DDoS_Detection_IE105.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "623d984a",
   "metadata": {
    "id": "623d984a"
   },
   "source": [
    "# DATA COLLECTION AND EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff0850f9",
   "metadata": {
    "id": "ff0850f9"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dense, Input,Dropout\n",
    "from sklearn import metrics\n",
    "import time\n",
    "import datetime\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "C-jKvJcZS13s",
   "metadata": {
    "id": "C-jKvJcZS13s"
   },
   "source": [
    "## IMPORT DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cbd3192",
   "metadata": {
    "id": "9cbd3192",
    "outputId": "7c90d142-4a2d-4115-a734-4e871ca9e6d2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_18668\\856072895.py:1: DtypeWarning: Columns (0,3,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('./CIC-DDoS-Dataset2019.csv')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./CIC-DDoS-Dataset2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56f93f6b",
   "metadata": {
    "id": "56f93f6b"
   },
   "outputs": [],
   "source": [
    "columns_to_skip = [1,2,4,7,87]\n",
    "\n",
    "# Duyệt qua tất cả các cột, bỏ qua các cột không phải mixed types\n",
    "for i, column in enumerate(df.columns):\n",
    "    if i not in columns_to_skip:\n",
    "        df[column] = pd.to_numeric(df[column].astype(str).str.replace(\"'\", \"\"), errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7b9c9a",
   "metadata": {
    "id": "mes3aAMoS6lV"
   },
   "source": [
    "## EDIT COLUMN NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8994df82",
   "metadata": {
    "id": "8994df82"
   },
   "outputs": [],
   "source": [
    "#Xóa bỏ các khoảng trắng trước và sau trong tên cột (nếu có)\n",
    "df = df.rename(columns=lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b1bfd61",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 725
    },
    "id": "3b1bfd61",
    "outputId": "50d9ee56-7be8-4eb1-994b-157c31136ad1"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>...</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>SimillarHTTP</th>\n",
       "      <th>Inbound</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>192.168.50.7-23.32.166.121-52380-443-6</td>\n",
       "      <td>192.168.50.7</td>\n",
       "      <td>52380.0</td>\n",
       "      <td>23.32.166.121</td>\n",
       "      <td>443.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34:28.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>192.168.50.7-23.32.166.121-52380-443-6</td>\n",
       "      <td>23.32.166.121</td>\n",
       "      <td>443.0</td>\n",
       "      <td>192.168.50.7</td>\n",
       "      <td>52380.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34:28.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64469.0</td>\n",
       "      <td>192.168.50.7-23.32.166.121-52380-443-6</td>\n",
       "      <td>23.32.166.121</td>\n",
       "      <td>443.0</td>\n",
       "      <td>192.168.50.7</td>\n",
       "      <td>52380.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34:28.6</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>171.0</td>\n",
       "      <td>192.168.50.253-224.0.0.5-0-0-0</td>\n",
       "      <td>192.168.50.253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0.0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34:28.7</td>\n",
       "      <td>115366430.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.073087e+00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.613862e+06</td>\n",
       "      <td>2.884856e+05</td>\n",
       "      <td>9988327.0</td>\n",
       "      <td>9110910.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>186.0</td>\n",
       "      <td>255.255.255.255-0.0.0.0-67-68-17</td>\n",
       "      <td>0.0.0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>255.255.255.255</td>\n",
       "      <td>67.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34:30.0</td>\n",
       "      <td>109157387.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.774939e+06</td>\n",
       "      <td>10054849.0</td>\n",
       "      <td>2647210.0</td>\n",
       "      <td>1.015194e+07</td>\n",
       "      <td>2.795046e+06</td>\n",
       "      <td>14600796.0</td>\n",
       "      <td>6304546.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>184.0</td>\n",
       "      <td>192.168.50.254-224.0.0.5-0-0-0</td>\n",
       "      <td>192.168.50.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0.0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34:32.7</td>\n",
       "      <td>115998008.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.237722e+06</td>\n",
       "      <td>4287600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.309195e+06</td>\n",
       "      <td>1.203884e+06</td>\n",
       "      <td>9988912.0</td>\n",
       "      <td>5659882.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>185.0</td>\n",
       "      <td>8.0.6.4-8.6.0.1-0-0-0</td>\n",
       "      <td>8.6.0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0.6.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34:33.6</td>\n",
       "      <td>74867914.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.743396e+07</td>\n",
       "      <td>2.412200e+07</td>\n",
       "      <td>54490788.0</td>\n",
       "      <td>20377122.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>194.0</td>\n",
       "      <td>192.168.50.6-74.125.28.94-58124-443-6</td>\n",
       "      <td>192.168.50.6</td>\n",
       "      <td>58124.0</td>\n",
       "      <td>74.125.28.94</td>\n",
       "      <td>443.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34:36.6</td>\n",
       "      <td>87964914.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.795089e+02</td>\n",
       "      <td>217977.0</td>\n",
       "      <td>217218.0</td>\n",
       "      <td>2.903140e+07</td>\n",
       "      <td>1.557271e+07</td>\n",
       "      <td>46962397.0</td>\n",
       "      <td>18895963.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>169864.0</td>\n",
       "      <td>192.168.50.6-4.2.2.4-57629-53-17</td>\n",
       "      <td>192.168.50.6</td>\n",
       "      <td>57629.0</td>\n",
       "      <td>4.2.2.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34:36.6</td>\n",
       "      <td>20823.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41274.0</td>\n",
       "      <td>192.168.50.6-8.8.8.8-57629-53-17</td>\n",
       "      <td>192.168.50.6</td>\n",
       "      <td>57629.0</td>\n",
       "      <td>8.8.8.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34:36.6</td>\n",
       "      <td>46885.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 88 columns</p>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0                                 Flow ID       Source IP  \\\n",
       "0         0.0  192.168.50.7-23.32.166.121-52380-443-6    192.168.50.7   \n",
       "1         1.0  192.168.50.7-23.32.166.121-52380-443-6   23.32.166.121   \n",
       "2     64469.0  192.168.50.7-23.32.166.121-52380-443-6   23.32.166.121   \n",
       "3       171.0          192.168.50.253-224.0.0.5-0-0-0  192.168.50.253   \n",
       "4       186.0        255.255.255.255-0.0.0.0-67-68-17         0.0.0.0   \n",
       "5       184.0          192.168.50.254-224.0.0.5-0-0-0  192.168.50.254   \n",
       "6       185.0                   8.0.6.4-8.6.0.1-0-0-0         8.6.0.1   \n",
       "7       194.0   192.168.50.6-74.125.28.94-58124-443-6    192.168.50.6   \n",
       "8    169864.0        192.168.50.6-4.2.2.4-57629-53-17    192.168.50.6   \n",
       "9     41274.0        192.168.50.6-8.8.8.8-57629-53-17    192.168.50.6   \n",
       "\n",
       "   Source Port   Destination IP  Destination Port  Protocol Timestamp  \\\n",
       "0      52380.0    23.32.166.121             443.0       6.0   34:28.6   \n",
       "1        443.0     192.168.50.7           52380.0       6.0   34:28.6   \n",
       "2        443.0     192.168.50.7           52380.0       6.0   34:28.6   \n",
       "3          0.0        224.0.0.5               0.0       0.0   34:28.7   \n",
       "4         68.0  255.255.255.255              67.0      17.0   34:30.0   \n",
       "5          0.0        224.0.0.5               0.0       0.0   34:32.7   \n",
       "6          0.0          8.0.6.4               0.0       0.0   34:33.6   \n",
       "7      58124.0     74.125.28.94             443.0       6.0   34:36.6   \n",
       "8      57629.0          4.2.2.4              53.0      17.0   34:36.6   \n",
       "9      57629.0          8.8.8.8              53.0      17.0   34:36.6   \n",
       "\n",
       "   Flow Duration  Total Fwd Packets  ...    Active Std  Active Max  \\\n",
       "0            1.0                2.0  ...  0.000000e+00         0.0   \n",
       "1            2.0                3.0  ...  0.000000e+00         0.0   \n",
       "2          218.0                1.0  ...  0.000000e+00         0.0   \n",
       "3    115366430.0               52.0  ...  1.073087e+00         8.0   \n",
       "4    109157387.0               16.0  ...  3.774939e+06  10054849.0   \n",
       "5    115998008.0               45.0  ...  1.237722e+06   4287600.0   \n",
       "6     74867914.0                6.0  ...  0.000000e+00         2.0   \n",
       "7     87964914.0               48.0  ...  3.795089e+02    217977.0   \n",
       "8        20823.0                2.0  ...  0.000000e+00         0.0   \n",
       "9        46885.0                2.0  ...  0.000000e+00         0.0   \n",
       "\n",
       "   Active Min     Idle Mean      Idle Std    Idle Max    Idle Min  \\\n",
       "0         0.0  0.000000e+00  0.000000e+00         0.0         0.0   \n",
       "1         0.0  0.000000e+00  0.000000e+00         0.0         0.0   \n",
       "2         0.0  0.000000e+00  0.000000e+00         0.0         0.0   \n",
       "3         4.0  9.613862e+06  2.884856e+05   9988327.0   9110910.0   \n",
       "4   2647210.0  1.015194e+07  2.795046e+06  14600796.0   6304546.0   \n",
       "5         4.0  9.309195e+06  1.203884e+06   9988912.0   5659882.0   \n",
       "6         2.0  3.743396e+07  2.412200e+07  54490788.0  20377122.0   \n",
       "7    217218.0  2.903140e+07  1.557271e+07  46962397.0  18895963.0   \n",
       "8         0.0  0.000000e+00  0.000000e+00         0.0         0.0   \n",
       "9         0.0  0.000000e+00  0.000000e+00         0.0         0.0   \n",
