iot_23.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: IoT-23 [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/iot_23.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.799744</td>\n",
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       "      <td>97.558887</td>\n",
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       "      <td>0.217322</td>\n",
       "      <td>0.456583</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.344039</td>\n",
       "      <td>0.725146</td>\n",
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       "      <td>0.998491</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.725146</td>\n",
       "      <td>155</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-7.021285e-01</td>\n",
       "      <td>8.752327</td>\n",
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       "      <td>0.231095</td>\n",
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       "      <td>1082.421593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.870985e+06</td>\n",
       "      <td>53068.485072</td>\n",
       "      <td>0.666439</td>\n",
       "      <td>0.514491</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>40.429382</td>\n",
       "      <td>176.315430</td>\n",
       "      <td>-0.099929</td>\n",
       "      <td>0.152260</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>239.255.255.250</td>\n",
       "      <td>192.168.1.132</td>\n",
       "      <td>36</td>\n",
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       "      <td>1.540469e+09</td>\n",
       "      <td>1.540469e+09</td>\n",
       "      <td>1900</td>\n",
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       "      <td>374</td>\n",
       "      <td>373.0</td>\n",
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       "      <td>370.0</td>\n",
       "      <td>379.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>60.611790</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.312913</td>\n",
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       "      <td>0.968015</td>\n",
       "      <td>0.005881</td>\n",
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       "      <td>161.169066</td>\n",
       "      <td>5.215497e+05</td>\n",
       "      <td>42537.728442</td>\n",
       "      <td>0.968015</td>\n",
       "      <td>0.760893</td>\n",
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       "      <td>2.038808</td>\n",
       "      <td>53.207736</td>\n",
       "      <td>5.686098</td>\n",
       "      <td>0.958765</td>\n",
       "      <td>0.666428</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>104.155.18.91</td>\n",
       "      <td>192.168.1.132</td>\n",
       "      <td>14</td>\n",
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       "      <td>117</td>\n",
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       "      <td>1.540469e+09</td>\n",
       "      <td>1.540470e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>37653</td>\n",
       "      <td>68</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>24.386947</td>\n",
       "      <td>12</td>\n",
       "      <td>17.647059</td>\n",
       "      <td>70.205311</td>\n",
       "      <td>0.785714</td>\n",
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       "      <td>7.831463e-05</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2.659380</td>\n",
       "      <td>0.749595</td>\n",
       "      <td>0.196881</td>\n",
       "      <td>0.566243</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.078883</td>\n",
       "      <td>0.748428</td>\n",
       "      <td>0.500969</td>\n",
       "      <td>0.806818</td>\n",
       "      <td>149.890171</td>\n",
       "      <td>150.058877</td>\n",
       "      <td>60.000115</td>\n",
       "      <td>240.320280</td>\n",
       "      <td>299.200274</td>\n",
       "      <td>23.015406</td>\n",
       "      <td>30.004427</td>\n",
       "      <td>0.017738</td>\n",
       "      <td>30.101376</td>\n",
       "      <td>0.764596</td>\n",
       "      <td>False</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.748428</td>\n",
       "      <td>219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.266439e+00</td>\n",
       "      <td>1.167456</td>\n",
       "      <td>0.799148</td>\n",
       "      <td>0.832723</td>\n",
       "      <td>0.086849</td>\n",
       "      <td>0.151707</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>434.247249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.256952e+06</td>\n",
       "      <td>-170402.665012</td>\n",
       "      <td>0.832723</td>\n",
       "      <td>0.504827</td>\n",
       "      <td>0.280721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.442301</td>\n",
       "      <td>104.245067</td>\n",
       "      <td>0.033575</td>\n",
       "      <td>0.313974</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.132</td>\n",
       "      <td>104.155.18.91</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>1.540469e+09</td>\n",
       "      <td>1.540470e+09</td>\n",
       "      <td>37653</td>\n",
       "      <td>443</td>\n",
       "      <td>69</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.340135</td>\n",
       "      <td>267</td>\n",
       "      <td>-0.617059</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>35</td>\n",
       "      <td>60</td>\n",
       "      <td>23.681355</td>\n",
       "      <td>13</td>\n",
       "      <td>18.840580</td>\n",
       "      <td>71.270930</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.550791</td>\n",
       "      <td>8.996254</td>\n",
       "      <td>1.637120</td>\n",
       "      <td>1.250235e-04</td>\n",
