classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3rR6UrwYmTq"
      },
      "source": [
        "# **Deteksi Serangan DDOS dengan Support Vector Machine**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvTktiSAVlXR"
      },
      "source": [
        "## **Data Input**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EnV_ZCDV60Q"
      },
      "source": [
        "### Import Library yang dibutuhkan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "QU0Ig1bQSCUL"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_selection import chi2, SelectKBest\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import metrics\n",
        "import pickle\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryxBoUGXVwRV"
      },
      "source": [
        "### Membaca dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "3VFn419iSCUO",
        "outputId": "5520c60f-cfc8-443b-955d-d684b75bcde4"
      },
      "outputs": [
        {
          "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></th>\n",
              "      <th>Flow ID</th>\n",
              "      <th>Source IP</th>\n",
              "      <th>Source Port</th>\n",
              "      <th>Destination IP</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>Protocol</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Flow Duration</th>\n",
              "      <th>Total Fwd Packets</th>\n",
              "      <th>Total Backward Packets</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>192.168.10.5-104.16.207.165-54865-443-6</td>\n",
              "      <td>104.16.207.165</td>\n",
              "      <td>443</td>\n",
              "      <td>192.168.10.5</td>\n",
              "      <td>54865</td>\n",
              "      <td>6</td>\n",
              "      <td>7/7/2017 3:30</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>192.168.10.5-104.16.28.216-55054-80-6</td>\n",
              "      <td>104.16.28.216</td>\n",
              "      <td>80</td>\n",
              "      <td>192.168.10.5</td>\n",
              "      <td>55054</td>\n",
              "      <td>6</td>\n",
              "      <td>7/7/2017 3:30</td>\n",
              "      <td>109</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>192.168.10.5-104.16.28.216-55055-80-6</td>\n",
              "      <td>104.16.28.216</td>\n",
              "      <td>80</td>\n",
              "      <td>192.168.10.5</td>\n",
              "      <td>55055</td>\n",
              "      <td>6</td>\n",
              "      <td>7/7/2017 3:30</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>192.168.10.16-104.17.241.25-46236-443-6</td>\n",
              "      <td>104.17.241.25</td>\n",
              "      <td>443</td>\n",
              "      <td>192.168.10.16</td>\n",
              "      <td>46236</td>\n",
              "      <td>6</td>\n",
              "      <td>7/7/2017 3:30</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>192.168.10.5-104.19.196.102-54863-443-6</td>\n",
              "      <td>104.19.196.102</td>\n",
              "      <td>443</td>\n",
              "      <td>192.168.10.5</td>\n",
              "      <td>54863</td>\n",
              "      <td>6</td>\n",
              "      <td>7/7/2017 3:30</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>BENIGN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 85 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Flow ID       Source IP   Source Port  \\\n",
              "0  192.168.10.5-104.16.207.165-54865-443-6  104.16.207.165           443   \n",
              "1    192.168.10.5-104.16.28.216-55054-80-6   104.16.28.216            80   \n",
              "2    192.168.10.5-104.16.28.216-55055-80-6   104.16.28.216            80   \n",
              "3  192.168.10.16-104.17.241.25-46236-443-6   104.17.241.25           443   \n",
              "4  192.168.10.5-104.19.196.102-54863-443-6  104.19.196.102           443   \n",
              "\n",
              "   Destination IP   Destination Port   Protocol      Timestamp  \\\n",
              "0    192.168.10.5              54865          6  7/7/2017 3:30   \n",
              "1    192.168.10.5              55054          6  7/7/2017 3:30   \n",
              "2    192.168.10.5              55055          6  7/7/2017 3:30   \n",
              "3   192.168.10.16              46236          6  7/7/2017 3:30   \n",
              "4    192.168.10.5              54863          6  7/7/2017 3:30   \n",
              "\n",
              "    Flow Duration   Total Fwd Packets   Total Backward Packets  ...  \\\n",
              "0               3                   2                        0  ...   \n",
              "1             109                   1                        1  ...   \n",
              "2              52                   1                        1  ...   \n",
              "3              34                   1                        1  ...   \n",
              "4               3                   2                        0  ...   \n",
              "\n",
              "    min_seg_size_forward  Active Mean   Active Std   Active Max   Active Min  \\\n",
              "0                     20          0.0          0.0            0            0   \n",
              "1                     20          0.0          0.0            0            0   \n",
              "2                     20          0.0          0.0            0            0   \n",
              "3                     20          0.0          0.0            0            0   \n",