       "\n",
       "   SimillarHTTP  Inbound   Label  \n",
       "0           0.0      0.0  BENIGN  \n",
       "1           0.0      1.0  BENIGN  \n",
       "2           0.0      1.0  BENIGN  \n",
       "3           0.0      0.0  BENIGN  \n",
       "4           0.0      0.0  BENIGN  \n",
       "5           0.0      0.0  BENIGN  \n",
       "6           0.0      0.0  BENIGN  \n",
       "7           0.0      0.0  BENIGN  \n",
       "8           0.0      0.0  BENIGN  \n",
       "9           0.0      0.0  BENIGN  \n",
       "\n",
       "[10 rows x 88 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "049a9d9a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 384
    },
    "id": "049a9d9a",
    "outputId": "c95b3c26-848e-4e47-9c7d-65fbffad2785"
   },
   "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>Unnamed: 0</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>...</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>SimillarHTTP</th>\n",
       "      <th>Inbound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>137116.000000</td>\n",
       "      <td>137116.000000</td>\n",
       "      <td>137116.000000</td>\n",
       "      <td>137116.000000</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>137116.00000</td>\n",
       "      <td>137116.000000</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>137116.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>1.371160e+05</td>\n",
       "      <td>135134.0</td>\n",
       "      <td>137116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95761.369774</td>\n",
       "      <td>35858.029639</td>\n",
       "      <td>20548.012953</td>\n",
       "      <td>10.098048</td>\n",
       "      <td>6.575324e+06</td>\n",
       "      <td>16.32641</td>\n",
       "      <td>4.698124</td>\n",
       "      <td>4.947366e+03</td>\n",
       "      <td>3.737773e+03</td>\n",
       "      <td>205.012004</td>\n",
       "      <td>...</td>\n",
       "      <td>4.906273e+04</td>\n",
       "      <td>2.100783e+04</td>\n",
       "      <td>7.492499e+04</td>\n",
       "      <td>3.588306e+04</td>\n",
       "      <td>2.390951e+06</td>\n",
       "      <td>2.400567e+05</td>\n",
       "      <td>2.626637e+06</td>\n",
       "      <td>2.180583e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.566331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>153370.296978</td>\n",
       "      <td>24735.070389</td>\n",
       "      <td>23169.488300</td>\n",
       "      <td>5.455740</td>\n",
       "      <td>2.345377e+07</td>\n",
       "      <td>318.51021</td>\n",
       "      <td>49.366888</td>\n",
       "      <td>5.277377e+04</td>\n",
       "      <td>1.011248e+05</td>\n",
       "      <td>419.745732</td>\n",
       "      <td>...</td>\n",
       "      <td>4.424027e+05</td>\n",
       "      <td>2.177489e+05</td>\n",
       "      <td>6.061227e+05</td>\n",
       "      <td>4.031814e+05</td>\n",
       "      <td>9.823704e+06</td>\n",
       "      <td>1.902224e+06</td>\n",
       "      <td>1.048392e+07</td>\n",
       "      <td>9.474628e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3287.000000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12789.000000</td>\n",
       "      <td>46955.500000</td>\n",
       "      <td>8161.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.480000e+02</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>124754.750000</td>\n",
       "      <td>57514.000000</td>\n",
       "      <td>42979.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.579800e+04</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.020000e+02</td>\n",
       "      <td>9.200000e+01</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>802795.000000</td>\n",
       "      <td>65534.000000</td>\n",
       "      <td>65533.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>85894.00000</td>\n",
       "      <td>8029.000000</td>\n",
       "      <td>1.526642e+07</td>\n",
       "      <td>1.289243e+07</td>\n",
       "      <td>32120.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.050800e+07</td>\n",
       "      <td>2.853681e+07</td>\n",
       "      <td>6.783473e+07</td>\n",
       "      <td>4.050800e+07</td>\n",
       "      <td>1.192194e+08</td>\n",
       "      <td>6.961402e+07</td>\n",
       "      <td>1.192194e+08</td>\n",
       "      <td>1.192194e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0    Source Port  Destination Port       Protocol  \\\n",
       "count  137116.000000  137116.000000     137116.000000  137116.000000   \n",
       "mean    95761.369774   35858.029639      20548.012953      10.098048   \n",
       "std    153370.296978   24735.070389      23169.488300       5.455740   \n",
       "min         0.000000       0.000000          0.000000       0.000000   \n",
       "25%      3287.000000     897.000000         80.000000       6.000000   \n",
       "50%     12789.000000   46955.500000       8161.500000       6.000000   \n",
       "75%    124754.750000   57514.000000      42979.000000      17.000000   \n",
       "max    802795.000000   65534.000000      65533.000000      17.000000   \n",
       "\n",
       "       Flow Duration  Total Fwd Packets  Total Backward Packets  \\\n",
       "count   1.371160e+05       137116.00000           137116.000000   \n",
       "mean    6.575324e+06           16.32641                4.698124   \n",
       "std     2.345377e+07          318.51021               49.366888   \n",
       "min     0.000000e+00            1.00000                0.000000   \n",
       "25%     2.000000e+00            2.00000                0.000000   \n",
       "50%     7.480000e+02            2.00000                1.000000   \n",
       "75%     4.579800e+04            4.00000                2.000000   \n",
       "max     1.200000e+08        85894.00000             8029.000000   \n",
       "\n",
       "       Total Length of Fwd Packets  Total Length of Bwd Packets  \\\n",
       "count                 1.371160e+05                 1.371160e+05   \n",
       "mean                  4.947366e+03                 3.737773e+03   \n",
       "std                   5.277377e+04                 1.011248e+05   \n",
       "min                   0.000000e+00                 0.000000e+00   \n",
       "25%                   1.200000e+01                 0.000000e+00   \n",
       "50%                   6.000000e+01                 0.000000e+00   \n",
       "75%                   8.020000e+02                 9.200000e+01   \n",
       "max                   1.526642e+07                 1.289243e+07   \n",
       "\n",
       "       Fwd Packet Length Max  ...   Active Mean    Active Std    Active Max  \\\n",
       "count          137116.000000  ...  1.371160e+05  1.371160e+05  1.371160e+05   \n",
       "mean              205.012004  ...  4.906273e+04  2.100783e+04  7.492499e+04   \n",
       "std               419.745732  ...  4.424027e+05  2.177489e+05  6.061227e+05   \n",
       "min                 0.000000  ...  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%                 6.000000  ...  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%                31.000000  ...  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%               383.000000  ...  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max             32120.000000  ...  4.050800e+07  2.853681e+07  6.783473e+07   \n",
       "\n",
       "         Active Min     Idle Mean      Idle Std      Idle Max      Idle Min  \\\n",
       "count  1.371160e+05  1.371160e+05  1.371160e+05  1.371160e+05  1.371160e+05   \n",
       "mean   3.588306e+04  2.390951e+06  2.400567e+05  2.626637e+06  2.180583e+06   \n",
       "std    4.031814e+05  9.823704e+06  1.902224e+06  1.048392e+07  9.474628e+06   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max    4.050800e+07  1.192194e+08  6.961402e+07  1.192194e+08  1.192194e+08   \n",
       "\n",
       "       SimillarHTTP        Inbound  \n",
       "count      135134.0  137116.000000  \n",
       "mean            0.0       0.566331  \n",
       "std             0.0       0.495583  \n",
       "min             0.0       0.000000  \n",
       "25%             0.0       0.000000  \n",
       "50%             0.0       1.000000  \n",
       "75%             0.0       1.000000  \n",
       "max             0.0       1.000000  \n",
       "\n",
       "[8 rows x 83 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d5c081b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3d5c081b",
    "outputId": "523c2770-6673-487b-88bc-f565dd11faf8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tổng số hàng trong dataset là: 137139\n"
     ]
    }
   ],
   "source": [
    "total_rows = len(df)\n",
    "print(\"Tổng số hàng trong dataset là:\", total_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2c7d05d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2c7d05d",
    "outputId": "3a6072b4-d8c8-49f5-f694-dd54724cf6b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total columns:  88\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Flow ID', 'Source IP', 'Source Port', 'Destination IP',\n",
       "       'Destination Port', 'Protocol', 'Timestamp', 'Flow Duration',\n",
       "       'Total Fwd Packets', 'Total Backward Packets',\n",
       "       'Total Length of Fwd Packets', 'Total Length of Bwd Packets',\n",
       "       'Fwd Packet Length Max', 'Fwd Packet Length Min',\n",
       "       'Fwd Packet Length Mean', 'Fwd Packet Length Std',\n",
       "       'Bwd Packet Length Max', 'Bwd Packet Length Min',\n",
       "       'Bwd Packet Length Mean', 'Bwd Packet Length Std', 'Flow Bytes/s',\n",
       "       'Flow Packets/s', 'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max',\n",
       "       'Flow IAT Min', 'Fwd IAT Total', 'Fwd IAT Mean', 'Fwd IAT Std',\n",
       "       'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Total', 'Bwd IAT Mean',\n",
       "       'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Fwd PSH Flags',\n",
       "       'Bwd PSH Flags', 'Fwd URG Flags', 'Bwd URG Flags', 'Fwd Header Length',\n",
       "       'Bwd Header Length', 'Fwd Packets/s', 'Bwd Packets/s',\n",
       "       'Min Packet Length', 'Max Packet Length', 'Packet Length Mean',\n",
       "       'Packet Length Std', 'Packet Length Variance', 'FIN Flag Count',\n",
       "       'SYN Flag Count', 'RST Flag Count', 'PSH Flag Count', 'ACK Flag Count',\n",
