0      1.0      0.0\n",
              "\n",
              "[5 rows x 123 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "61nrFDwsEWZ7",
        "outputId": "6aea4751-4098-4e9e-a856-2fe18e7ef89b"
      },
      "source": [
        "newdf_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
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              "      <th>root_shell</th>\n",
              "      <th>su_attempted</th>\n",
              "      <th>num_root</th>\n",
              "      <th>num_file_creations</th>\n",
              "      <th>num_shells</th>\n",
              "      <th>num_access_files</th>\n",
              "      <th>num_outbound_cmds</th>\n",
              "      <th>is_host_login</th>\n",
              "      <th>is_guest_login</th>\n",
              "      <th>count</th>\n",
              "      <th>srv_count</th>\n",
              "      <th>serror_rate</th>\n",
              "      <th>srv_serror_rate</th>\n",
              "      <th>rerror_rate</th>\n",
              "      <th>srv_rerror_rate</th>\n",
              "      <th>same_srv_rate</th>\n",
              "      <th>diff_srv_rate</th>\n",
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              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>label</th>\n",
              "      <th>Protocol_type_icmp</th>\n",
              "      <th>...</th>\n",
              "      <th>service_pm_dump</th>\n",
              "      <th>service_pop_2</th>\n",
              "      <th>service_pop_3</th>\n",
              "      <th>service_private</th>\n",
              "      <th>service_remote_job</th>\n",
              "      <th>service_shell</th>\n",
              "      <th>service_smtp</th>\n",
              "      <th>service_sql_net</th>\n",
              "      <th>service_ssh</th>\n",
              "      <th>service_sunrpc</th>\n",
              "      <th>service_supdup</th>\n",
              "      <th>service_systat</th>\n",
              "      <th>service_telnet</th>\n",
              "      <th>service_tftp_u</th>\n",
              "      <th>service_tim_i</th>\n",
              "      <th>service_time</th>\n",
              "      <th>service_urp_i</th>\n",
              "      <th>service_uucp</th>\n",
              "      <th>service_uucp_path</th>\n",
              "      <th>service_vmnet</th>\n",
              "      <th>service_whois</th>\n",
              "      <th>flag_OTH</th>\n",
              "      <th>flag_REJ</th>\n",
              "      <th>flag_RSTO</th>\n",
              "      <th>flag_RSTOS0</th>\n",
              "      <th>flag_RSTR</th>\n",
              "      <th>flag_S0</th>\n",
              "      <th>flag_S1</th>\n",
              "      <th>flag_S2</th>\n",
              "      <th>flag_S3</th>\n",
              "      <th>flag_SF</th>\n",
              "      <th>flag_SH</th>\n",
              "      <th>service_red_i</th>\n",
              "      <th>service_urh_i</th>\n",
              "      <th>service_http_8001</th>\n",
              "      <th>service_harvest</th>\n",
              "      <th>service_rje</th>\n",
              "      <th>service_printer</th>\n",
              "      <th>service_aol</th>\n",
              "      <th>service_http_2784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.04</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.3</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
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              "      <td>255</td>\n",
              "      <td>254</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255</td>\n",
              "      <td>79</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>511</td>\n",
              "      <td>511</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 123 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   duration  src_bytes  ...  service_aol  service_http_2784\n",
              "0        13        118  ...            0                  0\n",
              "1         0         44  ...            0                  0\n",
              "2         0          0  ...            0                  0\n",
