CSC791_IDSML_Validation_TrainTest_Split.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "from sklearn import preprocessing\n",
    "import sklearn as skl\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_pickle('processed_validation_data_df.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_df, test_df = train_test_split(data_df, test_size = .90, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Size = 282787 | Test Size = 2545089 | Test is 0.9000002121733768% of Train\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Size = {} | Test Size = {} | Test is {}% of Train\".format(len(train_df),len(test_df), len(test_df)/len(data_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow 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>Fwd Seg Size Min</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Active Max</th>\n",
       "      <th>Active Min</th>\n",
       "      <th>Idle Mean</th>\n",
       "      <th>Idle Std</th>\n",
       "      <th>Idle Max</th>\n",
       "      <th>Idle Min</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>282787.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.473112e+07</td>\n",
       "      <td>9.560998</td>\n",
       "      <td>10.774311</td>\n",
       "      <td>5.974760e+02</td>\n",
       "      <td>1.643504e+04</td>\n",
       "      <td>209.518942</td>\n",
       "      <td>18.620711</td>\n",
       "      <td>58.701718</td>\n",
       "      <td>69.877096</td>\n",
       "      <td>867.601389</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.966037e+03</td>\n",
       "      <td>8.045510e+04</td>\n",
       "      <td>4.193717e+04</td>\n",
       "      <td>1.527933e+05</td>\n",
       "      <td>5.644794e+04</td>\n",
       "      <td>8.238116e+06</td>\n",
       "      <td>5.166213e+05</td>\n",
       "      <td>8.628472e+06</td>\n",
       "      <td>7.832892e+06</td>\n",
       "      <td>1.062043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.360160e+07</td>\n",
       "      <td>742.557060</td>\n",
       "      <td>1007.094192</td>\n",
       "      <td>2.486795e+04</td>\n",
       "      <td>2.204908e+06</td>\n",
       "      <td>730.341538</td>\n",
       "      <td>60.295899</td>\n",
       "      <td>188.490010</td>\n",
       "      <td>286.713188</td>\n",
       "      <td>1938.836574</td>\n",
       "      <td>...</td>\n",
       "      <td>1.755734e+06</td>\n",
       "      <td>5.950294e+05</td>\n",
       "      <td>3.974262e+05</td>\n",
       "      <td>9.858843e+05</td>\n",
       "      <td>5.033897e+05</td>\n",
       "      <td>2.349453e+07</td>\n",
       "      <td>4.678662e+06</td>\n",
       "      <td>2.426009e+07</td>\n",
       "      <td>2.321777e+07</td>\n",
       "      <td>2.575128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.368707e+08</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000e+01</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.128100e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.400000e+01</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.151147e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>4.820000e+02</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>26.162951</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.200000e+01</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>199565.000000</td>\n",
       "      <td>268604.000000</td>\n",
       "      <td>1.290000e+07</td>\n",
       "      <td>5.850000e+08</td>\n",
       "      <td>24820.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>4575.218750</td>\n",
       "      <td>5796.500690</td>\n",
       "      <td>14480.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>6.360000e+07</td>\n",
       "      <td>7.000000e+07</td>\n",
       "      <td>9.900000e+07</td>\n",
       "      <td>6.360000e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>7.690000e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flow Duration   Tot Fwd Pkts   Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "count   2.827870e+05  282787.000000  282787.000000     2.827870e+05   \n",
       "mean    1.473112e+07       9.560998      10.774311     5.974760e+02   \n",
       "std     3.360160e+07     742.557060    1007.094192     2.486795e+04   \n",
       "min    -1.000000e+00       1.000000       0.000000     0.000000e+00   \n",
       "25%     1.540000e+02       2.000000       1.000000     8.000000e+00   \n",
       "50%     3.128100e+04       2.000000       2.000000     6.200000e+01   \n",
       "75%     3.151147e+06       5.000000       4.000000     1.870000e+02   \n",
       "max     1.200000e+08  199565.000000  268604.000000     1.290000e+07   \n",
       "\n",
       "       TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "count     2.827870e+05    282787.000000    282787.000000     282787.000000   \n",
       "mean      1.643504e+04       209.518942        18.620711         58.701718   \n",
       "std       2.204908e+06       730.341538        60.295899        188.490010   \n",
