4-Training_Experiment_Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8108e831",
   "metadata": {},
   "source": [
    "# Baseline Experiment 2: Training Random Forest Classifier\n",
    "\n",
    "Using Grid Search with 5-fold Cross-Validation, with a 70/30 train/test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ae1ec67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "import sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.utils import class_weight  # For balanced class weighted classification training\n",
    "\n",
    "# For reproducible results\n",
    "RANDOM_STATE_SEED = 420"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76272e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dataset = pd.read_csv(\"processed_friday_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a5d7038",
   "metadata": {},
   "outputs": [
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       "         Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "0               141385             9             7              553   \n",
       "1                  281             2             1               38   \n",
       "2               279824            11            15             1086   \n",
       "3                  132             2             0                0   \n",
       "4               274016             9            13             1285   \n",
       "...                ...           ...           ...              ...   \n",
       "1364426             25             1             1                0   \n",
       "1364427             37             1             1                0   \n",
       "1364428      112640543             3             0                0   \n",
       "1364429             41             1             1                0   \n",
       "1364430             36             1             1                0   \n",
       "\n",
       "         TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "0                 3773.0              202                0         61.444444   \n",
       "1                    0.0               38                0         19.000000   \n",
       "2                10527.0              385                0         98.727273   \n",
       "3                    0.0                0                0          0.000000   \n",
       "4                 6141.0              517                0        142.777778   \n",
       "...                  ...              ...              ...               ...   \n",
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       "\n",
       "         Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Active Std  Active Max  \\\n",
       "0              87.534438             1460  ...         0.0         0.0   \n",
       "1              26.870058                0  ...         0.0         0.0   \n",
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       "...                  ...              ...  ...         ...         ...   \n",
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       "\n",
       "         Active Min   Idle Mean  Idle Std    Idle Max    Idle Min  \\\n",
       "0               0.0         0.0  0.000000         0.0         0.0   \n",
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       "\n",
       "         Protocol_17  Protocol_6  Label  \n",
       "0                  0           1      0  \n",
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     "execution_count": 3,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c6c4cc8",
   "metadata": {},
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      " 50  Fwd Blk Rate Avg   1364431 non-null  int64  \n",
      " 51  Bwd Byts/b Avg     1364431 non-null  int64  \n",
      " 52  Bwd Pkts/b Avg     1364431 non-null  int64  \n",
      " 53  Bwd Blk Rate Avg   1364431 non-null  int64  \n",
      " 54  Subflow Fwd Pkts   1364431 non-null  int64  \n",
      " 55  Subflow Fwd Byts   1364431 non-null  int64  \n",
      " 56  Subflow Bwd Pkts   1364431 non-null  int64  \n",
      " 57  Subflow Bwd Byts   1364431 non-null  int64  \n",
      " 58  Init Fwd Win Byts  1364431 non-null  int64  \n",
      " 59  Init Bwd Win Byts  1364431 non-null  int64  \n",
      " 60  Fwd Act Data Pkts  1364431 non-null  int64  \n",
      " 61  Fwd Seg Size Min   1364431 non-null  int64  \n",
      " 62  Active Mean        1364431 non-null  float64\n",
      " 63  Active Std         1364431 non-null  float64\n",
      " 64  Active Max         1364431 non-null  float64\n",
      " 65  Active Min         1364431 non-null  float64\n",
      " 66  Idle Mean          1364431 non-null  float64\n",
      " 67  Idle Std           1364431 non-null  float64\n",
      " 68  Idle Max           1364431 non-null  float64\n",
      " 69  Idle Min           1364431 non-null  float64\n",
      " 70  Protocol_17        1364431 non-null  int64  \n",
      " 71  Protocol_6         1364431 non-null  int64  \n",
      " 72  Label              1364431 non-null  int64  \n",
      "dtypes: float64(35), int64(38)\n",
      "memory usage: 759.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60774e17",