              "4                     20          0.0          0.0            0            0   \n",
              "\n",
              "   Idle Mean   Idle Std   Idle Max   Idle Min   Label  \n",
              "0        0.0        0.0          0          0  BENIGN  \n",
              "1        0.0        0.0          0          0  BENIGN  \n",
              "2        0.0        0.0          0          0  BENIGN  \n",
              "3        0.0        0.0          0          0  BENIGN  \n",
              "4        0.0        0.0          0          0  BENIGN  \n",
              "\n",
              "[5 rows x 85 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = pd.read_csv(\"data/dataset_ISCX.csv\", low_memory=False)\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHm8FSclWsRO"
      },
      "source": [
        "### Pembersihan nama kolom pada dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UtvRZpTDSCUP"
      },
      "outputs": [],
      "source": [
        "dataset.columns = dataset.columns.str.strip()\n",
        "dataset.columns = dataset.columns.str.replace(\" \", \"_\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZhYlTRgW2kN"
      },
      "source": [
        "### Distribusi Data Setiap Kelas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "4Xaix5vPSCUP",
        "outputId": "008be575-1578-4c09-9a6a-18b2452b6c1e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "label = dataset[\"Label\"].unique()\n",
        "label_count = dataset[\"Label\"].value_counts()\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.pie(label_count, labels=label, autopct=\"%1.1f%%\")\n",
        "plt.title(\"Class Distribution\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rV5qWS32W8r5"
      },
      "source": [
        "## **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH458bGWXFVN"
      },
      "source": [
        "### Merubah data kategorikal menjadi numerik"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sb7mZSCtSCUP"
      },
      "outputs": [],
      "source": [
        "labelencoder = LabelEncoder()\n",
        "labelencoder.fit(dataset[\"Label\"])\n",
        "dataset[\"Label\"] = labelencoder.transform(dataset[\"Label\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHywMJ1FXNEG"
      },
      "source": [
        "### Menghapus Fitur yang memiliki nilai bukan float64 ataupun infinity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "hQgU73fGSCUQ",
        "outputId": "be752c0c-e911-4cfb-96e0-4e5ceeab9a31"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr 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>Source_Port</th>\n",
              "      <th>Destination_Port</th>\n",
              "      <th>Protocol</th>\n",
              "      <th>Flow_Duration</th>\n",
              "      <th>Total_Fwd_Packets</th>\n",
              "      <th>Total_Backward_Packets</th>\n",
              "      <th>Total_Length_of_Fwd_Packets</th>\n",
              "      <th>Total_Length_of_Bwd_Packets</th>\n",
              "      <th>Fwd_Packet_Length_Max</th>\n",
              "      <th>Fwd_Packet_Length_Min</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active_Mean</th>\n",
              "      <th>Active_Std</th>\n",
              "      <th>Active_Max</th>\n",
              "      <th>Active_Min</th>\n",
              "      <th>Idle_Mean</th>\n",
              "      <th>Idle_Std</th>\n",
              "      <th>Idle_Max</th>\n",
              "      <th>Idle_Min</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>443.0</td>\n",
              "      <td>54865.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80.0</td>\n",
              "      <td>55054.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80.0</td>\n",
              "      <td>55055.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>443.0</td>\n",
              "      <td>46236.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>443.0</td>\n",
              "      <td>54863.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225740</th>\n",
              "      <td>80.0</td>\n",
              "      <td>61374.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225741</th>\n",
              "      <td>80.0</td>\n",
              "      <td>61378.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225742</th>\n",
              "      <td>80.0</td>\n",
              "      <td>61375.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225743</th>\n",
              "      <td>80.0</td>\n",
              "      <td>61323.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225744</th>\n",
              "      <td>80.0</td>\n",
              "      <td>61326.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>225741 rows × 79 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Source_Port  Destination_Port  Protocol  Flow_Duration  \\\n",
              "0             443.0           54865.0       6.0            3.0   \n",
              "1              80.0           55054.0       6.0          109.0   \n",
              "2              80.0           55055.0       6.0           52.0   \n",
              "3             443.0           46236.0       6.0           34.0   \n",
              "4             443.0           54863.0       6.0            3.0   \n",
              "...             ...               ...       ...            ...   \n",
              "225740         80.0           61374.0       6.0           61.0   \n",
              "225741         80.0           61378.0       6.0           72.0   \n",