       "min       0.000000e+00         0.000000         0.000000          0.000000   \n",
       "25%       0.000000e+00         6.000000         0.000000          6.000000   \n",
       "50%       1.220000e+02        37.000000         2.000000         34.000000   \n",
       "75%       4.820000e+02        81.000000        36.000000         50.000000   \n",
       "max       5.850000e+08     24820.000000      2065.000000       4575.218750   \n",
       "\n",
       "       Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Fwd Seg Size Min   Active Mean  \\\n",
       "count    282787.000000    282787.000000  ...      2.827870e+05  2.827870e+05   \n",
       "mean         69.877096       867.601389  ...     -5.966037e+03  8.045510e+04   \n",
       "std         286.713188      1938.836574  ...      1.755734e+06  5.950294e+05   \n",
       "min           0.000000         0.000000  ...     -5.368707e+08  0.000000e+00   \n",
       "25%           0.000000         0.000000  ...      2.000000e+01  0.000000e+00   \n",
       "50%           0.000000        79.000000  ...      2.400000e+01  0.000000e+00   \n",
       "75%          26.162951       281.000000  ...      3.200000e+01  0.000000e+00   \n",
       "max        5796.500690     14480.000000  ...      7.800000e+01  6.360000e+07   \n",
       "\n",
       "         Active Std    Active Max    Active Min     Idle Mean      Idle Std  \\\n",
       "count  2.827870e+05  2.827870e+05  2.827870e+05  2.827870e+05  2.827870e+05   \n",
       "mean   4.193717e+04  1.527933e+05  5.644794e+04  8.238116e+06  5.166213e+05   \n",
       "std    3.974262e+05  9.858843e+05  5.033897e+05  2.349453e+07  4.678662e+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    7.000000e+07  9.900000e+07  6.360000e+07  1.200000e+08  7.690000e+07   \n",
       "\n",
       "           Idle Max      Idle Min          Label  \n",
       "count  2.827870e+05  2.827870e+05  282787.000000  \n",
       "mean   8.628472e+06  7.832892e+06       1.062043  \n",
       "std    2.426009e+07  2.321777e+07       2.575128  \n",
       "min    0.000000e+00  0.000000e+00       0.000000  \n",
       "25%    0.000000e+00  0.000000e+00       0.000000  \n",
       "50%    0.000000e+00  0.000000e+00       0.000000  \n",
       "75%    0.000000e+00  0.000000e+00       0.000000  \n",
       "max    1.200000e+08  1.200000e+08      14.000000  \n",
       "\n",
       "[8 rows x 78 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b30498b080>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_attack_types = train_df['Label'].value_counts()\n",
    "test_attack_types = test_df['Label'].value_counts()\n",
    "train_attack_types.plot(kind='barh', figsize=(20,10), fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3049e96d8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_attack_types.plot(kind='barh', figsize=(20,10), fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Y = train_df['Label']\n",
    "train_x_raw = train_df.drop(['Label'], axis=1)\n",
    "test_Y = test_df['Label']\n",
    "test_x_raw = test_df.drop(['Label'], \n",
    "                          axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow 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>Fwd Act Data Pkts</th>\n",
       "      <th>Fwd Seg Size Min</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282787.000000</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "      <td>2.827870e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.473112e+07</td>\n",
       "      <td>9.560998</td>\n",
       "      <td>10.774311</td>\n",
       "      <td>5.974760e+02</td>\n",
       "      <td>1.643504e+04</td>\n",
       "      <td>209.518942</td>\n",
       "      <td>18.620711</td>\n",
       "      <td>58.701718</td>\n",
       "      <td>69.877096</td>\n",
       "      <td>867.601389</td>\n",
       "      <td>...</td>\n",
       "      <td>5.119595</td>\n",
       "      <td>-5.966037e+03</td>\n",
       "      <td>8.045510e+04</td>\n",
       "      <td>4.193717e+04</td>\n",
       "      <td>1.527933e+05</td>\n",
       "      <td>5.644794e+04</td>\n",
       "      <td>8.238116e+06</td>\n",
       "      <td>5.166213e+05</td>\n",
       "      <td>8.628472e+06</td>\n",
       "      <td>7.832892e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.360160e+07</td>\n",
       "      <td>742.557060</td>\n",
       "      <td>1007.094192</td>\n",
       "      <td>2.486795e+04</td>\n",
       "      <td>2.204908e+06</td>\n",
       "      <td>730.341538</td>\n",
       "      <td>60.295899</td>\n",
       "      <td>188.490010</td>\n",
       "      <td>286.713188</td>\n",
       "      <td>1938.836574</td>\n",
       "      <td>...</td>\n",
       "      <td>578.631631</td>\n",
       "      <td>1.755734e+06</td>\n",
       "      <td>5.950294e+05</td>\n",
       "      <td>3.974262e+05</td>\n",
       "      <td>9.858843e+05</td>\n",
       "      <td>5.033897e+05</td>\n",
       "      <td>2.349453e+07</td>\n",
