ton_iot_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IoT malware detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of IoT malware\n",
    "\n",
    "Raw data from: TON_IoT [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_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",
    "\n",
    "# ML algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\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 xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import catboost as cb\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/ton_iot_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
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       "      <td>False</td>\n",
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       "      <td>1120.366194</td>\n",
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       "      <td>1.857421e+04</td>\n",
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       "      <td>0.020176</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>False</td>\n",
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       "      <td>0.136231</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>0.019443</td>\n",
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       "      <td>1.748741e+06</td>\n",
       "      <td>87651.532914</td>\n",
       "      <td>0.998411</td>\n",
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       "      <td>0.113074</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>192.168.1.152</td>\n",
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       "      <td>1.554220e+09</td>\n",
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       "      <td>833.488650</td>\n",
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       "      <td>2.061014e-10</td>\n",
       "      <td>-0.021359</td>\n",
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       "      <td>6.332938</td>\n",
       "      <td>0.563839</td>\n",
       "      <td>0.584333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.717256</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.544085</td>\n",
       "      <td>0.499061</td>\n",
       "      <td>0.976637</td>\n",
       "      <td>149.670896</td>\n",
       "      <td>148.796587</td>\n",
       "      <td>75.688963</td>\n",
       "      <td>225.342053</td>\n",
       "      <td>299.905281</td>\n",
       "      <td>0.124753</td>\n",
       "      <td>0.097319</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.869398</td>\n",
       "      <td>0.143493</td>\n",
       "      <td>False</td>\n",
       "      <td>1.982529</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.544085</td>\n",
       "      <td>328862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.533646e-09</td>\n",
       "      <td>0.027379</td>\n",
       "      <td>0.483864</td>\n",
       "      <td>0.999007</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.107110</td>\n",
       "      <td>10.272353</td>\n",
       "      <td>1.584060e+06</td>\n",
       "      <td>83069.432047</td>\n",
       "      <td>0.998212</td>\n",
       "      <td>0.681201</td>\n",
       "      <td>0.291605</td>\n",
       "      <td>0.155068</td>\n",
       "      <td>123.650232</td>\n",
       "      <td>0.124636</td>\n",
       "      <td>0.515143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.152.</td>\n",
       "      <td>192.168.1.190</td>\n",
       "      <td>2396</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.554220e+09</td>\n",
       "      <td>1.554221e+09</td>\n",
       "      <td>1880</td>\n",
       "      <td>43539</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</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>0.001213</td>\n",
       "      <td>0.534113</td>\n",
       "      <td>0.497263</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>149.131741</td>\n",
       "      <td>148.001476</td>\n",
       "      <td>75.339264</td>\n",
       "      <td>224.768017</td>\n",
       "      <td>299.905242</td>\n",
       "      <td>0.125221</td>\n",
       "      <td>0.097868</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.869425</td>\n",
       "      <td>0.144028</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534113</td>\n",
       "      <td>29400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>0.082049</td>\n",
       "      <td>1.389818e-09</td>\n",
       "      <td>0.205861</td>\n",
       "      <td>0.565317</td>\n",
       "      <td>0.998411</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.010743</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.423204</td>\n",
       "      <td>36.749028</td>\n",
       "      <td>1.070819e+06</td>\n",
       "      <td>67142.382666</td>\n",
       "      <td>0.997815</td>\n",
       "      <td>0.661169</td>\n",
       "      <td>0.341646</td>\n",
       "      <td>-0.484312</td>\n",
       "      <td>138.660387</td>\n",
       "      <td>0.976712</td>\n",
       "      <td>0.433093</td>\n",
       "      <td>0.831995</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467409</th>\n",
       "      <td>3467409</td>\n",
       "      <td>52.147.9.175.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2356</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51670</td>\n",
       "      <td>274</td>\n",
       "      <td>60.0</td>\n",
       "      <td>429.984883</td>\n",
       "      <td>184887</td>\n",
       "      <td>0.221574</td>\n",
       "      <td>60.00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1425</td>\n",
       "      <td>1365</td>\n",
       "      <td>60</td>\n",
       "      <td>156.928790</td>\n",
       "      <td>294</td>\n",
       "      <td>107.299270</td>\n",
       "      <td>493.208789</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.497692</td>\n",
       "      <td>1.772155</td>\n",
       "      <td>1.582852</td>\n",
       "      <td>2.287061e-09</td>\n",
       "      <td>2.665211e-09</td>\n",
       "      <td>-1.488372</td>\n",
       "      <td>4.800728</td>\n",
       "      <td>1.672933</td>\n",
       "      <td>0.483586</td>\n",
       "      <td>0.550311</td>\n",
       "      <td>False</td>\n",
       "      <td>0.652939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.549271</td>\n",
       "      <td>0.732143</td>\n",
       "      <td>0.124318</td>\n",
       "      <td>0.185845</td>\n",
       "      <td>0.025730</td>\n",
       "      <td>0.204397</td>\n",
       "      <td>0.226333</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.145608</td>\n",
       "      <td>0.155440</td>\n",
       "      <td>False</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.202904e-01</td>\n",
       "      <td>0.144114</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.006865</td>\n",
       "      <td>0.122027</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.12170</td>\n",
       "      <td>610.137224</td>\n",
       "      <td>1851.438312</td>\n",
       "      <td>2.318498e+05</td>\n",
       "      <td>29592.939850</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.500012</td>\n",
       "      <td>0.287566</td>\n",
       "      <td>-0.016614</td>\n",
       "      <td>1.180994</td>\n",
       "      <td>0.023824</td>\n",
       "      <td>-0.992936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467410</th>\n",
       "      <td>3467410</td>\n",
       "      <td>52.147.9.175.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2280</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51778</td>\n",
       "      <td>288</td>\n",
       "      <td>60.0</td>\n",
       "      <td>454.171774</td>\n",
       "      <td>206272</td>\n",
       "      <td>0.223899</td>\n",
       "      <td>60.00</td>\n",
       "      <td>271.5</td>\n",
       "      <td>60</td>\n",
       "      <td>1492</td>\n",
       "      <td>1432</td>\n",
       "      <td>60</td>\n",
       "      <td>157.698533</td>\n",
       "      <td>300</td>\n",
       "      <td>104.166667</td>\n",
       "      <td>518.255728</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.502013</td>\n",
       "      <td>1.770255</td>\n",
       "      <td>1.616712</td>\n",
       "      <td>-7.584549e-23</td>\n",
       "      <td>-8.994169e-23</td>\n",
       "      <td>-1.156028</td>\n",
       "      <td>4.991372</td>\n",
       "      <td>1.770951</td>\n",
       "      <td>0.533109</td>\n",
       "      <td>0.498566</td>\n",
       "      <td>False</td>\n",
       "      <td>0.677121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555707</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.044454</td>\n",
       "      <td>0.057543</td>\n",
       "      <td>0.016090</td>\n",
       "      <td>0.061109</td>\n",
       "      <td>0.079996</td>\n",
       "      <td>0.008888</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.017896</td>\n",
       "      <td>0.496668</td>\n",
       "      <td>False</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.982405e-01</td>\n",
       "      <td>0.325230</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.049807</td>\n",
       "      <td>0.013460</td>\n",
       "      <td>0.05183</td>\n",
       "      <td>249.036186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.596253e+07</td>\n",
       "      <td>-240826.912312</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.499121</td>\n",
       "      <td>0.281868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.529773</td>\n",
       "      <td>2.196577</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.995996</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467411</th>\n",
       "      <td>3467411</td>\n",
       "      <td>192.168.1.195.</td>\n",
       "      <td>52.147.9.175</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7763</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>51670</td>\n",
       "      <td>443</td>\n",
       "      <td>836</td>\n",
       "      <td>996.0</td>\n",
       "      <td>710.760156</td>\n",
       "      <td>505180</td>\n",
       "      <td>-0.080969</td>\n",
       "      <td>72.75</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>1520</td>\n",
       "      <td>85.019157</td>\n",
       "      <td>655</td>\n",
       "      <td>78.349282</td>\n",
       "      <td>1074.271893</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.962350</td>\n",
       "      <td>2.127300</td>\n",
       "      <td>-0.449867</td>\n",
       "      <td>8.355107e-10</td>\n",
       "      <td>9.907938e-10</td>\n",
       "      <td>-0.054759</td>\n",
       "      <td>0.912363</td>\n",
       "      <td>2.046439</td>\n",
       "      <td>0.616040</td>\n",
       "      <td>0.703213</td>\n",
       "      <td>False</td>\n",
       "      <td>0.753213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.417557</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.091755</td>\n",
       "      <td>0.021365</td>\n",
       "      <td>0.021328</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>0.219742</td>\n",
       "      <td>0.024416</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.157580</td>\n",
       "      <td>0.154942</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.337338e-01</td>\n",
       "      <td>0.608186</td>\n",
       "      <td>0.147124</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.560435</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>0.53373</td>\n",
       "      <td>2802.176106</td>\n",
       "      <td>2337.388916</td>\n",
       "      <td>9.613301e+03</td>\n",
       "      <td>2628.170455</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.513832</td>\n",