       "      <td>1.456953e-04</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>1.815167</td>\n",
       "      <td>0.845351</td>\n",
       "      <td>0.244361</td>\n",
       "      <td>0.545990</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.206162</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.501120</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>149.935146</td>\n",
       "      <td>150.041420</td>\n",
       "      <td>60.000127</td>\n",
       "      <td>240.320292</td>\n",
       "      <td>299.200297</td>\n",
       "      <td>29.920030</td>\n",
       "      <td>30.079890</td>\n",
       "      <td>28.858330</td>\n",
       "      <td>30.118849</td>\n",
       "      <td>0.993399</td>\n",
       "      <td>False</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>155</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.537466e+02</td>\n",
       "      <td>5.213503</td>\n",
       "      <td>0.999404</td>\n",
       "      <td>0.832723</td>\n",
       "      <td>0.024404</td>\n",
       "      <td>12.077722</td>\n",
       "      <td>0.05369</td>\n",
       "      <td>122.021539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.497040e+07</td>\n",
       "      <td>-353239.359292</td>\n",
       "      <td>0.832723</td>\n",
       "      <td>-2.906110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213.630434</td>\n",
       "      <td>1922.317814</td>\n",
       "      <td>-0.166681</td>\n",
       "      <td>0.005841</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>4060793</th>\n",
       "      <td>4060793</td>\n",
       "      <td>103.201.68.72</td>\n",
       "      <td>192.168.100.111</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1.532593e+09</td>\n",
       "      <td>1.532593e+09</td>\n",
       "      <td>81</td>\n",
       "      <td>58000</td>\n",
       "      <td>90</td>\n",
       "      <td>60.0</td>\n",
       "      <td>75.524830</td>\n",
       "      <td>5705</td>\n",
       "      <td>-0.087450</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>245</td>\n",
       "      <td>185</td>\n",
       "      <td>60</td>\n",
       "      <td>83.916478</td>\n",
       "      <td>50</td>\n",
       "      <td>55.555556</td>\n",
       "      <td>114.035813</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.397220</td>\n",
       "      <td>2.780542</td>\n",
       "      <td>0.840172</td>\n",
       "      <td>1.934414e-06</td>\n",
       "      <td>2.648806e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.977509</td>\n",
       "      <td>0.650022</td>\n",
       "      <td>0.251463</td>\n",
       "      <td>0.285759</td>\n",
       "      <td>False</td>\n",
       "      <td>0.467697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.662328</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>2.396589</td>\n",
       "      <td>3.295116</td>\n",
       "      <td>1.045401</td>\n",
       "      <td>3.295119</td>\n",
       "      <td>3.618435</td>\n",
       "      <td>0.723687</td>\n",
       "      <td>0.323316</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.080064</td>\n",
       "      <td>0.347916</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.791260e-02</td>\n",
       "      <td>0.431002</td>\n",
       "      <td>0.304071</td>\n",
       "      <td>0.957883</td>\n",
       "      <td>0.164291</td>\n",
       "      <td>0.085767</td>\n",
       "      <td>0.22377</td>\n",
       "      <td>821.456120</td>\n",
       "      <td>1996.693665</td>\n",
       "      <td>2.247969e+04</td>\n",
       "      <td>6673.865606</td>\n",
       "      <td>0.937420</td>\n",
       "      <td>0.610721</td>\n",
       "      <td>0.282812</td>\n",
       "      <td>0.323292</td>\n",
       "      <td>2.623404</td>\n",
       "      <td>1.426229</td>\n",
       "      <td>0.653812</td>\n",
       "      <td>0.056909</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060794</th>\n",
       "      <td>4060794</td>\n",
       "      <td>184.101.31.170</td>\n",
       "      <td>192.168.100.111</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>23</td>\n",
       "      <td>33206</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>60.0</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.605209</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.200344</td>\n",
       "      <td>3.080492</td>\n",
       "      <td>1.000679</td>\n",
       "      <td>3.284874</td>\n",
       "      <td>3.635673</td>\n",
       "      <td>0.908918</td>\n",
       "      <td>0.675739</td>\n",
       "      <td>0.204382</td>\n",
       "      <td>2.079813</td>\n",
       "      <td>0.437019</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>1.000679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060795</th>\n",
       "      <td>4060795</td>\n",
       "      <td>114.179.19.187</td>\n",
       "      <td>192.168.100.111</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>23</td>\n",
       "      <td>44152</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>60.0</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.386663</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>11.657316</td>\n",
       "      <td>7.453995</td>\n",
       "      <td>3.108478</td>\n",
       "      <td>23.668489</td>\n",
       "      <td>30.148543</td>\n",
       "      <td>4.306935</td>\n",
       "      <td>3.006775</td>\n",
       "      <td>0.265346</td>\n",
       "      <td>13.207719</td>\n",
       "      <td>0.326092</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>480</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>1.028664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060796</th>\n",
       "      <td>4060796</td>\n",
       "      <td>186.96.122.52</td>\n",