       "      <td>4.678662e+06</td>\n",
       "      <td>2.426009e+07</td>\n",
       "      <td>2.321777e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.368707e+08</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.540000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000e+01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.128100e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.200000e+01</td>\n",
       "      <td>1.220000e+02</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.400000e+01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.151147e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>4.820000e+02</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>26.162951</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.200000e+01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>199565.000000</td>\n",
       "      <td>268604.000000</td>\n",
       "      <td>1.290000e+07</td>\n",
       "      <td>5.850000e+08</td>\n",
       "      <td>24820.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>4575.218750</td>\n",
       "      <td>5796.500690</td>\n",
       "      <td>14480.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>189375.000000</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>6.360000e+07</td>\n",
       "      <td>7.000000e+07</td>\n",
       "      <td>9.900000e+07</td>\n",
       "      <td>6.360000e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>7.690000e+07</td>\n",
       "      <td>1.200000e+08</td>\n",
       "      <td>1.200000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flow Duration   Tot Fwd Pkts   Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "count   2.827870e+05  282787.000000  282787.000000     2.827870e+05   \n",
       "mean    1.473112e+07       9.560998      10.774311     5.974760e+02   \n",
       "std     3.360160e+07     742.557060    1007.094192     2.486795e+04   \n",
       "min    -1.000000e+00       1.000000       0.000000     0.000000e+00   \n",
       "25%     1.540000e+02       2.000000       1.000000     8.000000e+00   \n",
       "50%     3.128100e+04       2.000000       2.000000     6.200000e+01   \n",
       "75%     3.151147e+06       5.000000       4.000000     1.870000e+02   \n",
       "max     1.200000e+08  199565.000000  268604.000000     1.290000e+07   \n",
       "\n",
       "       TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "count     2.827870e+05    282787.000000    282787.000000     282787.000000   \n",
       "mean      1.643504e+04       209.518942        18.620711         58.701718   \n",
       "std       2.204908e+06       730.341538        60.295899        188.490010   \n",
       "min       0.000000e+00         0.000000         0.000000          0.000000   \n",
       "25%       0.000000e+00         6.000000         0.000000          6.000000   \n",
       "50%       1.220000e+02        37.000000         2.000000         34.000000   \n",
       "75%       4.820000e+02        81.000000        36.000000         50.000000   \n",
       "max       5.850000e+08     24820.000000      2065.000000       4575.218750   \n",
       "\n",
       "       Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Fwd Act Data Pkts  \\\n",
       "count    282787.000000    282787.000000  ...      282787.000000   \n",
       "mean         69.877096       867.601389  ...           5.119595   \n",
       "std         286.713188      1938.836574  ...         578.631631   \n",
       "min           0.000000         0.000000  ...           0.000000   \n",
       "25%           0.000000         0.000000  ...           0.000000   \n",
       "50%           0.000000        79.000000  ...           1.000000   \n",
       "75%          26.162951       281.000000  ...           2.000000   \n",
       "max        5796.500690     14480.000000  ...      189375.000000   \n",
       "\n",
       "       Fwd Seg Size Min   Active Mean    Active Std    Active Max  \\\n",
       "count      2.827870e+05  2.827870e+05  2.827870e+05  2.827870e+05   \n",
       "mean      -5.966037e+03  8.045510e+04  4.193717e+04  1.527933e+05   \n",
       "std        1.755734e+06  5.950294e+05  3.974262e+05  9.858843e+05   \n",
       "min       -5.368707e+08  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%        2.000000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%        2.400000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%        3.200000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max        7.800000e+01  6.360000e+07  7.000000e+07  9.900000e+07   \n",
       "\n",
       "         Active Min     Idle Mean      Idle Std      Idle Max      Idle Min  \n",
       "count  2.827870e+05  2.827870e+05  2.827870e+05  2.827870e+05  2.827870e+05  \n",
       "mean   5.644794e+04  8.238116e+06  5.166213e+05  8.628472e+06  7.832892e+06  \n",
       "std    5.033897e+05  2.349453e+07  4.678662e+06  2.426009e+07  2.321777e+07  \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    6.360000e+07  1.200000e+08  7.690000e+07  1.200000e+08  1.200000e+08  \n",