   "metadata": {},
   "source": [
    "### 1- Making an 70/30 train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31c18365",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df_dataset, test_size=0.3, random_state=RANDOM_STATE_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e5bf869",
   "metadata": {},
   "outputs": [
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       "         Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ca88705",
   "metadata": {},
   "outputs": [
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       "      <th>882513</th>\n",
       "      <td>67116</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413896</th>\n",
       "      <td>2681</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>155.0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189064</th>\n",
       "      <td>1391793</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1132</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>661</td>\n",
       "      <td>0</td>\n",
       "      <td>141.50</td>\n",
       "      <td>222.792023</td>\n",
       "      <td>1173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341771</th>\n",
       "      <td>4975198</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281286</th>\n",
       "      <td>4383470</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>935</td>\n",
       "      <td>668.0</td>\n",
       "      <td>935</td>\n",
       "      <td>0</td>\n",
       "      <td>187.00</td>\n",
       "      <td>418.144712</td>\n",
       "      <td>334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606144</th>\n",
       "      <td>15125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188978</th>\n",
       "      <td>1685281</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1144</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>677</td>\n",
       "      <td>0</td>\n",
       "      <td>143.00</td>\n",
       "      <td>227.969923</td>\n",
       "      <td>1173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666351</th>\n",
       "      <td>3661521</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1148</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>677</td>\n",
       "      <td>0</td>\n",
       "      <td>114.80</td>\n",
       "      <td>210.092466</td>\n",
       "      <td>1173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>409330 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "421031         5856314             4             4               97   \n",
       "1026751        4442833             5             3              935   \n",
       "882513           67116             3             0                0   \n",
       "413896            2681             1             1               34   \n",
       "189064         1391793             8             7             1132   \n",
       "...                ...           ...           ...              ...   \n",
       "341771         4975198             2             0                0   \n",
       "1281286        4383470             5             4              935   \n",
       "606144           15125             2             1                0   \n",
       "188978         1685281             8             7             1144   \n",
       "666351         3661521            10             7             1148   \n",
       "\n",
       "         TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "421031             231.0               97                0             24.25   \n",
       "1026751            348.0              935                0            187.00   \n",
       "882513               0.0                0                0              0.00   \n",
       "413896             155.0               34               34             34.00   \n",
       "189064            1581.0              661                0            141.50   \n",
       "...                  ...              ...              ...               ...   \n",
       "341771               0.0                0                0              0.00   \n",
       "1281286            668.0              935                0            187.00   \n",
       "606144               0.0                0                0              0.00   \n",
       "188978            1581.0              677                0            143.00   \n",
       "666351            1581.0              677                0            114.80   \n",
       "\n",
       "         Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Active Std  Active Max  \\\n",
       "421031         48.500000              231  ...         0.0         0.0   \n",
       "1026751       418.144712              348  ...         0.0         0.0   \n",
       "882513          0.000000                0  ...         0.0         0.0   \n",
       "413896          0.000000              155  ...         0.0         0.0   \n",
       "189064        222.792023             1173  ...         0.0         0.0   \n",
       "...                  ...              ...  ...         ...         ...   \n",