       "      <td>0.288496</td>\n",
       "      <td>0.027189</td>\n",
       "      <td>1.085204</td>\n",
       "      <td>0.147369</td>\n",
       "      <td>0.852677</td>\n",
       "      <td>0.671281</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467412</th>\n",
       "      <td>3467412</td>\n",
       "      <td>192.168.1.195.</td>\n",
       "      <td>52.147.9.175</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>51778</td>\n",
       "      <td>443</td>\n",
       "      <td>820</td>\n",
       "      <td>914.5</td>\n",
       "      <td>718.254133</td>\n",
       "      <td>515889</td>\n",
       "      <td>-0.066144</td>\n",
       "      <td>72.75</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>1520</td>\n",
       "      <td>87.591967</td>\n",
       "      <td>671</td>\n",
       "      <td>81.829268</td>\n",
       "      <td>1066.161620</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.974585</td>\n",
       "      <td>2.151745</td>\n",
       "      <td>-0.390486</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.032648</td>\n",
       "      <td>0.875264</td>\n",
       "      <td>2.046439</td>\n",
       "      <td>0.616040</td>\n",
       "      <td>0.712187</td>\n",
       "      <td>False</td>\n",
       "      <td>0.753213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418338</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.032386</td>\n",
       "      <td>0.017202</td>\n",
       "      <td>0.017188</td>\n",
       "      <td>0.058194</td>\n",
       "      <td>0.077416</td>\n",
       "      <td>0.008602</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.025017</td>\n",
       "      <td>0.343837</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.546102e-01</td>\n",
       "      <td>1.424689</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.389485</td>\n",
       "      <td>0.029427</td>\n",
       "      <td>0.41963</td>\n",
       "      <td>1947.424739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.664211e+06</td>\n",
       "      <td>37295.429950</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.518571</td>\n",
       "      <td>0.284595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.657879</td>\n",
       "      <td>3.133762</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.824852</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467413</th>\n",
       "      <td>3467413</td>\n",
       "      <td>13.107.3.128.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51672</td>\n",
       "      <td>199</td>\n",
       "      <td>60.0</td>\n",
       "      <td>221.115355</td>\n",
       "      <td>48892</td>\n",
       "      <td>0.052641</td>\n",
       "      <td>60.00</td>\n",
       "      <td>310.0</td>\n",
       "      <td>60</td>\n",
       "      <td>534</td>\n",
       "      <td>474</td>\n",
       "      <td>60</td>\n",
       "      <td>111.113243</td>\n",
       "      <td>184</td>\n",
       "      <td>92.462312</td>\n",
       "      <td>285.596619</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.628631</td>\n",
       "      <td>2.428597</td>\n",
       "      <td>0.355377</td>\n",
       "      <td>1.321433e-08</td>\n",
       "      <td>1.712773e-08</td>\n",
       "      <td>-0.112000</td>\n",
       "      <td>1.397409</td>\n",
       "      <td>1.664498</td>\n",
       "      <td>0.592906</td>\n",
       "      <td>0.507121</td>\n",
       "      <td>False</td>\n",
       "      <td>0.665736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.269733</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.107296</td>\n",
       "      <td>0.050451</td>\n",
       "      <td>0.024303</td>\n",
       "      <td>0.196795</td>\n",
       "      <td>0.397785</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.024890</td>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.200990</td>\n",
       "      <td>0.329855</td>\n",
       "      <td>False</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.995073e-01</td>\n",
       "      <td>0.503512</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.322149</td>\n",
       "      <td>0.464578</td>\n",
       "      <td>0.038735</td>\n",
       "      <td>0.50350</td>\n",
       "      <td>2322.888958</td>\n",
       "      <td>2556.938328</td>\n",
       "      <td>2.758580e+04</td>\n",
       "      <td>-7051.878833</td>\n",
       "      <td>0.066664</td>\n",
       "      <td>0.497605</td>\n",
       "      <td>0.277782</td>\n",
       "      <td>0.025319</td>\n",
       "      <td>1.083806</td>\n",
       "      <td>0.304453</td>\n",
       "      <td>0.315483</td>\n",
       "      <td>0.285079</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3467414 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP                     SRC_IP  PACKETS  \\\n",
       "0                 0     224.0.0.251              192.168.1.133       10   \n",
       "1                 1        ff02::fb  fe80::1087:c341:7263:ead3       11   \n",
       "2                 2  192.168.1.192.              192.168.1.152     2534   \n",
       "3                 3  192.168.1.190.              192.168.1.152     2405   \n",
       "4                 4  192.168.1.152.              192.168.1.190     2396   \n",
       "...             ...             ...                        ...      ...   \n",
       "3467409     3467409   52.147.9.175.              192.168.1.195       11   \n",
       "3467410     3467410   52.147.9.175.              192.168.1.195       10   \n",
       "3467411     3467411  192.168.1.195.               52.147.9.175       10   \n",
       "3467412     3467412  192.168.1.195.               52.147.9.175       10   \n",
       "3467413     3467413   13.107.3.128.              192.168.1.195        7   \n",
       "\n",
       "         PACKETS_REV    BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  \\\n",
       "0                  0      792          0  1.554220e+09  1.554221e+09   \n",
       "1                  0      858          0  1.554220e+09  1.554221e+09   \n",
       "2                  0  1536348          0  1.554220e+09  1.554221e+09   \n",
       "3                  0  1493560          0  1.554220e+09  1.554221e+09   \n",
       "4                  0        0          0  1.554220e+09  1.554221e+09   \n",
       "...              ...      ...        ...           ...           ...   \n",
       "3467409            0     2356          0  1.556365e+09  1.556365e+09   \n",
       "3467410            0     2280          0  1.556365e+09  1.556365e+09   \n",
       "3467411            0     7763          0  1.556365e+09  1.556365e+09   \n",
       "3467412            0     7600          0  1.556365e+09  1.556365e+09   \n",
       "3467413            0      974          0  1.556365e+09  1.556365e+09   \n",
       "\n",
       "         DST_PORT  SRC_PORT  MEAN  MEDIAN       STDEV     VAR  BURSTINESS  \\\n",
       "0            5353      5353   139   140.0   11.445523     131   -0.847845   \n",
       "1            5353      5353   138   130.0   11.575837     134   -0.845218   \n",
       "2           40571      1880   666   579.0  454.946151  206976   -0.188282   \n",
       "3           43539      1880   681   579.0  480.631876  231007   -0.172488   \n",
       "4            1880     43539    60    60.0    0.000000       0   -1.000000   \n",
       "...           ...       ...   ...     ...         ...     ...         ...   \n",
       "3467409       443     51670   274    60.0  429.984883  184887    0.221574   \n",
       "3467410       443     51778   288    60.0  454.171774  206272    0.223899   \n",
       "3467411     51670       443   836   996.0  710.760156  505180   -0.080969   \n",
       "3467412     51778       443   820   914.5  718.254133  515889   -0.066144   \n",
       "3467413       443     51672   199    60.0  221.115355   48892    0.052641   \n",
       "\n",
       "             Q1      Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0        130.00   150.0  126   150             24   150   \n",
       "1        128.00   150.0  126   150             24   150   \n",
       "2        417.00   933.0   64  2673           2609    64   \n",
       "3        418.00   933.0   64  2673           2609    64   \n",
       "4         60.00    60.0   60    60              0    60   \n",
       "...         ...     ...  ...   ...            ...   ...   \n",
       "3467409   60.00   232.0   60  1425           1365    60   \n",
       "3467410   60.00   271.5   60  1492           1432    60   \n",
       "3467411   72.75  1520.0   60  1520           1460  1520   \n",
       "3467412   72.75  1520.0   60  1520           1460  1520   \n",
       "3467413   60.00   310.0   60   534            474    60   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                        8.234189                  10           7.194245   \n",
       "1                        8.388288                  10           7.246377   \n",
       "2                       68.310233                 382          57.357357   \n",
       "3                       70.577368                 394          57.856094   \n",
       "4                        0.000000                   0           0.000000   \n",
       "...                           ...                 ...                ...   \n",
       "3467409                156.928790                 294         107.299270   \n",
       "3467410                157.698533                 300         104.166667   \n",
       "3467411                 85.019157                 655          78.349282   \n",
       "3467412                 87.591967                 671          81.829268   \n",
       "3467413                111.113243                 184          92.462312   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              139.625213            0.500000            0.500000   \n",
       "1              138.441980            0.545455            0.454545   \n",
       "2              806.747481            0.531965            0.468035   \n",
       "3              833.488650            0.523493            0.476507   \n",
       "4               60.000000            0.000000            0.000000   \n",
       "...                   ...                 ...                 ...   \n",
       "3467409        493.208789            0.727273            0.272727   \n",
       "3467410        518.255728            0.700000            0.300000   \n",
       "3467411       1074.271893            0.500000            0.500000   \n",
       "3467412       1066.161620            0.500000            0.500000   \n",
       "3467413        285.596619            0.714286            0.285714   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   -0.961074             24.201602              4.287953   \n",
       "1                   -1.036642             24.533863             -3.041708   \n",
       "2                    1.323234              3.119050              0.492374   \n",
       "3                    1.283727              3.045990              0.686459   \n",
       "4                    0.000000              0.000000              0.000000   \n",
       "...                       ...                   ...                   ...   \n",
       "3467409              0.497692              1.772155              1.582852   \n",
       "3467410              0.502013              1.770255              1.616712   \n",
       "3467411             -0.962350              2.127300             -0.449867   \n",
       "3467412             -0.974585              2.151745             -0.390486   \n",