              "3         0         53  ...            0                  0\n",
              "4         0          0  ...            0                  0\n",
              "\n",
              "[5 rows x 123 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsTZfN44Q1F1"
      },
      "source": [
        "#Split the test and train data into training and testing \n",
        "# train set:\n",
        "x=newdf.drop([\"label\"],1)  # features\n",
        "y=newlabeldf  # label\n",
        "# test set:\n",
        "xtest=newdf_test.drop(['label'],1) # features\n",
        "ytest=newlabeldf_test # label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srFsDiztFP6B",
        "outputId": "e8979915-4ab9-454a-cb0d-a4d8438d2508"
      },
      "source": [
        "print(\"X: \",x.shape)\n",
        "print(\"Y: \",y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X:  (110368, 122)\n",
            "Y:  (110368,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0In7wHkFYAV",
        "outputId": "07cf70c1-ed25-4112-d5d8-c67876371189"
      },
      "source": [
        "print(\"X: \",xtest.shape)\n",
        "print(\"Y: \",ytest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X:  (11850, 122)\n",
            "Y:  (11850,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzz4U8OcFkYO"
      },
      "source": [
        "## Min max normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q52hwAZRFnZ8",
        "outputId": "01b4f153-8269-4f4d-bb00-5039bcb967cc"
      },
      "source": [
        "# scaling\n",
        "scale = MinMaxScaler()\n",
        "scale = preprocessing.StandardScaler().fit(x)\n",
        "X_train_scaled=scale.transform(x)\n",
        "print(X_train_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.11036564 -0.0078166  -0.00514408 ... -0.01997061  0.8257417\n",
            "  -0.04529787]\n",
            " [-0.11036564 -0.00789016 -0.00514408 ... -0.01997061  0.8257417\n",
            "  -0.04529787]\n",
            " [-0.11036564 -0.00792129 -0.00514408 ... -0.01997061 -1.21103246\n",
            "  -0.04529787]\n",
            " ...\n",
            " [-0.11036564 -0.00792129 -0.00514408 ... -0.01997061 -1.21103246\n",
            "  -0.04529787]\n",
            " [-0.11036564 -0.00792129 -0.00514408 ... -0.01997061 -1.21103246\n",
            "  -0.04529787]\n",
            " [-0.11036564 -0.00792129 -0.00514408 ... -0.01997061 -1.21103246\n",
            "  -0.04529787]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2r9LdLDDFxQj",
        "outputId": "6c1f0f5f-f793-4cd5-abd1-e386ef75f748"
      },
      "source": [
        "# scaling\n",
        "scale = MinMaxScaler()\n",
        "scale = preprocessing.StandardScaler().fit(xtest)\n",
        "X_test_scaled=scale.transform(xtest)\n",
        "print(X_test_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.2096739  -0.02966205  0.05008971 ...  0.          0.\n",
            "   0.        ]\n",
            " [-0.21644699 -0.02977555 -0.05139586 ...  0.          0.\n",
            "   0.        ]\n",
            " [-0.21644699 -0.02984304 -0.04955447 ...  0.          0.\n",
            "   0.        ]\n",
            " ...\n",
            " [-0.21592598 -0.02984304 -0.05076811 ...  0.          0.\n",
            "   0.        ]\n",
            " [-0.21644699 -0.02984304 -0.05139586 ...  0.          0.\n",
            "   0.        ]\n",
            " [-0.21644699 -0.02978016 -0.05139586 ...  0.          0.\n",
            "   0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yh6pGSmFCTDe"
      },
      "source": [
        "## Building the RNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVn0H8R0CSfO"
      },
      "source": [
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import datetime\n",
        "import time\n",
        "\n",
        "sample = X_train_scaled.shape[0]\n",
        "features = X_train_scaled.shape[1]\n",
        "#Train: convert 2D to 3D for input RNN\n",
        "x_train = np.reshape(X_train_scaled,(sample,features,1)) #shape  = (125973, 18, 1)\n",
        "#Test: convert 2D to 3D for input RNN\n",
        "\n",
