ids_cic_multiclass.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-class classification of intrusion in IDS using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: multi-class classification of intrusion in IDS\n",
    "\n",
    "Raw data from: CIC-IDS-2017 [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "import lightgbm as lgb\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/ids_cic_multiclass.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>0.305000</td>\n",
       "      <td>0.066748</td>\n",
       "      <td>0.607308</td>\n",
       "      <td>0.859725</td>\n",
       "      <td>0.071644</td>\n",
       "      <td>0.076293</td>\n",
       "      <td>4.816055e-05</td>\n",
       "      <td>0.154162</td>\n",
       "      <td>0.464730</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.432243e-01</td>\n",
       "      <td>2.593818e-01</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.804711</td>\n",
       "      <td>2.518095e-01</td>\n",
       "      <td>5.471546e-03</td>\n",
       "      <td>2.593700e-01</td>\n",
       "      <td>1.259048e+03</td>\n",
       "      <td>2504.566025</td>\n",
       "      <td>7.652101e+03</td>\n",
       "      <td>-370.725100</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.508904</td>\n",
       "      <td>0.286044</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>1.030072</td>\n",
       "      <td>2.495717e-02</td>\n",
       "      <td>0.798044</td>\n",
       "      <td>9.998273e-01</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>65.152.202.208</td>\n",
       "      <td>192.168.10.14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>49435</td>\n",
       "      <td>131</td>\n",
       "      <td>60.0</td>\n",
       "      <td>143.499129</td>\n",
       "      <td>20593</td>\n",
       "      <td>0.045534</td>\n",
       "      <td>60.00</td>\n",
       "      <td>131.75</td>\n",
       "      <td>60</td>\n",
       "      <td>347</td>\n",
       "      <td>287</td>\n",
       "      <td>60</td>\n",
       "      <td>109.541320</td>\n",
       "      <td>107</td>\n",
       "      <td>81.679389</td>\n",
       "      <td>181.113914</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.494777</td>\n",
       "      <td>2.320572</td>\n",
       "      <td>0.090267</td>\n",
       "      <td>2.538128e-07</td>\n",
       "      <td>4.396166e-07</td>\n",
       "      <td>-0.979094</td>\n",
       "      <td>1.328335</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.405639</td>\n",
       "      <td>0.217397</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.399578</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.053222</td>\n",
       "      <td>0.039891</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>0.133196</td>\n",
       "      <td>0.133196</td>\n",
       "      <td>0.044399</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>8.893013e-05</td>\n",
       "      <td>0.093305</td>\n",
       "      <td>0.475844</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.832371e+01</td>\n",
       "      <td>4.236853e+00</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.011832</td>\n",
       "      <td>-1.130712e-04</td>\n",
       "      <td>5.508040e-01</td>\n",
       "      <td>1.018000e-02</td>\n",
       "      <td>-5.653560e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.344089e+07</td>\n",
       "      <td>-336552.987623</td>\n",
       "      <td>0.011832</td>\n",
       "      <td>-25.100995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-37470.665715</td>\n",
       "      <td>8.480894e+01</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>1.188339e-01</td>\n",
       "      <td>BENIGN</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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189497</th>\n",
       "      <td>2189497</td>\n",
       "      <td>630307</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>39462</td>\n",
       "      <td>96</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.035203</td>\n",
       "      <td>5046</td>\n",
       "      <td>-0.149458</td>\n",
       "      <td>60.00</td>\n",
       "      <td>83.25</td>\n",
       "      <td>60</td>\n",
       "      <td>264</td>\n",
       "      <td>204</td>\n",
       "      <td>60</td>\n",
       "      <td>73.995003</td>\n",
       "      <td>51</td>\n",
       "      <td>53.125000</td>\n",
       "      <td>117.376744</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.506791</td>\n",
       "      <td>3.209676</td>\n",
       "      <td>1.037912</td>\n",
       "      <td>2.789839e-07</td>\n",
       "      <td>3.308342e-07</td>\n",
       "      <td>-2.096774</td>\n",
       "      <td>3.432402</td>\n",
       "      <td>1.356780</td>\n",
       "      <td>0.408431</td>\n",
       "      <td>0.463774</td>\n",
       "      <td>False</td>\n",
       "      <td>0.597940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.310055</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>1.797256</td>\n",
       "      <td>0.121327</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>5.745610</td>\n",
       "      <td>5.796573</td>\n",
       "      <td>0.644064</td>\n",
       "      <td>0.050943</td>\n",
       "      <td>2.002716e-05</td>\n",
       "      <td>5.530482</td>\n",
       "      <td>0.116457</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.246662e+01</td>\n",
       "      <td>1.095870e+03</td>\n",
       "      <td>0.515055</td>\n",
       "      <td>0.687696</td>\n",
       "      <td>3.230312e-01</td>\n",
       "      <td>1.560095e+01</td>\n",
       "      <td>7.238000e-02</td>\n",
       "      <td>1.615156e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.432130e+07</td>\n",
       "      <td>346770.111107</td>\n",
       "      <td>0.687696</td>\n",
       "      <td>0.653235</td>\n",
       "      <td>0.177846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3392.459355</td>\n",
       "      <td>2.673920e+03</td>\n",
       "      <td>0.172641</td>\n",
       "      <td>4.493058e-03</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189498</th>\n",
       "      <td>2189498</td>\n",
       "      <td>630308</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>39460</td>\n",
       "      <td>443</td>\n",
       "      <td>290</td>\n",
       "      <td>60.0</td>\n",
       "      <td>486.092584</td>\n",
       "      <td>236287</td>\n",
       "      <td>0.252666</td>\n",
       "      <td>60.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>167.618132</td>\n",
       "      <td>349</td>\n",
       "      <td>120.344828</td>\n",
       "      <td>554.688451</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.473161</td>\n",
       "      <td>1.666349</td>\n",
       "      <td>1.688049</td>\n",
       "      <td>6.288019e-09</td>\n",
       "      <td>6.874713e-09</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>4.653261</td>\n",
       "      <td>1.668839</td>\n",
       "      <td>0.400208</td>\n",
       "      <td>0.649600</td>\n",
       "      <td>False</td>\n",
       "      <td>0.634363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.349754</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>22.826011</td>\n",
       "      <td>10.262953</td>\n",
       "      <td>0.084918</td>\n",
       "      <td>51.047208</td>\n",
       "      <td>65.263079</td>\n",
       "      <td>3.839005</td>\n",
       "      <td>0.057308</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>10.240159</td>\n",
       "      <td>0.374897</td>\n",
       "      <td>False</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.829702e-01</td>\n",
       "      <td>5.167880e+00</td>\n",
       "      <td>0.784447</td>\n",
       "      <td>0.784645</td>\n",
       "      <td>1.435307e-01</td>\n",
       "      <td>8.838691e-02</td>\n",
       "      <td>1.527800e-01</td>\n",
       "      <td>7.176535e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056822e+07</td>\n",
       "      <td>185629.457917</td>\n",
       "      <td>0.784645</td>\n",
       "      <td>0.504609</td>\n",
       "      <td>0.287035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.005401</td>\n",
       "      <td>3.400897e+01</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>5.023208e-01</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189499</th>\n",
       "      <td>2189499</td>\n",
       "      <td>630309</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3395</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>39462</td>\n",
       "      <td>443</td>\n",
       "      <td>484</td>\n",
       "      <td>85.5</td>\n",
       "      <td>655.179365</td>\n",
       "      <td>429260</td>\n",
       "      <td>0.150266</td>\n",
       "      <td>60.00</td>\n",
       "      <td>743.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>135.367637</td>\n",
       "      <td>517</td>\n",
       "      <td>106.818182</td>\n",
       "      <td>781.167156</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.647151</td>\n",
       "      <td>2.085688</td>\n",
       "      <td>0.553750</td>\n",
       "      <td>1.333370e-09</td>\n",
       "      <td>1.663004e-09</td>\n",
       "      <td>-0.241581</td>\n",
       "      <td>1.663755</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.543073</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134493</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.772732</td>\n",
       "      <td>0.066410</td>\n",
       "      <td>0.063314</td>\n",
       "      <td>0.122979</td>\n",
       "      <td>5.745534</td>\n",
       "      <td>0.820791</td>\n",
       "      <td>0.012340</td>\n",
       "      <td>4.816055e-05</td>\n",
       "      <td>5.622555</td>\n",
       "      <td>0.145982</td>\n",
       "      <td>False</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.279188e+01</td>\n",
       "      <td>2.103826e+02</td>\n",
       "      <td>0.187453</td>\n",
       "      <td>0.562933</td>\n",
       "      <td>1.349265e-01</td>\n",
       "      <td>3.329814e+00</td>\n",
       "      <td>8.099000e-02</td>\n",
       "      <td>6.746327e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.671677e+07</td>\n",
       "      <td>279177.135887</td>\n",
       "      <td>0.562933</td>\n",
       "      <td>0.587287</td>\n",
       "      <td>0.257633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1559.238495</td>\n",
       "      <td>7.828635e+02</td>\n",
       "      <td>0.375480</td>\n",
       "      <td>1.882113e-02</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189500</th>\n",
       "      <td>2189500</td>\n",
       "      <td>630310</td>\n",
       "      <td>52.201.108.198</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>861</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>54364</td>\n",
       "      <td>131</td>\n",
       "      <td>60.0</td>\n",
       "      <td>161.588985</td>\n",
       "      <td>26111</td>\n",
       "      <td>0.104546</td>\n",
       "      <td>60.00</td>\n",
       "      <td>91.50</td>\n",
       "      <td>60</td>\n",
       "      <td>605</td>\n",
       "      <td>545</td>\n",
       "      <td>60</td>\n",
       "      <td>123.350370</td>\n",
       "      <td>107</td>\n",
       "      <td>81.679389</td>\n",
       "      <td>203.207816</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.439386</td>\n",
       "      <td>2.060784</td>\n",
       "      <td>1.882886</td>\n",
       "      <td>1.777567e-07</td>\n",
       "      <td>2.042269e-07</td>\n",
       "      <td>-3.507937</td>\n",
       "      <td>6.223583</td>\n",
       "      <td>1.207519</td>\n",
       "      <td>0.336829</td>\n",
       "      <td>0.428646</td>\n",
       "      <td>False</td>\n",
       "      <td>0.551030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.288032</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.475818</td>\n",
       "      <td>0.112988</td>\n",
       "      <td>0.102043</td>\n",
       "      <td>1.600927</td>\n",
       "      <td>1.651963</td>\n",
       "      <td>0.150178</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>4.792213e-05</td>\n",
       "      <td>1.434194</td>\n",
       "      <td>0.104713</td>\n",
       "      <td>False</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1581</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.537524e+01</td>\n",
       "      <td>4.604367e+01</td>\n",
       "      <td>0.765176</td>\n",
       "      <td>0.765375</td>\n",
       "      <td>8.548223e-02</td>\n",
       "      <td>9.080691e-01</td>\n",
       "      <td>8.999000e-02</td>\n",