       "3467413              0.628631              2.428597              0.355377   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      1.333899e-04                1.581810e-04   \n",
       "1                     -2.862945e-19               -3.336314e-19   \n",
       "2                      3.118073e-09                3.119921e-09   \n",
       "3                      2.059728e-10                2.061014e-10   \n",
       "4                      0.000000e+00                0.000000e+00   \n",
       "...                             ...                         ...   \n",
       "3467409                2.287061e-09                2.665211e-09   \n",
       "3467410               -7.584549e-23               -8.994169e-23   \n",
       "3467411                8.355107e-10                9.907938e-10   \n",
       "3467412                0.000000e+00                0.000000e+00   \n",
       "3467413                1.321433e-08                1.712773e-08   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.100000  0.874133  1.485475        0.447173        0.675573   \n",
       "1               0.090909  0.902084  1.539485        0.445011        0.715793   \n",
       "2               0.034884  2.775578  6.380793        0.564312        0.578712   \n",
       "3              -0.021359  3.295854  6.332938        0.563839        0.584333   \n",
       "4               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "3467409        -1.488372  4.800728  1.672933        0.483586        0.550311   \n",
       "3467410        -1.156028  4.991372  1.770951        0.533109        0.498566   \n",
       "3467411        -0.054759  0.912363  2.046439        0.616040        0.703213   \n",
       "3467412        -0.032648  0.875264  2.046439        0.616040        0.712187   \n",
       "3467413        -0.112000  1.397409  1.664498        0.592906        0.507121   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.602060             0.000000   \n",
       "1                        False   0.602060             0.000000   \n",
       "2                        False   0.714878             0.001000   \n",
       "3                        False   0.717256             0.001000   \n",
       "4                        False   0.301030             0.001213   \n",
       "...                        ...        ...                  ...   \n",
       "3467409                  False   0.652939             0.000000   \n",
       "3467410                  False   0.677121             0.000000   \n",
       "3467411                  False   0.753213             0.000000   \n",
       "3467412                  False   0.753213             0.000000   \n",
       "3467413                  False   0.665736             0.000000   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                1.000000           0.267197                  0.625000   \n",
       "1                1.000000           0.262991                  0.575000   \n",
       "2                0.542108           0.498117                  0.975651   \n",
       "3                0.544085           0.499061                  0.976637   \n",
       "4                0.534113           0.497263                  0.875000   \n",
       "...                   ...                ...                       ...   \n",
       "3467409          0.000000           0.549271                  0.732143   \n",
       "3467410          0.000000           0.555707                  0.708333   \n",
       "3467411          0.000000           0.417557                  0.500000   \n",
       "3467412          0.000000           0.418338                  0.500000   \n",
       "3467413          0.000000           0.269733                  0.687500   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               12.751683           11.546521        3.973819       14.582449   \n",
       "1               12.550874           10.536604        3.973608       14.582614   \n",
       "2              149.388050          148.001666       75.689032      224.696690   \n",
       "3              149.670896          148.796587       75.688963      225.342053   \n",
       "4              149.131741          148.001476       75.339264      224.768017   \n",
       "...                   ...                 ...             ...             ...   \n",
       "3467409          0.124318            0.185845        0.025730        0.204397   \n",
       "3467410          0.044454            0.057543        0.016090        0.061109   \n",
       "3467411          0.091755            0.021365        0.021328        0.206023   \n",
       "3467412          0.032386            0.017202        0.017188        0.058194   \n",
       "3467413          0.107296            0.050451        0.024303        0.196795   \n",
       "\n",
       "           DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0         47.723840        5.302649          2.930586       0.105342   \n",
       "1         47.723620        4.772362          2.422591       0.104397   \n",
       "2        299.905341        0.118399          0.088413       0.000009   \n",
       "3        299.905281        0.124753          0.097319       0.000013   \n",
       "4        299.905242        0.125221          0.097868       0.000033   \n",
       "...             ...             ...               ...            ...   \n",
       "3467409    0.226333        0.022633          0.009434       0.000118   \n",
       "3467410    0.079996        0.008888          0.011698       0.000783   \n",
       "3467411    0.219742        0.024416          0.001552       0.000005   \n",
       "3467412    0.077416        0.008602          0.001893       0.000004   \n",
       "3467413    0.397785        0.066298          0.024890       0.000672   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            26.960027               0.196686       False          1.111111   \n",
       "1            26.960008               0.177016       False          1.000000   \n",
       "2             0.869108               0.136231       False          1.950257   \n",
       "3             0.869398               0.143493       False          1.982529   \n",
       "4             0.869425               0.144028       False          0.000000   \n",
       "...                ...                    ...         ...               ...   \n",
       "3467409       0.145608               0.155440       False          1.400000   \n",
       "3467410       0.017896               0.496668       False          1.555556   \n",
       "3467411       0.157580               0.154942       False          1.333333   \n",
       "3467412       0.025017               0.343837       False          1.333333   \n",
       "3467413       0.200990               0.329855       False          1.666667   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0             1.000000               1242   \n",
       "1             False        0.0             1.000000               1368   \n",
       "2              True        0.0             0.542108             340327   \n",
       "3             False        0.0             0.544085             328862   \n",
       "4             False        0.0             0.534113              29400   \n",
       "...             ...        ...                  ...                ...   \n",
       "3467409        True        0.0             0.000000               3016   \n",
       "3467410        True        0.0             0.000000               2880   \n",
       "3467411        True        0.0             0.000000               8363   \n",
       "3467412        True        0.0             0.000000               8200   \n",
       "3467413       False        0.0             0.000000               1394   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER  MAX_POWER  \\\n",
       "0               1.0        False    0  0.000000  1.034798e-04   0.953453   \n",
       "1               1.0        False    0  0.000000  1.786456e-05   0.953825   \n",
       "2               1.0        False    0  0.000000  1.936269e-08   0.019443   \n",
       "3               1.0        False    0  0.000000  3.533646e-09   0.027379   \n",
       "4               1.0         True   60  0.082049  1.389818e-09   0.205861   \n",
       "...             ...          ...  ...       ...           ...        ...   \n",
       "3467409         1.0        False    0  0.000000  1.202904e-01   0.144114   \n",
       "3467410         1.0        False    0  0.000000 -7.982405e-01   0.325230   \n",
       "3467411         1.0        False    0  0.000000  5.337338e-01   0.608186   \n",
       "3467412         1.0        False    0  0.000000 -1.546102e-01   1.424689   \n",
       "3467413         1.0        False    0  0.000000  2.995073e-01   0.503512   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  \\\n",
       "0              0.765573        0.999801    0.249463   0.245946     0.01341   \n",
       "1              0.467772        0.044413    0.224073   0.248517     0.03408   \n",
       "2              0.149309        0.999007    0.000200   0.000867     0.00000   \n",
       "3              0.483864        0.999007    0.000221   0.001266     0.00000   \n",
       "4              0.565317        0.998411    0.001485   0.010743     0.00000   \n",
       "...                 ...             ...         ...        ...         ...   \n",
       "3467409        0.999801        0.006865    0.122027   0.002360     0.12170   \n",
       "3467410        0.008653        0.008455    0.049807   0.013460     0.05183   \n",
       "3467411        0.147124        0.999801    0.560435   0.023381     0.53373   \n",
       "3467412        0.008653        0.008455    0.389485   0.029427     0.41963   \n",
       "3467413        0.006667        0.322149    0.464578   0.038735     0.50350   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0            1247.316951       1683.058849       1.656597e+04   \n",
       "1            1120.366194       1534.385582       1.857421e+04   \n",
       "2               0.997638         10.317111       1.748741e+06   \n",
       "3               1.107110         10.272353       1.584060e+06   \n",
       "4               7.423204         36.749028       1.070819e+06   \n",
       "...                  ...               ...                ...   \n",
       "3467409       610.137224       1851.438312       2.318498e+05   \n",
       "3467410       249.036186               NaN       1.596253e+07   \n",
       "3467411      2802.176106       2337.388916       9.613301e+03   \n",
       "3467412      1947.424739               NaN       1.664211e+06   \n",
       "3467413      2322.888958       2556.938328       2.758580e+04   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0              5761.260206          0.020375           0.468631   \n",
       "1              6672.787492          0.020176           0.479667   \n",
       "2             87651.532914          0.998411           0.632954   \n",
       "3             83069.432047          0.998212           0.681201   \n",
       "4             67142.382666          0.997815           0.661169   \n",
       "...                    ...               ...                ...   \n",
       "3467409       29592.939850          0.006667           0.500012   \n",
       "3467410     -240826.912312          0.008455           0.499121   \n",
       "3467411        2628.170455          0.006667           0.513832   \n",
       "3467412       37295.429950          0.008455           0.518571   \n",