       "       'URG Flag Count', 'CWE Flag Count', 'ECE Flag Count', 'Down/Up Ratio',\n",
       "       'Average Packet Size', 'Avg Fwd Segment Size', 'Avg Bwd Segment Size',\n",
       "       'Fwd Header Length.1', 'Fwd Avg Bytes/Bulk', 'Fwd Avg Packets/Bulk',\n",
       "       'Fwd Avg Bulk Rate', 'Bwd Avg Bytes/Bulk', 'Bwd Avg Packets/Bulk',\n",
       "       'Bwd Avg Bulk Rate', 'Subflow Fwd Packets', 'Subflow Fwd Bytes',\n",
       "       'Subflow Bwd Packets', 'Subflow Bwd Bytes', 'Init_Win_bytes_forward',\n",
       "       'Init_Win_bytes_backward', 'act_data_pkt_fwd', 'min_seg_size_forward',\n",
       "       'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean',\n",
       "       'Idle Std', 'Idle Max', 'Idle Min', 'SimillarHTTP', 'Inbound', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total columns: \", len(df.columns))\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "529da742",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "529da742",
    "outputId": "af682cf2-1e70-468a-afc4-a966565b7467"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 137139 entries, 0 to 137138\n",
      "Data columns (total 88 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   Unnamed: 0                   137116 non-null  float64\n",
      " 1   Flow ID                      137139 non-null  object \n",
      " 2   Source IP                    137139 non-null  object \n",
      " 3   Source Port                  137116 non-null  float64\n",
      " 4   Destination IP               137139 non-null  object \n",
      " 5   Destination Port             137116 non-null  float64\n",
      " 6   Protocol                     137116 non-null  float64\n",
      " 7   Timestamp                    137139 non-null  object \n",
      " 8   Flow Duration                137116 non-null  float64\n",
      " 9   Total Fwd Packets            137116 non-null  float64\n",
      " 10  Total Backward Packets       137116 non-null  float64\n",
      " 11  Total Length of Fwd Packets  137116 non-null  float64\n",
      " 12  Total Length of Bwd Packets  137116 non-null  float64\n",
      " 13  Fwd Packet Length Max        137116 non-null  float64\n",
      " 14  Fwd Packet Length Min        137116 non-null  float64\n",
      " 15  Fwd Packet Length Mean       137116 non-null  float64\n",
      " 16  Fwd Packet Length Std        137116 non-null  float64\n",
      " 17  Bwd Packet Length Max        137116 non-null  float64\n",
      " 18  Bwd Packet Length Min        137116 non-null  float64\n",
      " 19  Bwd Packet Length Mean       137116 non-null  float64\n",
      " 20  Bwd Packet Length Std        137116 non-null  float64\n",
      " 21  Flow Bytes/s                 136213 non-null  float64\n",
      " 22  Flow Packets/s               137116 non-null  float64\n",
      " 23  Flow IAT Mean                137116 non-null  float64\n",
      " 24  Flow IAT Std                 137116 non-null  float64\n",
      " 25  Flow IAT Max                 137116 non-null  float64\n",
      " 26  Flow IAT Min                 137116 non-null  float64\n",
      " 27  Fwd IAT Total                137116 non-null  float64\n",
      " 28  Fwd IAT Mean                 137116 non-null  float64\n",
      " 29  Fwd IAT Std                  137116 non-null  float64\n",
      " 30  Fwd IAT Max                  137116 non-null  float64\n",
      " 31  Fwd IAT Min                  137116 non-null  float64\n",
      " 32  Bwd IAT Total                137116 non-null  float64\n",
      " 33  Bwd IAT Mean                 137116 non-null  float64\n",
      " 34  Bwd IAT Std                  137116 non-null  float64\n",
      " 35  Bwd IAT Max                  137116 non-null  float64\n",
      " 36  Bwd IAT Min                  137116 non-null  float64\n",
      " 37  Fwd PSH Flags                137116 non-null  float64\n",
      " 38  Bwd PSH Flags                137116 non-null  float64\n",
      " 39  Fwd URG Flags                137116 non-null  float64\n",
      " 40  Bwd URG Flags                137116 non-null  float64\n",
      " 41  Fwd Header Length            137116 non-null  float64\n",
      " 42  Bwd Header Length            137116 non-null  float64\n",
      " 43  Fwd Packets/s                137116 non-null  float64\n",
      " 44  Bwd Packets/s                137116 non-null  float64\n",
      " 45  Min Packet Length            137116 non-null  float64\n",
      " 46  Max Packet Length            137116 non-null  float64\n",
      " 47  Packet Length Mean           137116 non-null  float64\n",
      " 48  Packet Length Std            137116 non-null  float64\n",
      " 49  Packet Length Variance       137116 non-null  float64\n",
      " 50  FIN Flag Count               137116 non-null  float64\n",
      " 51  SYN Flag Count               137116 non-null  float64\n",
      " 52  RST Flag Count               137116 non-null  float64\n",
      " 53  PSH Flag Count               137116 non-null  float64\n",
      " 54  ACK Flag Count               137116 non-null  float64\n",
      " 55  URG Flag Count               137116 non-null  float64\n",
      " 56  CWE Flag Count               137116 non-null  float64\n",
      " 57  ECE Flag Count               137116 non-null  float64\n",
      " 58  Down/Up Ratio                137116 non-null  float64\n",
      " 59  Average Packet Size          137116 non-null  float64\n",
      " 60  Avg Fwd Segment Size         137116 non-null  float64\n",
      " 61  Avg Bwd Segment Size         137116 non-null  float64\n",
      " 62  Fwd Header Length.1          137116 non-null  float64\n",
      " 63  Fwd Avg Bytes/Bulk           137116 non-null  float64\n",
      " 64  Fwd Avg Packets/Bulk         137116 non-null  float64\n",
      " 65  Fwd Avg Bulk Rate            137116 non-null  float64\n",
      " 66  Bwd Avg Bytes/Bulk           137116 non-null  float64\n",
      " 67  Bwd Avg Packets/Bulk         137116 non-null  float64\n",
      " 68  Bwd Avg Bulk Rate            137116 non-null  float64\n",
      " 69  Subflow Fwd Packets          137116 non-null  float64\n",
      " 70  Subflow Fwd Bytes            137116 non-null  float64\n",
      " 71  Subflow Bwd Packets          137116 non-null  float64\n",
      " 72  Subflow Bwd Bytes            137116 non-null  float64\n",
      " 73  Init_Win_bytes_forward       137116 non-null  float64\n",
      " 74  Init_Win_bytes_backward      137116 non-null  float64\n",
      " 75  act_data_pkt_fwd             137116 non-null  float64\n",
      " 76  min_seg_size_forward         137116 non-null  float64\n",
      " 77  Active Mean                  137116 non-null  float64\n",
      " 78  Active Std                   137116 non-null  float64\n",
      " 79  Active Max                   137116 non-null  float64\n",
      " 80  Active Min                   137116 non-null  float64\n",
      " 81  Idle Mean                    137116 non-null  float64\n",
      " 82  Idle Std                     137116 non-null  float64\n",
      " 83  Idle Max                     137116 non-null  float64\n",
      " 84  Idle Min                     137116 non-null  float64\n",
      " 85  SimillarHTTP                 135134 non-null  float64\n",
      " 86  Inbound                      137116 non-null  float64\n",
      " 87  Label                        137139 non-null  object \n",
      "dtypes: float64(83), object(5)\n",
      "memory usage: 92.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kDKKVLRETA12",
   "metadata": {
    "id": "kDKKVLRETA12"
   },
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7b0bcca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e7b0bcca",
    "outputId": "4e8e2a2d-60d4-463e-96e1-4b19334ea67d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Bwd Packet Length Std', 'Bwd IAT Std', 'Fwd PSH Flags',\n",
      "       'Bwd PSH Flags', 'Fwd URG Flags', 'Bwd URG Flags', 'FIN Flag Count',\n",
      "       'SYN Flag Count', 'RST Flag Count', 'PSH Flag Count', 'CWE Flag Count',\n",
      "       'ECE Flag Count', 'Fwd Avg Bytes/Bulk', 'Fwd Avg Packets/Bulk',\n",
      "       'Fwd Avg Bulk Rate', 'Bwd Avg Bytes/Bulk', 'Bwd Avg Packets/Bulk',\n",
      "       'Bwd Avg Bulk Rate', 'Active Mean', 'Active Std', 'Active Max',\n",
      "       'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max', 'Idle Min',\n",
      "       'SimillarHTTP'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Đếm số value = 0 trong mỗi cột\n",
    "zeroCounts = (df == 0).sum()\n",
    "#Đếm số dòng mỗi cột\n",
    "totalCounts = df.count()\n",
    "# Điều kiện: tổng value ==0 >= 80% tổng số dòng\n",
    "condition = zeroCounts / totalCounts >= 0.8\n",
    "#Láy ra các cột đủ điều kiện\n",
    "conditionColumns = zeroCounts[condition].index\n",
    "print(conditionColumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6df1e005",
   "metadata": {
    "id": "6df1e005"
   },
   "outputs": [],
   "source": [
    "#Xóa bỏ các cột có tỉ lệ value = 0 lớn hơn 80% số dòng và các cột khác không cần thiết để giảm lượng tính toán\n",
    "df=df.drop('Bwd PSH Flags',axis=1)\n",
    "df=df.drop('Fwd URG Flags',axis=1)\n",
    "df=df.drop('Bwd URG Flags',axis=1)\n",
    "df=df.drop('FIN Flag Count',axis=1)\n",
    "df=df.drop('PSH Flag Count',axis=1)\n",
    "df=df.drop('ECE Flag Count',axis=1)\n",
    "df=df.drop('Fwd Avg Bytes/Bulk',axis=1)\n",
    "df=df.drop('Fwd Avg Packets/Bulk',axis=1)\n",
    "df=df.drop('Fwd Avg Bulk Rate',axis=1)\n",
    "df=df.drop('Bwd Avg Bytes/Bulk',axis=1)\n",
    "df=df.drop('Bwd Avg Packets/Bulk',axis=1)\n",
    "df=df.drop('Bwd Avg Bulk Rate',axis=1)\n",
    "df=df.drop('Unnamed: 0',axis=1)\n",
    "df=df.drop('SimillarHTTP', axis=1)\n",
    "df=df.drop('Flow ID', axis=1)\n",
    "df=df.drop('Timestamp', axis=1)\n",
    "df=df.drop('Source IP',axis=1)\n",
    "df=df.drop('Flow Duration',axis=1)\n",
    "df=df.drop('Total Fwd Packets',axis=1)\n",
    "df=df.drop('Total Backward Packets',axis=1)\n",
    "df=df.drop('Total Length of Bwd Packets',axis=1)\n",
    "df=df.drop('Fwd Packet Length Std',axis=1)\n",
    "df=df.drop('Flow IAT Max',axis=1)\n",
    "df=df.drop('Flow IAT Min',axis=1)\n",
    "df=df.drop('Fwd IAT Total',axis=1)\n",
    "df=df.drop('Fwd IAT Max',axis=1)\n",
    "df=df.drop('Fwd IAT Min',axis=1)\n",
    "df=df.drop('Bwd IAT Total',axis=1)\n",
    "df=df.drop('Bwd IAT Mean',axis=1)\n",
    "df=df.drop('Bwd IAT Std',axis=1)\n",
    "df=df.drop('Bwd IAT Max',axis=1)\n",