        "x_test = np.reshape(X_test_scaled,(X_test_scaled.shape[0],X_test_scaled.shape[1],1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-H0PaQlGMoi",
        "outputId": "61dfa00e-0c77-43f1-b236-1ca45f4050f7"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(110368, 122, 1)\n",
            "(110368,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55D-p8KYGNuW",
        "outputId": "72b2da5f-dbee-49c2-997a-2be5bce6bd65"
      },
      "source": [
        "print(x_test.shape)\n",
        "print(ytest.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11850, 122, 1)\n",
            "(11850,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMNTIS-siAit"
      },
      "source": [
        "# **RNN Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dzAvXi0Geau"
      },
      "source": [
        "### Metrics list to evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pp_ZcT0Gdkt"
      },
      "source": [
        "import tensorflow as tf\n",
        "METRICS = [\n",
        "      tf.keras.metrics.BinaryAccuracy(name='accuracy'),\n",
        "      tf.keras.metrics.Precision(name='precision'),\n",
        "      tf.keras.metrics.Recall(name='recall')\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5zGeFpWGlkz"
      },
      "source": [
        "### Custom Callback functions: recall, precision, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjPZjuPXGKY5"
      },
      "source": [
        "import tensorflow.keras.backend as K\n",
        "def recall(y_true, y_pred):\n",
        "    y_true = K.ones_like(y_true) \n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    all_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    \n",
        "    recall = true_positives / (all_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision(y_true, y_pred):\n",
        "    y_true = K.ones_like(y_true) \n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    \n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_score(y_true, y_pred):\n",
        "    prec = precision(y_true, y_pred)\n",
        "    rec = recall(y_true, y_pred)\n",
        "    return 2*((prec*rec)/(prec+rec+K.epsilon()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sT3NixhpGskH",
        "outputId": "9a879935-e3b7-4e97-e33a-41148864353a"
      },
      "source": [
        "from keras import optimizers \n",
        "model = Sequential()\n",
        "model.add(SimpleRNN(80,input_shape=(features,x_train.shape[2]), activation='relu'))\n",
        "model.add(Dense(5, activation='softmax'))\n",
        "opt = keras.optimizers.Adam(learning_rate=0.01)\n",
        "model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy', 'mae', recall,precision,f1_score])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn_1 (SimpleRNN)    (None, 80)                6560      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 405       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,965\n",
            "Trainable params: 6,965\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLGRXXJiGt_z"
      },
      "source": [
        "mcp = ModelCheckpoint('RNN_multicalss1.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tnOaq99LEup",
        "outputId": "b6c2c172-1d14-4fab-c022-72cf35e28cce"
      },
      "source": [
        "# multi-class classification\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# define baseline model\n",
        "def baseline_model():\n",
        "\t# create model\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Dense(60, input_dim=122, activation='relu'))\n",
        "\tmodel.add(Dense(5, activation='softmax'))\n",
        "\t# Compile model\n",
        "  \n",
        "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "estimator = KerasClassifier(build_fn=baseline_model, epochs=20, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=10, shuffle=True)\n",
        "results = cross_val_score(estimator,xtest,ytest, cv=kfold)\n",
        "print(\"Accuracy: %.2f%%\" % (results.mean()*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 89.22%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_6MA4s2BQOl",
        "outputId": "87915944-b3d8-471c-dc8a-2e8659c4aec8"