       "3467413       -7051.878833          0.066664           0.497605   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.316306       -0.194402        3.822014      19.214476   \n",
       "1               0.336345       -0.237233        4.256757      17.573316   \n",
       "2               0.306152        0.167644       97.442938       0.113074   \n",
       "3               0.291605        0.155068      123.650232       0.124636   \n",
       "4               0.341646       -0.484312      138.660387       0.976712   \n",
       "...                  ...             ...             ...            ...   \n",
       "3467409         0.287566       -0.016614        1.180994       0.023824   \n",
       "3467410         0.281868             NaN        6.529773       2.196577   \n",
       "3467411         0.288496        0.027189        1.085204       0.147369   \n",
       "3467412         0.284595             NaN        3.657879       3.133762   \n",
       "3467413         0.277782        0.025319        1.083806       0.304453   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                  0.234228          0.015639  False  \n",
       "1                 -0.423359          0.015325  False  \n",
       "2                  0.849697          1.000000  False  \n",
       "3                  0.515143          1.000000  False  \n",
       "4                  0.433093          0.831995  False  \n",
       "...                     ...               ...    ...  \n",
       "3467409           -0.992936          1.000000   True  \n",
       "3467410           -0.000199          0.995996   True  \n",
       "3467411            0.852677          0.671281   True  \n",
       "3467412           -0.000199          0.824852   True  \n",
       "3467413            0.315483          0.285079   True  \n",
       "\n",
       "[3467414 rows x 84 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# df['LABEL'] = df.LABEL.astype('category')\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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>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",
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       "      <td>0</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>-0.961074</td>\n",
       "      <td>24.201602</td>\n",
       "      <td>4.287953</td>\n",
       "      <td>1.333899e-04</td>\n",
       "      <td>1.581810e-04</td>\n",
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       "      <td>12.751683</td>\n",
       "      <td>11.546521</td>\n",
       "      <td>3.973819</td>\n",
       "      <td>14.582449</td>\n",
       "      <td>47.723840</td>\n",
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       "      <td>0.105342</td>\n",
       "      <td>26.960027</td>\n",
       "      <td>0.196686</td>\n",
       "      <td>False</td>\n",
       "      <td>1.111111</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.034798e-04</td>\n",
       "      <td>0.953453</td>\n",
       "      <td>0.765573</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.249463</td>\n",
       "      <td>0.245946</td>\n",
       "      <td>0.01341</td>\n",
       "      <td>1247.316951</td>\n",
       "      <td>1683.058849</td>\n",
       "      <td>1.656597e+04</td>\n",
       "      <td>5761.260206</td>\n",
       "      <td>0.020375</td>\n",
       "      <td>0.468631</td>\n",
       "      <td>0.316306</td>\n",
       "      <td>-0.194402</td>\n",
       "      <td>3.822014</td>\n",
       "      <td>19.214476</td>\n",
       "      <td>0.234228</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ff02::fb</td>\n",
       "      <td>fe80::1087:c341:7263:ead3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>858</td>\n",
       "      <td>0</td>\n",
       "      <td>1.554220e+09</td>\n",
       "      <td>1.554221e+09</td>\n",
       "      <td>5353</td>\n",
       "      <td>5353</td>\n",
       "      <td>138</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11.575837</td>\n",
       "      <td>134</td>\n",
       "      <td>-0.845218</td>\n",
       "      <td>128.00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>126</td>\n",
       "      <td>150</td>\n",
       "      <td>24</td>\n",
       "      <td>150</td>\n",
       "      <td>8.388288</td>\n",
       "      <td>10</td>\n",
       "      <td>7.246377</td>\n",
       "      <td>138.441980</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>-1.036642</td>\n",
       "      <td>24.533863</td>\n",
       "      <td>-3.041708</td>\n",
       "      <td>-2.862945e-19</td>\n",
       "      <td>-3.336314e-19</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.902084</td>\n",
       "      <td>1.539485</td>\n",
       "      <td>0.445011</td>\n",
       "      <td>0.715793</td>\n",
       "      <td>False</td>\n",
       "      <td>0.602060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262991</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>12.550874</td>\n",
       "      <td>10.536604</td>\n",
       "      <td>3.973608</td>\n",
       "      <td>14.582614</td>\n",
       "      <td>47.723620</td>\n",
       "      <td>4.772362</td>\n",
       "      <td>2.422591</td>\n",
       "      <td>0.104397</td>\n",
       "      <td>26.960008</td>\n",
       "      <td>0.177016</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.786456e-05</td>\n",
       "      <td>0.953825</td>\n",
       "      <td>0.467772</td>\n",
       "      <td>0.044413</td>\n",
       "      <td>0.224073</td>\n",
       "      <td>0.248517</td>\n",
       "      <td>0.03408</td>\n",
       "      <td>1120.366194</td>\n",
       "      <td>1534.385582</td>\n",
       "      <td>1.857421e+04</td>\n",
       "      <td>6672.787492</td>\n",
       "      <td>0.020176</td>\n",
       "      <td>0.479667</td>\n",
       "      <td>0.336345</td>\n",
       "      <td>-0.237233</td>\n",
       "      <td>4.256757</td>\n",
       "      <td>17.573316</td>\n",
       "      <td>-0.423359</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>192.168.1.192.</td>\n",
       "      <td>192.168.1.152</td>\n",
       "      <td>2534</td>\n",
       "      <td>0</td>\n",
       "      <td>1536348</td>\n",
       "      <td>0</td>\n",
       "      <td>1.554220e+09</td>\n",
       "      <td>1.554221e+09</td>\n",
       "      <td>40571</td>\n",
       "      <td>1880</td>\n",
       "      <td>666</td>\n",
       "      <td>579.0</td>\n",
       "      <td>454.946151</td>\n",
       "      <td>206976</td>\n",
       "      <td>-0.188282</td>\n",
       "      <td>417.00</td>\n",
       "      <td>933.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2673</td>\n",
       "      <td>2609</td>\n",
       "      <td>64</td>\n",
       "      <td>68.310233</td>\n",
       "      <td>382</td>\n",
       "      <td>57.357357</td>\n",
       "      <td>806.747481</td>\n",
       "      <td>0.531965</td>\n",
       "      <td>0.468035</td>\n",
       "      <td>1.323234</td>\n",
       "      <td>3.119050</td>\n",
       "      <td>0.492374</td>\n",
       "      <td>3.118073e-09</td>\n",
       "      <td>3.119921e-09</td>\n",
       "      <td>0.034884</td>\n",
       "      <td>2.775578</td>\n",
       "      <td>6.380793</td>\n",
       "      <td>0.564312</td>\n",
       "      <td>0.578712</td>\n",
       "      <td>False</td>\n",
       "      <td>0.714878</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.542108</td>\n",
       "      <td>0.498117</td>\n",
       "      <td>0.975651</td>\n",
       "      <td>149.388050</td>\n",
       "      <td>148.001666</td>\n",
       "      <td>75.689032</td>\n",
       "      <td>224.696690</td>\n",
       "      <td>299.905341</td>\n",
       "      <td>0.118399</td>\n",
       "      <td>0.088413</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.869108</td>\n",
       "      <td>0.136231</td>\n",
       "      <td>False</td>\n",
       "      <td>1.950257</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542108</td>\n",
       "      <td>340327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.936269e-08</td>\n",
       "      <td>0.019443</td>\n",
       "      <td>0.149309</td>\n",
       "      <td>0.999007</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.997638</td>\n",
       "      <td>10.317111</td>\n",
       "      <td>1.748741e+06</td>\n",
       "      <td>87651.532914</td>\n",
       "      <td>0.998411</td>\n",
       "      <td>0.632954</td>\n",
       "      <td>0.306152</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>97.442938</td>\n",
       "      <td>0.113074</td>\n",
       "      <td>0.849697</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>192.168.1.190.</td>\n",
       "      <td>192.168.1.152</td>\n",
       "      <td>2405</td>\n",
       "      <td>0</td>\n",
       "      <td>1493560</td>\n",
       "      <td>0</td>\n",
       "      <td>1.554220e+09</td>\n",
       "      <td>1.554221e+09</td>\n",
       "      <td>43539</td>\n",
       "      <td>1880</td>\n",
       "      <td>681</td>\n",
       "      <td>579.0</td>\n",
       "      <td>480.631876</td>\n",
       "      <td>231007</td>\n",
       "      <td>-0.172488</td>\n",
       "      <td>418.00</td>\n",
       "      <td>933.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2673</td>\n",
       "      <td>2609</td>\n",
       "      <td>64</td>\n",
       "      <td>70.577368</td>\n",
       "      <td>394</td>\n",
       "      <td>57.856094</td>\n",
       "      <td>833.488650</td>\n",
       "      <td>0.523493</td>\n",
       "      <td>0.476507</td>\n",
       "      <td>1.283727</td>\n",
       "      <td>3.045990</td>\n",
       "      <td>0.686459</td>\n",
       "      <td>2.059728e-10</td>\n",
       "      <td>2.061014e-10</td>\n",
       "      <td>-0.021359</td>\n",
       "      <td>3.295854</td>\n",
       "      <td>6.332938</td>\n",
       "      <td>0.563839</td>\n",
       "      <td>0.584333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.717256</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.544085</td>\n",
       "      <td>0.499061</td>\n",
       "      <td>0.976637</td>\n",
       "      <td>149.670896</td>\n",
       "      <td>148.796587</td>\n",
       "      <td>75.688963</td>\n",
       "      <td>225.342053</td>\n",
       "      <td>299.905281</td>\n",
       "      <td>0.124753</td>\n",
       "      <td>0.097319</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.869398</td>\n",
       "      <td>0.143493</td>\n",
       "      <td>False</td>\n",
       "      <td>1.982529</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.544085</td>\n",
       "      <td>328862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.533646e-09</td>\n",
       "      <td>0.027379</td>\n",
       "      <td>0.483864</td>\n",
       "      <td>0.999007</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.107110</td>\n",
       "      <td>10.272353</td>\n",
       "      <td>1.584060e+06</td>\n",
       "      <td>83069.432047</td>\n",
       "      <td>0.998212</td>\n",
       "      <td>0.681201</td>\n",
       "      <td>0.291605</td>\n",
       "      <td>0.155068</td>\n",
       "      <td>123.650232</td>\n",
       "      <td>0.124636</td>\n",
       "      <td>0.515143</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.152.</td>\n",
       "      <td>192.168.1.190</td>\n",
       "      <td>2396</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.554220e+09</td>\n",
       "      <td>1.554221e+09</td>\n",
       "      <td>1880</td>\n",