    "df=df.drop('Bwd IAT Min',axis=1)\n",
    "df=df.drop('Fwd Header Length',axis=1)\n",
    "df=df.drop('Bwd Header Length',axis=1)\n",
    "df=df.drop('Bwd Packets/s',axis=1)\n",
    "df=df.drop('SYN Flag Count',axis=1)\n",
    "df=df.drop('Down/Up Ratio',axis=1)\n",
    "df=df.drop('Fwd Header Length.1',axis=1)\n",
    "df=df.drop('Subflow Fwd Packets',axis=1)\n",
    "df=df.drop('Subflow Bwd Packets',axis=1)\n",
    "df=df.drop('Subflow Bwd Bytes',axis=1)\n",
    "df=df.drop('act_data_pkt_fwd',axis=1)\n",
    "df=df.drop('min_seg_size_forward',axis=1)\n",
    "df=df.drop('Active Mean',axis=1)\n",
    "df=df.drop('Active Std',axis=1)\n",
    "df=df.drop('Active Max',axis=1)\n",
    "df=df.drop('Active Min',axis=1)\n",
    "df=df.drop('Idle Mean',axis=1)\n",
    "df=df.drop('Idle Max',axis=1)\n",
    "df=df.drop('Idle Min',axis=1)\n",
    "df=df.drop('Packet Length Std',axis=1)\n",
    "df=df.drop('Inbound',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0990a057",
   "metadata": {},
   "source": [
    "# DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bC9b6EQZTlYD",
   "metadata": {
    "id": "bC9b6EQZTlYD"
   },
   "source": [
    "## CHECK NULL & NaN VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7a9aebf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a7a9aebf",
    "outputId": "25a80b02-d83c-408c-d69d-ee2ce5e019e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source Port                     23\n",
       "Destination IP                   0\n",
       "Destination Port                23\n",
       "Protocol                        23\n",
       "Total Length of Fwd Packets     23\n",
       "Fwd Packet Length Max           23\n",
       "Fwd Packet Length Min           23\n",
       "Fwd Packet Length Mean          23\n",
       "Bwd Packet Length Max           23\n",
       "Bwd Packet Length Min           23\n",
       "Bwd Packet Length Mean          23\n",
       "Bwd Packet Length Std           23\n",
       "Flow Bytes/s                   926\n",
       "Flow Packets/s                  23\n",
       "Flow IAT Mean                   23\n",
       "Flow IAT Std                    23\n",
       "Fwd IAT Mean                    23\n",
       "Fwd IAT Std                     23\n",
       "Fwd PSH Flags                   23\n",
       "Fwd Packets/s                   23\n",
       "Min Packet Length               23\n",
       "Max Packet Length               23\n",
       "Packet Length Mean              23\n",
       "Packet Length Variance          23\n",
       "RST Flag Count                  23\n",
       "ACK Flag Count                  23\n",
       "URG Flag Count                  23\n",
       "CWE Flag Count                  23\n",
       "Average Packet Size             23\n",
       "Avg Fwd Segment Size            23\n",
       "Avg Bwd Segment Size            23\n",
       "Subflow Fwd Bytes               23\n",
       "Init_Win_bytes_forward          23\n",
       "Init_Win_bytes_backward         23\n",
       "Idle Std                        23\n",
       "Label                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Đếm giá trị null trong cột\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa896680",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "aa896680",
    "outputId": "85eecc9c-8164-4fb7-b769-94a21c0b2674"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nullCounts = df.isnull().sum()\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.bar(nullCounts.index, nullCounts.values,color = 'lightpink')\n",
    "\n",
    "plt.xlabel('Columns')\n",
    "plt.ylabel('Number of Null Values')\n",
    "plt.title('Null Value Distribution')\n",
    "\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f15a35c",
   "metadata": {
    "id": "8f15a35c"
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46ce6e5f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "46ce6e5f",
    "outputId": "40f6aa7b-0d65-4ff7-f338-d568222eb725"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source Port                    0\n",
       "Destination IP                 0\n",
       "Destination Port               0\n",
       "Protocol                       0\n",
       "Total Length of Fwd Packets    0\n",
       "Fwd Packet Length Max          0\n",
       "Fwd Packet Length Min          0\n",
       "Fwd Packet Length Mean         0\n",
       "Bwd Packet Length Max          0\n",
       "Bwd Packet Length Min          0\n",
       "Bwd Packet Length Mean         0\n",
       "Bwd Packet Length Std          0\n",
       "Flow Bytes/s                   0\n",
       "Flow Packets/s                 0\n",
       "Flow IAT Mean                  0\n",
       "Flow IAT Std                   0\n",
       "Fwd IAT Mean                   0\n",
       "Fwd IAT Std                    0\n",
       "Fwd PSH Flags                  0\n",
       "Fwd Packets/s                  0\n",
       "Min Packet Length              0\n",
       "Max Packet Length              0\n",
       "Packet Length Mean             0\n",
       "Packet Length Variance         0\n",
       "RST Flag Count                 0\n",
       "ACK Flag Count                 0\n",
       "URG Flag Count                 0\n",
       "CWE Flag Count                 0\n",
       "Average Packet Size            0\n",
       "Avg Fwd Segment Size           0\n",
       "Avg Bwd Segment Size           0\n",
       "Subflow Fwd Bytes              0\n",
       "Init_Win_bytes_forward         0\n",
       "Init_Win_bytes_backward        0\n",
       "Idle Std                       0\n",
       "Label                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22652a19",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22652a19",
    "outputId": "102414c7-ee2c-43f0-9520-d9c8888b68e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BENIGN', 'TFTP', 'DrDoS_DNS', 'DrDoS_LDAP', 'DrDoS_MSSQL',\n",
       "       'DrDoS_NetBIOS', 'DrDoS_NTP', 'DrDoS_SNMP', 'DrDoS_SSDP',\n",
       "       'DrDoS_UDP', 'UDP-lag', 'WebDDoS', 'Syn'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "449e7c5c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "449e7c5c",
    "outputId": "18c4a9f0-4483-470c-cec2-cfd001a1efb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label\n",
      "BENIGN           71688\n",
      "Syn              27418\n",
      "DrDoS_NTP        14500\n",
      "TFTP              5261\n",
      "UDP-lag           3800\n",
      "DrDoS_DNS         3400\n",
      "DrDoS_UDP         2160\n",
      "DrDoS_MSSQL       2000\n",
      "DrDoS_NetBIOS     1706\n",
      "DrDoS_LDAP        1600\n",
      "DrDoS_SNMP        1510\n",
      "DrDoS_SSDP         770\n",
      "WebDDoS            400\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "valueCounts = df['Label'].value_counts()\n",
    "print(valueCounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Jmcj5brXTV4g",
   "metadata": {
    "id": "Jmcj5brXTV4g"
   },
   "source": [
    "# DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0VYA_uEEZVHQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "id": "0VYA_uEEZVHQ",
    "outputId": "15224b74-4ac4-4405-b593-1540d1d50066"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labelCounts = df['Label'].value_counts()\n",
    "\n",
    "threshold = 0.03\n",
    "smallLabels = labelCounts[labelCounts / labelCounts.sum() < threshold].index\n",
    "df['Label'] = df['Label'].apply(lambda x: 'Others' if x in smallLabels else x)\n",
    "\n",
    "labelCounts = df['Label'].value_counts()\n",
    "\n",
    "benignCount = labelCounts['BENIGN']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "labelCounts.plot(kind='bar', color='skyblue')\n",
    "plt.bar('Benign', benignCount, color='green')\n",
    "plt.legend(['Others', 'Benign'])\n",
    "plt.title('Biểu đồ thể hiện số lượng BENIGN và các kiểu tấn công DDoS')\n",
    "plt.xlabel('Label')\n",
    "plt.ylabel('Quantity')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "86068f7a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 523
    },
    "id": "86068f7a",
    "outputId": "fb122b55-b81d-43bd-c4cc-012dc5deb811"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labelCounts = df['Label'].value_counts()\n",
    "\n",
    "threshold = 0.03\n",
    "smallLabels = labelCounts[labelCounts/labelCounts.sum() < threshold].index\n",
    "df['Label'] = df['Label'].apply(lambda x: 'Others' if x in smallLabels else x)\n",
    "\n",
    "labelCounts = df['Label'].value_counts()\n",
    "\n",
    "labels = labelCounts.index.tolist()\n",
    "sizes = labelCounts.values.tolist()\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Biểu đồ thể hiện các loại tấn công DDoS')\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.legend(labels, loc=\"upper right\",bbox_to_anchor=(1.3, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "JL9y1hCYU4_E",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 601
    },
    "id": "JL9y1hCYU4_E",
    "outputId": "6f4f4e80-aefe-4f7c-c9e4-6ea4b7786a3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "protocolCounts = df['Protocol'].value_counts()\n",
    "protocolPercentage = protocolCounts / protocolCounts.sum() * 100\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=[7,7])\n",
    "ax1.pie(protocolPercentage, explode=(0.1, 0, 0), labels=protocolPercentage.index, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "ax1.legend(protocolPercentage.index, loc=\"best\")\n",
    "plt.title('Biểu đồ thể hiện sự phân bổ của Protocol', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2OdxqW_MVDsk",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "2OdxqW_MVDsk",