      },
      "source": [
        "# multi-class classification\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "# define baseline model\n",
        "def baseline_model():\n",
        "\t# create model\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Dense(80, input_dim=122, activation='relu'))\n",
        "\tmodel.add(Dense(5, activation='softmax'))\n",
        "\t# Compile model\n",
        "  \n",
        "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "estimator = KerasClassifier(build_fn=baseline_model, epochs=20, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=10, shuffle=True)\n",
        "results = cross_val_score(estimator,xtest,ytest, cv=kfold)\n",
        "print(\"Accuracy: %.2f%%\" % (results.mean()*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 90.96%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHeFCTSSAWNs"
      },
      "source": [
        "# Step 2: the value of every feature is mapped to the [0,1] range linearly\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import preprocessing\n",
        "# Training Set\n",
        "scale = MinMaxScaler()\n",
        "scale = preprocessing.StandardScaler().fit(x)\n",
        "x=scale.transform(x)\n",
        "scaletest = preprocessing.StandardScaler().fit(xtest)\n",
        "xtest=scaletest.transform(xtest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bu7S66qJcw56",
        "outputId": "641cde00-1b3f-49ad-99cd-c7b8144b8b0f"
      },
      "source": [
        "to_drop_DoS = [0,1]\n",
        "to_drop_Probe = [0,2]\n",
        "to_drop_R2L = [0,3]\n",
        "to_drop_U2R = [0,4]\n",
        "\n",
        "# Filter all rows with label value except itself\n",
        "# isin filter function\n",
        "\n",
        "DoS_df=newdf[newdf['label'].isin(to_drop_DoS)];\n",
        "Probe_df=newdf[newdf['label'].isin(to_drop_Probe)];\n",
        "R2L_df=newdf[newdf['label'].isin(to_drop_R2L)];\n",
        "U2R_df=newdf[newdf['label'].isin(to_drop_U2R)];\n",
        "\n",
        "\n",
        "\n",
        "#test\n",
        "DoS_df_test=newdf_test[newdf_test['label'].isin(to_drop_DoS)];\n",
        "Probe_df_test=newdf_test[newdf_test['label'].isin(to_drop_Probe)];\n",
        "R2L_df_test=newdf_test[newdf_test['label'].isin(to_drop_R2L)];\n",
        "U2R_df_test=newdf_test[newdf_test['label'].isin(to_drop_U2R)];\n",
        "\n",
        "\n",
        "print('Train Set:')\n",
        "print('Dimensions of DoS:' ,DoS_df.shape)\n",
        "print('Dimensions of Probe:' ,Probe_df.shape)\n",
        "print('Dimensions of R2L:' ,R2L_df.shape)\n",
        "print('Dimensions of U2R:' ,U2R_df.shape)\n",
        "print()\n",
        "print('Test Set:')\n",
        "print('Dimensions of DoS:' ,DoS_df_test.shape)\n",
        "print('Dimensions of Probe:' ,Probe_df_test.shape)\n",
        "print('Dimensions of R2L:' ,R2L_df_test.shape)\n",
        "print('Dimensions of U2R:' ,U2R_df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Set:\n",
            "Dimensions of DoS: (113270, 123)\n",
            "Dimensions of Probe: (78999, 123)\n",
            "Dimensions of R2L: (68338, 123)\n",
            "Dimensions of U2R: (67395, 123)\n",
            "\n",
            "Test Set:\n",
            "Dimensions of DoS: (17171, 123)\n",
            "Dimensions of Probe: (12132, 123)\n",
            "Dimensions of R2L: (12596, 123)\n",
            "Dimensions of U2R: (9778, 123)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8Qtz742ZVGk"
      },
      "source": [
        "**`Split the training set and testing set`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VeQhD09c6qT"
      },
      "source": [
        "# Split dataframes into X & Y\n",
        "# X Properties, Y result variable\n",
        "\n",
        "X_DoS = DoS_df.drop('label',1)\n",
        "Y_DoS = DoS_df.label\n",
        "\n",
        "X_Probe = Probe_df.drop('label',1)\n",
        "Y_Probe = Probe_df.label\n",
        "\n",
        "X_R2L = R2L_df.drop('label',1)\n",
        "Y_R2L = R2L_df.label\n",
        "\n",
        "X_U2R = U2R_df.drop('label',1)\n",
        "Y_U2R = U2R_df.label\n",
        "\n",