       "      <td>43539</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</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>0.001213</td>\n",
       "      <td>0.534113</td>\n",
       "      <td>0.497263</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>149.131741</td>\n",
       "      <td>148.001476</td>\n",
       "      <td>75.339264</td>\n",
       "      <td>224.768017</td>\n",
       "      <td>299.905242</td>\n",
       "      <td>0.125221</td>\n",
       "      <td>0.097868</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.869425</td>\n",
       "      <td>0.144028</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534113</td>\n",
       "      <td>29400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>0.082049</td>\n",
       "      <td>1.389818e-09</td>\n",
       "      <td>0.205861</td>\n",
       "      <td>0.565317</td>\n",
       "      <td>0.998411</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.010743</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.423204</td>\n",
       "      <td>36.749028</td>\n",
       "      <td>1.070819e+06</td>\n",
       "      <td>67142.382666</td>\n",
       "      <td>0.997815</td>\n",
       "      <td>0.661169</td>\n",
       "      <td>0.341646</td>\n",
       "      <td>-0.484312</td>\n",
       "      <td>138.660387</td>\n",
       "      <td>0.976712</td>\n",
       "      <td>0.433093</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467409</th>\n",
       "      <td>3467409</td>\n",
       "      <td>52.147.9.175.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2356</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51670</td>\n",
       "      <td>274</td>\n",
       "      <td>60.0</td>\n",
       "      <td>429.984883</td>\n",
       "      <td>184887</td>\n",
       "      <td>0.221574</td>\n",
       "      <td>60.00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1425</td>\n",
       "      <td>1365</td>\n",
       "      <td>60</td>\n",
       "      <td>156.928790</td>\n",
       "      <td>294</td>\n",
       "      <td>107.299270</td>\n",
       "      <td>493.208789</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.497692</td>\n",
       "      <td>1.772155</td>\n",
       "      <td>1.582852</td>\n",
       "      <td>2.287061e-09</td>\n",
       "      <td>2.665211e-09</td>\n",
       "      <td>-1.488372</td>\n",
       "      <td>4.800728</td>\n",
       "      <td>1.672933</td>\n",
       "      <td>0.483586</td>\n",
       "      <td>0.550311</td>\n",
       "      <td>False</td>\n",
       "      <td>0.652939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.549271</td>\n",
       "      <td>0.732143</td>\n",
       "      <td>0.124318</td>\n",
       "      <td>0.185845</td>\n",
       "      <td>0.025730</td>\n",
       "      <td>0.204397</td>\n",
       "      <td>0.226333</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.145608</td>\n",
       "      <td>0.155440</td>\n",
       "      <td>False</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.202904e-01</td>\n",
       "      <td>0.144114</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.006865</td>\n",
       "      <td>0.122027</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.12170</td>\n",
       "      <td>610.137224</td>\n",
       "      <td>1851.438312</td>\n",
       "      <td>2.318498e+05</td>\n",
       "      <td>29592.939850</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.500012</td>\n",
       "      <td>0.287566</td>\n",
       "      <td>-0.016614</td>\n",
       "      <td>1.180994</td>\n",
       "      <td>0.023824</td>\n",
       "      <td>-0.992936</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467410</th>\n",
       "      <td>3467410</td>\n",
       "      <td>52.147.9.175.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2280</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51778</td>\n",
       "      <td>288</td>\n",
       "      <td>60.0</td>\n",
       "      <td>454.171774</td>\n",
       "      <td>206272</td>\n",
       "      <td>0.223899</td>\n",
       "      <td>60.00</td>\n",
       "      <td>271.5</td>\n",
       "      <td>60</td>\n",
       "      <td>1492</td>\n",
       "      <td>1432</td>\n",
       "      <td>60</td>\n",
       "      <td>157.698533</td>\n",
       "      <td>300</td>\n",
       "      <td>104.166667</td>\n",
       "      <td>518.255728</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.502013</td>\n",
       "      <td>1.770255</td>\n",
       "      <td>1.616712</td>\n",
       "      <td>-7.584549e-23</td>\n",
       "      <td>-8.994169e-23</td>\n",
       "      <td>-1.156028</td>\n",
       "      <td>4.991372</td>\n",
       "      <td>1.770951</td>\n",
       "      <td>0.533109</td>\n",
       "      <td>0.498566</td>\n",
       "      <td>False</td>\n",
       "      <td>0.677121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555707</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.044454</td>\n",
       "      <td>0.057543</td>\n",
       "      <td>0.016090</td>\n",
       "      <td>0.061109</td>\n",
       "      <td>0.079996</td>\n",
       "      <td>0.008888</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.017896</td>\n",
       "      <td>0.496668</td>\n",
       "      <td>False</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.982405e-01</td>\n",
       "      <td>0.325230</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.049807</td>\n",
       "      <td>0.013460</td>\n",
       "      <td>0.05183</td>\n",
       "      <td>249.036186</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.596253e+07</td>\n",
       "      <td>-240826.912312</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.499121</td>\n",
       "      <td>0.281868</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>6.529773</td>\n",
       "      <td>2.196577</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467411</th>\n",
       "      <td>3467411</td>\n",
       "      <td>192.168.1.195.</td>\n",
       "      <td>52.147.9.175</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7763</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>51670</td>\n",
       "      <td>443</td>\n",
       "      <td>836</td>\n",
       "      <td>996.0</td>\n",
       "      <td>710.760156</td>\n",
       "      <td>505180</td>\n",
       "      <td>-0.080969</td>\n",
       "      <td>72.75</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>1520</td>\n",
       "      <td>85.019157</td>\n",
       "      <td>655</td>\n",
       "      <td>78.349282</td>\n",
       "      <td>1074.271893</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.962350</td>\n",
       "      <td>2.127300</td>\n",
       "      <td>-0.449867</td>\n",
       "      <td>8.355107e-10</td>\n",
       "      <td>9.907938e-10</td>\n",
       "      <td>-0.054759</td>\n",
       "      <td>0.912363</td>\n",
       "      <td>2.046439</td>\n",
       "      <td>0.616040</td>\n",
       "      <td>0.703213</td>\n",
       "      <td>False</td>\n",
       "      <td>0.753213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.417557</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.091755</td>\n",
       "      <td>0.021365</td>\n",
       "      <td>0.021328</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>0.219742</td>\n",
       "      <td>0.024416</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.157580</td>\n",
       "      <td>0.154942</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.337338e-01</td>\n",
       "      <td>0.608186</td>\n",
       "      <td>0.147124</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.560435</td>\n",
       "      <td>0.023381</td>\n",
       "      <td>0.53373</td>\n",
       "      <td>2802.176106</td>\n",
       "      <td>2337.388916</td>\n",
       "      <td>9.613301e+03</td>\n",
       "      <td>2628.170455</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.513832</td>\n",
       "      <td>0.288496</td>\n",
       "      <td>0.027189</td>\n",
       "      <td>1.085204</td>\n",
       "      <td>0.147369</td>\n",
       "      <td>0.852677</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467412</th>\n",
       "      <td>3467412</td>\n",
       "      <td>192.168.1.195.</td>\n",
       "      <td>52.147.9.175</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>51778</td>\n",
       "      <td>443</td>\n",
       "      <td>820</td>\n",
       "      <td>914.5</td>\n",
       "      <td>718.254133</td>\n",
       "      <td>515889</td>\n",
       "      <td>-0.066144</td>\n",
       "      <td>72.75</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>1520</td>\n",
       "      <td>87.591967</td>\n",
       "      <td>671</td>\n",
       "      <td>81.829268</td>\n",
       "      <td>1066.161620</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.974585</td>\n",
       "      <td>2.151745</td>\n",
       "      <td>-0.390486</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.032648</td>\n",
       "      <td>0.875264</td>\n",
       "      <td>2.046439</td>\n",
       "      <td>0.616040</td>\n",
       "      <td>0.712187</td>\n",
       "      <td>False</td>\n",
       "      <td>0.753213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418338</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.032386</td>\n",
       "      <td>0.017202</td>\n",
       "      <td>0.017188</td>\n",
       "      <td>0.058194</td>\n",
       "      <td>0.077416</td>\n",
       "      <td>0.008602</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.025017</td>\n",
       "      <td>0.343837</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.546102e-01</td>\n",
       "      <td>1.424689</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.389485</td>\n",
       "      <td>0.029427</td>\n",
       "      <td>0.41963</td>\n",
       "      <td>1947.424739</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.664211e+06</td>\n",
       "      <td>37295.429950</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.518571</td>\n",
       "      <td>0.284595</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.657879</td>\n",
       "      <td>3.133762</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467413</th>\n",
       "      <td>3467413</td>\n",
       "      <td>13.107.3.128.</td>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>1.556365e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>51672</td>\n",
       "      <td>199</td>\n",
       "      <td>60.0</td>\n",
       "      <td>221.115355</td>\n",
       "      <td>48892</td>\n",
       "      <td>0.052641</td>\n",
       "      <td>60.00</td>\n",
       "      <td>310.0</td>\n",
       "      <td>60</td>\n",
       "      <td>534</td>\n",
       "      <td>474</td>\n",
       "      <td>60</td>\n",
       "      <td>111.113243</td>\n",
       "      <td>184</td>\n",
       "      <td>92.462312</td>\n",
       "      <td>285.596619</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.628631</td>\n",
       "      <td>2.428597</td>\n",
       "      <td>0.355377</td>\n",
       "      <td>1.321433e-08</td>\n",
       "      <td>1.712773e-08</td>\n",
       "      <td>-0.112000</td>\n",
       "      <td>1.397409</td>\n",
       "      <td>1.664498</td>\n",
       "      <td>0.592906</td>\n",
       "      <td>0.507121</td>\n",
       "      <td>False</td>\n",
       "      <td>0.665736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.269733</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.107296</td>\n",
       "      <td>0.050451</td>\n",
       "      <td>0.024303</td>\n",
       "      <td>0.196795</td>\n",