    "outputId": "684e449c-7468-4bba-92e0-798b50b417d7"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '23.32.166.121'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Để trực quan ma trận, cần phải thực hiện Label Encoding trước \u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m correlationMatrix \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mcorr()\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m10\u001b[39m])\n\u001b[0;32m      7\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(correlationMatrix, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:11049\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  11047\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  11048\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 11049\u001b[0m mat \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m, na_value\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnan, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m  11051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  11052\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:1993\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1992\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1993\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mas_array(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(result, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1694\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1692\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1694\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interleave(dtype\u001b[38;5;241m=\u001b[39mdtype, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1695\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1696\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1753\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1751\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1752\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1753\u001b[0m     result[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1754\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1756\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '23.32.166.121'"
     ]
    }
   ],
   "source": [
    "#Để trực quan ma trận, cần phải thực hiện Label Encoding trước \n",
    "import seaborn as sns\n",
    "\n",
    "correlationMatrix = df.corr()\n",
    "\n",
    "plt.figure(figsize=[12,10])\n",
    "sns.heatmap(correlationMatrix, annot=False, cmap='coolwarm', linewidths=0.5)\n",
    "plt.title('Ma trận tương quan của các feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6VCNNkDVT-e8",
   "metadata": {
    "id": "6VCNNkDVT-e8"
   },
   "source": [
    "# LABEL ENDCODING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7155444c",
   "metadata": {
    "id": "7155444c"
   },
   "outputs": [],
   "source": [
    "# BENIGN = 0 và DDoS = 1\n",
    "df['Label'] = np.where(df['Label'] == 'BENIGN', 0, 1)\n",
    "df['Label'] = df['Label'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c9d32b0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9c9d32b0",
    "outputId": "7039eb14-a1ce-49b4-a0c4-1fd442bcac56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eaf85b07",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eaf85b07",
    "outputId": "aa814776-64c6-42ca-9d60-00bd79d006c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of categorical features is 1 and they are : \n",
      " ['Destination IP']\n"
     ]
    }
   ],
   "source": [
    "categorical_features = [feature for feature in df.columns if df[feature].dtypes == 'O']\n",
    "print(\"The number of categorical features is\",len(categorical_features),\"and they are : \\n\",categorical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c123a8fa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c123a8fa",
    "outputId": "c4714908-06f2-48d4-c5eb-a204d7e0635a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Source Port',\n",
       "  'Destination Port',\n",
       "  'Protocol',\n",
       "  'Total Length of Fwd Packets',\n",
       "  'Fwd Packet Length Max',\n",
       "  'Fwd Packet Length Min',\n",
       "  'Fwd Packet Length Mean',\n",
       "  'Bwd Packet Length Max',\n",
       "  'Bwd Packet Length Min',\n",
       "  'Bwd Packet Length Mean',\n",
       "  'Bwd Packet Length Std',\n",
       "  'Flow Bytes/s',\n",
       "  'Flow Packets/s',\n",
       "  'Flow IAT Mean',\n",
       "  'Flow IAT Std',\n",
       "  'Fwd IAT Mean',\n",
       "  'Fwd IAT Std',\n",
       "  'Fwd PSH Flags',\n",
       "  'Fwd Packets/s',\n",
       "  'Min Packet Length',\n",
       "  'Max Packet Length',\n",
       "  'Packet Length Mean',\n",
       "  'Packet Length Variance',\n",
       "  'RST Flag Count',\n",
       "  'ACK Flag Count',\n",
       "  'URG Flag Count',\n",
       "  'CWE Flag Count',\n",
       "  'Average Packet Size',\n",
       "  'Avg Fwd Segment Size',\n",
       "  'Avg Bwd Segment Size',\n",
       "  'Subflow Fwd Bytes',\n",
       "  'Init_Win_bytes_forward',\n",
       "  'Init_Win_bytes_backward',\n",
       "  'Idle Std',\n",
       "  'Label'],\n",
       " 35)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features = [feature for feature in df.columns if df[feature].dtypes != 'O']\n",
    "num_features, len(num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05b626af",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "05b626af",
    "outputId": "8f1b7739-cddf-427b-f085-b451171b11fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source Port                    36712\n",
       "Destination Port               41379\n",
       "Protocol                           3\n",
       "Total Length of Fwd Packets     4430\n",
       "Fwd Packet Length Max           1996\n",
       "Fwd Packet Length Min            632\n",
       "Fwd Packet Length Mean          6870\n",
       "Bwd Packet Length Max            993\n",
       "Bwd Packet Length Min            201\n",
       "Bwd Packet Length Mean          5971\n",
       "Bwd Packet Length Std           6172\n",
       "Flow Bytes/s                   59983\n",
       "Flow Packets/s                 52994\n",
       "Flow IAT Mean                  52621\n",
       "Flow IAT Std                   59775\n",
       "Fwd IAT Mean                   37200\n",
       "Fwd IAT Std                    40196\n",
       "Fwd PSH Flags                      2\n",
       "Fwd Packets/s                  52715\n",
       "Min Packet Length                603\n",
       "Max Packet Length               1841\n",
       "Packet Length Mean              9065\n",
       "Packet Length Variance          8840\n",
       "RST Flag Count                     2\n",
       "ACK Flag Count                     2\n",
       "URG Flag Count                     2\n",
       "CWE Flag Count                     2\n",
       "Average Packet Size             9271\n",
       "Avg Fwd Segment Size            6663\n",
       "Avg Bwd Segment Size            5871\n",
       "Subflow Fwd Bytes               4430\n",
       "Init_Win_bytes_forward          1526\n",
       "Init_Win_bytes_backward         1381\n",
       "Idle Std                        8909\n",
       "Label                              2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_features].nunique(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6404ad53",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6404ad53",
    "outputId": "12139a7b-0bbe-40c6-f392-c8b1f4f0f774"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of discrete features is 7 and they are : \n",
      " ['Protocol', 'Fwd PSH Flags', 'RST Flag Count', 'ACK Flag Count', 'URG Flag Count', 'CWE Flag Count', 'Label']\n"
     ]
    }
   ],
   "source": [
    "discrete_feature = [feature for feature in num_features if df[feature].nunique()<=15 and feature != 'label']\n",
    "print(\"The number of discrete features is\",len(discrete_feature),\"and they are : \\n\",discrete_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f13e412f",
   "metadata": {
    "id": "f13e412f"
   },
   "outputs": [],
   "source": [
    "#Hàm này xử lí các dữ liệu dạng category hay object sang loại numeric\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "def labelEncoding(data):\n",
    "    columnsToEncode = list(data.select_dtypes(include=['category', 'object']))\n",
    "    le = LabelEncoder()\n",
    "    for feature in columnsToEncode:\n",
    "        try:\n",
    "            data[feature] = le.fit_transform(data[feature])\n",
    "        except:\n",
    "            print ('error' + feature)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "516afaf5",
   "metadata": {
    "id": "516afaf5"
   },
   "outputs": [],
   "source": [
    "df = labelEncoding(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vd0fgco1UQKa",
   "metadata": {
    "id": "vd0fgco1UQKa"
   },
   "source": [
    "## OUTLIER HANDLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2913c0f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d2913c0f",
    "outputId": "4b8ee154-c27c-46f8-fa69-02d94fb565fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Có giá trị vô cùng trong cột 'Flow Bytes/s'\n",
      "Có giá trị vô cùng trong cột 'Flow Packets/s'\n"
     ]
    }
   ],
   "source": [
    "#Kiểm tra lại trong dataset có giá trị vô cùng không\n",
    "isInf = np.isinf(df)\n",
    "for column in isInf.columns:\n",
    "    if isInf[column].any():\n",
    "        print(f\"Có giá trị vô cùng trong cột '{column}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "faf066e6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "21247fda",
    "outputId": "a55a3df7-4b85-419a-c090-74c20d9fde07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Có giá trị quá lớn trong cột 'Flow Bytes/s'\n",
      "Có giá trị quá lớn trong cột 'Flow Packets/s'\n"
     ]
    }
   ],
   "source": [
    "#Kiểm tra lại trong dataset có giá trị quá lớn không\n",
    "isLarge = np.abs(df) > 1e9\n",
    "for column in isLarge.columns:\n",
    "    if isLarge[column].any():\n",
    "        print(f\"Có giá trị quá lớn trong cột '{column}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "058aa76c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "058aa76c",
    "outputId": "73f43578-8a7a-48f4-8511-5504f908569f"
   },
   "outputs": [],
   "source": [
    "#Loại bỏ các giá trị quá lớn và vô cùng bằng việc thay thế giá trị đó thành NaN rồi loại bỏ\n",
    "df[isInf] = np.nan\n",
    "df[isLarge] = np.nan\n",
    "nanColumns = df.columns[df.isna().any()].tolist()\n",
    "df = df.dropna(subset=nanColumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d42f075",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7d42f075",
    "outputId": "3c53bdac-83af-40ac-da9a-2262bb7f0fbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 128649 entries, 0 to 137138\n",
      "Data columns (total 36 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   Source Port                  128649 non-null  float64\n",
      " 1   Destination IP               128649 non-null  int32  \n",
      " 2   Destination Port             128649 non-null  float64\n",
      " 3   Protocol                     128649 non-null  float64\n",
      " 4   Total Length of Fwd Packets  128649 non-null  float64\n",
      " 5   Fwd Packet Length Max        128649 non-null  float64\n",