       "341771          0.000000                0  ...         0.0         0.0   \n",
       "1281286       418.144712              334  ...         0.0         0.0   \n",
       "606144          0.000000                0  ...         0.0         0.0   \n",
       "188978        227.969923             1173  ...         0.0         0.0   \n",
       "666351        210.092466             1173  ...         0.0         0.0   \n",
       "\n",
       "         Active Min  Idle Mean  Idle Std  Idle Max  Idle Min  Protocol_17  \\\n",
       "421031          0.0        0.0       0.0       0.0       0.0            0   \n",
       "1026751         0.0        0.0       0.0       0.0       0.0            0   \n",
       "882513          0.0        0.0       0.0       0.0       0.0            0   \n",
       "413896          0.0        0.0       0.0       0.0       0.0            1   \n",
       "189064          0.0        0.0       0.0       0.0       0.0            0   \n",
       "...             ...        ...       ...       ...       ...          ...   \n",
       "341771          0.0        0.0       0.0       0.0       0.0            0   \n",
       "1281286         0.0        0.0       0.0       0.0       0.0            0   \n",
       "606144          0.0        0.0       0.0       0.0       0.0            0   \n",
       "188978          0.0        0.0       0.0       0.0       0.0            0   \n",
       "666351          0.0        0.0       0.0       0.0       0.0            0   \n",
       "\n",
       "         Protocol_6  Label  \n",
       "421031            1      0  \n",
       "1026751           1      0  \n",
       "882513            1      1  \n",
       "413896            0      0  \n",
       "189064            1      0  \n",
       "...             ...    ...  \n",
       "341771            1      0  \n",
       "1281286           1      0  \n",
       "606144            1      0  \n",
       "188978            1      0  \n",
       "666351            1      0  \n",
       "\n",
       "[409330 rows x 73 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6275d9",
   "metadata": {},
   "source": [
    "#### MinMax Scaling of numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1c92111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Flow Duration', 'Tot Fwd Pkts', 'Tot Bwd Pkts', 'TotLen Fwd Pkts',\n",
       "       'TotLen Bwd Pkts', 'Fwd Pkt Len Max', 'Fwd Pkt Len Min',\n",
       "       'Fwd Pkt Len Mean', 'Fwd Pkt Len Std', 'Bwd Pkt Len Max',\n",
       "       'Bwd Pkt Len Min', 'Bwd Pkt Len Mean', 'Bwd Pkt Len Std',\n",
       "       'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min',\n",
       "       'Fwd IAT Tot', 'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max',\n",
       "       'Fwd IAT Min', 'Bwd IAT Tot', 'Bwd IAT Mean', 'Bwd IAT Std',\n",
       "       'Bwd IAT Max', 'Bwd IAT Min', 'Fwd Header Len', 'Bwd Header Len',\n",
       "       'Fwd Pkts/s', 'Bwd Pkts/s', 'Pkt Len Min', 'Pkt Len Max',\n",
       "       'Pkt Len Mean', 'Pkt Len Std', 'Pkt Len Var', 'FIN Flag Cnt',\n",
       "       'SYN Flag Cnt', 'RST Flag Cnt', 'PSH Flag Cnt', 'ACK Flag Cnt',\n",
       "       'URG Flag Cnt', 'CWE Flag Count', 'ECE Flag Cnt', 'Down/Up Ratio',\n",
       "       'Pkt Size Avg', 'Fwd Seg Size Avg', 'Bwd Seg Size Avg',\n",
       "       'Fwd Byts/b Avg', 'Fwd Pkts/b Avg', 'Fwd Blk Rate Avg',\n",
       "       'Bwd Byts/b Avg', 'Bwd Pkts/b Avg', 'Bwd Blk Rate Avg',\n",
       "       'Subflow Fwd Pkts', 'Subflow Fwd Byts', 'Subflow Bwd Pkts',\n",
       "       'Subflow Bwd Byts', 'Init Fwd Win Byts', 'Init Bwd Win Byts',\n",
       "       'Fwd Act Data Pkts', 'Fwd Seg Size Min', 'Active Mean', 'Active Std',\n",
       "       'Active Max', 'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max',\n",
       "       'Idle Min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols = train.columns[:-3]\n",
    "numerical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b43985e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = MinMaxScaler().fit(train[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f03cef58",
   "metadata": {},
   "outputs": [],
   "source": [
    "train[numerical_cols] = min_max_scaler.transform(train[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2266f45",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Tot Fwd Pkts</th>\n",
       "      <th>Tot Bwd Pkts</th>\n",
       "      <th>TotLen Fwd Pkts</th>\n",
       "      <th>TotLen Bwd Pkts</th>\n",
       "      <th>Fwd Pkt Len Max</th>\n",
       "      <th>Fwd Pkt Len Min</th>\n",
       "      <th>Fwd Pkt Len Mean</th>\n",
       "      <th>Fwd Pkt Len Std</th>\n",
       "      <th>Bwd Pkt Len Max</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>Protocol_17</th>\n",
       "      <th>Protocol_6</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.000014</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.011676</td>\n",
       "      <td>0.023288</td>\n",
       "      <td>0.023288</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>205523</th>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.111951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074429</td>\n",