       "      <td>0.397785</td>\n",
       "      <td>0.066298</td>\n",
       "      <td>0.024890</td>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.200990</td>\n",
       "      <td>0.329855</td>\n",
       "      <td>False</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.995073e-01</td>\n",
       "      <td>0.503512</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.322149</td>\n",
       "      <td>0.464578</td>\n",
       "      <td>0.038735</td>\n",
       "      <td>0.50350</td>\n",
       "      <td>2322.888958</td>\n",
       "      <td>2556.938328</td>\n",
       "      <td>2.758580e+04</td>\n",
       "      <td>-7051.878833</td>\n",
       "      <td>0.066664</td>\n",
       "      <td>0.497605</td>\n",
       "      <td>0.277782</td>\n",
       "      <td>0.025319</td>\n",
       "      <td>1.083806</td>\n",
       "      <td>0.304453</td>\n",
       "      <td>0.315483</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3467414 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP                     SRC_IP  PACKETS  \\\n",
       "0                 0     224.0.0.251              192.168.1.133       10   \n",
       "1                 1        ff02::fb  fe80::1087:c341:7263:ead3       11   \n",
       "2                 2  192.168.1.192.              192.168.1.152     2534   \n",
       "3                 3  192.168.1.190.              192.168.1.152     2405   \n",
       "4                 4  192.168.1.152.              192.168.1.190     2396   \n",
       "...             ...             ...                        ...      ...   \n",
       "3467409     3467409   52.147.9.175.              192.168.1.195       11   \n",
       "3467410     3467410   52.147.9.175.              192.168.1.195       10   \n",
       "3467411     3467411  192.168.1.195.               52.147.9.175       10   \n",
       "3467412     3467412  192.168.1.195.               52.147.9.175       10   \n",
       "3467413     3467413   13.107.3.128.              192.168.1.195        7   \n",
       "\n",
       "         PACKETS_REV    BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  \\\n",
       "0                  0      792          0  1.554220e+09  1.554221e+09   \n",
       "1                  0      858          0  1.554220e+09  1.554221e+09   \n",
       "2                  0  1536348          0  1.554220e+09  1.554221e+09   \n",
       "3                  0  1493560          0  1.554220e+09  1.554221e+09   \n",
       "4                  0        0          0  1.554220e+09  1.554221e+09   \n",
       "...              ...      ...        ...           ...           ...   \n",
       "3467409            0     2356          0  1.556365e+09  1.556365e+09   \n",
       "3467410            0     2280          0  1.556365e+09  1.556365e+09   \n",
       "3467411            0     7763          0  1.556365e+09  1.556365e+09   \n",
       "3467412            0     7600          0  1.556365e+09  1.556365e+09   \n",
       "3467413            0      974          0  1.556365e+09  1.556365e+09   \n",
       "\n",
       "         DST_PORT  SRC_PORT  MEAN  MEDIAN       STDEV     VAR  BURSTINESS  \\\n",
       "0            5353      5353   139   140.0   11.445523     131   -0.847845   \n",
       "1            5353      5353   138   130.0   11.575837     134   -0.845218   \n",
       "2           40571      1880   666   579.0  454.946151  206976   -0.188282   \n",
       "3           43539      1880   681   579.0  480.631876  231007   -0.172488   \n",
       "4            1880     43539    60    60.0    0.000000       0   -1.000000   \n",
       "...           ...       ...   ...     ...         ...     ...         ...   \n",
       "3467409       443     51670   274    60.0  429.984883  184887    0.221574   \n",
       "3467410       443     51778   288    60.0  454.171774  206272    0.223899   \n",
       "3467411     51670       443   836   996.0  710.760156  505180   -0.080969   \n",
       "3467412     51778       443   820   914.5  718.254133  515889   -0.066144   \n",
       "3467413       443     51672   199    60.0  221.115355   48892    0.052641   \n",
       "\n",
       "             Q1      Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0        130.00   150.0  126   150             24   150   \n",
       "1        128.00   150.0  126   150             24   150   \n",
       "2        417.00   933.0   64  2673           2609    64   \n",
       "3        418.00   933.0   64  2673           2609    64   \n",
       "4         60.00    60.0   60    60              0    60   \n",
       "...         ...     ...  ...   ...            ...   ...   \n",
       "3467409   60.00   232.0   60  1425           1365    60   \n",
       "3467410   60.00   271.5   60  1492           1432    60   \n",
       "3467411   72.75  1520.0   60  1520           1460  1520   \n",
       "3467412   72.75  1520.0   60  1520           1460  1520   \n",
       "3467413   60.00   310.0   60   534            474    60   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                        8.234189                  10           7.194245   \n",
       "1                        8.388288                  10           7.246377   \n",
       "2                       68.310233                 382          57.357357   \n",
       "3                       70.577368                 394          57.856094   \n",
       "4                        0.000000                   0           0.000000   \n",
       "...                           ...                 ...                ...   \n",
       "3467409                156.928790                 294         107.299270   \n",
       "3467410                157.698533                 300         104.166667   \n",
       "3467411                 85.019157                 655          78.349282   \n",
       "3467412                 87.591967                 671          81.829268   \n",
       "3467413                111.113243                 184          92.462312   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              139.625213            0.500000            0.500000   \n",
       "1              138.441980            0.545455            0.454545   \n",
       "2              806.747481            0.531965            0.468035   \n",
       "3              833.488650            0.523493            0.476507   \n",
       "4               60.000000            0.000000            0.000000   \n",
       "...                   ...                 ...                 ...   \n",
       "3467409        493.208789            0.727273            0.272727   \n",
       "3467410        518.255728            0.700000            0.300000   \n",
       "3467411       1074.271893            0.500000            0.500000   \n",
       "3467412       1066.161620            0.500000            0.500000   \n",
       "3467413        285.596619            0.714286            0.285714   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   -0.961074             24.201602              4.287953   \n",
       "1                   -1.036642             24.533863             -3.041708   \n",
       "2                    1.323234              3.119050              0.492374   \n",
       "3                    1.283727              3.045990              0.686459   \n",
       "4                    0.000000              0.000000              0.000000   \n",
       "...                       ...                   ...                   ...   \n",
       "3467409              0.497692              1.772155              1.582852   \n",
       "3467410              0.502013              1.770255              1.616712   \n",
       "3467411             -0.962350              2.127300             -0.449867   \n",
       "3467412             -0.974585              2.151745             -0.390486   \n",
       "3467413              0.628631              2.428597              0.355377   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      1.333899e-04                1.581810e-04   \n",
       "1                     -2.862945e-19               -3.336314e-19   \n",
       "2                      3.118073e-09                3.119921e-09   \n",
       "3                      2.059728e-10                2.061014e-10   \n",
       "4                      0.000000e+00                0.000000e+00   \n",
       "...                             ...                         ...   \n",
       "3467409                2.287061e-09                2.665211e-09   \n",
       "3467410               -7.584549e-23               -8.994169e-23   \n",
       "3467411                8.355107e-10                9.907938e-10   \n",
       "3467412                0.000000e+00                0.000000e+00   \n",
       "3467413                1.321433e-08                1.712773e-08   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.100000  0.874133  1.485475        0.447173        0.675573   \n",
       "1               0.090909  0.902084  1.539485        0.445011        0.715793   \n",
       "2               0.034884  2.775578  6.380793        0.564312        0.578712   \n",
       "3              -0.021359  3.295854  6.332938        0.563839        0.584333   \n",
       "4               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "3467409        -1.488372  4.800728  1.672933        0.483586        0.550311   \n",
       "3467410        -1.156028  4.991372  1.770951        0.533109        0.498566   \n",
       "3467411        -0.054759  0.912363  2.046439        0.616040        0.703213   \n",
       "3467412        -0.032648  0.875264  2.046439        0.616040        0.712187   \n",
       "3467413        -0.112000  1.397409  1.664498        0.592906        0.507121   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.602060             0.000000   \n",
       "1                        False   0.602060             0.000000   \n",
       "2                        False   0.714878             0.001000   \n",
       "3                        False   0.717256             0.001000   \n",
       "4                        False   0.301030             0.001213   \n",
       "...                        ...        ...                  ...   \n",
       "3467409                  False   0.652939             0.000000   \n",
       "3467410                  False   0.677121             0.000000   \n",
       "3467411                  False   0.753213             0.000000   \n",
       "3467412                  False   0.753213             0.000000   \n",
       "3467413                  False   0.665736             0.000000   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                1.000000           0.267197                  0.625000   \n",
       "1                1.000000           0.262991                  0.575000   \n",
       "2                0.542108           0.498117                  0.975651   \n",
       "3                0.544085           0.499061                  0.976637   \n",
       "4                0.534113           0.497263                  0.875000   \n",
       "...                   ...                ...                       ...   \n",
       "3467409          0.000000           0.549271                  0.732143   \n",
       "3467410          0.000000           0.555707                  0.708333   \n",
       "3467411          0.000000           0.417557                  0.500000   \n",
       "3467412          0.000000           0.418338                  0.500000   \n",
       "3467413          0.000000           0.269733                  0.687500   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               12.751683           11.546521        3.973819       14.582449   \n",