        "# test set\n",
        "X_DoS_test = DoS_df_test.drop('label',1)\n",
        "Y_DoS_test = DoS_df_test.label\n",
        "\n",
        "X_Probe_test = Probe_df_test.drop('label',1)\n",
        "Y_Probe_test = Probe_df_test.label\n",
        "\n",
        "X_R2L_test = R2L_df_test.drop('label',1)\n",
        "Y_R2L_test = R2L_df_test.label\n",
        "\n",
        "X_U2R_test = U2R_df_test.drop('label',1)\n",
        "Y_U2R_test = U2R_df_test.label\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl7gee3KdAb5"
      },
      "source": [
        "**Since the column names will be deleted at this stage, we save the column names for later use.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nWS5oukdFS6"
      },
      "source": [
        "colNames=list(X_DoS)\n",
        "colNames_test=list(X_DoS_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dvi-Oglzc5Bs"
      },
      "source": [
        "**Step 2: Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEBafDjodRcT"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "scaler1 = preprocessing.StandardScaler().fit(X_DoS)\n",
        "X_DoS=scaler1.transform(X_DoS) \n",
        "\n",
        "scaler2 = preprocessing.StandardScaler().fit(X_Probe)\n",
        "X_Probe=scaler2.transform(X_Probe)\n",
        "\n",
        "scaler3 = preprocessing.StandardScaler().fit(X_R2L)\n",
        "X_R2L=scaler3.transform(X_R2L)\n",
        "\n",
        "scaler4 = preprocessing.StandardScaler().fit(X_U2R)\n",
        "X_U2R=scaler4.transform(X_U2R) \n",
        "\n",
        "# test data\n",
        "scaler5 = preprocessing.StandardScaler().fit(X_DoS_test)\n",
        "X_DoS_test=scaler5.transform(X_DoS_test) \n",
        "\n",
        "scaler6 = preprocessing.StandardScaler().fit(X_Probe_test)\n",
        "X_Probe_test=scaler6.transform(X_Probe_test) \n",
        "\n",
        "scaler7 = preprocessing.StandardScaler().fit(X_R2L_test)\n",
        "X_R2L_test=scaler7.transform(X_R2L_test) \n",
        "\n",
        "scaler8 = preprocessing.StandardScaler().fit(X_U2R_test)\n",
        "X_U2R_test=scaler8.transform(X_U2R_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJpQzMD1Zjt1"
      },
      "source": [
        "**Build the Compartive Machine learning Models:**\n",
        "\n",
        "**DoS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "nVVPnrOQFltr",
        "outputId": "74c65ed5-2bb6-407f-84d9-347dd338151a"
      },
      "source": [
        "#1. DoS\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "mlp = MLPClassifier(solver='adam', learning_rate_init = 0.0005, learning_rate = 'adaptive', activation=\"relu\", max_iter=2000, random_state=42)\n",
        "dec = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
        "ran = RandomForestClassifier(n_estimators=100)\n",
        "svm = SVC(random_state=1)\n",
        "naive = GaussianNB()\n",
        "\n",
        "models = {\"J48\" : dec,\n",
        "          \"NB\" : naive,\n",
        "          \"RF\" : ran,\n",
        "          \"MLP\" : mlp,\n",
        "          \"SVM\" : svm\n",
        "          }\n",
        "scores= { }\n",
        "\n",
        "for key, value in models.items():    \n",
        "    model = value\n",
        "    model.fit(X_DoS, Y_DoS.astype(int))\n",
        "    scores[key] = model.score(X_DoS_test , Y_DoS_test)\n",
        "\n",
        "# results\n",
        "scores_frame = pd.DataFrame(scores, index=[\"Accuracy Score\"]).T\n",
        "scores_frame.sort_values(by=[\"Accuracy Score\"], axis=0 ,ascending=False, inplace=True)\n",
        "print('DoS')\n",
        "print(scores_frame)\n",
        "print(\"\\n\")\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=scores_frame.index,y=scores_frame[\"Accuracy Score\"])\n",
        "plt.ylim(0, 1)\n",
        "plt.ylabel(\"Accuracy%\")\n",
        "plt.xlabel(\"DoS\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DoS\n",
            "     Accuracy Score\n",
            "MLP        0.903092\n",
            "SVM        0.868849\n",
            "J48        0.834314\n",
            "RF         0.822200\n",