       "\n",
       "[8 rows x 77 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df_raw = pd.concat([train_x_raw, test_x_raw])\n",
    "\n",
    "train_x = combined_df_raw[:len(train_x_raw)]\n",
    "test_x = combined_df_raw[len(train_x_raw):]\n",
    "\n",
    "\n",
    "train_x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.827870e+05\n",
       "mean     1.473112e+07\n",
       "std      3.360160e+07\n",
       "min     -1.000000e+00\n",
       "25%      1.540000e+02\n",
       "50%      3.128100e+04\n",
       "75%      3.151147e+06\n",
       "max      1.200000e+08\n",
       "Name: Flow Duration, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example statistics for the 'duration' feature before scaling\n",
    "train_x['Flow Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.827870e+05\n",
       "mean     6.532871e-19\n",
       "std      1.000002e+00\n",
       "min     -4.384060e-01\n",
       "25%     -4.384014e-01\n",
       "50%     -4.374751e-01\n",
       "75%     -3.446262e-01\n",
       "max      3.132859e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Experimenting with StandardScaler on the single 'duration' feature\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "durations = train_x['Flow Duration'].values.reshape(-1, 1)\n",
    "standard_scaler = StandardScaler().fit(durations)\n",
    "scaled_durations = standard_scaler.transform(durations)\n",
    "pd.Series(scaled_durations.flatten()).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    282787.000000\n",
       "mean          0.122759\n",
       "std           0.280013\n",
       "min           0.000000\n",
       "25%           0.000001\n",
       "50%           0.000261\n",
       "75%           0.026260\n",
       "max           1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Experimenting with MinMaxScaler on the single 'duration' feature\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "min_max_scaler = MinMaxScaler().fit(durations)\n",
    "min_max_scaled_durations = min_max_scaler.transform(durations)\n",
    "pd.Series(min_max_scaled_durations.flatten()).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    282787.000000\n",
       "mean          4.665144\n",
       "std          10.663813\n",
       "min          -0.009928\n",
       "25%          -0.009878\n",
       "50%           0.000000\n",
       "75%           0.990122\n",
       "max          38.073305\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Experimenting with RobustScaler on the single 'duration' feature\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "min_max_scaler = RobustScaler().fit(durations)\n",
    "robust_scaled_durations = min_max_scaler.transform(durations)\n",
    "pd.Series(robust_scaled_durations.flatten()).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Let's proceed with StandardScaler- Apply to all the numeric columns\n",
    "\n",
    "standard_scaler = StandardScaler()\n",
    "\n",
    "print(type(train_x))\n",
    "\n",
    "train_x_scaled = pd.DataFrame(standard_scaler.fit_transform(train_x), columns=train_x.columns)\n",
    "\n",
    "test_x_scaled = pd.DataFrame(standard_scaler.fit_transform(test_x), columns=test_x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y_bin = train_Y\n",
    "test_Y_bin = test_Y\n",
    "type(test_x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "282787 282787\n"
     ]
    }
   ],
   "source": [
    "print(len(train_Y), len(train_x_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x_scaled.to_pickle('validation_test_x.pkl')\n",
    "test_Y.to_pickle('validation_test_Y.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow 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>Fwd Act Data Pkts</th>\n",
       "      <th>Fwd Seg Size Min</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.439230</td>\n",
       "      <td>-0.009785</td>\n",
       "      <td>-0.008389</td>\n",
       "      <td>-0.081972</td>\n",
       "      <td>-0.007107</td>\n",
       "      <td>-0.274570</td>\n",
       "      <td>-0.310483</td>\n",
       "      <td>-0.283501</td>\n",
       "      <td>-0.217658</td>\n",
       "      <td>-0.444638</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006933</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>-0.124901</td>\n",
       "      <td>-0.104518</td>\n",
       "      <td>-0.148825</td>\n",
       "      <td>-0.100110</td>\n",
       "      <td>-0.352292</td>\n",
       "      <td>-0.109416</td>\n",
       "      <td>-0.357228</td>\n",
       "      <td>-0.339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.439739</td>\n",
       "      <td>-0.009785</td>\n",
       "      <td>-0.008389</td>\n",
       "      <td>-0.071678</td>\n",
       "      <td>-0.006898</td>\n",
       "      <td>-0.235468</td>\n",
       "      <td>0.335620</td>\n",
       "      <td>-0.103310</td>\n",
       "      <td>-0.245367</td>\n",