       "1               12.550874           10.536604        3.973608       14.582614   \n",
       "2              149.388050          148.001666       75.689032      224.696690   \n",
       "3              149.670896          148.796587       75.688963      225.342053   \n",
       "4              149.131741          148.001476       75.339264      224.768017   \n",
       "...                   ...                 ...             ...             ...   \n",
       "3467409          0.124318            0.185845        0.025730        0.204397   \n",
       "3467410          0.044454            0.057543        0.016090        0.061109   \n",
       "3467411          0.091755            0.021365        0.021328        0.206023   \n",
       "3467412          0.032386            0.017202        0.017188        0.058194   \n",
       "3467413          0.107296            0.050451        0.024303        0.196795   \n",
       "\n",
       "           DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0         47.723840        5.302649          2.930586       0.105342   \n",
       "1         47.723620        4.772362          2.422591       0.104397   \n",
       "2        299.905341        0.118399          0.088413       0.000009   \n",
       "3        299.905281        0.124753          0.097319       0.000013   \n",
       "4        299.905242        0.125221          0.097868       0.000033   \n",
       "...             ...             ...               ...            ...   \n",
       "3467409    0.226333        0.022633          0.009434       0.000118   \n",
       "3467410    0.079996        0.008888          0.011698       0.000783   \n",
       "3467411    0.219742        0.024416          0.001552       0.000005   \n",
       "3467412    0.077416        0.008602          0.001893       0.000004   \n",
       "3467413    0.397785        0.066298          0.024890       0.000672   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            26.960027               0.196686       False          1.111111   \n",
       "1            26.960008               0.177016       False          1.000000   \n",
       "2             0.869108               0.136231       False          1.950257   \n",
       "3             0.869398               0.143493       False          1.982529   \n",
       "4             0.869425               0.144028       False          0.000000   \n",
       "...                ...                    ...         ...               ...   \n",
       "3467409       0.145608               0.155440       False          1.400000   \n",
       "3467410       0.017896               0.496668       False          1.555556   \n",
       "3467411       0.157580               0.154942       False          1.333333   \n",
       "3467412       0.025017               0.343837       False          1.333333   \n",
       "3467413       0.200990               0.329855       False          1.666667   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0             1.000000               1242   \n",
       "1             False        0.0             1.000000               1368   \n",
       "2              True        0.0             0.542108             340327   \n",
       "3             False        0.0             0.544085             328862   \n",
       "4             False        0.0             0.534113              29400   \n",
       "...             ...        ...                  ...                ...   \n",
       "3467409        True        0.0             0.000000               3016   \n",
       "3467410        True        0.0             0.000000               2880   \n",
       "3467411        True        0.0             0.000000               8363   \n",
       "3467412        True        0.0             0.000000               8200   \n",
       "3467413       False        0.0             0.000000               1394   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER  MAX_POWER  \\\n",
       "0               1.0        False    0  0.000000  1.034798e-04   0.953453   \n",
       "1               1.0        False    0  0.000000  1.786456e-05   0.953825   \n",
       "2               1.0        False    0  0.000000  1.936269e-08   0.019443   \n",
       "3               1.0        False    0  0.000000  3.533646e-09   0.027379   \n",
       "4               1.0         True   60  0.082049  1.389818e-09   0.205861   \n",
       "...             ...          ...  ...       ...           ...        ...   \n",
       "3467409         1.0        False    0  0.000000  1.202904e-01   0.144114   \n",
       "3467410         1.0        False    0  0.000000 -7.982405e-01   0.325230   \n",
       "3467411         1.0        False    0  0.000000  5.337338e-01   0.608186   \n",
       "3467412         1.0        False    0  0.000000 -1.546102e-01   1.424689   \n",
       "3467413         1.0        False    0  0.000000  2.995073e-01   0.503512   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  \\\n",
       "0              0.765573        0.999801    0.249463   0.245946     0.01341   \n",
       "1              0.467772        0.044413    0.224073   0.248517     0.03408   \n",
       "2              0.149309        0.999007    0.000200   0.000867     0.00000   \n",
       "3              0.483864        0.999007    0.000221   0.001266     0.00000   \n",
       "4              0.565317        0.998411    0.001485   0.010743     0.00000   \n",
       "...                 ...             ...         ...        ...         ...   \n",
       "3467409        0.999801        0.006865    0.122027   0.002360     0.12170   \n",
       "3467410        0.008653        0.008455    0.049807   0.013460     0.05183   \n",
       "3467411        0.147124        0.999801    0.560435   0.023381     0.53373   \n",
       "3467412        0.008653        0.008455    0.389485   0.029427     0.41963   \n",
       "3467413        0.006667        0.322149    0.464578   0.038735     0.50350   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0            1247.316951       1683.058849       1.656597e+04   \n",
       "1            1120.366194       1534.385582       1.857421e+04   \n",
       "2               0.997638         10.317111       1.748741e+06   \n",
       "3               1.107110         10.272353       1.584060e+06   \n",
       "4               7.423204         36.749028       1.070819e+06   \n",
       "...                  ...               ...                ...   \n",
       "3467409       610.137224       1851.438312       2.318498e+05   \n",
       "3467410       249.036186         -1.000000       1.596253e+07   \n",
       "3467411      2802.176106       2337.388916       9.613301e+03   \n",
       "3467412      1947.424739         -1.000000       1.664211e+06   \n",
       "3467413      2322.888958       2556.938328       2.758580e+04   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0              5761.260206          0.020375           0.468631   \n",
       "1              6672.787492          0.020176           0.479667   \n",
       "2             87651.532914          0.998411           0.632954   \n",
       "3             83069.432047          0.998212           0.681201   \n",
       "4             67142.382666          0.997815           0.661169   \n",
       "...                    ...               ...                ...   \n",
       "3467409       29592.939850          0.006667           0.500012   \n",
       "3467410     -240826.912312          0.008455           0.499121   \n",
       "3467411        2628.170455          0.006667           0.513832   \n",
       "3467412       37295.429950          0.008455           0.518571   \n",
       "3467413       -7051.878833          0.066664           0.497605   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.316306       -0.194402        3.822014      19.214476   \n",
       "1               0.336345       -0.237233        4.256757      17.573316   \n",
       "2               0.306152        0.167644       97.442938       0.113074   \n",
       "3               0.291605        0.155068      123.650232       0.124636   \n",
       "4               0.341646       -0.484312      138.660387       0.976712   \n",
       "...                  ...             ...             ...            ...   \n",
       "3467409         0.287566       -0.016614        1.180994       0.023824   \n",
       "3467410         0.281868       -1.000000        6.529773       2.196577   \n",
       "3467411         0.288496        0.027189        1.085204       0.147369   \n",
       "3467412         0.284595       -1.000000        3.657879       3.133762   \n",
       "3467413         0.277782        0.025319        1.083806       0.304453   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                  0.234228              True  False  \n",
       "1                 -0.423359              True  False  \n",
       "2                  0.849697              True  False  \n",
       "3                  0.515143              True  False  \n",
       "4                  0.433093              True  False  \n",
       "...                     ...               ...    ...  \n",
       "3467409           -0.992936              True   True  \n",
       "3467410           -0.000199              True   True  \n",
       "3467411            0.852677              True   True  \n",
       "3467412           -0.000199              True   True  \n",
       "3467413            0.315483              True   True  \n",
       "\n",
       "[3467414 rows x 84 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IoT malware: 3456561\tclear: 10853\n"
     ]
    }
   ],
   "source": [
    "print(f\"IoT malware: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3467414, 77)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IoT malware: 3456561\tclear: 10853\n"
     ]
    }
   ],
   "source": [
    "print(f\"IoT malware: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=500000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "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']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, 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)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\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, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [04:10<00:00,  2.51s/trial, best loss: -0.9995737389778093]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.22554399934482494, 'gamma': 4.879915242838943, 'max_depth': 18.0, 'min_child_weight': 6.0, 'n_estimators': 220.0, 'reg_alpha': 3.0, 'reg_lambda': 0.6901824367091948}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(200000), features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL']  # 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",
    "      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",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.95 & 99.96 & 99.99 & 99.98 \\\\\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL']  # Labels\n",
    "    best_results = (0,\"\",None,None, 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_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",
    "        for F, N in zip(\n",
    "                [ features], # statistics_features, time_features, distribution_features, frequency_features, behavior_features, \n",
    "                [ \"All\"], # \"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \n",
    "        ):\n",