            "NB         0.769087\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4]), <a list of 5 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIldW8MSkAu-"
      },
      "source": [
        "**Probe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "E1BGEqGejrtV",
        "outputId": "d758f3d7-4859-4f3b-bb46-dc8fe88f4b3d"
      },
      "source": [
        "# 2.Probe\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "mlp = MLPClassifier(solver='adam', learning_rate_init = 0.0005, learning_rate = 'adaptive', activation=\"relu\", max_iter=2000, random_state=42)\n",
        "dec = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
        "ran = RandomForestClassifier(n_estimators=100)\n",
        "svm = SVC(random_state=1)\n",
        "naive = GaussianNB()\n",
        "\n",
        "models = {\"J48\" : dec,\n",
        "          \"NB\" : naive,\n",
        "          \"RF\" : ran,\n",
        "          \"MLP\" : mlp,\n",
        "          \"SVM\" : svm\n",
        "          }\n",
        "scores= { }\n",
        "\n",
        "for key, value in models.items():    \n",
        "    model1 = value\n",
        "    model1.fit(X_Probe, Y_Probe.astype(int))\n",
        "    scores[key] = model1.score(X_Probe_test , Y_Probe_test)\n",
        "\n",
        "\n",
        "# results\n",
        "scores_frame = pd.DataFrame(scores, index=[\"Accuracy Score\"]).T\n",
        "scores_frame.sort_values(by=[\"Accuracy Score\"], axis=0 ,ascending=False, inplace=True)\n",
        "print('Probe')\n",
        "print(scores_frame)\n",
        "print('\\n')\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=scores_frame.index,y=scores_frame[\"Accuracy Score\"])\n",
        "plt.ylim(0, 1)\n",
        "plt.ylabel(\"Accuracy%\")\n",
        "plt.xlabel(\"Probe\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probe\n",
            "     Accuracy Score\n",
            "MLP        0.902819\n",
            "RF         0.896555\n",
            "SVM        0.876690\n",
            "J48        0.395648\n",
            "NB         0.189252\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4]), <a list of 5 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imOebEHSnBVJ"
      },
      "source": [
        "**R2L**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "_60ezi47m2BP",
        "outputId": "c00367c1-a6fc-44ca-8068-6a88cc9fda7b"
      },
      "source": [
        "# 3.R2L\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "mlp = MLPClassifier(solver='adam', learning_rate_init = 0.0005, learning_rate = 'adaptive', activation=\"relu\", max_iter=2000, random_state=42)\n",
        "dec = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
        "ran = RandomForestClassifier(n_estimators=100)\n",
        "svm = SVC(random_state=1)\n",
        "naive = GaussianNB()\n",
        "\n",
        "models = {\"J48\" : dec,\n",
        "          \"NB\" : naive,\n",
        "          \"RF\" : ran,\n",
        "          \"MLP\" : mlp,\n",
        "          \"SVM\" : svm\n",
        "          }\n",
        "scores= { }\n",
        "\n",
        "for key, value in models.items():    \n",
        "    model2 = value\n",
        "    model2.fit(X_R2L, Y_R2L.astype(int))\n",
        "    scores[key] = model2.score(X_R2L_test , Y_R2L_test)\n",
        "\n",
        "\n",
        "# results\n",
        "scores_frame = pd.DataFrame(scores, index=[\"Accuracy Score\"]).T\n",
        "scores_frame.sort_values(by=[\"Accuracy Score\"], axis=0 ,ascending=False, inplace=True)\n",
        "print('R2L')\n",
        "print(scores_frame)\n",
        "print('\\n')\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=scores_frame.index,y=scores_frame[\"Accuracy Score\"])\n",
        "plt.ylim(0, 1)\n",
        "plt.ylabel(\"Accuracy%\")\n",
        "plt.xlabel(\"R2L\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2L\n",
            "     Accuracy Score\n",
            "MLP        0.775087\n",
            "SVM        0.771038\n",
            "J48        0.770959\n",
            "NB         0.770959\n",