       "      <td>4.274112e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.999190e+06</td>\n",
       "      <td>71016.140797</td>\n",
       "      <td>0.765375</td>\n",
       "      <td>0.510688</td>\n",
       "      <td>0.305019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538.634368</td>\n",
       "      <td>1.643887e+02</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>5.506893e-02</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189501</th>\n",
       "      <td>2189501</td>\n",
       "      <td>630311</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>52.201.108.198</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5299</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>54364</td>\n",
       "      <td>443</td>\n",
       "      <td>589</td>\n",
       "      <td>170.5</td>\n",
       "      <td>646.557035</td>\n",
       "      <td>418037</td>\n",
       "      <td>0.046584</td>\n",
       "      <td>67.75</td>\n",
       "      <td>1197.75</td>\n",
       "      <td>60</td>\n",
       "      <td>1508</td>\n",
       "      <td>1448</td>\n",
       "      <td>60</td>\n",
       "      <td>109.771992</td>\n",
       "      <td>585</td>\n",
       "      <td>99.320883</td>\n",
       "      <td>851.008284</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.818180</td>\n",
       "      <td>2.469233</td>\n",
       "      <td>0.176552</td>\n",
       "      <td>3.329829e-09</td>\n",
       "      <td>3.948692e-09</td>\n",
       "      <td>0.077434</td>\n",
       "      <td>1.159487</td>\n",
       "      <td>2.646439</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.672581</td>\n",
       "      <td>True</td>\n",
       "      <td>0.877121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230450</td>\n",
       "      <td>0.313889</td>\n",
       "      <td>0.369001</td>\n",
       "      <td>0.053009</td>\n",
       "      <td>0.051316</td>\n",
       "      <td>0.115952</td>\n",
       "      <td>1.601222</td>\n",
       "      <td>0.177914</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>4.601479e-05</td>\n",
       "      <td>1.485224</td>\n",
       "      <td>0.119789</td>\n",
       "      <td>False</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5899</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.894657e+02</td>\n",
       "      <td>3.101176e+01</td>\n",
       "      <td>0.549821</td>\n",
       "      <td>0.549623</td>\n",
       "      <td>1.564855e-01</td>\n",
       "      <td>2.770690e+00</td>\n",
       "      <td>2.094300e-01</td>\n",
       "      <td>7.824273e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.196021e+07</td>\n",
       "      <td>-319707.025556</td>\n",
       "      <td>0.549623</td>\n",
       "      <td>0.481767</td>\n",
       "      <td>0.327933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198.176621</td>\n",
       "      <td>4.415406e+02</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>2.455167e-02</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2189502 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0   index          DST_IP                     SRC_IP  \\\n",
       "0                 0       0       ff02::1:2  fe80::6d07:ea13:6f73:ed41   \n",
       "1                 1       1  192.168.10.255              192.168.10.25   \n",
       "2                 2       2   13.78.188.147              192.168.10.14   \n",
       "3                 3       3   192.168.10.14              13.78.188.147   \n",
       "4                 4       4  65.152.202.208              192.168.10.14   \n",
       "...             ...     ...             ...                        ...   \n",
       "2189497     2189497  630307   54.239.27.131              192.168.10.16   \n",
       "2189498     2189498  630308   192.168.10.16              54.239.27.131   \n",
       "2189499     2189499  630309   192.168.10.16              54.239.27.131   \n",
       "2189500     2189500  630310  52.201.108.198              192.168.10.16   \n",
       "2189501     2189501  630311   192.168.10.16             52.201.108.198   \n",
       "\n",
       "         PACKETS  PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  \\\n",
       "0             20            0   2060          0  1.499429e+09  1.499429e+09   \n",
       "1             36            0   7776          0  1.499429e+09  1.499429e+09   \n",
       "2             15            0   7118          0  1.499429e+09  1.499429e+09   \n",
       "3             13            0   5852          0  1.499429e+09  1.499429e+09   \n",
       "4              4            0    287          0  1.499429e+09  1.499429e+09   \n",
       "...          ...          ...    ...        ...           ...           ...   \n",
       "2189497       10            0    361          0  1.499285e+09  1.499285e+09   \n",
       "2189498       18            0   4153          0  1.499285e+09  1.499285e+09   \n",
       "2189499        8            0   3395          0  1.499285e+09  1.499285e+09   \n",
       "2189500       12            0    861          0  1.499285e+09  1.499285e+09   \n",
       "2189501       10            0   5299          0  1.499285e+09  1.499285e+09   \n",
       "\n",
       "         DST_PORT  SRC_PORT  MEAN  MEDIAN       STDEV     VAR  BURSTINESS  \\\n",
       "0             547       546   163   163.0    0.000000       0   -1.000000   \n",
       "1             138       138   276   276.0    0.000000       0   -1.000000   \n",
       "2             443     49442   534   881.0  406.987715  165640   -0.134978   \n",
       "3           49442       443   510   305.0  482.231272  232547   -0.027986   \n",
       "4              80     49435   131    60.0  143.499129   20593    0.045534   \n",
       "...           ...       ...   ...     ...         ...     ...         ...   \n",
       "2189497       443     39462    96    60.0   71.035203    5046   -0.149458   \n",
       "2189498     39460       443   290    60.0  486.092584  236287    0.252666   \n",
       "2189499     39462       443   484    85.5  655.179365  429260    0.150266   \n",
       "2189500       443     54364   131    60.0  161.588985   26111    0.104546   \n",
       "2189501     54364       443   589   170.5  646.557035  418037    0.046584   \n",
       "\n",
       "             Q1       Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0        163.00   163.00  163   163              0   163   \n",
       "1        276.00   276.00  276   276              0   276   \n",
       "2         60.00   897.00   60   945            885    60   \n",
       "3        289.00   417.00   60  1520           1460   289   \n",
       "4         60.00   131.75   60   347            287    60   \n",
       "...         ...      ...  ...   ...            ...   ...   \n",
       "2189497   60.00    83.25   60   264            204    60   \n",
       "2189498   60.00   106.00   60  1520           1460    60   \n",
       "2189499   60.00   743.00   60  1520           1460    60   \n",
       "2189500   60.00    91.50   60   605            545    60   \n",
       "2189501   67.75  1197.75   60  1508           1448    60   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                        0.000000                   0           0.000000   \n",
       "1                        0.000000                   0           0.000000   \n",
       "2                       76.214928                 386          72.284644   \n",
       "3                       94.555151                 362          70.980392   \n",
       "4                      109.541320                 107          81.679389   \n",
       "...                           ...                 ...                ...   \n",
       "2189497                 73.995003                  51          53.125000   \n",
       "2189498                167.618132                 349         120.344828   \n",
       "2189499                135.367637                 517         106.818182   \n",
       "2189500                123.350370                 107          81.679389   \n",
       "2189501                109.771992                 585          99.320883   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              163.000000            0.000000            0.000000   \n",
       "1              276.000000            0.000000            0.000000   \n",
       "2              663.568334            0.466667            0.533333   \n",
       "3              689.141383            0.769231            0.230769   \n",
       "4              181.113914            0.750000            0.250000   \n",
       "...                   ...                 ...                 ...   \n",
       "2189497        117.376744            0.800000            0.200000   \n",
       "2189498        554.688451            0.777778            0.222222   \n",
       "2189499        781.167156            0.625000            0.250000   \n",
       "2189500        203.207816            0.750000            0.250000   \n",
       "2189501        851.008284            0.600000            0.400000   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    0.000000              0.000000              0.000000   \n",
       "1                    0.000000              0.000000              0.000000   \n",
       "2                    1.164654              1.771552             -0.436380   \n",
       "3                    0.458286              2.540275              1.060541   \n",
       "4                    0.494777              2.320572              0.090267   \n",
       "...                       ...                   ...                   ...   \n",
       "2189497              0.506791              3.209676              1.037912   \n",
       "2189498              0.473161              1.666349              1.688049   \n",
       "2189499              0.647151              2.085688              0.553750   \n",
       "2189500              0.439386              2.060784              1.882886   \n",
       "2189501              0.818180              2.469233              0.176552   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      0.000000e+00                0.000000e+00   \n",
       "1                      0.000000e+00                0.000000e+00   \n",
       "2                      7.911427e-09                8.819036e-09   \n",
       "3                      1.371894e-09                1.557723e-09   \n",
       "4                      2.538128e-07                4.396166e-07   \n",
       "...                             ...                         ...   \n",
       "2189497                2.789839e-07                3.308342e-07   \n",
       "2189498                6.288019e-09                6.874713e-09   \n",
       "2189499                1.333370e-09                1.663004e-09   \n",
       "2189500                1.777567e-07                2.042269e-07   \n",
       "2189501                3.329829e-09                3.948692e-09   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "1               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2              -0.132616  0.980343  2.282589        0.584247        0.723222   \n",
       "3              -2.453125  3.076312  2.777363        0.750549        0.671154   \n",
       "4              -0.979094  1.328335  0.811278        0.405639        0.217397   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "2189497        -2.096774  3.432402  1.356780        0.408431        0.463774   \n",
       "2189498        -9.000000  4.653261  1.668839        0.400208        0.649600   \n",
       "2189499        -0.241581  1.663755  1.750000        0.583333        0.543073   \n",
       "2189500        -3.507937  6.223583  1.207519        0.336829        0.428646   \n",
       "2189501         0.077434  1.159487  2.646439        0.796658        0.672581   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.301030                  0.0   \n",
       "1                        False   0.301030                  0.0   \n",
       "2                        False   0.802060                  0.0   \n",
       "3                         True   0.909752                  0.0   \n",
       "4                        False   0.477121                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "2189497                  False   0.597940                  0.0   \n",
       "2189498                  False   0.634363                  0.0   \n",
       "2189499                  False   0.698970                  0.0   \n",
       "2189500                  False   0.551030                  0.0   \n",
       "2189501                   True   0.877121                  0.0   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                     1.0           0.550000                  0.875000   \n",