      " 6   Fwd Packet Length Min        128649 non-null  float64\n",
      " 7   Fwd Packet Length Mean       128649 non-null  float64\n",
      " 8   Bwd Packet Length Max        128649 non-null  float64\n",
      " 9   Bwd Packet Length Min        128649 non-null  float64\n",
      " 10  Bwd Packet Length Mean       128649 non-null  float64\n",
      " 11  Bwd Packet Length Std        128649 non-null  float64\n",
      " 12  Flow Bytes/s                 128649 non-null  float64\n",
      " 13  Flow Packets/s               128649 non-null  float64\n",
      " 14  Flow IAT Mean                128649 non-null  float64\n",
      " 15  Flow IAT Std                 128649 non-null  float64\n",
      " 16  Fwd IAT Mean                 128649 non-null  float64\n",
      " 17  Fwd IAT Std                  128649 non-null  float64\n",
      " 18  Fwd PSH Flags                128649 non-null  float64\n",
      " 19  Fwd Packets/s                128649 non-null  float64\n",
      " 20  Min Packet Length            128649 non-null  float64\n",
      " 21  Max Packet Length            128649 non-null  float64\n",
      " 22  Packet Length Mean           128649 non-null  float64\n",
      " 23  Packet Length Variance       128649 non-null  float64\n",
      " 24  RST Flag Count               128649 non-null  float64\n",
      " 25  ACK Flag Count               128649 non-null  float64\n",
      " 26  URG Flag Count               128649 non-null  float64\n",
      " 27  CWE Flag Count               128649 non-null  float64\n",
      " 28  Average Packet Size          128649 non-null  float64\n",
      " 29  Avg Fwd Segment Size         128649 non-null  float64\n",
      " 30  Avg Bwd Segment Size         128649 non-null  float64\n",
      " 31  Subflow Fwd Bytes            128649 non-null  float64\n",
      " 32  Init_Win_bytes_forward       128649 non-null  float64\n",
      " 33  Init_Win_bytes_backward      128649 non-null  float64\n",
      " 34  Idle Std                     128649 non-null  float64\n",
      " 35  Label                        128649 non-null  int32  \n",
      "dtypes: float64(34), int32(2)\n",
      "memory usage: 35.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "I0HIqyQJUUsH",
   "metadata": {
    "id": "I0HIqyQJUUsH"
   },
   "source": [
    "# DATA SPLITTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d7dacb5",
   "metadata": {
    "id": "4d7dacb5"
   },
   "outputs": [],
   "source": [
    "X = df.drop(['Label'], axis=1)\n",
    "y = df['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eaaada47",
   "metadata": {
    "id": "eaaada47"
   },
   "outputs": [],
   "source": [
    "ms = MinMaxScaler()\n",
    "X = ms.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "330ee42e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "330ee42e",
    "outputId": "80c0234e-d33b-404c-9c8a-1e0763bde143"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90054, 35) (38595, 35)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3)\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qlxWjwv4UX0m",
   "metadata": {
    "id": "qlxWjwv4UX0m"
   },
   "source": [
    "# MODEL TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c055b463",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c055b463",
    "outputId": "56b3acea-70e6-49a0-ec95-dfcc29db5d89"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Input_Layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,260</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Hidden_Layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Dropout_Layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Hidden_Layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">506</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Output_Layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Input_Layer (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │         \u001b[38;5;34m1,260\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Hidden_Layer_1 (\u001b[38;5;33mDense\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m)             │           \u001b[38;5;34m792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Dropout_Layer (\u001b[38;5;33mDropout\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Hidden_Layer_2 (\u001b[38;5;33mDense\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m)             │           \u001b[38;5;34m506\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ Output_Layer (\u001b[38;5;33mDense\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m23\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,581</span> (10.08 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,581\u001b[0m (10.08 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,581</span> (10.08 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,581\u001b[0m (10.08 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(Input(shape=(35,)))\n",
    "model.add(Dense(35,name = \"Input_Layer\"))\n",
    "model.add(Dense(22 , activation=\"relu\" , name=\"Hidden_Layer_1\"))\n",
    "model.add(Dropout(0.5, name=\"Dropout_Layer\"))\n",
    "model.add(Dense(22 , activation=\"relu\" , name=\"Hidden_Layer_2\"))\n",
    "model.add(Dense(1 , activation=\"sigmoid\" , name=\"Output_Layer\"))\n",
    "opt = keras.optimizers.Adam(learning_rate=0.0015)\n",
    "model.compile( optimizer=opt, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "726cf4af",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "726cf4af",
    "outputId": "6ed3878a-b738-4af5-cccd-77fe2350c89d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "704/704 - 4s - 6ms/step - accuracy: 0.9479 - loss: 0.1380 - val_accuracy: 0.9643 - val_loss: 0.0634\n",
      "Epoch 2/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9748 - loss: 0.0604 - val_accuracy: 0.9816 - val_loss: 0.0449\n",
      "Epoch 3/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9798 - loss: 0.0490 - val_accuracy: 0.9836 - val_loss: 0.0366\n",
      "Epoch 4/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9820 - loss: 0.0419 - val_accuracy: 0.9863 - val_loss: 0.0288\n",
      "Epoch 5/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9855 - loss: 0.0351 - val_accuracy: 0.9942 - val_loss: 0.0262\n",
      "Epoch 6/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9869 - loss: 0.0328 - val_accuracy: 0.9876 - val_loss: 0.0231\n",
      "Epoch 7/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9878 - loss: 0.0307 - val_accuracy: 0.9921 - val_loss: 0.0211\n",
      "Epoch 8/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9892 - loss: 0.0281 - val_accuracy: 0.9941 - val_loss: 0.0192\n",
      "Epoch 9/50\n",
      "704/704 - 2s - 2ms/step - accuracy: 0.9895 - loss: 0.0266 - val_accuracy: 0.9961 - val_loss: 0.0154\n",
      "Epoch 10/50\n",
      "704/704 - 2s - 2ms/step - accuracy: 0.9894 - loss: 0.0273 - val_accuracy: 0.9932 - val_loss: 0.0211\n",
      "Epoch 11/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9901 - loss: 0.0255 - val_accuracy: 0.9947 - val_loss: 0.0174\n",
      "Epoch 12/50\n",
      "704/704 - 3s - 4ms/step - accuracy: 0.9913 - loss: 0.0233 - val_accuracy: 0.9960 - val_loss: 0.0142\n",
      "Epoch 13/50\n",
      "704/704 - 3s - 4ms/step - accuracy: 0.9919 - loss: 0.0224 - val_accuracy: 0.9953 - val_loss: 0.0190\n",
      "Epoch 14/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9918 - loss: 0.0222 - val_accuracy: 0.9932 - val_loss: 0.0175\n",
      "Epoch 15/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9923 - loss: 0.0207 - val_accuracy: 0.9974 - val_loss: 0.0110\n",
      "Epoch 16/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9925 - loss: 0.0203 - val_accuracy: 0.9962 - val_loss: 0.0133\n",
      "Epoch 17/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9921 - loss: 0.0204 - val_accuracy: 0.9962 - val_loss: 0.0144\n",
      "Epoch 18/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9921 - loss: 0.0213 - val_accuracy: 0.9961 - val_loss: 0.0139\n",
      "Epoch 19/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9924 - loss: 0.0205 - val_accuracy: 0.9972 - val_loss: 0.0141\n",
      "Epoch 20/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9925 - loss: 0.0212 - val_accuracy: 0.9953 - val_loss: 0.0163\n",
      "Epoch 21/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9929 - loss: 0.0198 - val_accuracy: 0.9949 - val_loss: 0.0140\n",
      "Epoch 22/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9929 - loss: 0.0203 - val_accuracy: 0.9968 - val_loss: 0.0119\n",
      "Epoch 23/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9929 - loss: 0.0189 - val_accuracy: 0.9972 - val_loss: 0.0118\n",
      "Epoch 24/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9927 - loss: 0.0199 - val_accuracy: 0.9972 - val_loss: 0.0119\n",
      "Epoch 25/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9927 - loss: 0.0195 - val_accuracy: 0.9973 - val_loss: 0.0121\n",
      "Epoch 26/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9931 - loss: 0.0185 - val_accuracy: 0.9970 - val_loss: 0.0120\n",
      "Epoch 27/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9929 - loss: 0.0196 - val_accuracy: 0.9970 - val_loss: 0.0141\n",
      "Epoch 28/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9935 - loss: 0.0181 - val_accuracy: 0.9972 - val_loss: 0.0123\n",
      "Epoch 29/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9934 - loss: 0.0192 - val_accuracy: 0.9967 - val_loss: 0.0165\n",
      "Epoch 30/50\n",
      "704/704 - 3s - 4ms/step - accuracy: 0.9935 - loss: 0.0179 - val_accuracy: 0.9974 - val_loss: 0.0106\n",
      "Epoch 31/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9934 - loss: 0.0189 - val_accuracy: 0.9964 - val_loss: 0.0146\n",
      "Epoch 32/50\n",
      "704/704 - 3s - 4ms/step - accuracy: 0.9938 - loss: 0.0182 - val_accuracy: 0.9967 - val_loss: 0.0118\n",
      "Epoch 33/50\n",
      "704/704 - 3s - 4ms/step - accuracy: 0.9936 - loss: 0.0177 - val_accuracy: 0.9971 - val_loss: 0.0137\n",
      "Epoch 34/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9935 - loss: 0.0184 - val_accuracy: 0.9973 - val_loss: 0.0151\n",
      "Epoch 35/50\n",
      "704/704 - 2s - 3ms/step - accuracy: 0.9936 - loss: 0.0181 - val_accuracy: 0.9973 - val_loss: 0.0120\n",
      "Epoch 36/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9940 - loss: 0.0169 - val_accuracy: 0.9973 - val_loss: 0.0102\n",
      "Epoch 37/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9945 - loss: 0.0169 - val_accuracy: 0.9973 - val_loss: 0.0096\n",