       "      <td>0.182314</td>\n",
       "      <td>0.012891</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>334013</th>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.021918</td>\n",
       "      <td>0.021918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244653</th>\n",
       "      <td>0.000786</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1049785</th>\n",
       "      <td>0.038428</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.321085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160103</td>\n",
       "      <td>0.452839</td>\n",
       "      <td>0.043048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>178094</th>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.111951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074429</td>\n",
       "      <td>0.182314</td>\n",
       "      <td>0.012891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>510579</th>\n",
       "      <td>0.004509</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.048077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051941</td>\n",
       "      <td>0.060962</td>\n",
       "      <td>0.016690</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242184</th>\n",
       "      <td>0.028369</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.321085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.128082</td>\n",
       "      <td>0.405031</td>\n",
       "      <td>0.037063</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928689</th>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>955101 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "358501        0.000016      0.000000      0.000014         0.000031   \n",
       "205523        0.000085      0.000046      0.000058         0.000296   \n",
       "334013        0.000247      0.000000      0.000014         0.000029   \n",
       "1244653       0.000786      0.000046      0.000000         0.000000   \n",
       "1309914       0.000501      0.000046      0.000000         0.000000   \n",
       "...                ...           ...           ...              ...   \n",
       "1049785       0.038428      0.000070      0.000072         0.000850   \n",
       "178094        0.000099      0.000046      0.000058         0.000296   \n",
       "510579        0.004509      0.000116      0.000072         0.000413   \n",
       "1242184       0.028369      0.000093      0.000043         0.000850   \n",
       "928689        0.000276      0.000023      0.000000         0.000000   \n",
       "\n",
       "         TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "358501          0.000002         0.011676         0.023288          0.023288   \n",
       "205523          0.000001         0.111951         0.000000          0.074429   \n",
       "334013          0.000001         0.010989         0.021918          0.021918   \n",
       "1244653         0.000000         0.000000         0.000000          0.000000   \n",
       "1309914         0.000000         0.000000         0.000000          0.000000   \n",
       "...                  ...              ...              ...               ...   \n",
       "1049785         0.000004         0.321085         0.000000          0.160103   \n",
       "178094          0.000001         0.111951         0.000000          0.074429   \n",
       "510579          0.000003         0.048077         0.000000          0.051941   \n",
       "1242184         0.000003         0.321085         0.000000          0.128082   \n",
       "928689          0.000000         0.000000         0.000000          0.000000   \n",
       "\n",
       "         Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Active Std  Active Max  \\\n",
       "358501          0.000000         0.024286  ...         0.0         0.0   \n",
       "205523          0.182314         0.012891  ...         0.0         0.0   \n",
       "334013          0.000000         0.013927  ...         0.0         0.0   \n",
       "1244653         0.000000         0.000000  ...         0.0         0.0   \n",
       "1309914         0.000000         0.000000  ...         0.0         0.0   \n",
       "...                  ...              ...  ...         ...         ...   \n",
       "1049785         0.452839         0.043048  ...         0.0         0.0   \n",
       "178094          0.182314         0.012891  ...         0.0         0.0   \n",
       "510579          0.060962         0.016690  ...         0.0         0.0   \n",
       "1242184         0.405031         0.037063  ...         0.0         0.0   \n",
       "928689          0.000000         0.000000  ...         0.0         0.0   \n",
       "\n",
       "         Active Min  Idle Mean  Idle Std  Idle Max  Idle Min  Protocol_17  \\\n",
       "358501          0.0        0.0       0.0       0.0       0.0            1   \n",
       "205523          0.0        0.0       0.0       0.0       0.0            0   \n",
       "334013          0.0        0.0       0.0       0.0       0.0            1   \n",