       "1                     1.0           0.666240                  0.875000   \n",
       "2                     0.0           0.421537                  0.375000   \n",
       "3                     0.0           0.430102                  0.375000   \n",
       "4                     0.0           0.399578                  0.791667   \n",
       "...                   ...                ...                       ...   \n",
       "2189497               0.0           0.310055                  0.767857   \n",
       "2189498               1.0           0.349754                  0.750000   \n",
       "2189499               0.0           0.134493                  0.625000   \n",
       "2189500               0.0           0.288032                  0.791667   \n",
       "2189501               0.0           0.230450                  0.313889   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               17.606277           32.011393        0.000019       32.011403   \n",
       "1               20.248109           30.352675        0.000060       30.391601   \n",
       "2                0.412525            0.411548        0.130263        0.676120   \n",
       "3                0.369769            0.305000        0.066748        0.607308   \n",
       "4                0.053222            0.039891        0.039802        0.133196   \n",
       "...                   ...                 ...             ...             ...   \n",
       "2189497          1.797256            0.121327        0.062660        5.745610   \n",
       "2189498         22.826011           10.262953        0.084918       51.047208   \n",
       "2189499          0.772732            0.066410        0.063314        0.122979   \n",
       "2189500          0.475818            0.112988        0.102043        1.600927   \n",
       "2189501          0.369001            0.053009        0.051316        0.115952   \n",
       "\n",
       "          DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        32.011410        1.684811          0.000002   9.536743e-07   \n",
       "1        30.391613        0.868332          0.000002   9.536743e-07   \n",
       "2         0.978622        0.069902          0.072875   7.050037e-04   \n",
       "3         0.859725        0.071644          0.076293   4.816055e-05   \n",
       "4         0.133196        0.044399          0.039802   8.893013e-05   \n",
       "...            ...             ...               ...            ...   \n",
       "2189497   5.796573        0.644064          0.050943   2.002716e-05   \n",
       "2189498  65.263079        3.839005          0.057308   3.099442e-06   \n",
       "2189499   5.745534        0.820791          0.012340   4.816055e-05   \n",
       "2189500   1.651963        0.150178          0.009250   4.792213e-05   \n",
       "2189501   1.601222        0.177914          0.001429   4.601479e-05   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            32.011359               0.052632        True          0.000000   \n",
       "1            30.352596               0.028608        True          0.000000   \n",
       "2             0.124533               0.561310       False          1.714286   \n",
       "3             0.154162               0.464730       False          1.333333   \n",
       "4             0.093305               0.475844       False          1.333333   \n",
       "...                ...                    ...         ...               ...   \n",
       "2189497       5.530482               0.116457       False          1.333333   \n",
       "2189498      10.240159               0.374897       False          0.941176   \n",
       "2189499       5.622555               0.145982       False          1.142857   \n",
       "2189500       1.434194               0.104713       False          0.909091   \n",
       "2189501       1.485224               0.119789       False          1.555556   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0                  1.0               1793   \n",
       "1             False        0.0                  1.0               6624   \n",
       "2             False        0.0                  0.0               8018   \n",
       "3              True        0.0                  0.0               6632   \n",
       "4              True        0.0                  0.0                527   \n",
       "...             ...        ...                  ...                ...   \n",
       "2189497        True        0.0                  0.0                961   \n",
       "2189498        True        0.0                  1.0               4782   \n",
       "2189499       False        0.0                  0.0               3875   \n",
       "2189500        True        0.0                  0.0               1581   \n",
       "2189501        True        0.0                  0.0               5899   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER     MAX_POWER  \\\n",
       "0               1.0         True  163  0.000011 -2.169255e+13  4.075619e+13   \n",
       "1               1.0         True  276  0.000047           NaN           NaN   \n",
       "2               1.0        False    0  0.000000  4.635747e-01  5.417682e-01   \n",
       "3               1.0        False    0  0.000000  2.432243e-01  2.593818e-01   \n",
       "4               1.0        False    0  0.000000 -3.832371e+01  4.236853e+00   \n",
       "...             ...          ...  ...       ...           ...           ...   \n",
       "2189497         1.0        False    0  0.000000 -1.246662e+01  1.095870e+03   \n",
       "2189498         1.0        False    0  0.000000 -8.829702e-01  5.167880e+00   \n",
       "2189499         1.0        False    0  0.000000 -1.279188e+01  2.103826e+02   \n",
       "2189500         1.0        False    0  0.000000 -3.537524e+01  4.604367e+01   \n",
       "2189501         1.0        False    0  0.000000 -1.894657e+02  3.101176e+01   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ    POWER_MEAN     POWER_STD  \\\n",
       "0              0.008256        0.008057  2.054117e+10  8.478452e+11   \n",
       "1                   NaN             NaN           NaN           NaN   \n",
       "2              0.999801        0.149508  5.289119e-01  1.840534e-02   \n",
       "3              0.006667        0.804711  2.518095e-01  5.471546e-03   \n",
       "4              0.012031        0.011832 -1.130712e-04  5.508040e-01   \n",
       "...                 ...             ...           ...           ...   \n",
       "2189497        0.515055        0.687696  3.230312e-01  1.560095e+01   \n",
       "2189498        0.784447        0.784645  1.435307e-01  8.838691e-02   \n",
       "2189499        0.187453        0.562933  1.349265e-01  3.329814e+00   \n",
       "2189500        0.765176        0.765375  8.548223e-02  9.080691e-01   \n",
       "2189501        0.549821        0.549623  1.564855e-01  2.770690e+00   \n",
       "\n",
       "           POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0        1.772407e+13     1.027058e+14               NaN       6.361159e+06   \n",
       "1        0.000000e+00              NaN               NaN                NaN   \n",
       "2        5.417600e-01     2.644559e+03       2427.727179       2.657057e+04   \n",
       "3        2.593700e-01     1.259048e+03       2504.566025       7.652101e+03   \n",
       "4        1.018000e-02    -5.653560e-01               NaN       2.344089e+07   \n",
       "...               ...              ...               ...                ...   \n",
       "2189497  7.238000e-02     1.615156e+03               NaN       2.432130e+07   \n",
       "2189498  1.527800e-01     7.176535e+02               NaN       1.056822e+07   \n",
       "2189499  8.099000e-02     6.746327e+02               NaN       1.671677e+07   \n",
       "2189500  8.999000e-02     4.274112e+02               NaN       8.999190e+06   \n",
       "2189501  2.094300e-01     7.824273e+02               NaN       2.196021e+07   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            127555.760160          0.008057           0.007112   \n",
       "1                      NaN          0.006667                NaN   \n",
       "2             -8887.782747          0.006667           0.495092   \n",
       "3              -370.725100          0.006667           0.508904   \n",
       "4           -336552.987623          0.011832         -25.100995   \n",
       "...                    ...               ...                ...   \n",
       "2189497      346770.111107          0.687696           0.653235   \n",
       "2189498      185629.457917          0.784645           0.504609   \n",
       "2189499      279177.135887          0.562933           0.587287   \n",
       "2189500       71016.140797          0.765375           0.510688   \n",
       "2189501     -319707.025556          0.549623           0.481767   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0                    NaN             NaN     1984.122308   1.192864e+14   \n",
       "1                    NaN             NaN             NaN            NaN   \n",
       "2               0.284281       -0.022448        1.024307   8.116715e-02   \n",
       "3               0.286044        0.020387        1.030072   2.495717e-02   \n",
       "4                    NaN             NaN   -37470.665715   8.480894e+01   \n",
       "...                  ...             ...             ...            ...   \n",
       "2189497         0.177846             NaN     3392.459355   2.673920e+03   \n",
       "2189498         0.287035             NaN       36.005401   3.400897e+01   \n",
       "2189499         0.257633             NaN     1559.238495   7.828635e+02   \n",
       "2189500         0.305019             NaN      538.634368   1.643887e+02   \n",
       "2189501         0.327933             NaN      198.176621   4.415406e+02   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF   LABEL  \n",
       "0                 -0.000199      5.673240e-14  BENIGN  \n",
       "1                  0.000000               NaN  BENIGN  \n",
       "2                 -0.850293      7.979590e-01  BENIGN  \n",
       "3                  0.798044      9.998273e-01  BENIGN  \n",
       "4                 -0.000199      1.188339e-01  BENIGN  \n",
       "...                     ...               ...     ...  \n",
       "2189497            0.172641      4.493058e-03  BENIGN  \n",
       "2189498            0.000199      5.023208e-01  BENIGN  \n",
       "2189499            0.375480      1.882113e-02  BENIGN  \n",
       "2189500            0.000199      5.506893e-02  BENIGN  \n",
       "2189501           -0.000199      2.455167e-02  BENIGN  \n",
       "\n",
       "[2189502 rows x 85 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BENIGN                        1699049\n",
       "DDoS                           182672\n",
       "DoS Slowhttptest                77419\n",
       "DoS GoldenEye                   71372\n",
       "DoS slowloris                   69569\n",
       "DoS Hulk                        69043\n",
       "FTP-Patator                      7982\n",
       "SSH-Patator                      5960\n",
       "Bot                              2470\n",
       "PortScan                         1611\n",
       "Web Attack – Brute Force         1596\n",
       "Web Attack – XSS                  681\n",
       "Infiltration                       44\n",
       "Web Attack – Sql Injection         24\n",
       "Heartbleed                         10\n",
       "Name: LABEL, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.LABEL.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.LABEL == \"Web Attack – Sql Injection\", \"LABEL\"] = \"Web Attack\"\n",
    "df.loc[df.LABEL == \"Web Attack – XSS\", \"LABEL\"] = \"Web Attack\"\n",
    "df.loc[df.LABEL == \"Web Attack – Brute Force\", \"LABEL\"] = \"Web Attack\"\n",
    "\n",
    "df.loc[df.LABEL == \"DoS Slowhttptest\", \"LABEL\"] = \"DDoS\"\n",
    "df.loc[df.LABEL == \"DoS GoldenEye\", \"LABEL\"] = \"DDoS\"\n",
    "df.loc[df.LABEL == \"DoS slowloris\", \"LABEL\"] = \"DDoS\"\n",
    "df.loc[df.LABEL == \"DoS Hulk\", \"LABEL\"] = \"DDoS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BENIGN          1699049\n",
       "DDoS             470075\n",
       "FTP-Patator        7982\n",
       "SSH-Patator        5960\n",
       "Bot                2470\n",
       "Web Attack         2301\n",
       "PortScan           1611\n",
       "Infiltration         44\n",
       "Heartbleed           10\n",