       "      <td>-0.322979</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006933</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>-0.124901</td>\n",
       "      <td>-0.104518</td>\n",
       "      <td>-0.148825</td>\n",
       "      <td>-0.100110</td>\n",
       "      <td>-0.352292</td>\n",
       "      <td>-0.109416</td>\n",
       "      <td>-0.357228</td>\n",
       "      <td>-0.339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.439743</td>\n",
       "      <td>-0.009785</td>\n",
       "      <td>-0.010395</td>\n",
       "      <td>-0.081818</td>\n",
       "      <td>-0.007113</td>\n",
       "      <td>-0.281553</td>\n",
       "      <td>-0.211083</td>\n",
       "      <td>-0.280811</td>\n",
       "      <td>-0.245367</td>\n",
       "      <td>-0.447718</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006933</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>-0.124901</td>\n",
       "      <td>-0.104518</td>\n",
       "      <td>-0.148825</td>\n",
       "      <td>-0.100110</td>\n",
       "      <td>-0.352292</td>\n",
       "      <td>-0.109416</td>\n",
       "      <td>-0.357228</td>\n",
       "      <td>-0.339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.436862</td>\n",
       "      <td>-0.009785</td>\n",
       "      <td>-0.008389</td>\n",
       "      <td>-0.073215</td>\n",
       "      <td>-0.006985</td>\n",
       "      <td>-0.242451</td>\n",
       "      <td>0.252786</td>\n",
       "      <td>-0.130204</td>\n",
       "      <td>-0.245367</td>\n",
       "      <td>-0.373285</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006933</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>-0.124901</td>\n",
       "      <td>-0.104518</td>\n",
       "      <td>-0.148825</td>\n",
       "      <td>-0.100110</td>\n",
       "      <td>-0.352292</td>\n",
       "      <td>-0.109416</td>\n",
       "      <td>-0.357228</td>\n",
       "      <td>-0.339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.219320</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>-0.006383</td>\n",
       "      <td>-0.075058</td>\n",
       "      <td>-0.002004</td>\n",
       "      <td>-0.262002</td>\n",
       "      <td>-0.310483</td>\n",
       "      <td>-0.275433</td>\n",
       "      <td>-0.225215</td>\n",
       "      <td>4.049021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>1.735827</td>\n",
       "      <td>-0.104518</td>\n",
       "      <td>1.032962</td>\n",
       "      <td>1.982027</td>\n",
       "      <td>-0.090851</td>\n",
       "      <td>-0.109416</td>\n",
       "      <td>-0.103655</td>\n",
       "      <td>-0.074978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
       "0      -0.439230     -0.009785     -0.008389        -0.081972   \n",
       "1      -0.439739     -0.009785     -0.008389        -0.071678   \n",
       "2      -0.439743     -0.009785     -0.010395        -0.081818   \n",
       "3      -0.436862     -0.009785     -0.008389        -0.073215   \n",
       "4      -0.219320     -0.001795     -0.006383        -0.075058   \n",
       "\n",
       "   TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
       "0        -0.007107        -0.274570        -0.310483         -0.283501   \n",
       "1        -0.006898        -0.235468         0.335620         -0.103310   \n",
       "2        -0.007113        -0.281553        -0.211083         -0.280811   \n",
       "3        -0.006985        -0.242451         0.252786         -0.130204   \n",
       "4        -0.002004        -0.262002        -0.310483         -0.275433   \n",
       "\n",
       "   Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Fwd Act Data Pkts  Fwd Seg Size Min  \\\n",
       "0        -0.217658        -0.444638  ...          -0.006933          0.002460   \n",
       "1        -0.245367        -0.322979  ...          -0.006933          0.002447   \n",
       "2        -0.245367        -0.447718  ...          -0.006933          0.002447   \n",
       "3        -0.245367        -0.373285  ...          -0.006933          0.002447   \n",
       "4        -0.225215         4.049021  ...           0.000844          0.002447   \n",
       "\n",
       "   Active Mean  Active Std  Active Max  Active Min  Idle Mean  Idle Std  \\\n",
       "0    -0.124901   -0.104518   -0.148825   -0.100110  -0.352292 -0.109416   \n",
       "1    -0.124901   -0.104518   -0.148825   -0.100110  -0.352292 -0.109416   \n",
       "2    -0.124901   -0.104518   -0.148825   -0.100110  -0.352292 -0.109416   \n",
       "3    -0.124901   -0.104518   -0.148825   -0.100110  -0.352292 -0.109416   \n",
       "4     1.735827   -0.104518    1.032962    1.982027  -0.090851 -0.109416   \n",
       "\n",
       "   Idle Max  Idle Min  \n",
       "0 -0.357228 -0.339386  \n",
       "1 -0.357228 -0.339386  \n",
       "2 -0.357228 -0.339386  \n",
       "3 -0.357228 -0.339386  \n",
       "4 -0.103655 -0.074978  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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