       "1244653         0.0        0.0       0.0       0.0       0.0            0   \n",
       "1309914         0.0        0.0       0.0       0.0       0.0            0   \n",
       "...             ...        ...       ...       ...       ...          ...   \n",
       "1049785         0.0        0.0       0.0       0.0       0.0            0   \n",
       "178094          0.0        0.0       0.0       0.0       0.0            0   \n",
       "510579          0.0        0.0       0.0       0.0       0.0            0   \n",
       "1242184         0.0        0.0       0.0       0.0       0.0            0   \n",
       "928689          0.0        0.0       0.0       0.0       0.0            0   \n",
       "\n",
       "         Protocol_6  Label  \n",
       "358501            0      0  \n",
       "205523            1      1  \n",
       "334013            0      0  \n",
       "1244653           1      1  \n",
       "1309914           1      1  \n",
       "...             ...    ...  \n",
       "1049785           1      0  \n",
       "178094            1      1  \n",
       "510579            1      0  \n",
       "1242184           1      0  \n",
       "928689            1      1  \n",
       "\n",
       "[955101 rows x 73 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b3897a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test[numerical_cols] = min_max_scaler.transform(test[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0513c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Flow Duration</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188978</th>\n",
       "      <td>0.014044</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.001039</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.232486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097945</td>\n",
       "      <td>0.220821</td>\n",
       "      <td>0.135014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666351</th>\n",
       "      <td>0.030513</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.232486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078630</td>\n",
       "      <td>0.203504</td>\n",
       "      <td>0.135014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>409330 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "421031        0.048803      0.000070      0.000058         0.000088   \n",
       "1026751       0.037024      0.000093      0.000043         0.000850   \n",
       "882513        0.000559      0.000046      0.000000         0.000000   \n",
       "413896        0.000022      0.000000      0.000014         0.000031   \n",
       "189064        0.011598      0.000162      0.000101         0.001029   \n",
       "...                ...           ...           ...              ...   \n",
       "341771        0.041460      0.000023      0.000000         0.000000   \n",
       "1281286       0.036529      0.000093      0.000058         0.000850   \n",
       "606144        0.000126      0.000023      0.000014         0.000000   \n",
       "188978        0.014044      0.000162      0.000101         0.001039   \n",
       "666351        0.030513      0.000209      0.000101         0.001043   \n",
       "\n",
       "         TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "421031          0.000002         0.033310         0.000000          0.016610   \n",
       "1026751         0.000003         0.321085         0.000000          0.128082   \n",
       "882513          0.000000         0.000000         0.000000          0.000000   \n",
       "413896          0.000002         0.011676         0.023288          0.023288   \n",
       "189064          0.000016         0.226992         0.000000          0.096918   \n",
       "...                  ...              ...              ...               ...   \n",
       "341771          0.000000         0.000000         0.000000          0.000000   \n",
       "1281286         0.000007         0.321085         0.000000          0.128082   \n",
       "606144          0.000000         0.000000         0.000000          0.000000   \n",
       "188978          0.000016         0.232486         0.000000          0.097945   \n",
       "666351          0.000016         0.232486         0.000000          0.078630   \n",
       "\n",
       "         Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Active Std  Active Max  \\\n",
       "421031          0.046979         0.026588  ...         0.0         0.0   \n",
       "1026751         0.405031         0.040055  ...         0.0         0.0   \n",
       "882513          0.000000         0.000000  ...         0.0         0.0   \n",
       "413896          0.000000         0.017841  ...         0.0         0.0   \n",
       "189064          0.215805         0.135014  ...         0.0         0.0   \n",
       "...                  ...              ...  ...         ...         ...   \n",
       "341771          0.000000         0.000000  ...         0.0         0.0   \n",
       "1281286         0.405031         0.038444  ...         0.0         0.0   \n",
       "606144          0.000000         0.000000  ...         0.0         0.0   \n",