       "Name: LABEL, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.LABEL.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "\n",
    "def handle_df(df):\n",
    "    \n",
    "    bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "    for F in features:\n",
    "        if F in bool_features:\n",
    "            continue\n",
    "        # if F in distribution_features:\n",
    "        #     df.replace([np.inf], 0.5, inplace=True)\n",
    "        #     df.replace([-np.inf], 0.5, inplace=True)\n",
    "        #     df.loc[df[F].isnull(), F] = 0.5\n",
    "        # elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        #     df.replace([np.inf], 0.5, inplace=True)\n",
    "        #     df.replace([-np.inf], 0.5, inplace=True)\n",
    "        #     df.loc[df[F].isnull(), F] = 0.5\n",
    "        # elif F in frequency_features: \n",
    "        #     df.replace([np.inf], -1, inplace=True)\n",
    "        #     df.replace([-np.inf], -1, inplace=True)\n",
    "        #     df.loc[df[F].isnull(), F] = -1\n",
    "        # else:\n",
    "        #     df.replace([np.inf], 0, inplace=True)\n",
    "        #     df.replace([-np.inf], 0, inplace=True)\n",
    "        #     df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "    for F in bool_features:\n",
    "        df.loc[df[F].isnull(), F] = False\n",
    "        df[F] = df[F].astype('bool')\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = handle_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ff02::1:2</td>\n",
       "      <td>fe80::6d07:ea13:6f73:ed41</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>547</td>\n",
       "      <td>546</td>\n",
       "      <td>163</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>163.00</td>\n",
       "      <td>163.00</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</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>0.000000e+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>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>17.606277</td>\n",
       "      <td>32.011393</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>32.011403</td>\n",
       "      <td>32.011410</td>\n",
       "      <td>1.684811</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>32.011359</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1793</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>163</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>-2.169255e+13</td>\n",
       "      <td>4.075619e+13</td>\n",
       "      <td>0.008256</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>2.054117e+10</td>\n",
       "      <td>8.478452e+11</td>\n",
       "      <td>1.772407e+13</td>\n",
       "      <td>1.027058e+14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.361159e+06</td>\n",
       "      <td>127555.760160</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1984.122308</td>\n",
       "      <td>1.192864e+14</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>192.168.10.255</td>\n",
       "      <td>192.168.10.25</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>7776</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>276</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>276.00</td>\n",
       "      <td>276.00</td>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>276.000000</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>0.000000e+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>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666240</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>20.248109</td>\n",
       "      <td>30.352675</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>30.391601</td>\n",
       "      <td>30.391613</td>\n",
       "      <td>0.868332</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>30.352596</td>\n",
       "      <td>0.028608</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>276</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>13.78.188.147</td>\n",
       "      <td>192.168.10.14</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>7118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>49442</td>\n",
       "      <td>534</td>\n",
       "      <td>881.0</td>\n",
       "      <td>406.987715</td>\n",
       "      <td>165640</td>\n",
       "      <td>-0.134978</td>\n",
       "      <td>60.00</td>\n",
       "      <td>897.00</td>\n",
       "      <td>60</td>\n",
       "      <td>945</td>\n",
       "      <td>885</td>\n",
       "      <td>60</td>\n",
       "      <td>76.214928</td>\n",
       "      <td>386</td>\n",
       "      <td>72.284644</td>\n",
       "      <td>663.568334</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.164654</td>\n",
       "      <td>1.771552</td>\n",
       "      <td>-0.436380</td>\n",
       "      <td>7.911427e-09</td>\n",
       "      <td>8.819036e-09</td>\n",
       "      <td>-0.132616</td>\n",
       "      <td>0.980343</td>\n",
       "      <td>2.282589</td>\n",
       "      <td>0.584247</td>\n",
       "      <td>0.723222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.802060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.421537</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.412525</td>\n",
       "      <td>0.411548</td>\n",
       "      <td>0.130263</td>\n",
       "      <td>0.676120</td>\n",
       "      <td>0.978622</td>\n",
       "      <td>0.069902</td>\n",
       "      <td>0.072875</td>\n",
       "      <td>7.050037e-04</td>\n",
       "      <td>0.124533</td>\n",
       "      <td>0.561310</td>\n",
       "      <td>False</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.635747e-01</td>\n",
       "      <td>5.417682e-01</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.149508</td>\n",
       "      <td>5.289119e-01</td>\n",
       "      <td>1.840534e-02</td>\n",
       "      <td>5.417600e-01</td>\n",
       "      <td>2.644559e+03</td>\n",
       "      <td>2427.727179</td>\n",
       "      <td>2.657057e+04</td>\n",
       "      <td>-8887.782747</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.495092</td>\n",
       "      <td>0.284281</td>\n",
       "      <td>-0.022448</td>\n",
       "      <td>1.024307</td>\n",
       "      <td>8.116715e-02</td>\n",
       "      <td>-0.850293</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>192.168.10.14</td>\n",
       "      <td>13.78.188.147</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>5852</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>49442</td>\n",
       "      <td>443</td>\n",
       "      <td>510</td>\n",
       "      <td>305.0</td>\n",
       "      <td>482.231272</td>\n",
       "      <td>232547</td>\n",
       "      <td>-0.027986</td>\n",
       "      <td>289.00</td>\n",
       "      <td>417.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>289</td>\n",
       "      <td>94.555151</td>\n",
       "      <td>362</td>\n",
       "      <td>70.980392</td>\n",
       "      <td>689.141383</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.458286</td>\n",
       "      <td>2.540275</td>\n",
       "      <td>1.060541</td>\n",
       "      <td>1.371894e-09</td>\n",
       "      <td>1.557723e-09</td>\n",
       "      <td>-2.453125</td>\n",
       "      <td>3.076312</td>\n",
       "      <td>2.777363</td>\n",
       "      <td>0.750549</td>\n",
       "      <td>0.671154</td>\n",
       "      <td>True</td>\n",
       "      <td>0.909752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.430102</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.369769</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>0.066748</td>\n",
       "      <td>0.607308</td>\n",
       "      <td>0.859725</td>\n",
       "      <td>0.071644</td>\n",
       "      <td>0.076293</td>\n",
       "      <td>4.816055e-05</td>\n",
       "      <td>0.154162</td>\n",
       "      <td>0.464730</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.432243e-01</td>\n",
       "      <td>2.593818e-01</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.804711</td>\n",
       "      <td>2.518095e-01</td>\n",
       "      <td>5.471546e-03</td>\n",
       "      <td>2.593700e-01</td>\n",
       "      <td>1.259048e+03</td>\n",
       "      <td>2504.566025</td>\n",
       "      <td>7.652101e+03</td>\n",
       "      <td>-370.725100</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.508904</td>\n",
       "      <td>0.286044</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>1.030072</td>\n",
       "      <td>2.495717e-02</td>\n",
       "      <td>0.798044</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>65.152.202.208</td>\n",
       "      <td>192.168.10.14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>1.499429e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>49435</td>\n",
       "      <td>131</td>\n",
       "      <td>60.0</td>\n",
       "      <td>143.499129</td>\n",
       "      <td>20593</td>\n",
       "      <td>0.045534</td>\n",
       "      <td>60.00</td>\n",
       "      <td>131.75</td>\n",
       "      <td>60</td>\n",
       "      <td>347</td>\n",
       "      <td>287</td>\n",
       "      <td>60</td>\n",
       "      <td>109.541320</td>\n",
       "      <td>107</td>\n",
       "      <td>81.679389</td>\n",
       "      <td>181.113914</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.494777</td>\n",
       "      <td>2.320572</td>\n",
       "      <td>0.090267</td>\n",
       "      <td>2.538128e-07</td>\n",
       "      <td>4.396166e-07</td>\n",
       "      <td>-0.979094</td>\n",
       "      <td>1.328335</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.405639</td>\n",
       "      <td>0.217397</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.399578</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.053222</td>\n",
       "      <td>0.039891</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>0.133196</td>\n",
       "      <td>0.133196</td>\n",
       "      <td>0.044399</td>\n",
       "      <td>0.039802</td>\n",
       "      <td>8.893013e-05</td>\n",
       "      <td>0.093305</td>\n",
       "      <td>0.475844</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.832371e+01</td>\n",
       "      <td>4.236853e+00</td>\n",
       "      <td>0.012031</td>\n",
       "      <td>0.011832</td>\n",
       "      <td>-1.130712e-04</td>\n",
       "      <td>5.508040e-01</td>\n",
       "      <td>1.018000e-02</td>\n",
       "      <td>-5.653560e-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.344089e+07</td>\n",
       "      <td>-336552.987623</td>\n",
       "      <td>0.011832</td>\n",
       "      <td>-25.100995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-37470.665715</td>\n",
       "      <td>8.480894e+01</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</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",
       "      <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",
       "      <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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189497</th>\n",
       "      <td>2189497</td>\n",
       "      <td>630307</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>10</td>\n",
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       "      <td>361</td>\n",
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       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>39462</td>\n",
       "      <td>96</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.035203</td>\n",
       "      <td>5046</td>\n",
       "      <td>-0.149458</td>\n",
       "      <td>60.00</td>\n",
       "      <td>83.25</td>\n",
       "      <td>60</td>\n",
       "      <td>264</td>\n",
       "      <td>204</td>\n",
       "      <td>60</td>\n",
       "      <td>73.995003</td>\n",
       "      <td>51</td>\n",
       "      <td>53.125000</td>\n",
       "      <td>117.376744</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.506791</td>\n",
       "      <td>3.209676</td>\n",
       "      <td>1.037912</td>\n",
       "      <td>2.789839e-07</td>\n",
       "      <td>3.308342e-07</td>\n",
       "      <td>-2.096774</td>\n",
       "      <td>3.432402</td>\n",
       "      <td>1.356780</td>\n",
       "      <td>0.408431</td>\n",
       "      <td>0.463774</td>\n",
       "      <td>False</td>\n",
       "      <td>0.597940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.310055</td>\n",
       "      <td>0.767857</td>\n",
       "      <td>1.797256</td>\n",
       "      <td>0.121327</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>5.745610</td>\n",
       "      <td>5.796573</td>\n",
       "      <td>0.644064</td>\n",
       "      <td>0.050943</td>\n",