            "RF         0.770959\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'R2L')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSY0yxCVJapQhLklSowxxSZIaZYhLktQoQ1ySpEYZ4pIkNcoQlySpUYa4JEmNMsQlSWqUIS5JUqMMcUmSGmWIS5LUKENckqRGGeKSJDXKEJckqVGGuCRJjTLEJUlqlCEuSVKjDHFJkhpliEuS1ChDXJKkRhnikiQ1yhCXJKlRhrgkSY0yxCVJapQhLklSowxxSZIaZYhLktQoQ1ySpEb1GuJJjk9yR5KtSc6eYfuzklzSbf9GksP6rEeSpEnSW4gn2QfYAJwAHAWsS3LUtG6nATuq6meBjwEf6aseSZImTZ9n4muArVV1V1U9ClwMnDytz8nAn3fLlwPHJUmPNUmSNDH6DPFDgHuG2tu6dTP2qarHgQeBn+qxJkmSJsaycRcwiiTrgfVd86Ekd4yznj1wMHD/uIsAyB/8yrhL6MuSOcYAfGgiB5SW1DHO+yfyGMNSOs6TOzC6dI4xcOYfztvlhbNt6DPE7wVWDbVXdutm6rMtyTLgOcD3pu+oqi4ALuipzt4l2VJVq8ddxyTzGPfPY7w4PM79m6Rj3Odw+nXAEUkOT7IcWAtsnNZnIzB1avhLwP+pquqxJkmSJkZvZ+JV9XiSM4ArgX2Az1bVrUnOA7ZU1Ubgz4DPJ9kKfJ9B0EuSpBH0ek28qjYBm6atO2do+UfA2/usYYlo9lJAQzzG/fMYLw6Pc/8m5hjH0WtJktrktKuSJDXKEH+aklSSvxhqL0uyPclfde1Tk3xyhvfdneTmJDcl+WqSn1nMuluS5INJbu2O1Y1JPpTkw9P6HJ3k9m757iR/O237jUluWcy6W5XkoaHlg5JsG/4ZTrJu6Gf3K0kOHk+l7ep+b5w/1P7NJOd2y+cmubf7mf37JJ9O4u/qPZTkiam//0muSPLcbv1hSR7utk29lo+73t3lD8bT90PgZUn269pv4qlfpZvNG6rqFcAW4D/3UVzrkrwGeAvwqu5YvRG4CnjntK5rgYuG2gcmWdXt4yWLUeuE+l1g81Sj+yroH/GTn92bgDPGVFvLHgHeOsc/gD5WVUczmLL65cDrF62yyfNwVR1dVS9jcAP16UPb7uy2Tb0eHVONe8wQXxibgF/sltexa5iMYjPwswta0eR4PnB/VT0CUFX3V9VmYEeSfznU7x3setwv5SdBvyd/Jnu9JMcAPw18dXh199q/myL5IOC+MZTXuscZ3Fz16/P0Ww7sC+zovaK9wzU8debQphniC+NiYG2SfYFXAN/Yzfe/Bbh5wauaDF8FViX5VpJPJZk6I7mI7iuJSV4NfL+q/mHofV8A3totnwhcsVgFT4Ju+PZ84DeH11fVY8B7Gfy83sfgTPHPFr3AybAB+OUkz5lh268nuRH4DvCtqrpxcUubPN1DuY5j1/lKXjQ0lL5hTKU9LYb4Aqiqm4DDGJzxbZq79y6u6v6iHgR8eL7Oe6Oqegg4hsG0u9uBS5KcClwC/FIXNtOH0mEw89+OJGuB24Gdi1b0ZHgfsKmqtg2vTPJMBiH+SuAFDIbTf2vxy2tfVf0AuBB4/wybp4bTn8dg1MM5NPbcft3v2f/HYGTpfw1tGx5OP33mty9tTcyd3oiNwB8AxzL6Q1zeUFVLZv7epaqqngCuBq5OcjPwK1X1uSTfZnCt8G3Aa2Z46yUMznZOXaRSJ8lrgH+T5H3AAcDy7oa3LwBU1Z0ASS4Fzh5ble37OPBN4L/PtLGqHkvyFeB1DEb8tPserqqjkzybweRjpwN/POaaFowhvnA+CzxQVTcnOXbcxUyKJD8HPDk0VH408I/d8kUMnkN/1/Qzxs4XGVxTv5LBWaNGVFW/PLXcjXysrqqzk7wAOCrJiqrazuBGztvHVGbzqur73T+ETmPwO2QX3X0HrwVuWOzaJk1V7UzyfuBLST417noWisPpC6SqtlXVbP+6O7X7ms7Ua+WiFte2A4A/T3JbkpsYXIM9t9t2GfBSZrlprar+uao+0uIdp+PS3X3+yGzbq+o+4HeAzd2fx9HAf1uk8ibV+QyeqjVs6pr4LQymrZ6Y0BmnqrqBwSWgdeOuZaE4Y5ukH0vy88BnqmrNuGuRND/PxCUBkOQ9DEY1fnvctUgajWfikiQ1yjNxSZIaZYhLktQoQ1ySpEYZ4pKAOZ/2dHSSa4aeJPfOofdcnWT1+KqW9m6GuKQpsz3taSdwSlW9FDge+PhUwEsaL2dskzSTaxg8zIeq+tbUyqq6L8l3gRXAA2OqTVLHM3FJu5jlaU9T29YweDzmnYtdl6SnMsQlTZnraU8keT7weeBXq+rJMdQnaRpDXNKUh7vHX74QCD+5Jk6Sg4AvAx+sqmvHVJ+kaQxxSbuoqp0MnnF9VpJlSZYzeCLchVV1+XirkzTMaVclAZDkoao6YKh9BXApUAyed33rUPdTq+rGJFcDLwEe69ZfU1VvX6SSpb2eIS5JUqMcTpckqVGGuCRJjTLEJUlqlCEuSVKjDHFJkhpliEuS1ChDXJKkRhnikiQ16v8DHngaJV/e39oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKaeZXmhrptO"
      },
      "source": [
        "**U2R**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "2akpnvLRrdfS",
        "outputId": "60acb620-b604-45d8-99c1-bda311fa4b3a"
      },
      "source": [
        "# 4.U2R\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "mlp = MLPClassifier(solver='adam', learning_rate_init = 0.0005, learning_rate = 'adaptive', activation=\"relu\", max_iter=2000, random_state=42)\n",
        "dec = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
        "ran = RandomForestClassifier(n_estimators=100)\n",
        "svm = SVC(random_state=1)\n",
        "naive = GaussianNB()\n",
        "\n",
        "models = {\"J48\" : dec,\n",
        "          \"NB\" : naive,\n",
        "          \"RF\" : ran,\n",
        "          \"MLP\" : mlp,\n",
        "          \"SVM\" : svm\n",
        "          }\n",
        "scores= { }\n",
        "\n",
        "for key, value in models.items():    \n",
        "    model3 = value\n",
        "    model3.fit(X_U2R, Y_U2R.astype(int))\n",
        "    scores[key] = model3.score(X_U2R_test , Y_U2R_test)\n",
        "\n",
        "\n",
        "# results\n",
        "scores_frame = pd.DataFrame(scores, index=[\"Accuracy Score\"]).T\n",
        "scores_frame.sort_values(by=[\"Accuracy Score\"], axis=0 ,ascending=False, inplace=True)\n",
        "print('U2R')\n",
        "print(scores_frame)\n",
        "print('\\n')\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.barplot(x=scores_frame.index,y=scores_frame['Accuracy Score'])\n",
        "plt.ylim(0, 1)\n",