       "188978          0.220821         0.135014  ...         0.0         0.0   \n",
       "666351          0.203504         0.135014  ...         0.0         0.0   \n",
       "\n",
       "         Active Min  Idle Mean  Idle Std  Idle Max  Idle Min  Protocol_17  \\\n",
       "421031          0.0        0.0       0.0       0.0       0.0            0   \n",
       "1026751         0.0        0.0       0.0       0.0       0.0            0   \n",
       "882513          0.0        0.0       0.0       0.0       0.0            0   \n",
       "413896          0.0        0.0       0.0       0.0       0.0            1   \n",
       "189064          0.0        0.0       0.0       0.0       0.0            0   \n",
       "...             ...        ...       ...       ...       ...          ...   \n",
       "341771          0.0        0.0       0.0       0.0       0.0            0   \n",
       "1281286         0.0        0.0       0.0       0.0       0.0            0   \n",
       "606144          0.0        0.0       0.0       0.0       0.0            0   \n",
       "188978          0.0        0.0       0.0       0.0       0.0            0   \n",
       "666351          0.0        0.0       0.0       0.0       0.0            0   \n",
       "\n",
       "         Protocol_6  Label  \n",
       "421031            1      0  \n",
       "1026751           1      0  \n",
       "882513            1      1  \n",
       "413896            0      0  \n",
       "189064            1      0  \n",
       "...             ...    ...  \n",
       "341771            1      0  \n",
       "1281286           1      0  \n",
       "606144            1      0  \n",
       "188978            1      0  \n",
       "666351            1      0  \n",
       "\n",
       "[409330 rows x 73 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fab8c76",
   "metadata": {},
   "source": [
    "### 2- Checking label distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "543594fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full dataset:\n",
      "\n",
      "Benign: 1074342\n",
      "Malicious: 290089\n",
      "---------------\n",
      "Training set:\n",
      "\n",
      "Benign: 751849\n",
      "Malicious: 203252\n",
      "---------------\n",
      "Test set:\n",
      "\n",
      "Benign: 322493\n",
      "Malicious: 86837\n"
     ]
    }
   ],
   "source": [
    "print(\"Full dataset:\\n\")\n",
    "print(\"Benign: \" + str(df_dataset[\"Label\"].value_counts()[[0]].sum()))\n",
    "print(\"Malicious: \" + str(df_dataset[\"Label\"].value_counts()[[1]].sum()))\n",
    "print(\"---------------\")\n",
    "\n",
    "print(\"Training set:\\n\")\n",
    "print(\"Benign: \" + str(train[\"Label\"].value_counts()[[0]].sum()))\n",
    "print(\"Malicious: \" + str(train[\"Label\"].value_counts()[[1]].sum()))\n",
    "print(\"---------------\")\n",
    "\n",
    "print(\"Test set:\\n\")\n",
    "print(\"Benign: \" + str(test[\"Label\"].value_counts()[[0]].sum()))\n",
    "print(\"Malicious: \" + str(test[\"Label\"].value_counts()[[1]].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7364f0a",
   "metadata": {},
   "source": [
    "### 3- Splitting to X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "48c2c210",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "(955101, 72)\n",
      "(955101,)\n"
     ]
    }
   ],
   "source": [
    "y_train = np.array(train.pop(\"Label\")) # pop removes \"Label\" from the dataframe\n",
    "X_train = train.values\n",
    "\n",
    "print(type(X_train))\n",
    "print(type(y_train))\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbd95949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "(409330, 72)\n",
      "(409330,)\n"
     ]
    }
   ],
   "source": [
    "y_test = np.array(test.pop(\"Label\")) # pop removes \"Label\" from the dataframe\n",
    "X_test = test.values\n",
    "\n",
    "print(type(X_test))\n",
    "print(type(y_test))\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a685d3c",
   "metadata": {},
   "source": [
    "### 4- Fitting Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81bd3d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6351681  2.34954884]\n",
      "{0: 0.6351680989134786, 1: 2.3495488359278136}\n"
     ]
    }
   ],
   "source": [
    "# Calculating class weights for balanced class weighted classifier training\n",
    "class_weights = class_weight.compute_class_weight(\n",
    "    class_weight='balanced',\n",
    "    classes=np.unique(y_train),\n",
    "    y=y_train\n",
    ")\n",
    "\n",
    "print(class_weights)\n",
    "\n",
    "# Must be in dict format for scikitlearn\n",
    "class_weights = {\n",
    "    0: class_weights[0],\n",
    "    1: class_weights[1]\n",
    "}\n",
    "\n",
    "print(class_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a7acec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    criterion='gini',\n",
    "    max_depth=None,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    min_weight_fraction_leaf=0.0,\n",
    "    max_features='auto',\n",
    "    max_leaf_nodes=None,\n",