       "      <td>2.002716e-05</td>\n",
       "      <td>5.530482</td>\n",
       "      <td>0.116457</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.246662e+01</td>\n",
       "      <td>1.095870e+03</td>\n",
       "      <td>0.515055</td>\n",
       "      <td>0.687696</td>\n",
       "      <td>3.230312e-01</td>\n",
       "      <td>1.560095e+01</td>\n",
       "      <td>7.238000e-02</td>\n",
       "      <td>1.615156e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.432130e+07</td>\n",
       "      <td>346770.111107</td>\n",
       "      <td>0.687696</td>\n",
       "      <td>0.653235</td>\n",
       "      <td>0.177846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3392.459355</td>\n",
       "      <td>2.673920e+03</td>\n",
       "      <td>0.172641</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189498</th>\n",
       "      <td>2189498</td>\n",
       "      <td>630308</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>18</td>\n",
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       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>39460</td>\n",
       "      <td>443</td>\n",
       "      <td>290</td>\n",
       "      <td>60.0</td>\n",
       "      <td>486.092584</td>\n",
       "      <td>236287</td>\n",
       "      <td>0.252666</td>\n",
       "      <td>60.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>167.618132</td>\n",
       "      <td>349</td>\n",
       "      <td>120.344828</td>\n",
       "      <td>554.688451</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.473161</td>\n",
       "      <td>1.666349</td>\n",
       "      <td>1.688049</td>\n",
       "      <td>6.288019e-09</td>\n",
       "      <td>6.874713e-09</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>4.653261</td>\n",
       "      <td>1.668839</td>\n",
       "      <td>0.400208</td>\n",
       "      <td>0.649600</td>\n",
       "      <td>False</td>\n",
       "      <td>0.634363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.349754</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>22.826011</td>\n",
       "      <td>10.262953</td>\n",
       "      <td>0.084918</td>\n",
       "      <td>51.047208</td>\n",
       "      <td>65.263079</td>\n",
       "      <td>3.839005</td>\n",
       "      <td>0.057308</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>10.240159</td>\n",
       "      <td>0.374897</td>\n",
       "      <td>False</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4782</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-8.829702e-01</td>\n",
       "      <td>5.167880e+00</td>\n",
       "      <td>0.784447</td>\n",
       "      <td>0.784645</td>\n",
       "      <td>1.435307e-01</td>\n",
       "      <td>8.838691e-02</td>\n",
       "      <td>1.527800e-01</td>\n",
       "      <td>7.176535e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056822e+07</td>\n",
       "      <td>185629.457917</td>\n",
       "      <td>0.784645</td>\n",
       "      <td>0.504609</td>\n",
       "      <td>0.287035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.005401</td>\n",
       "      <td>3.400897e+01</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189499</th>\n",
       "      <td>2189499</td>\n",
       "      <td>630309</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>54.239.27.131</td>\n",
       "      <td>8</td>\n",
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       "      <td>3395</td>\n",
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       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>39462</td>\n",
       "      <td>443</td>\n",
       "      <td>484</td>\n",
       "      <td>85.5</td>\n",
       "      <td>655.179365</td>\n",
       "      <td>429260</td>\n",
       "      <td>0.150266</td>\n",
       "      <td>60.00</td>\n",
       "      <td>743.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>135.367637</td>\n",
       "      <td>517</td>\n",
       "      <td>106.818182</td>\n",
       "      <td>781.167156</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.647151</td>\n",
       "      <td>2.085688</td>\n",
       "      <td>0.553750</td>\n",
       "      <td>1.333370e-09</td>\n",
       "      <td>1.663004e-09</td>\n",
       "      <td>-0.241581</td>\n",
       "      <td>1.663755</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.543073</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134493</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.772732</td>\n",
       "      <td>0.066410</td>\n",
       "      <td>0.063314</td>\n",
       "      <td>0.122979</td>\n",
       "      <td>5.745534</td>\n",
       "      <td>0.820791</td>\n",
       "      <td>0.012340</td>\n",
       "      <td>4.816055e-05</td>\n",
       "      <td>5.622555</td>\n",
       "      <td>0.145982</td>\n",
       "      <td>False</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.279188e+01</td>\n",
       "      <td>2.103826e+02</td>\n",
       "      <td>0.187453</td>\n",
       "      <td>0.562933</td>\n",
       "      <td>1.349265e-01</td>\n",
       "      <td>3.329814e+00</td>\n",
       "      <td>8.099000e-02</td>\n",
       "      <td>6.746327e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.671677e+07</td>\n",
       "      <td>279177.135887</td>\n",
       "      <td>0.562933</td>\n",
       "      <td>0.587287</td>\n",
       "      <td>0.257633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1559.238495</td>\n",
       "      <td>7.828635e+02</td>\n",
       "      <td>0.375480</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189500</th>\n",
       "      <td>2189500</td>\n",
       "      <td>630310</td>\n",
       "      <td>52.201.108.198</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>12</td>\n",
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       "      <td>861</td>\n",
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       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>54364</td>\n",
       "      <td>131</td>\n",
       "      <td>60.0</td>\n",
       "      <td>161.588985</td>\n",
       "      <td>26111</td>\n",
       "      <td>0.104546</td>\n",
       "      <td>60.00</td>\n",
       "      <td>91.50</td>\n",
       "      <td>60</td>\n",
       "      <td>605</td>\n",
       "      <td>545</td>\n",
       "      <td>60</td>\n",
       "      <td>123.350370</td>\n",
       "      <td>107</td>\n",
       "      <td>81.679389</td>\n",
       "      <td>203.207816</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.439386</td>\n",
       "      <td>2.060784</td>\n",
       "      <td>1.882886</td>\n",
       "      <td>1.777567e-07</td>\n",
       "      <td>2.042269e-07</td>\n",
       "      <td>-3.507937</td>\n",
       "      <td>6.223583</td>\n",
       "      <td>1.207519</td>\n",
       "      <td>0.336829</td>\n",
       "      <td>0.428646</td>\n",
       "      <td>False</td>\n",
       "      <td>0.551030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.288032</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.475818</td>\n",
       "      <td>0.112988</td>\n",
       "      <td>0.102043</td>\n",
       "      <td>1.600927</td>\n",
       "      <td>1.651963</td>\n",
       "      <td>0.150178</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>4.792213e-05</td>\n",
       "      <td>1.434194</td>\n",
       "      <td>0.104713</td>\n",
       "      <td>False</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1581</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.537524e+01</td>\n",
       "      <td>4.604367e+01</td>\n",
       "      <td>0.765176</td>\n",
       "      <td>0.765375</td>\n",
       "      <td>8.548223e-02</td>\n",
       "      <td>9.080691e-01</td>\n",
       "      <td>8.999000e-02</td>\n",
       "      <td>4.274112e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.999190e+06</td>\n",
       "      <td>71016.140797</td>\n",
       "      <td>0.765375</td>\n",
       "      <td>0.510688</td>\n",
       "      <td>0.305019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538.634368</td>\n",
       "      <td>1.643887e+02</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189501</th>\n",
       "      <td>2189501</td>\n",
       "      <td>630311</td>\n",
       "      <td>192.168.10.16</td>\n",
       "      <td>52.201.108.198</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5299</td>\n",
       "      <td>0</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>1.499285e+09</td>\n",
       "      <td>54364</td>\n",
       "      <td>443</td>\n",
       "      <td>589</td>\n",
       "      <td>170.5</td>\n",
       "      <td>646.557035</td>\n",
       "      <td>418037</td>\n",
       "      <td>0.046584</td>\n",
       "      <td>67.75</td>\n",
       "      <td>1197.75</td>\n",
       "      <td>60</td>\n",
       "      <td>1508</td>\n",
       "      <td>1448</td>\n",
       "      <td>60</td>\n",
       "      <td>109.771992</td>\n",
       "      <td>585</td>\n",
       "      <td>99.320883</td>\n",
       "      <td>851.008284</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.818180</td>\n",
       "      <td>2.469233</td>\n",
       "      <td>0.176552</td>\n",
       "      <td>3.329829e-09</td>\n",
       "      <td>3.948692e-09</td>\n",
       "      <td>0.077434</td>\n",
       "      <td>1.159487</td>\n",
       "      <td>2.646439</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.672581</td>\n",
       "      <td>True</td>\n",
       "      <td>0.877121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230450</td>\n",
       "      <td>0.313889</td>\n",
       "      <td>0.369001</td>\n",
       "      <td>0.053009</td>\n",
       "      <td>0.051316</td>\n",
       "      <td>0.115952</td>\n",
       "      <td>1.601222</td>\n",
       "      <td>0.177914</td>\n",
       "      <td>0.001429</td>\n",
       "      <td>4.601479e-05</td>\n",
       "      <td>1.485224</td>\n",
       "      <td>0.119789</td>\n",
       "      <td>False</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5899</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.894657e+02</td>\n",
       "      <td>3.101176e+01</td>\n",
       "      <td>0.549821</td>\n",
       "      <td>0.549623</td>\n",
       "      <td>1.564855e-01</td>\n",
       "      <td>2.770690e+00</td>\n",
       "      <td>2.094300e-01</td>\n",
       "      <td>7.824273e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.196021e+07</td>\n",
       "      <td>-319707.025556</td>\n",
       "      <td>0.549623</td>\n",
       "      <td>0.481767</td>\n",
       "      <td>0.327933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198.176621</td>\n",
       "      <td>4.415406e+02</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2189502 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0   index          DST_IP                     SRC_IP  \\\n",
       "0                 0       0       ff02::1:2  fe80::6d07:ea13:6f73:ed41   \n",
       "1                 1       1  192.168.10.255              192.168.10.25   \n",
       "2                 2       2   13.78.188.147              192.168.10.14   \n",
       "3                 3       3   192.168.10.14              13.78.188.147   \n",
       "4                 4       4  65.152.202.208              192.168.10.14   \n",
       "...             ...     ...             ...                        ...   \n",
       "2189497     2189497  630307   54.239.27.131              192.168.10.16   \n",
       "2189498     2189498  630308   192.168.10.16              54.239.27.131   \n",
       "2189499     2189499  630309   192.168.10.16              54.239.27.131   \n",
       "2189500     2189500  630310  52.201.108.198              192.168.10.16   \n",
       "2189501     2189501  630311   192.168.10.16             52.201.108.198   \n",
       "\n",
       "         PACKETS  PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  \\\n",
       "0             20            0   2060          0  1.499429e+09  1.499429e+09   \n",
       "1             36            0   7776          0  1.499429e+09  1.499429e+09   \n",
       "2             15            0   7118          0  1.499429e+09  1.499429e+09   \n",
       "3             13            0   5852          0  1.499429e+09  1.499429e+09   \n",
       "4              4            0    287          0  1.499429e+09  1.499429e+09   \n",
       "...          ...          ...    ...        ...           ...           ...   \n",
       "2189497       10            0    361          0  1.499285e+09  1.499285e+09   \n",
       "2189498       18            0   4153          0  1.499285e+09  1.499285e+09   \n",