              "225742         80.0           61375.0       6.0           75.0   \n",
              "225743         80.0           61323.0       6.0           48.0   \n",
              "225744         80.0           61326.0       6.0           68.0   \n",
              "\n",
              "        Total_Fwd_Packets  Total_Backward_Packets  \\\n",
              "0                     2.0                     0.0   \n",
              "1                     1.0                     1.0   \n",
              "2                     1.0                     1.0   \n",
              "3                     1.0                     1.0   \n",
              "4                     2.0                     0.0   \n",
              "...                   ...                     ...   \n",
              "225740                1.0                     1.0   \n",
              "225741                1.0                     1.0   \n",
              "225742                1.0                     1.0   \n",
              "225743                2.0                     0.0   \n",
              "225744                1.0                     1.0   \n",
              "\n",
              "        Total_Length_of_Fwd_Packets  Total_Length_of_Bwd_Packets  \\\n",
              "0                              12.0                          0.0   \n",
              "1                               6.0                          6.0   \n",
              "2                               6.0                          6.0   \n",
              "3                               6.0                          6.0   \n",
              "4                              12.0                          0.0   \n",
              "...                             ...                          ...   \n",
              "225740                          6.0                          6.0   \n",
              "225741                          6.0                          6.0   \n",
              "225742                          6.0                          6.0   \n",
              "225743                         12.0                          0.0   \n",
              "225744                          6.0                          6.0   \n",
              "\n",
              "        Fwd_Packet_Length_Max  Fwd_Packet_Length_Min  ...  \\\n",
              "0                         6.0                    6.0  ...   \n",
              "1                         6.0                    6.0  ...   \n",
              "2                         6.0                    6.0  ...   \n",
              "3                         6.0                    6.0  ...   \n",
              "4                         6.0                    6.0  ...   \n",
              "...                       ...                    ...  ...   \n",
              "225740                    6.0                    6.0  ...   \n",
              "225741                    6.0                    6.0  ...   \n",
              "225742                    6.0                    6.0  ...   \n",
              "225743                    6.0                    6.0  ...   \n",
              "225744                    6.0                    6.0  ...   \n",
              "\n",
              "        min_seg_size_forward  Active_Mean  Active_Std  Active_Max  Active_Min  \\\n",
              "0                       20.0          0.0         0.0         0.0         0.0   \n",
              "1                       20.0          0.0         0.0         0.0         0.0   \n",
              "2                       20.0          0.0         0.0         0.0         0.0   \n",
              "3                       20.0          0.0         0.0         0.0         0.0   \n",
              "4                       20.0          0.0         0.0         0.0         0.0   \n",
              "...                      ...          ...         ...         ...         ...   \n",
              "225740                  20.0          0.0         0.0         0.0         0.0   \n",
              "225741                  20.0          0.0         0.0         0.0         0.0   \n",
              "225742                  20.0          0.0         0.0         0.0         0.0   \n",
              "225743                  20.0          0.0         0.0         0.0         0.0   \n",
              "225744                  20.0          0.0         0.0         0.0         0.0   \n",
              "\n",
              "        Idle_Mean  Idle_Std  Idle_Max  Idle_Min  Label  \n",
              "0             0.0       0.0       0.0       0.0    0.0  \n",
              "1             0.0       0.0       0.0       0.0    0.0  \n",
              "2             0.0       0.0       0.0       0.0    0.0  \n",
              "3             0.0       0.0       0.0       0.0    0.0  \n",
              "4             0.0       0.0       0.0       0.0    0.0  \n",
              "...           ...       ...       ...       ...    ...  \n",
              "225740        0.0       0.0       0.0       0.0    0.0  \n",
              "225741        0.0       0.0       0.0       0.0    0.0  \n",
              "225742        0.0       0.0       0.0       0.0    0.0  \n",
              "225743        0.0       0.0       0.0       0.0    0.0  \n",
              "225744        0.0       0.0       0.0       0.0    0.0  \n",
              "\n",
              "[225741 rows x 79 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = dataset.drop(\n",
        "    [\n",
        "        \"Timestamp\",\n",
        "        \"Source_IP\",\n",
        "        \"Destination_IP\",\n",