      "Epoch 38/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9940 - loss: 0.0173 - val_accuracy: 0.9972 - val_loss: 0.0121\n",
      "Epoch 39/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9942 - loss: 0.0165 - val_accuracy: 0.9937 - val_loss: 0.0217\n",
      "Epoch 40/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9939 - loss: 0.0173 - val_accuracy: 0.9973 - val_loss: 0.0092\n",
      "Epoch 41/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9943 - loss: 0.0159 - val_accuracy: 0.9966 - val_loss: 0.0114\n",
      "Epoch 42/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9943 - loss: 0.0157 - val_accuracy: 0.9976 - val_loss: 0.0099\n",
      "Epoch 43/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9946 - loss: 0.0159 - val_accuracy: 0.9975 - val_loss: 0.0123\n",
      "Epoch 44/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9939 - loss: 0.0169 - val_accuracy: 0.9971 - val_loss: 0.0128\n",
      "Epoch 45/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9939 - loss: 0.0167 - val_accuracy: 0.9942 - val_loss: 0.0195\n",
      "Epoch 46/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9940 - loss: 0.0167 - val_accuracy: 0.9969 - val_loss: 0.0115\n",
      "Epoch 47/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9944 - loss: 0.0150 - val_accuracy: 0.9974 - val_loss: 0.0106\n",
      "Epoch 48/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9938 - loss: 0.0173 - val_accuracy: 0.9961 - val_loss: 0.0138\n",
      "Epoch 49/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9940 - loss: 0.0174 - val_accuracy: 0.9965 - val_loss: 0.0143\n",
      "Epoch 50/50\n",
      "704/704 - 1s - 2ms/step - accuracy: 0.9941 - loss: 0.0171 - val_accuracy: 0.9976 - val_loss: 0.0118\n",
      "\u001b[1m1207/1207\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n",
      "\u001b[1m1207/1207\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9980 - loss: 0.0107\n",
      "Validation Loss:  0.011822675354778767\n",
      "Accuracy:  0.9975903630256653\n"
     ]
    }
   ],
   "source": [
    "from timeit import default_timer as timer\n",
    "startTime = timer()\n",
    "historyOrg = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    batch_size=128,\n",
    "    epochs=50, verbose=2,\n",
    "    callbacks=None,\n",
    "    validation_data=(X_test,y_test),\n",
    "    shuffle=True,\n",
    "    class_weight=None,\n",
    "    sample_weight=None,\n",
    "    initial_epoch=0)\n",
    "endTime = timer()\n",
    "timeTaken = endTime - startTime\n",
    "predictions = model.predict(X_test)\n",
    "loss, accuracy = model.evaluate(X_test,y_test)\n",
    "print(\"Training time: \", timeTaken)\n",
    "print(\"Validation Loss: \",loss)\n",
    "print(\"Accuracy: \",accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e06c34f1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "e06c34f1",
    "outputId": "be3ded20-ccee-4174-b21d-c450594c77cd"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'historyOrg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m loss \u001b[38;5;241m=\u001b[39m historyOrg\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      2\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m historyOrg\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      3\u001b[0m epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mlen\u001b[39m(loss) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'historyOrg' is not defined"
     ]
    }
   ],
   "source": [
    "loss = historyOrg.history['loss']\n",
    "val_loss = historyOrg.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'g', label = 'Training Loss')\n",
    "plt.plot(epochs, val_loss, 'r', label = 'Validation Loss')\n",
    "plt.title('Loss v/s No. of epochs')\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7dffb012",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "7dffb012",
    "outputId": "523ec344-dcc9-41fe-d142-0e2af08079c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = historyOrg.history['accuracy']\n",
    "val_loss = historyOrg.history['val_accuracy']\n",
    "plt.plot(epochs, loss, 'g', label = 'Training accuracy')\n",
    "plt.plot(epochs, val_loss, 'r', label = 'Validation accuracy')\n",
    "plt.title('Accuracy Scores v/s Number of Epochs')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.ylabel('Accuracy Score')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8X4ydDRtUeCS",
   "metadata": {
    "id": "8X4ydDRtUeCS"
   },
   "source": [
    "# CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2270dfb",
   "metadata": {
    "id": "e2270dfb"
   },
   "outputs": [],
   "source": [
    "targetLabel = (predictions >0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b07b5f62",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "b07b5f62",
    "outputId": "2e9fd87e-4edc-402b-d276-0614dcd0ae06"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "\n",
    "# Example confusion matrix\n",
    "# Replace this with your actual confusion matrix\n",
    "cm = confusion_matrix(y_test,targetLabel)\n",
    "# Plot confusion matrix\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.RdPu)\n",
    "plt.title('Confusion matrix')\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(2)\n",
    "plt.xticks(tick_marks, ['Benign', 'Malign'], rotation=45)\n",
    "plt.yticks(tick_marks, ['Benign', 'Malign'])\n",
    "\n",
    "plt.ylabel('Real label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.grid(False)  # Xóa đường kẻ\n",
    "# Add text annotations\n",
    "thresh = cm.max() / 2.\n",
    "for i, j in np.ndindex(cm.shape):\n",
    "    plt.text(j, i, format(cm[i, j], 'd'),\n",
    "             ha=\"center\", va=\"center\",\n",
    "             color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a46c44ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a46c44ac",
    "outputId": "23c3dca4-e1de-478a-8ef7-c2a0773b1389"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:  \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0   0.997784  0.997832  0.997808     21213\n",
      "           1   0.997353  0.997296  0.997325     17382\n",
      "\n",
      "    accuracy                       0.997590     38595\n",
      "   macro avg   0.997569  0.997564  0.997566     38595\n",
      "weighted avg   0.997590  0.997590  0.997590     38595\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print (\"Classification Report: \", \"\\n\",  classification_report(y_test,targetLabel,digits = 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c4fe336b",
   "metadata": {
    "id": "c4fe336b",
    "outputId": "332846a7-d79a-41b4-a87a-1e549eac755d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save model? (y/n) y\n",
      "Named model: model.keras\n"
     ]
    }
   ],
   "source": [
    "save = input(\"Save model? (y/n) \")\n",
    "if save == 'y':\n",
    "    named = input('Named model: ')\n",
    "    model.save(named)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JmkoD8d8UhFj",
   "metadata": {
    "id": "JmkoD8d8UhFj"
   },
   "source": [
    "# PREDICT MODEL IN REAL TIME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1571cb0d",
   "metadata": {
    "id": "1571cb0d"
   },
   "outputs": [],
   "source": [
    "def getRecentFile(directory, fullPath=True):\n",
    "    # Lấy danh sách tất cả các file trong thư mục\n",
    "    files = [os.path.join(directory, f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]\n",
    "    \n",
    "    # Kiểm tra xem có file nào trong thư mục không\n",
    "    if not files:\n",
    "        return None\n",
    "    # Tìm file có thời gian chỉnh sửa gần nhất\n",
    "    mostRecentlyFile = max(files, key=os.path.getmtime)\n",
    "    #Trả về đường dẫn đầy đủ hoặc chỉ tên tệp\n",
    "    if fullPath:\n",
    "        return mostRecentlyFile\n",
    "    else:\n",
    "        return os.path.basename(mostRecentlyFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "95e63861",
   "metadata": {
    "id": "95e63861"
   },
   "outputs": [],
   "source": [
    "def readNewLines(directory,lastPosition):\n",
    "    #Mở file csv từ đường dẫn\n",
    "    with open(directory, 'r') as file:\n",
    "        # Di chuyển con trỏ tới vị trí cuối cùng đã đọc\n",
    "        file.seek(lastPosition)\n",
    "        \n",
    "        # Đọc các dòng mới\n",
    "        lines = file.readlines()\n",
    "        \n",
    "        # Cập nhật vị trí cuối cùng đã đọc\n",
    "        newPosition = file.tell()\n",
    "        \n",
    "    return lines, newPosition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2fd58839",
   "metadata": {
    "id": "2fd58839"
   },
   "outputs": [],
   "source": [
    "def processingLines(lines):\n",
    "    df = None\n",
    "    if lines:\n",
    "        # Chuyển các dòng thành DataFrame\n",
    "        from io import StringIO\n",
    "        data = ''.join(lines)\n",
    "        df = pd.read_csv(StringIO(data))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f044247d",
   "metadata": {
    "id": "f044247d"
   },
   "outputs": [],
   "source": [
    "def processingData(df):\n",
    "    #Xóa các cột có giá trị NULL nếu có\n",
    "    df.isnull().sum()\n",
    "    df.dropna(inplace=True)\n",
    "    #Xóa các cột có giá trị quá lớn hoặc INF nếu có\n",
    "    isInf = np.isinf(df)\n",
    "    isLarge = np.abs(df) > 1e10\n",
    "    df[isInf] = np.nan  # Thay thế các giá trị vô cùng bằng NaN\n",
    "    df[isLarge] = np.nan  # Thay thế các giá trị quá lớn bằng NaN\n",
    "    # Loại bỏ các giá trị NaN\n",
    "    nanColumns = df.columns[df.isna().any()].tolist()\n",
    "    df = df.dropna(subset=nanColumns)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "65763ce7",
   "metadata": {
    "id": "65763ce7"
   },
   "outputs": [],
   "source": [
    "def dropColumns(df):\n",
    "    #Xóa dấu cách các cột (nếu có)\n",
    "    df = df.rename(columns=lambda x: x.strip())\n",
    "    #Lựa chọn đặc trưng\n",
    "    df=df.drop('Bwd PSH Flags',axis=1)\n",
    "    df=df.drop('Fwd URG Flags',axis=1)\n",
    "    df=df.drop('Bwd URG Flags',axis=1)\n",
    "    df=df.drop('FIN Flag Count',axis=1)\n",
    "    df=df.drop('PSH Flag Count',axis=1)\n",
    "    df=df.drop('ECE Flag Count',axis=1)\n",
    "    df=df.drop('Fwd Avg Bytes/Bulk',axis=1)\n",
    "    df=df.drop('Fwd Avg Packets/Bulk',axis=1)\n",
    "    df=df.drop('Fwd Avg Bulk Rate',axis=1)\n",
    "    df=df.drop('Bwd Avg Bytes/Bulk',axis=1)\n",