       "2189499        8            0   3395          0  1.499285e+09  1.499285e+09   \n",
       "2189500       12            0    861          0  1.499285e+09  1.499285e+09   \n",
       "2189501       10            0   5299          0  1.499285e+09  1.499285e+09   \n",
       "\n",
       "         DST_PORT  SRC_PORT  MEAN  MEDIAN       STDEV     VAR  BURSTINESS  \\\n",
       "0             547       546   163   163.0    0.000000       0   -1.000000   \n",
       "1             138       138   276   276.0    0.000000       0   -1.000000   \n",
       "2             443     49442   534   881.0  406.987715  165640   -0.134978   \n",
       "3           49442       443   510   305.0  482.231272  232547   -0.027986   \n",
       "4              80     49435   131    60.0  143.499129   20593    0.045534   \n",
       "...           ...       ...   ...     ...         ...     ...         ...   \n",
       "2189497       443     39462    96    60.0   71.035203    5046   -0.149458   \n",
       "2189498     39460       443   290    60.0  486.092584  236287    0.252666   \n",
       "2189499     39462       443   484    85.5  655.179365  429260    0.150266   \n",
       "2189500       443     54364   131    60.0  161.588985   26111    0.104546   \n",
       "2189501     54364       443   589   170.5  646.557035  418037    0.046584   \n",
       "\n",
       "             Q1       Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0        163.00   163.00  163   163              0   163   \n",
       "1        276.00   276.00  276   276              0   276   \n",
       "2         60.00   897.00   60   945            885    60   \n",
       "3        289.00   417.00   60  1520           1460   289   \n",
       "4         60.00   131.75   60   347            287    60   \n",
       "...         ...      ...  ...   ...            ...   ...   \n",
       "2189497   60.00    83.25   60   264            204    60   \n",
       "2189498   60.00   106.00   60  1520           1460    60   \n",
       "2189499   60.00   743.00   60  1520           1460    60   \n",
       "2189500   60.00    91.50   60   605            545    60   \n",
       "2189501   67.75  1197.75   60  1508           1448    60   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                        0.000000                   0           0.000000   \n",
       "1                        0.000000                   0           0.000000   \n",
       "2                       76.214928                 386          72.284644   \n",
       "3                       94.555151                 362          70.980392   \n",
       "4                      109.541320                 107          81.679389   \n",
       "...                           ...                 ...                ...   \n",
       "2189497                 73.995003                  51          53.125000   \n",
       "2189498                167.618132                 349         120.344828   \n",
       "2189499                135.367637                 517         106.818182   \n",
       "2189500                123.350370                 107          81.679389   \n",
       "2189501                109.771992                 585          99.320883   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              163.000000            0.000000            0.000000   \n",
       "1              276.000000            0.000000            0.000000   \n",
       "2              663.568334            0.466667            0.533333   \n",
       "3              689.141383            0.769231            0.230769   \n",
       "4              181.113914            0.750000            0.250000   \n",
       "...                   ...                 ...                 ...   \n",
       "2189497        117.376744            0.800000            0.200000   \n",
       "2189498        554.688451            0.777778            0.222222   \n",
       "2189499        781.167156            0.625000            0.250000   \n",
       "2189500        203.207816            0.750000            0.250000   \n",
       "2189501        851.008284            0.600000            0.400000   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    0.000000              0.000000              0.000000   \n",
       "1                    0.000000              0.000000              0.000000   \n",
       "2                    1.164654              1.771552             -0.436380   \n",
       "3                    0.458286              2.540275              1.060541   \n",
       "4                    0.494777              2.320572              0.090267   \n",
       "...                       ...                   ...                   ...   \n",
       "2189497              0.506791              3.209676              1.037912   \n",
       "2189498              0.473161              1.666349              1.688049   \n",
       "2189499              0.647151              2.085688              0.553750   \n",
       "2189500              0.439386              2.060784              1.882886   \n",
       "2189501              0.818180              2.469233              0.176552   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      0.000000e+00                0.000000e+00   \n",
       "1                      0.000000e+00                0.000000e+00   \n",
       "2                      7.911427e-09                8.819036e-09   \n",
       "3                      1.371894e-09                1.557723e-09   \n",
       "4                      2.538128e-07                4.396166e-07   \n",
       "...                             ...                         ...   \n",
       "2189497                2.789839e-07                3.308342e-07   \n",
       "2189498                6.288019e-09                6.874713e-09   \n",
       "2189499                1.333370e-09                1.663004e-09   \n",
       "2189500                1.777567e-07                2.042269e-07   \n",
       "2189501                3.329829e-09                3.948692e-09   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "1               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2              -0.132616  0.980343  2.282589        0.584247        0.723222   \n",
       "3              -2.453125  3.076312  2.777363        0.750549        0.671154   \n",
       "4              -0.979094  1.328335  0.811278        0.405639        0.217397   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "2189497        -2.096774  3.432402  1.356780        0.408431        0.463774   \n",
       "2189498        -9.000000  4.653261  1.668839        0.400208        0.649600   \n",
       "2189499        -0.241581  1.663755  1.750000        0.583333        0.543073   \n",
       "2189500        -3.507937  6.223583  1.207519        0.336829        0.428646   \n",
       "2189501         0.077434  1.159487  2.646439        0.796658        0.672581   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.301030                  0.0   \n",
       "1                        False   0.301030                  0.0   \n",
       "2                        False   0.802060                  0.0   \n",
       "3                         True   0.909752                  0.0   \n",
       "4                        False   0.477121                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "2189497                  False   0.597940                  0.0   \n",
       "2189498                  False   0.634363                  0.0   \n",
       "2189499                  False   0.698970                  0.0   \n",
       "2189500                  False   0.551030                  0.0   \n",
       "2189501                   True   0.877121                  0.0   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                     1.0           0.550000                  0.875000   \n",
       "1                     1.0           0.666240                  0.875000   \n",
       "2                     0.0           0.421537                  0.375000   \n",
       "3                     0.0           0.430102                  0.375000   \n",
       "4                     0.0           0.399578                  0.791667   \n",
       "...                   ...                ...                       ...   \n",
       "2189497               0.0           0.310055                  0.767857   \n",
       "2189498               1.0           0.349754                  0.750000   \n",
       "2189499               0.0           0.134493                  0.625000   \n",
       "2189500               0.0           0.288032                  0.791667   \n",
       "2189501               0.0           0.230450                  0.313889   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               17.606277           32.011393        0.000019       32.011403   \n",
       "1               20.248109           30.352675        0.000060       30.391601   \n",
       "2                0.412525            0.411548        0.130263        0.676120   \n",
       "3                0.369769            0.305000        0.066748        0.607308   \n",
       "4                0.053222            0.039891        0.039802        0.133196   \n",
       "...                   ...                 ...             ...             ...   \n",
       "2189497          1.797256            0.121327        0.062660        5.745610   \n",
       "2189498         22.826011           10.262953        0.084918       51.047208   \n",
       "2189499          0.772732            0.066410        0.063314        0.122979   \n",
       "2189500          0.475818            0.112988        0.102043        1.600927   \n",
       "2189501          0.369001            0.053009        0.051316        0.115952   \n",
       "\n",
       "          DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        32.011410        1.684811          0.000002   9.536743e-07   \n",
       "1        30.391613        0.868332          0.000002   9.536743e-07   \n",
       "2         0.978622        0.069902          0.072875   7.050037e-04   \n",
       "3         0.859725        0.071644          0.076293   4.816055e-05   \n",
       "4         0.133196        0.044399          0.039802   8.893013e-05   \n",
       "...            ...             ...               ...            ...   \n",
       "2189497   5.796573        0.644064          0.050943   2.002716e-05   \n",
       "2189498  65.263079        3.839005          0.057308   3.099442e-06   \n",
       "2189499   5.745534        0.820791          0.012340   4.816055e-05   \n",
       "2189500   1.651963        0.150178          0.009250   4.792213e-05   \n",
       "2189501   1.601222        0.177914          0.001429   4.601479e-05   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            32.011359               0.052632        True          0.000000   \n",
       "1            30.352596               0.028608        True          0.000000   \n",
       "2             0.124533               0.561310       False          1.714286   \n",
       "3             0.154162               0.464730       False          1.333333   \n",
       "4             0.093305               0.475844       False          1.333333   \n",
       "...                ...                    ...         ...               ...   \n",
       "2189497       5.530482               0.116457       False          1.333333   \n",
       "2189498      10.240159               0.374897       False          0.941176   \n",
       "2189499       5.622555               0.145982       False          1.142857   \n",
       "2189500       1.434194               0.104713       False          0.909091   \n",
       "2189501       1.485224               0.119789       False          1.555556   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0                  1.0               1793   \n",
       "1             False        0.0                  1.0               6624   \n",
       "2             False        0.0                  0.0               8018   \n",
       "3              True        0.0                  0.0               6632   \n",
       "4              True        0.0                  0.0                527   \n",
       "...             ...        ...                  ...                ...   \n",
       "2189497        True        0.0                  0.0                961   \n",
       "2189498        True        0.0                  1.0               4782   \n",
       "2189499       False        0.0                  0.0               3875   \n",
       "2189500        True        0.0                  0.0               1581   \n",