        "plt.ylabel(\"Accuracy%\")\n",
        "plt.xlabel(\"U2R\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "U2R\n",
            "     Accuracy Score\n",
            "J48        0.994784\n",
            "MLP        0.993864\n",
            "NB         0.993148\n",
            "RF         0.993148\n",
            "SVM        0.993148\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4]), <a list of 5 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dnH9hPel4Pl"
      },
      "source": [
        "**Step 5: Prediction & Evaluation (validation):**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "5BEe8Ar1mH83",
        "outputId": "cb1af306-e8aa-46b3-bf60-42a794558b82"
      },
      "source": [
        "# 1.DoS\n",
        "Y_DoS_pred=model.predict(X_DoS_test)\n",
        "# Create confusion matrix\n",
        "print('Dos')\n",
        "pd.crosstab(Y_DoS_test, Y_DoS_pred, rownames=['Actual attacks'], colnames=['Predicted attacks'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Predicted attacks</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual attacks</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9546</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2087</td>\n",
              "      <td>5373</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted attacks     0     1\n",
              "Actual attacks               \n",
              "0                  9546   165\n",
              "1                  2087  5373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "xUIiP1bZmdeR",
        "outputId": "b3e11504-7004-4528-b4c1-a68695b958da"
      },
      "source": [
        "#2.Probe\n",
        "Y_Probe_pred=model1.predict(X_Probe_test)\n",
        "\n",
        "# Create confusion matrix\n",
        "print('Probe')\n",
        "pd.crosstab(Y_Probe_test, Y_Probe_pred, rownames=['Actual attacks'], colnames=['Predicted attacks'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Predicted attacks</th>\n",
              "      <th>0</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual attacks</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9233</td>\n",
              "      <td>478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1018</td>\n",
              "      <td>1403</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted attacks     0     2\n",
              "Actual attacks               \n",
              "0                  9233   478\n",
              "2                  1018  1403"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "z7ro_ovNmkIz",
        "outputId": "5b24b5b9-b9c2-4cf0-867f-45aa411c30f6"
      },
      "source": [
        "#3.R2L\n",
        "Y_R2L_pred=model2.predict(X_R2L_test)\n",
        "# Create confusion matrix\n",
        "print('R2L')\n",
        "pd.crosstab(Y_R2L_test, Y_R2L_pred, rownames=['Actual attacks'], colnames=['Predicted attacks'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Predicted attacks</th>\n",
              "      <th>0</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual attacks</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2883</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted attacks     0  3\n",
              "Actual attacks            \n",
              "0                  9710  1\n",
              "3                  2883  2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "b8MtOKsemrAN",
        "outputId": "98fc2cba-7595-4043-cb95-6d9f4443561a"
      },
      "source": [
        "#4.U2R\n",
        "Y_U2R_pred=model3.predict(X_U2R_test)\n",
        "print('U2R')\n",
        "# Create confusion matrix\n",
        "pd.crosstab(Y_U2R_test, Y_U2R_pred, rownames=['Actual attacks'], colnames=['Predicted attacks'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Predicted attacks</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Actual attacks</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Predicted attacks     0\n",
              "Actual attacks         \n",
              "0                  9711\n",
              "4                    67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    }
  ]
}