        "        \"Flow_ID\",\n",
        "        \"Flow_Bytes/s\",\n",
        "        \"Flow_Packets/s\",\n",
        "    ],\n",
        "    axis=1,\n",
        ")\n",
        "dataset = dataset.astype(\"float64\")\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1Jore4gXWaF"
      },
      "source": [
        "### Menampilkan Fitur yang tersedia"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqMvG0-8SCUQ",
        "outputId": "0506dba0-5531-4a3f-b257-5698240cc357"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 225741 entries, 0 to 225744\n",
            "Data columns (total 79 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   Source_Port                  225741 non-null  float64\n",
            " 1   Destination_Port             225741 non-null  float64\n",
            " 2   Protocol                     225741 non-null  float64\n",
            " 3   Flow_Duration                225741 non-null  float64\n",
            " 4   Total_Fwd_Packets            225741 non-null  float64\n",
            " 5   Total_Backward_Packets       225741 non-null  float64\n",
            " 6   Total_Length_of_Fwd_Packets  225741 non-null  float64\n",
            " 7   Total_Length_of_Bwd_Packets  225741 non-null  float64\n",
            " 8   Fwd_Packet_Length_Max        225741 non-null  float64\n",
            " 9   Fwd_Packet_Length_Min        225741 non-null  float64\n",
            " 10  Fwd_Packet_Length_Mean       225741 non-null  float64\n",
            " 11  Fwd_Packet_Length_Std        225741 non-null  float64\n",
            " 12  Bwd_Packet_Length_Max        225741 non-null  float64\n",
            " 13  Bwd_Packet_Length_Min        225741 non-null  float64\n",
            " 14  Bwd_Packet_Length_Mean       225741 non-null  float64\n",
            " 15  Bwd_Packet_Length_Std        225741 non-null  float64\n",
            " 16  Flow_IAT_Mean                225741 non-null  float64\n",
            " 17  Flow_IAT_Std                 225741 non-null  float64\n",
            " 18  Flow_IAT_Max                 225741 non-null  float64\n",
            " 19  Flow_IAT_Min                 225741 non-null  float64\n",
            " 20  Fwd_IAT_Total                225741 non-null  float64\n",
            " 21  Fwd_IAT_Mean                 225741 non-null  float64\n",
            " 22  Fwd_IAT_Std                  225741 non-null  float64\n",
            " 23  Fwd_IAT_Max                  225741 non-null  float64\n",
            " 24  Fwd_IAT_Min                  225741 non-null  float64\n",
            " 25  Bwd_IAT_Total                225741 non-null  float64\n",
            " 26  Bwd_IAT_Mean                 225741 non-null  float64\n",
            " 27  Bwd_IAT_Std                  225741 non-null  float64\n",
            " 28  Bwd_IAT_Max                  225741 non-null  float64\n",
            " 29  Bwd_IAT_Min                  225741 non-null  float64\n",
            " 30  Fwd_PSH_Flags                225741 non-null  float64\n",
            " 31  Bwd_PSH_Flags                225741 non-null  float64\n",
            " 32  Fwd_URG_Flags                225741 non-null  float64\n",
            " 33  Bwd_URG_Flags                225741 non-null  float64\n",
            " 34  Fwd_Header_Length            225741 non-null  float64\n",
            " 35  Bwd_Header_Length            225741 non-null  float64\n",
            " 36  Fwd_Packets/s                225741 non-null  float64\n",
            " 37  Bwd_Packets/s                225741 non-null  float64\n",
            " 38  Min_Packet_Length            225741 non-null  float64\n",
            " 39  Max_Packet_Length            225741 non-null  float64\n",
            " 40  Packet_Length_Mean           225741 non-null  float64\n",
            " 41  Packet_Length_Std            225741 non-null  float64\n",
            " 42  Packet_Length_Variance       225741 non-null  float64\n",
            " 43  FIN_Flag_Count               225741 non-null  float64\n",
            " 44  SYN_Flag_Count               225741 non-null  float64\n",
            " 45  RST_Flag_Count               225741 non-null  float64\n",
            " 46  PSH_Flag_Count               225741 non-null  float64\n",
            " 47  ACK_Flag_Count               225741 non-null  float64\n",
            " 48  URG_Flag_Count               225741 non-null  float64\n",
            " 49  CWE_Flag_Count               225741 non-null  float64\n",
            " 50  ECE_Flag_Count               225741 non-null  float64\n",
            " 51  Down/Up_Ratio                225741 non-null  float64\n",
            " 52  Average_Packet_Size          225741 non-null  float64\n",
            " 53  Avg_Fwd_Segment_Size         225741 non-null  float64\n",
            " 54  Avg_Bwd_Segment_Size         225741 non-null  float64\n",
            " 55  Fwd_Header_Length.1          225741 non-null  float64\n",
            " 56  Fwd_Avg_Bytes/Bulk           225741 non-null  float64\n",
            " 57  Fwd_Avg_Packets/Bulk         225741 non-null  float64\n",
            " 58  Fwd_Avg_Bulk_Rate            225741 non-null  float64\n",
            " 59  Bwd_Avg_Bytes/Bulk           225741 non-null  float64\n",
            " 60  Bwd_Avg_Packets/Bulk         225741 non-null  float64\n",
            " 61  Bwd_Avg_Bulk_Rate            225741 non-null  float64\n",
            " 62  Subflow_Fwd_Packets          225741 non-null  float64\n",
            " 63  Subflow_Fwd_Bytes            225741 non-null  float64\n",