       "2189501        True        0.0                  0.0               5899   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER     MAX_POWER  \\\n",
       "0               1.0         True  163  0.000011 -2.169255e+13  4.075619e+13   \n",
       "1               1.0         True  276  0.000047           NaN           NaN   \n",
       "2               1.0        False    0  0.000000  4.635747e-01  5.417682e-01   \n",
       "3               1.0        False    0  0.000000  2.432243e-01  2.593818e-01   \n",
       "4               1.0        False    0  0.000000 -3.832371e+01  4.236853e+00   \n",
       "...             ...          ...  ...       ...           ...           ...   \n",
       "2189497         1.0        False    0  0.000000 -1.246662e+01  1.095870e+03   \n",
       "2189498         1.0        False    0  0.000000 -8.829702e-01  5.167880e+00   \n",
       "2189499         1.0        False    0  0.000000 -1.279188e+01  2.103826e+02   \n",
       "2189500         1.0        False    0  0.000000 -3.537524e+01  4.604367e+01   \n",
       "2189501         1.0        False    0  0.000000 -1.894657e+02  3.101176e+01   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ    POWER_MEAN     POWER_STD  \\\n",
       "0              0.008256        0.008057  2.054117e+10  8.478452e+11   \n",
       "1                   NaN             NaN           NaN           NaN   \n",
       "2              0.999801        0.149508  5.289119e-01  1.840534e-02   \n",
       "3              0.006667        0.804711  2.518095e-01  5.471546e-03   \n",
       "4              0.012031        0.011832 -1.130712e-04  5.508040e-01   \n",
       "...                 ...             ...           ...           ...   \n",
       "2189497        0.515055        0.687696  3.230312e-01  1.560095e+01   \n",
       "2189498        0.784447        0.784645  1.435307e-01  8.838691e-02   \n",
       "2189499        0.187453        0.562933  1.349265e-01  3.329814e+00   \n",
       "2189500        0.765176        0.765375  8.548223e-02  9.080691e-01   \n",
       "2189501        0.549821        0.549623  1.564855e-01  2.770690e+00   \n",
       "\n",
       "           POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0        1.772407e+13     1.027058e+14               NaN       6.361159e+06   \n",
       "1        0.000000e+00              NaN               NaN                NaN   \n",
       "2        5.417600e-01     2.644559e+03       2427.727179       2.657057e+04   \n",
       "3        2.593700e-01     1.259048e+03       2504.566025       7.652101e+03   \n",
       "4        1.018000e-02    -5.653560e-01               NaN       2.344089e+07   \n",
       "...               ...              ...               ...                ...   \n",
       "2189497  7.238000e-02     1.615156e+03               NaN       2.432130e+07   \n",
       "2189498  1.527800e-01     7.176535e+02               NaN       1.056822e+07   \n",
       "2189499  8.099000e-02     6.746327e+02               NaN       1.671677e+07   \n",
       "2189500  8.999000e-02     4.274112e+02               NaN       8.999190e+06   \n",
       "2189501  2.094300e-01     7.824273e+02               NaN       2.196021e+07   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            127555.760160          0.008057           0.007112   \n",
       "1                      NaN          0.006667                NaN   \n",
       "2             -8887.782747          0.006667           0.495092   \n",
       "3              -370.725100          0.006667           0.508904   \n",
       "4           -336552.987623          0.011832         -25.100995   \n",
       "...                    ...               ...                ...   \n",
       "2189497      346770.111107          0.687696           0.653235   \n",
       "2189498      185629.457917          0.784645           0.504609   \n",
       "2189499      279177.135887          0.562933           0.587287   \n",
       "2189500       71016.140797          0.765375           0.510688   \n",
       "2189501     -319707.025556          0.549623           0.481767   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0                    NaN             NaN     1984.122308   1.192864e+14   \n",
       "1                    NaN             NaN             NaN            NaN   \n",
       "2               0.284281       -0.022448        1.024307   8.116715e-02   \n",
       "3               0.286044        0.020387        1.030072   2.495717e-02   \n",
       "4                    NaN             NaN   -37470.665715   8.480894e+01   \n",
       "...                  ...             ...             ...            ...   \n",
       "2189497         0.177846             NaN     3392.459355   2.673920e+03   \n",
       "2189498         0.287035             NaN       36.005401   3.400897e+01   \n",
       "2189499         0.257633             NaN     1559.238495   7.828635e+02   \n",
       "2189500         0.305019             NaN      538.634368   1.643887e+02   \n",
       "2189501         0.327933             NaN      198.176621   4.415406e+02   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF   LABEL  \n",
       "0                 -0.000199              True  BENIGN  \n",
       "1                  0.000000             False  BENIGN  \n",
       "2                 -0.850293              True  BENIGN  \n",
       "3                  0.798044              True  BENIGN  \n",
       "4                 -0.000199              True  BENIGN  \n",
       "...                     ...               ...     ...  \n",
       "2189497            0.172641              True  BENIGN  \n",
       "2189498            0.000199              True  BENIGN  \n",
       "2189499            0.375480              True  BENIGN  \n",
       "2189500            0.000199              True  BENIGN  \n",
       "2189501           -0.000199              True  BENIGN  \n",
       "\n",
       "[2189502 rows x 85 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    for f in features:\n",
    "        df[f] = df[f][indices_to_keep].astype(np.float64)\n",
    "    return df\n",
    "# [indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(876203, 85)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BENIGN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19288</th>\n",
       "      <td>Bot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28891</th>\n",
       "      <td>DDoS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213752</th>\n",
       "      <td>FTP-Patator</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920929</th>\n",
       "      <td>Infiltration</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21902</th>\n",
       "      <td>PortScan</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238850</th>\n",
       "      <td>SSH-Patator</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910176</th>\n",
       "      <td>Web Attack</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                LABEL  LABEL_code\n",
       "2              BENIGN           0\n",
       "19288             Bot           1\n",
       "28891            DDoS           2\n",
       "1213752   FTP-Patator           3\n",
       "920929   Infiltration           4\n",
       "21902        PortScan           5\n",
       "1238850   SSH-Patator           6\n",
       "910176     Web Attack           7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BENIGN',\n",
       " 'Bot',\n",
       " 'DDoS',\n",
       " 'FTP-Patator',\n",
       " 'Infiltration',\n",
       " 'PortScan',\n",
       " 'SSH-Patator',\n",
       " 'Web Attack']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications_array = []\n",
    "for index, row in df[[\"LABEL\", \"LABEL_code\"]].drop_duplicates().sort_values(\"LABEL_code\").iterrows():\n",
    "    classifications_array.append(row[0])\n",
    "classifications_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    686004\n",
       "2    181173\n",
       "6      5721\n",
       "3      2473\n",
       "1       498\n",
       "5       154\n",
       "7       149\n",
       "4        31\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.LABEL_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 100000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "543342.1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.shape[0]*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    607688\n",
       "2.0    160507\n",
       "6.0      5077\n",
       "3.0      2206\n",
       "1.0       436\n",
       "5.0       133\n",
       "7.0       131\n",
       "4.0        25\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.LABEL_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    78316\n",
       "2    20666\n",
       "6      644\n",
       "3      267\n",
       "1       62\n",
       "5       21\n",
       "7       18\n",
       "4        6\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation.LABEL_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_features[features]\n",
    "y = df_features.LABEL_code\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    425382\n",
       "2.0    112355\n",
       "6.0      3554\n",
       "3.0      1544\n",
       "1.0       305\n",
       "5.0        93\n",
       "7.0        92\n",
       "4.0        17\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    182306\n",
       "2.0     48152\n",
       "6.0      1523\n",
       "3.0       662\n",
       "1.0       131\n",
       "5.0        40\n",
       "7.0        39\n",
       "4.0         8\n",
       "Name: LABEL_code, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred,average='weighted')\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy, \"  \", space)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 10, 40, 5),\n",
    "        'gamma': hp.uniform ('gamma', 1,4),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,90,5),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 3),\n",
    "        'n_estimators': hp.quniform('n_estimators', 50, 400, 50),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [1:47:04<00:00, 64.24s/trial, best loss: -0.9992339020824815]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.17563178843903846, 'gamma': 2.037933701860277, 'max_depth': 30.0, 'min_child_weight': 12.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4753326305809569}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned_old(best_hyperparams, X_train=None, X_test=None, y_train=None, y_test=None):\n",
    "      if X_train is None:\n",
    "            X=df_features[features]  # Features\n",
    "            y=df_features['LABEL_code']  # Labels\n",
    "            #Split on train and test\n",
    "            X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "            X_train = X_train_a[features]\n",
    "            X_test = X_test_a[features] \n",
    "      \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      model.fit(X_train, y_train)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      print(classification_report(y_test, y_pred,  digits=4))  # target_names=classifications_array,\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9996    0.9997    0.9997    182306\n",
      "         1.0     1.0000    0.9542    0.9766       131\n",
      "         2.0     0.9982    0.9990    0.9986     48152\n",
      "         3.0     1.0000    1.0000    1.0000       662\n",
      "         4.0     0.0000    0.0000    0.0000         8\n",
      "         5.0     0.0000    0.0000    0.0000        40\n",
      "         6.0     1.0000    0.9961    0.9980      1523\n",
      "         7.0     1.0000    0.8462    0.9167        39\n",
      "\n",
      "    accuracy                         0.9993    232861\n",
      "   macro avg     0.7497    0.7244    0.7362    232861\n",
      "weighted avg     0.9991    0.9993    0.9992    232861\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned_old(best_hyperparams, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned_old(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    report = None\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y) # 70% training and 30% test\n",
    "        # sm = SMOTE(random_state=100)\n",
    "        # X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "        # X_test, y_test = sm.fit_resample(X_test, y_test)\n",
    "        \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "        )\n",
    "        model.fit(X_train, y_train, ) # eval_set=evaluation, eval_metric=[\"auc\",\"error\",\"logloss\"], verbose=False\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        F1 = f1_score(y_test, y_pred, average='macro')\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            report = classification_report(y_test, y_pred, target_names=classifications_array, digits=4)\n",