    "                X_train = X_train_a[F]\n",
    "                X_test = X_test_a[F] \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",
    "                accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "                precision = precision_score(y_test, y_pred)  * 100\n",
    "                recall = recall_score(y_test, y_pred)  * 100\n",
    "                F1 = f1_score(y_test, y_pred)  * 100\n",
    "                if N == \"All\":\n",
    "                    tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\hline\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_f1 = F1\n",
    "                elif N in [\"Statistical-based\", \"Distribution-based\", \"Behavior-based\"]:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                else:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \\\\rowcolor{LigthGray} \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "        if best_results[0] < tmp_f1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (tmp_f1, tmp_str, matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "\n",
      "\n",
      "99.9777483216883\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.96 & 99.96 & 99.99 & 99.98 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.95 & 99.96 & 99.99 & 99.97 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL']  # Labels\n",
    "    \n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"\"\n",
    "tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "tmp_str += \"\\n\"\n",
    "tmp_str += \"\\hline\"\n",
    "tmp_str += \"\\n\"\n",
    "print(tmp_str)\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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"TON_IoT-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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_df = 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_df]\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.29030323, 0.19660047, 0.014353969, 0.23355688, 0.17590284, 0.014353969]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF            0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000000\n",
       "PERCENT_BELOW_MEAN          0.000000\n",
       "SPECTRAL_BANDWIDTH          0.000000\n",
       "BENFORD_LAW_PRESENTED       0.000000\n",
       "                              ...   \n",
       "CNT_DISTRIBUTION            0.051489\n",
       "VAR                         0.054626\n",
       "MIN_POWER                   0.066764\n",
       "MEDIAN_SCALED_TIME          0.108287\n",
       "TRANSIENTS                  0.125623\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PERIODICITY_SCDF', 'AREA_VALUES_DISTRIBUTION', 'PERCENT_BELOW_MEAN',\n",
       "       'SPECTRAL_BANDWIDTH', 'BENFORD_LAW_PRESENTED', 'CNT_NZ_DISTRIBUTION',\n",
       "       'PERIODICITY', 'TIME', 'CNT_ZEROS', 'BYTES_REV', 'MEAN',\n",
       "       'SPECTRAL_SKEWNESS', 'P_BENFORD', 'POWER_MEAN', 'PEARSON_SK1_SKEWNESS',\n",
       "       'SPECTRAL_CREST', 'Q3_SCALED_TIME', 'GALTON_SKEWNESS',\n",
       "       'COEFFICIENT_OF_VARIATION', 'PEARSON_SK2_SKEWNESS', 'MODE',\n",
       "       'TIME_DISTRIBUTION', 'MAX_POWER_FREQ', 'Q1', 'MIN', 'MAX_POWER',\n",
       "       'ROOT_MEAN_SQUARE', 'PACKETS', 'BURSTINESS',\n",
       "       'FISHER_PEARSON_g1_SKEWNESS', 'MEAN_SCALED_TIME', 'SPECTRAL_SLOPE',\n",
       "       'MEAN_SCALED_DIFFTIMES', 'SPECTRAL_FLUX', 'Q1_SCALED_TIME', 'BYTES',\n",
       "       'SPECTRAL_ENERGY', 'VAL', 'MAX_DIFFTIMES', 'BIGGEST_CNT_1_SEC', 'STDEV',\n",
       "       'DIRECTIONS', 'SPECTRAL_CENTROID', 'SIG_SPACES', 'PERCENT_DEVIATION',\n",
       "       'PERCENT_ABOVE_MEAN', 'FISHER_PEARSON_G1_SKEWNESS', 'PACKETS_REV',\n",
       "       'DURATION', 'NORMAL_DISTRIBUTION', 'SPECTRAL_ENTROPY', 'KURTOSIS',\n",
       "       'MIN_POWER_FREQ', 'MAX', 'SCALED_ENTROPY', 'Q3', 'MEDIAN_DIFFTIMES',\n",
       "       'POWER_STD', 'SPECTRAL_SPREAD', 'SPECTRAL_KURTOSIS',\n",
       "       'FISHER_MI_3_SKEWNESS', 'MIN_DIFFTIMES', 'MIN_MINUS_MAX',\n",
       "       'HURST_EXPONENT', 'AVERAGE_DISPERSION', 'ENTROPY', 'MEAN_DIFFTIMES',\n",
       "       'SWITCHING_METRIC', 'POWER_MODE', 'SPECTRAL_ROLLOFF', 'MEDIAN',\n",
       "       'CNT_DISTRIBUTION', 'VAR', 'MIN_POWER', 'MEDIAN_SCALED_TIME',\n",
       "       'TRANSIENTS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'BYTES_REV': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'P_BENFORD': 0.0009695774060674012,\n",
       " 'POWER_MEAN': 0.0011988227488473058,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.001284839934669435,\n",
       " 'SPECTRAL_CREST': 0.001308799022808671,\n",
       " 'Q3_SCALED_TIME': 0.0014540856936946511,\n",
       " 'GALTON_SKEWNESS': 0.001463739899918437,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0015105133643373847,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.001557622104883194,\n",
       " 'MODE': 0.00170783675275743,\n",
       " 'TIME_DISTRIBUTION': 0.0019190531456843019,\n",
       " 'MAX_POWER_FREQ': 0.0019369912333786488,\n",
       " 'Q1': 0.0019938338082283735,\n",
       " 'MIN': 0.002005920745432377,\n",
       " 'MAX_POWER': 0.002326883375644684,\n",
       " 'ROOT_MEAN_SQUARE': 0.0024632448330521584,\n",
       " 'PACKETS': 0.00270800176076591,\n",
       " 'BURSTINESS': 0.0028108374681323767,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0028867903165519238,\n",
       " 'MEAN_SCALED_TIME': 0.0029103378765285015,\n",
       " 'SPECTRAL_SLOPE': 0.0029190194327384233,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.00298628699965775,\n",
       " 'SPECTRAL_FLUX': 0.0030670305714011192,\n",
       " 'Q1_SCALED_TIME': 0.0033265340607613325,\n",
       " 'BYTES': 0.0034914547577500343,\n",
       " 'SPECTRAL_ENERGY': 0.004174066241830587,\n",
       " 'VAL': 0.0046260724775493145,\n",
       " 'MAX_DIFFTIMES': 0.005052951630204916,\n",
       " 'BIGGEST_CNT_1_SEC': 0.005465730559080839,\n",
       " 'STDEV': 0.005468388088047504,\n",
       " 'DIRECTIONS': 0.005611776374280453,\n",
       " 'SPECTRAL_CENTROID': 0.005843297578394413,\n",
       " 'SIG_SPACES': 0.006307313218712807,\n",
       " 'PERCENT_DEVIATION': 0.006987186148762703,\n",
       " 'PERCENT_ABOVE_MEAN': 0.007045568432658911,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0070465742610394955,\n",
       " 'PACKETS_REV': 0.008154512383043766,\n",
       " 'DURATION': 0.008450562134385109,\n",
       " 'NORMAL_DISTRIBUTION': 0.009579064324498177,\n",
       " 'SPECTRAL_ENTROPY': 0.009633342735469341,\n",
       " 'KURTOSIS': 0.009719254449009895,\n",
       " 'MIN_POWER_FREQ': 0.010241657495498657,\n",
       " 'MAX': 0.011470873840153217,\n",
       " 'SCALED_ENTROPY': 0.014144114218652248,\n",
       " 'Q3': 0.014710080809891224,\n",
       " 'MEDIAN_DIFFTIMES': 0.015684444457292557,\n",
       " 'POWER_STD': 0.016000766307115555,\n",
       " 'SPECTRAL_SPREAD': 0.018081799149513245,\n",
       " 'SPECTRAL_KURTOSIS': 0.01825079321861267,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.019088217988610268,\n",
       " 'MIN_DIFFTIMES': 0.02052406221628189,\n",
       " 'MIN_MINUS_MAX': 0.02054743655025959,\n",
       " 'HURST_EXPONENT': 0.0253263171762228,\n",
       " 'AVERAGE_DISPERSION': 0.02762746624648571,\n",
       " 'ENTROPY': 0.02789471298456192,\n",
       " 'MEAN_DIFFTIMES': 0.02792452834546566,\n",
       " 'SWITCHING_METRIC': 0.028268776834011078,\n",
       " 'POWER_MODE': 0.029804468154907227,\n",
       " 'SPECTRAL_ROLLOFF': 0.04200538992881775,\n",
       " 'MEDIAN': 0.04424213245511055,\n",
       " 'CNT_DISTRIBUTION': 0.051488567143678665,\n",
       " 'VAR': 0.05462603643536568,\n",
       " 'MIN_POWER': 0.06676375865936279,\n",
       " 'MEDIAN_SCALED_TIME': 0.10828666388988495,\n",
       " 'TRANSIENTS': 0.1256231814622879}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=10):\n",
    "    if best_hyperparams is None:\n",
    "        df_to_tunne = df_features.sample(100000)\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_to_tunne, selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL']  # 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, y, test_size=0.30, stratify=y) # 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",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:04<00:00,  1.56trial/s, best loss: -0.999047921294827]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.13049320889889846, 'gamma': 1.3622291862124185, 'max_depth': 26.0, 'min_child_weight': 12.0, 'n_estimators': 380.0, 'reg_alpha': 1.0, 'reg_lambda': 0.6092375655651708}\n",
      "100%|██████████| 100/100 [00:34<00:00,  2.88trial/s, best loss: -0.9993482729232466]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.36984344823866544, 'gamma': 1.2532764368973446, 'max_depth': 14.0, 'min_child_weight': 0.0, 'n_estimators': 100.0, 'reg_alpha': 2.0, 'reg_lambda': 0.34964515832763715}\n",
      "100%|██████████| 100/100 [00:37<00:00,  2.64trial/s, best loss: -0.9995153824301065]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.1071558464171134, 'gamma': 4.638587424046174, 'max_depth': 10.0, 'min_child_weight': 2.0, 'n_estimators': 160.0, 'reg_alpha': 1.0, 'reg_lambda': 0.0995821556601394}\n",
      "100%|██████████| 100/100 [00:24<00:00,  4.12trial/s, best loss: -0.9990644525376725]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2902482104155284, 'gamma': 7.1415856627438945, 'max_depth': 16.0, 'min_child_weight': 3.0, 'n_estimators': 340.0, 'reg_alpha': 2.0, 'reg_lambda': 0.38803881723908124}\n",
      "100%|██████████| 100/100 [01:00<00:00,  1.64trial/s, best loss: -0.9991144084081074]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5525689414075513, 'gamma': 1.1336323033147462, 'max_depth': 30.0, 'min_child_weight': 4.0, 'n_estimators': 240.0, 'reg_alpha': 3.0, 'reg_lambda': 0.4216007400039594}\n",
      "100%|██████████| 100/100 [00:38<00:00,  2.60trial/s, best loss: -0.9991646339425937]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3657134685687228, 'gamma': 7.476921096913444, 'max_depth': 8.0, 'min_child_weight': 4.0, 'n_estimators': 220.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8872710118657312}\n",
      "100%|██████████| 100/100 [01:01<00:00,  1.64trial/s, best loss: -0.9992313604919293]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9190974047634872, 'gamma': 6.456839160884971, 'max_depth': 10.0, 'min_child_weight': 5.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4772898374310539}\n",
      "100%|██████████| 100/100 [00:51<00:00,  1.95trial/s, best loss: -0.9991646339425936]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8954514057767908, 'gamma': 2.9049897726057337, 'max_depth': 20.0, 'min_child_weight': 16.0, 'n_estimators': 380.0, 'reg_alpha': 2.0, 'reg_lambda': 0.6666204718480699}\n",
      "100%|██████████| 100/100 [00:50<00:00,  1.98trial/s, best loss: -0.9989643537233154]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8576380928175504, 'gamma': 4.490783744917173, 'max_depth': 10.0, 'min_child_weight': 12.0, 'n_estimators': 260.0, 'reg_alpha': 5.0, 'reg_lambda': 0.8687424485051479}\n",
      "100%|██████████| 100/100 [01:09<00:00,  1.44trial/s, best loss: -0.9993482511405604]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.03591344594754392, 'gamma': 2.9439195321676737, 'max_depth': 6.0, 'min_child_weight': 12.0, 'n_estimators': 180.0, 'reg_alpha': 1.0, 'reg_lambda': 0.31961278949595967}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"TON_IoT-binary\"\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",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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