    "    df=df.drop('Bwd Avg Packets/Bulk',axis=1)\n",
    "    df=df.drop('Bwd Avg Bulk Rate',axis=1)\n",
    "    df=df.drop('Flow ID', axis=1)\n",
    "    df=df.drop('Timestamp', axis=1)\n",
    "    df=df.drop('Source IP',axis=1)\n",
    "    df=df.drop('Flow Duration',axis=1)\n",
    "    df=df.drop('Total Fwd Packets',axis=1)\n",
    "    df=df.drop('Total Backward Packets',axis=1)\n",
    "    df=df.drop('Total Length of Bwd Packets',axis=1)\n",
    "    df=df.drop('Fwd Packet Length Std',axis=1)\n",
    "    df=df.drop('Flow IAT Max',axis=1)\n",
    "    df=df.drop('Flow IAT Min',axis=1)\n",
    "    df=df.drop('Fwd IAT Total',axis=1)\n",
    "    df=df.drop('Fwd IAT Max',axis=1)\n",
    "    df=df.drop('Fwd IAT Min',axis=1)\n",
    "    df=df.drop('Bwd IAT Total',axis=1)\n",
    "    df=df.drop('Bwd IAT Mean',axis=1)\n",
    "    df=df.drop('Bwd IAT Std',axis=1)\n",
    "    df=df.drop('Bwd IAT Max',axis=1)\n",
    "    df=df.drop('Bwd IAT Min',axis=1)\n",
    "    df=df.drop('Fwd Header Length',axis=1)\n",
    "    df=df.drop('Bwd Header Length',axis=1)\n",
    "    df=df.drop('Bwd Packets/s',axis=1)\n",
    "    df=df.drop('SYN Flag Count',axis=1)\n",
    "    df=df.drop('Down/Up Ratio',axis=1)\n",
    "    df=df.drop('Subflow Fwd Packets',axis=1)\n",
    "    df=df.drop('Subflow Bwd Packets',axis=1)\n",
    "    df=df.drop('Subflow Bwd Bytes',axis=1)\n",
    "    df=df.drop('act_data_pkt_fwd',axis=1)\n",
    "    df=df.drop('min_seg_size_forward',axis=1)\n",
    "    df=df.drop('Active Mean',axis=1)\n",
    "    df=df.drop('Active Std',axis=1)\n",
    "    df=df.drop('Active Max',axis=1)\n",
    "    df=df.drop('Active Min',axis=1)\n",
    "    df=df.drop('Idle Mean',axis=1)\n",
    "    df=df.drop('Idle Max',axis=1)\n",
    "    df=df.drop('Idle Min',axis=1)\n",
    "    df=df.drop('Packet Length Std',axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e7129d1b",
   "metadata": {
    "id": "e7129d1b"
   },
   "outputs": [],
   "source": [
    "def predictFlow(modelname):\n",
    "    mlp_live_iteration = 0 #Bằng 1 nếu chỉ đọc file hoàn chỉnh, lớn hơn 1 nếu đọc một file đang được ghi\n",
    "    hostile = 0 # Đếm số lượng hostile flow mà model đã dự đoán được\n",
    "    safe = 0 #Đếm số lượng safe flow mà model đã dự đoán\n",
    "    start = time.time() #Biến điều kiện ghi vào file log.txt trạng thái của flow sau 60s\n",
    "    stop = False #Điều kiện dừng vòng lặp while\n",
    "    lastPosition = 0 #Vị trí cuối cùng con trỏ trỏ đến trong file csv (ban đầu là 0)\n",
    "    directoryPath = '.\\\\CICFlowmeter\\\\bin\\\\data\\\\daily' #Đường dẫn đến folder chứa file csv\n",
    "    directory = getRecentFile(directoryPath, fullPath=True) #lấy ra file được chỉnh sửa hoặc tạo mới nhất\n",
    "    host = [] #Khởi tạo mảng dùng để lưu giá trị các hostile sau mỗi 30s\n",
    "    while not stop:\n",
    "        line, lastPosition = readNewLines(directory, lastPosition) #Lấy ra dòng mới đọc và vị trí cuối cùng con trỏ trỏ đến trong file csv\n",
    "        if (line == [] or line == None): #Kiểm tra xem line đã đọc hết dòng trong file csv chưa (csv dừng cập nhật dòng mới)\n",
    "            print('Finish!')\n",
    "            #stop = True\n",
    "            return mlp_live_iteration\n",
    "        else:\n",
    "            df = processingLines(line) #Biến các dòng dữ liệu đã đọc thành DataFrame\n",
    "            #Rename cột để đồng bộ features với Model\n",
    "            new_col = ['Flow ID', 'Source IP', 'Source Port', 'Destination IP',\n",
    "            'Destination Port', 'Protocol', 'Timestamp', 'Flow Duration',\n",
    "            'Total Fwd Packets', 'Total Backward Packets',\n",
    "            'Total Length of Fwd Packets', 'Total Length of Bwd Packets',\n",
    "            'Fwd Packet Length Max', 'Fwd Packet Length Min',\n",
    "            'Fwd Packet Length Mean', 'Fwd Packet Length Std',\n",
    "            'Bwd Packet Length Max', 'Bwd Packet Length Min',\n",
    "            'Bwd Packet Length Mean', 'Bwd Packet Length Std', 'Flow Bytes/s',\n",
    "            'Flow Packets/s', 'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max',\n",
    "            'Flow IAT Min', 'Fwd IAT Total', 'Fwd IAT Mean', 'Fwd IAT Std',\n",
    "            'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Total', 'Bwd IAT Mean',\n",
    "            'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Fwd PSH Flags',\n",
    "            'Bwd PSH Flags', 'Fwd URG Flags', 'Bwd URG Flags', 'Fwd Header Length',\n",
    "            'Bwd Header Length', 'Fwd Packets/s', 'Bwd Packets/s',\n",
    "            'Min Packet Length', 'Max Packet Length', 'Packet Length Mean',\n",
    "            'Packet Length Std', 'Packet Length Variance', 'FIN Flag Count',\n",
    "            'SYN Flag Count', 'RST Flag Count', 'PSH Flag Count', 'ACK Flag Count',\n",
    "            'URG Flag Count', 'CWE Flag Count', 'ECE Flag Count', 'Down/Up Ratio',\n",
    "            'Average Packet Size', 'Avg Fwd Segment Size', 'Avg Bwd Segment Size',\n",
    "            'Fwd Avg Bytes/Bulk', 'Fwd Avg Packets/Bulk',\n",
    "            'Fwd Avg Bulk Rate', 'Bwd Avg Bytes/Bulk', 'Bwd Avg Packets/Bulk',\n",
    "            'Bwd Avg Bulk Rate', 'Subflow Fwd Packets', 'Subflow Fwd Bytes',\n",
    "            'Subflow Bwd Packets', 'Subflow Bwd Bytes', 'Init_Win_bytes_forward',\n",
    "            'Init_Win_bytes_backward', 'act_data_pkt_fwd', 'min_seg_size_forward',\n",
    "            'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean',\n",
    "            'Idle Std', 'Idle Max', 'Idle Min', 'Label']\n",
    "            df.columns = new_col\n",
    "            df = dropColumns(df) #Xóa cột không cần thiết (Feature Selection)\n",
    "            df = labelEncoding(df) #Mã hóa cột cate và obj\n",
    "            df = processingData(df) #Xử lí dữ liệu trước khi dự đoán\n",
    "            X = df.drop('Label', axis = 1) # Lấy ra bộ dữ liệu X để dự đoán\n",
    "            from keras.models import load_model\n",
    "            model = keras.models.load_model(modelname) # Load Model\n",
    "            model.compile( optimizer=opt, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
    "            predictions = model.predict(X) #Dự đoán X\n",
    "            #Đếm số safe và hostile flow\n",
    "            for check in predictions:\n",
    "                if check > 0.5:\n",
    "                    hostile += 1\n",
    "                else:\n",
    "                    safe += 1\n",
    "            host.append(hostile)\n",
    "            print(\"Safe Flows: \", safe)\n",
    "            print(\"Possible Hostile Flows: \", hostile)\n",
    "            print(\"Percentage of Hostile Flows: \", 100 * hostile/(safe + hostile))\n",
    "            print (\"\\n\")\n",
    "            mlp_live_iteration += 1\n",
    "            #Sau mỗi 60s sẽ ghi trạng thái vào được file log.txt\n",
    "            if (time.time() - start > 90):\n",
    "                for i in range(3, len(host)):\n",
    "                    temp = (host[i] - host[i - 3])/90\n",
    "                    if ( temp > 0.67):\n",
    "                        testwrite = open('log.txt', 'a+')\n",
    "                        testwrite.write('Attack Detected at: ')\n",
    "                        testwrite.write(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "                        testwrite.write('\\n')\n",
    "                        testwrite.write('Flows collected: ')\n",
    "                        testwrite.write(str(safe + hostile))\n",
    "                        testwrite.write('\\n')\n",
    "                        return (\"Attack\")\n",
    "                    else:\n",
    "                        testwrite = open('log.txt', 'a+')\n",
    "                        testwrite.write('Normal Activity Detected at: ')\n",
    "                        testwrite.write(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "                        testwrite.write('\\n')\n",
    "                        testwrite.write('Flows collected: ')\n",
    "                        testwrite.write(str(safe + hostile))\n",
    "                        testwrite.write('\\n \\n')\n",
    "                        return mlp_live_iteration\n",
    "            time.sleep(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "300540da",
   "metadata": {
    "id": "300540da",
    "outputId": "3ef9207b-ae9a-42da-9fa7-02f2bd70450a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selection (0 or 1): 1\n",
      "Please enter model: model.keras\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
      "Safe Flows:  3\n",
      "Possible Hostile Flows:  0\n",
      "Percentage of Hostile Flows:  0.0\n",
      "\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step\n",
      "Safe Flows:  14\n",
      "Possible Hostile Flows:  1\n",
      "Percentage of Hostile Flows:  6.666666666666667\n",
      "\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step\n",
      "Safe Flows:  19\n",
      "Possible Hostile Flows:  1\n",
      "Percentage of Hostile Flows:  5.0\n",
      "\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n",
      "Safe Flows:  24\n",
      "Possible Hostile Flows:  1\n",
      "Percentage of Hostile Flows:  4.0\n",
      "\n",
      "\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
      "Safe Flows:  27\n",
      "Possible Hostile Flows:  1\n",
      "Percentage of Hostile Flows:  3.5714285714285716\n",
      "\n",
      "\n",
      "Finish!\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    live = True\n",
    "    select = input('Selection (0 or 1): ')\n",
    "    if (select == \"1\"):\n",
    "        modelname = input(\"Please enter model: \")\n",
    "        while live:\n",
    "            result = predictFlow(modelname)\n",
    "            if result == \"Attack\": #nếu return là attack sẽ dừng lại và in ra cảnh báo DDoS\n",
    "                live = False\n",
    "                print(\"DDoS ATTACK DETECTED !!!\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "            elif result == 1: #mlp_live_prediction = 1 thực hiện trên 1 file csv hoàn chỉnh\n",
    "                live = False\n",
    "            else:\n",
    "                time.sleep(30)\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a2d5ce1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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