    "        \n",
    "    print(\"\")\n",
    "    print(report)\n",
    "    return best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      BENIGN     0.9997    0.9997    0.9997    182306\n",
      "         Bot     1.0000    0.9695    0.9845       131\n",
      "        DDoS     0.9982    0.9993    0.9987     48152\n",
      " FTP-Patator     1.0000    1.0000    1.0000       662\n",
      "Infiltration     1.0000    0.2500    0.4000         8\n",
      "    PortScan     0.0000    0.0000    0.0000        40\n",
      " SSH-Patator     1.0000    0.9967    0.9984      1523\n",
      "  Web Attack     1.0000    0.8462    0.9167        39\n",
      "\n",
      "    accuracy                         0.9994    232861\n",
      "   macro avg     0.8747    0.7577    0.7872    232861\n",
      "weighted avg     0.9992    0.9994    0.9993    232861\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned_old(best_hyperparams, cycles=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      BENIGN     0.9996    0.9999    0.9997     78316\n",
      "         Bot     1.0000    1.0000    1.0000        62\n",
      "        DDoS     0.9986    0.9988    0.9987     20666\n",
      " FTP-Patator     1.0000    1.0000    1.0000       267\n",
      "Infiltration     0.0000    0.0000    0.0000         6\n",
      "    PortScan     0.0000    0.0000    0.0000        21\n",
      " SSH-Patator     1.0000    0.9938    0.9969       644\n",
      "  Web Attack     1.0000    0.9444    0.9714        18\n",
      "\n",
      "    accuracy                         0.9994    100000\n",
      "   macro avg     0.7498    0.7421    0.7458    100000\n",
      "weighted avg     0.9991    0.9994    0.9992    100000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = best_model.predict(X_validation)\n",
    "\n",
    "print(classification_report(y_validation, y_pred, target_names=classifications_array,digits=4))  # \n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_validation, y_pred, target_names=classifications_array,digits=4)\n",
    "\n",
    "import csv\n",
    "\n",
    "problem = \"IDS_CIC-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    report = classification_report(y_validation, y_pred, target_names=classifications_array, digits=4)\n",
    "    tmp = report.split(\"weighted avg\")\n",
    "    tmp_2 = tmp[0].split(\"macro avg\")\n",
    "    accuracy = tmp_2[0].split(\"accuracy\")[1].split()[0]\n",
    "    _ = tmp_2[1].split()\n",
    "    m_precision = _[0]\n",
    "    m_recall = _[1]\n",
    "    m_f1 = _[2]\n",
    "    _ = tmp[1].split()\n",
    "    w_precision = _[0]\n",
    "    w_recall = _[1]\n",
    "    w_f1 = _[2]\n",
    "    writer.writerow([problem, \"-\", accuracy, m_precision, m_recall, m_f1, w_precision, w_recall, w_f1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "f_def = feat_importances[default_faetures].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf, f_def]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \"Default\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.30517298, 0.05538407, 0.049256764, 0.20016813, 0.2193771, 0.17064095]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF      0.000000\n",
       "SIG_SPACES            0.000000\n",
       "CNT_ZEROS             0.000000\n",
       "VAL                   0.000453\n",
       "PERCENT_BELOW_MEAN    0.000613\n",
       "                        ...   \n",
       "MIN_POWER             0.057322\n",
       "AVERAGE_DISPERSION    0.066295\n",
       "DIRECTIONS            0.070380\n",
       "PACKETS_REV           0.113311\n",
       "SWITCHING_METRIC      0.133562\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'VAL': 0.00045309215784072876,\n",
       " 'PERCENT_BELOW_MEAN': 0.0006130770780146122,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0007248417823575437,\n",
       " 'TIME': 0.0008029410382732749,\n",
       " 'CNT_DISTRIBUTION': 0.0008739119512028992,\n",
       " 'SPECTRAL_CREST': 0.0009226549882441759,\n",
       " 'SPECTRAL_ENTROPY': 0.0009510175441391766,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0009837172692641616,\n",
       " 'PERCENT_DEVIATION': 0.0010166865540668368,\n",
       " 'GALTON_SKEWNESS': 0.0010857434244826436,\n",
       " 'HURST_EXPONENT': 0.001139150932431221,\n",
       " 'MODE': 0.001156626851297915,\n",
       " 'SPECTRAL_SPREAD': 0.001180641003884375,\n",
       " 'SPECTRAL_SKEWNESS': 0.0013878516620025039,\n",
       " 'TRANSIENTS': 0.0013883940409868956,\n",
       " 'SPECTRAL_FLUX': 0.001517808879725635,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0016056058229878545,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0017730072140693665,\n",
       " 'Q1': 0.001891349907964468,\n",
       " 'BURSTINESS': 0.0021578369196504354,\n",
       " 'MEDIAN_SCALED_TIME': 0.0022160049993544817,\n",
       " 'POWER_STD': 0.0024983747862279415,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0025161372032016516,\n",
       " 'SPECTRAL_ENERGY': 0.0025347352493554354,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.0025501816999167204,\n",
       " 'Q3_SCALED_TIME': 0.0026646435726433992,\n",
       " 'MIN': 0.002777234185487032,\n",
       " 'DURATION': 0.002813589759171009,\n",
       " 'MAX_POWER': 0.0030598680023103952,\n",
       " 'MAX_POWER_FREQ': 0.0033655345905572176,\n",
       " 'STDEV': 0.0034998590126633644,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.003571992041543126,\n",
       " 'VAR': 0.003817387158051133,\n",
       " 'TIME_DISTRIBUTION': 0.0038271606899797916,\n",
       " 'MEAN': 0.0038837683387100697,\n",
       " 'MAX_DIFFTIMES': 0.004114439245313406,\n",
       " 'MEAN_SCALED_TIME': 0.004505644552409649,\n",
       " 'P_BENFORD': 0.0047403182834386826,\n",
       " 'SPECTRAL_CENTROID': 0.004797131754457951,\n",
       " 'PERIODICITY': 0.00496693467721343,\n",
       " 'PACKETS': 0.005064444150775671,\n",
       " 'MEAN_DIFFTIMES': 0.005282786674797535,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.005665716249495745,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0058273873291909695,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.006640431471168995,\n",
       " 'BIGGEST_CNT_1_SEC': 0.00709841214120388,\n",
       " 'Q1_SCALED_TIME': 0.00717684393748641,\n",
       " 'BYTES': 0.0072581106796860695,\n",
       " 'PERCENT_ABOVE_MEAN': 0.007651018910109997,\n",
       " 'MEDIAN_DIFFTIMES': 0.00800144113600254,\n",
       " 'SPECTRAL_KURTOSIS': 0.008356316946446896,\n",
       " 'MAX': 0.009736337698996067,\n",
       " 'MIN_POWER_FREQ': 0.010986118577420712,\n",
       " 'SPECTRAL_SLOPE': 0.011506425216794014,\n",
       " 'ROOT_MEAN_SQUARE': 0.011823243461549282,\n",
       " 'MIN_MINUS_MAX': 0.011982232332229614,\n",
       " 'MIN_DIFFTIMES': 0.012781291268765926,\n",
       " 'BENFORD_LAW_PRESENTED': 0.014087259769439697,\n",
       " 'POWER_MEAN': 0.01641819253563881,\n",
       " 'MEDIAN': 0.018130000680685043,\n",
       " 'KURTOSIS': 0.02090967819094658,\n",
       " 'NORMAL_DISTRIBUTION': 0.022983353585004807,\n",
       " 'SPECTRAL_ROLLOFF': 0.027378024533391,\n",
       " 'SCALED_ENTROPY': 0.03063519112765789,\n",
       " 'ENTROPY': 0.037445779889822006,\n",
       " 'POWER_MODE': 0.045001767575740814,\n",
       " 'BYTES_REV': 0.0450076088309288,\n",
       " 'Q3': 0.04594788700342178,\n",
       " 'MIN_POWER': 0.05732195824384689,\n",
       " 'AVERAGE_DISPERSION': 0.06629456579685211,\n",
       " 'DIRECTIONS': 0.07038042694330215,\n",
       " 'PACKETS_REV': 0.11331076920032501,\n",
       " 'SWITCHING_METRIC': 0.13356207311153412}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, cycles = 5, best_hyperparams=None):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(200000), selected_features, verbose=False)\n",
    "    X_dos=df_features[selected_features]  # Features\n",
    "    y_dos=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_dos, y_dos, test_size=0.30, stratify=y_dos) # 70% training and 30% test\n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        F1 = f1_score(y_test, y_pred, average='macro')\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    print(F1)\n",
    "\n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    \n",
    "    report = classification_report(y_validation,y_pred)\n",
    "    return report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [22:37<00:00, 13.57s/trial, best loss: -0.9981423345793341]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.18373504797827522, 'gamma': 1.192790095455379, 'max_depth': 40.0, 'min_child_weight': 6.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.49970587330798083}\n",
      "0.7397365973620632\n",
      "100%|██████████| 100/100 [21:15<00:00, 12.75s/trial, best loss: -0.9987927799984455]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5891294898529246, 'gamma': 2.3384776982122046, 'max_depth': 35.0, 'min_child_weight': 0.0, 'n_estimators': 350.0, 'reg_alpha': 0.0, 'reg_lambda': 0.32238586482163334}\n",
      "0.7674044680761811\n",
      "100%|██████████| 100/100 [24:12<00:00, 14.52s/trial, best loss: -0.9981949825150883]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4560402819516892, 'gamma': 1.3472846933782883, 'max_depth': 30.0, 'min_child_weight': 9.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.01813343889028335}\n",
      "0.7369657666760218\n",
      "100%|██████████| 100/100 [18:48<00:00, 11.28s/trial, best loss: -0.998219526302945]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.0766772692676471, 'gamma': 1.4176686481031426, 'max_depth': 25.0, 'min_child_weight': 3.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3872682216523175}\n",
      "0.7632318954935489\n",
      "100%|██████████| 100/100 [28:06<00:00, 16.87s/trial, best loss: -0.9983591275576]  \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.47751951489615235, 'gamma': 2.293339356576467, 'max_depth': 30.0, 'min_child_weight': 3.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.1863286609051374}\n",
      "0.7618925932892983\n",
      "100%|██████████| 100/100 [31:18<00:00, 18.79s/trial, best loss: -0.9985677764162203]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.13674161173912694, 'gamma': 1.7782220814104899, 'max_depth': 25.0, 'min_child_weight': 3.0, 'n_estimators': 350.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5057113074678294}\n",
      "0.78975164693287\n",
      "100%|██████████| 100/100 [22:36<00:00, 13.57s/trial, best loss: -0.9981820901466785]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.1992971773154164, 'gamma': 2.6380897380973853, 'max_depth': 25.0, 'min_child_weight': 21.0, 'n_estimators': 350.0, 'reg_alpha': 0.0, 'reg_lambda': 0.15224815772549422}\n",
      "0.7226574458854378\n",
      "100%|██████████| 100/100 [40:38<00:00, 24.38s/trial, best loss: -0.9979298357237854]  \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8325730359003088, 'gamma': 1.7148873026863793, 'max_depth': 35.0, 'min_child_weight': 3.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.30845073325349204}\n",
      "0.7864789844529858\n",
      "100%|██████████| 100/100 [22:36<00:00, 13.56s/trial, best loss: -0.9976368105946382]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.45184312350524125, 'gamma': 3.2808997985895347, 'max_depth': 35.0, 'min_child_weight': 12.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3473783449245875}\n",
      "0.7338063563496589\n",
      "100%|██████████| 100/100 [31:23<00:00, 18.84s/trial, best loss: -0.9974800479001243]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7886852121330891, 'gamma': 1.6254208258827816, 'max_depth': 25.0, 'min_child_weight': 15.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.47327469472090977}\n",
      "0.728090254012631\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = \"IDS_CIC-multiclass\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        tmp = report.split(\"weighted avg\")\n",
    "        tmp_2 = tmp[0].split(\"macro avg\")\n",
    "        accuracy = tmp_2[0].split(\"accuracy\")[1].split()[0]\n",
    "        _ = tmp_2[1].split()\n",
    "        m_precision = _[0]\n",
    "        m_recall = _[1]\n",
    "        m_f1 = _[2]\n",
    "        _ = tmp[1].split()\n",
    "        w_precision = _[0]\n",
    "        w_recall = _[1]\n",
    "        w_f1 = _[2]\n",
    "        writer.writerow([problem, f_set, accuracy, m_precision, m_recall, m_f1, w_precision, w_recall, w_f1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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