            " 64  Subflow_Bwd_Packets          225741 non-null  float64\n",
            " 65  Subflow_Bwd_Bytes            225741 non-null  float64\n",
            " 66  Init_Win_bytes_forward       225741 non-null  float64\n",
            " 67  Init_Win_bytes_backward      225741 non-null  float64\n",
            " 68  act_data_pkt_fwd             225741 non-null  float64\n",
            " 69  min_seg_size_forward         225741 non-null  float64\n",
            " 70  Active_Mean                  225741 non-null  float64\n",
            " 71  Active_Std                   225741 non-null  float64\n",
            " 72  Active_Max                   225741 non-null  float64\n",
            " 73  Active_Min                   225741 non-null  float64\n",
            " 74  Idle_Mean                    225741 non-null  float64\n",
            " 75  Idle_Std                     225741 non-null  float64\n",
            " 76  Idle_Max                     225741 non-null  float64\n",
            " 77  Idle_Min                     225741 non-null  float64\n",
            " 78  Label                        225741 non-null  float64\n",
            "dtypes: float64(79)\n",
            "memory usage: 137.8 MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(dataset.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iB9Pz_qYXcVt"
      },
      "source": [
        "### Scaling Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLAtKxDZXxem"
      },
      "source": [
        "Scalling menggunakan MinMaxScalling dengan formula :\n",
        "\n",
        "![MinMaxFormula](https://img-blog.csdnimg.cn/20201127162915978.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "zba7KICaSCUQ"
      },
      "outputs": [],
      "source": [
        "X = dataset.drop([\"Label\"], axis=1)\n",
        "y = dataset[\"Label\"]\n",
        "scaler = MinMaxScaler().fit(X)\n",
        "X = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUVSyPB6YUoK"
      },
      "source": [
        "### Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_Q_xW28UTLZ",
        "outputId": "e4710787-30c5-4fd7-f868-732e5d746c79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 225741 entries, 0 to 225740\n",
            "Data columns (total 8 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   Destination_Port             225741 non-null  float64\n",
            " 1   Protocol                     225741 non-null  float64\n",
            " 2   Flow_Duration                225741 non-null  float64\n",
            " 3   Total_Fwd_Packets            225741 non-null  float64\n",
            " 4   Total_Backward_Packets       225741 non-null  float64\n",
            " 5   Total_Length_of_Fwd_Packets  225741 non-null  float64\n",
            " 6   Total_Length_of_Bwd_Packets  225741 non-null  float64\n",
            " 7   Fwd_Packet_Length_Max        225741 non-null  float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 13.8 MB\n"
          ]
        }
      ],
      "source": [
        "XBest = SelectKBest(chi2, k=8).fit_transform(X, y)\n",
        "XBest = pd.DataFrame(XBest)\n",
        "XBest.columns = dataset.columns[1:9]\n",
        "XBest.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OosMO6IYb0v"
      },
      "source": [
        "## **Data Spliting**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "CFOWK9yySCUQ"
      },
      "outputs": [],
      "source": [
        "train_X, test_X, train_y, test_y = train_test_split(XBest, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEVU-VCjYeEE"
      },
      "source": [
        "## **Model Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fOxZg_YuSCUR"
      },
      "outputs": [],
      "source": [
        "model = SVC()\n",
        "model.fit(train_X, train_y)\n",
        "pred_y = model.predict(test_X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooy11X6Mb0uF"
      },
      "source": [
        "## **Model Evaluation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3EiqJFNhVwd"
      },
      "source": [
        "### Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Vg99jeQicPNY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      Benign       1.00      0.93      0.96     19417\n",
            "        DDoS       0.95      1.00      0.97     25732\n",
            "\n",
            "    accuracy                           0.97     45149\n",
            "   macro avg       0.97      0.96      0.97     45149\n",
            "weighted avg       0.97      0.97      0.97     45149\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report: \\n\", classification_report(test_y, pred_y, target_names=[\"Benign\", \"DDoS\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6XuS2jLcHj_"
      },
      "source": [
        "### Visualise confusion matrix matplotlib\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "n80DDeKLcKq4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2d43c5d7a50>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(test_y, pred_y), display_labels=[\"Benign\", \"DDoS\"])\n",
        "cm_display.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVu9kfdyoy-D"
      },
      "source": [
        "## **Export Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "3T6eHLRUox2b"
      },
      "outputs": [],
      "source": [
        "if not os.path.exists(\"model\"):\n",
        "    os.mkdir(\"model\")\n",
        "filename =\"model/svm.pkl\"\n",
        "pickle.dump(model, open(filename, 'wb'))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "jvTktiSAVlXR"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}