    "    min_impurity_decrease=0.0,\n",
    "    bootstrap=True,\n",
    "    oob_score=False,\n",
    "    n_jobs=None,\n",
    "    random_state=None,\n",
    "    verbose=0,\n",
    "    warm_start=False,\n",
    "    class_weight=class_weights,\n",
    "    ccp_alpha=0.0,\n",
    "    max_samples=None\n",
    ")\n",
    "\n",
    "hyperparameters = {\n",
    "    'n_estimators': [50, 75, 100, 125, 150]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e6b82d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(\n",
    "    estimator=model,\n",
    "    param_grid=hyperparameters,\n",
    "    cv=5,\n",
    "    verbose=1,\n",
    "    n_jobs=-1  # Use all available CPU cores\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d25e5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(class_weight={0: 0.6351680989134786,\n",
       "                                                            1: 2.3495488359278136}),\n",
       "             n_jobs=-1, param_grid={'n_estimators': [50, 75, 100, 125, 150]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d9d523",
   "metadata": {},
   "source": [
    "### 5- Extracting best performing model in the 5-fold cross-validation Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6bf908a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score on Validation set: \n",
      "\n",
      "0.9996345936035459\n",
      "---------------\n",
      "Best performing hyperparameters on Validation set: \n",
      "{'n_estimators': 125}\n",
      "---------------\n",
      "RandomForestClassifier(class_weight={0: 0.6351680989134786,\n",
      "                                     1: 2.3495488359278136},\n",
      "                       n_estimators=125)\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score on Validation set: \\n\")\n",
    "print(clf.best_score_ )\n",
    "print(\"---------------\")\n",
    "print(\"Best performing hyperparameters on Validation set: \")\n",
    "print(clf.best_params_)\n",
    "print(\"---------------\")\n",
    "print(clf.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "474556a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5d4fdaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight={0: 0.6351680989134786,\n",
       "                                     1: 2.3495488359278136},\n",
       "                       n_estimators=125)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad5790a",
   "metadata": {},
   "source": [
    "### 6- Evaluating on Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c249a2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f243cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c04efba",
   "metadata": {},
   "source": [
    "#### 6.1 Accuracy on Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6cd74f1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9990374514450443\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19954e7c",
   "metadata": {},
   "source": [
    "#### 6.2 Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "28600683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[322130    363]\n",
      " [    31  86806]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, predictions)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c9dafd8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tamer/anaconda3/envs/cic-ids-2018/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f8adcd1db50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model, X_test, y_test, cmap=\"cividis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0376fe06",
   "metadata": {},
   "source": [
    "#### 6.3 Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "264ebb85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0    0.99990   0.99887   0.99939    322493\n",
      "           1    0.99584   0.99964   0.99774     86837\n",
      "\n",
      "    accuracy                        0.99904    409330\n",
      "   macro avg    0.99787   0.99926   0.99856    409330\n",
      "weighted avg    0.99904   0.99904   0.99904    409330\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions, digits=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321f9f0e",
   "metadata": {},
   "source": [
    "### 7- Saving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39a1108c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trained_models/random-forest-classifier.pkl']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model, \"trained_models/random-forest-classifier.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3eedd6f",
   "metadata": {},
   "source": [
    "### 8- Testing loading model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d090ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"trained_models/random-forest-classifier.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "43896d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight={0: 0.6351680989134786,\n",
       "                                     1: 2.3495488359278136},\n",
       "                       n_estimators=125)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "269e1d15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}