       "      <td>192.168.100.111</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>23</td>\n",
       "      <td>55398</td>\n",
       "      <td>62</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.912741</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>4.561979</td>\n",
       "      <td>1</td>\n",
       "      <td>1.612903</td>\n",
       "      <td>62.782498</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>-0.353553</td>\n",
       "      <td>43.487067</td>\n",
       "      <td>377.023753</td>\n",
       "      <td>3.163372e-02</td>\n",
       "      <td>3.214350e-02</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>31.611184</td>\n",
       "      <td>1.255631</td>\n",
       "      <td>0.191120</td>\n",
       "      <td>0.512283</td>\n",
       "      <td>False</td>\n",
       "      <td>0.540279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.470660</td>\n",
       "      <td>0.753906</td>\n",
       "      <td>8.841241</td>\n",
       "      <td>7.870761</td>\n",
       "      <td>3.325352</td>\n",
       "      <td>14.148180</td>\n",
       "      <td>18.784777</td>\n",
       "      <td>0.199838</td>\n",
       "      <td>0.231122</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.432504</td>\n",
       "      <td>0.462049</td>\n",
       "      <td>False</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5958</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.629717e-05</td>\n",
       "      <td>0.012329</td>\n",
       "      <td>0.338837</td>\n",
       "      <td>0.264337</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.00090</td>\n",
       "      <td>20.211983</td>\n",
       "      <td>152.424197</td>\n",
       "      <td>1.466892e+04</td>\n",
       "      <td>4254.083695</td>\n",
       "      <td>0.089312</td>\n",
       "      <td>0.452776</td>\n",
       "      <td>0.250489</td>\n",
       "      <td>-0.018027</td>\n",
       "      <td>3.049933</td>\n",
       "      <td>0.167340</td>\n",
       "      <td>-0.074500</td>\n",
       "      <td>0.734515</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060797</th>\n",
       "      <td>4060797</td>\n",
       "      <td>192.168.100.111</td>\n",
       "      <td>186.96.122.52</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "      <td>0</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>1.532595e+09</td>\n",
       "      <td>55398</td>\n",
       "      <td>23</td>\n",
       "      <td>65</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9.327379</td>\n",
       "      <td>88</td>\n",
       "      <td>-0.749019</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60</td>\n",
       "      <td>123</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>14.349814</td>\n",
       "      <td>5</td>\n",
       "      <td>7.692308</td>\n",
       "      <td>66.186472</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.214423</td>\n",
       "      <td>14.151885</td>\n",
       "      <td>11.904795</td>\n",
       "      <td>6.485859e-04</td>\n",
       "      <td>6.572660e-04</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>17.514511</td>\n",
       "      <td>2.163352</td>\n",
       "      <td>0.316609</td>\n",
       "      <td>0.576896</td>\n",
       "      <td>False</td>\n",
       "      <td>0.708925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468941</td>\n",
       "      <td>0.805594</td>\n",
       "      <td>8.815622</td>\n",
       "      <td>7.659130</td>\n",
       "      <td>3.113725</td>\n",
       "      <td>14.400031</td>\n",
       "      <td>18.799019</td>\n",
       "      <td>0.166363</td>\n",
       "      <td>0.231113</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.432505</td>\n",
       "      <td>0.384650</td>\n",
       "      <td>False</td>\n",
       "      <td>0.814159</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7470</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.801399e-05</td>\n",
       "      <td>0.151968</td>\n",
       "      <td>0.564125</td>\n",
       "      <td>0.989471</td>\n",
       "      <td>0.033182</td>\n",
       "      <td>0.033495</td>\n",
       "      <td>0.02663</td>\n",
       "      <td>165.907746</td>\n",
       "      <td>711.290334</td>\n",
       "      <td>2.672506e+04</td>\n",
       "      <td>8302.213474</td>\n",
       "      <td>0.871463</td>\n",
       "      <td>0.612411</td>\n",
       "      <td>0.304072</td>\n",
       "      <td>0.059420</td>\n",
       "      <td>4.579908</td>\n",
       "      <td>1.606854</td>\n",
       "      <td>0.425345</td>\n",
       "      <td>0.126679</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4060798 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0           DST_IP           SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0    104.155.18.91    192.168.1.132       12            0   \n",
       "1                 1    192.168.1.132    104.155.18.91       10            0   \n",
       "2                 2  239.255.255.250    192.168.1.132       36            0   \n",
       "3                 3    104.155.18.91    192.168.1.132       14            0   \n",
       "4                 4    192.168.1.132    104.155.18.91       11            0   \n",
       "...             ...              ...              ...      ...          ...   \n",
       "4060793     4060793    103.201.68.72  192.168.100.111        6            0   \n",
       "4060794     4060794   184.101.31.170  192.168.100.111        5            0   \n",
       "4060795     4060795   114.179.19.187  192.168.100.111        8            0   \n",
       "4060796     4060796    186.96.122.52  192.168.100.111       95            0   \n",
       "4060797     4060797  192.168.100.111    186.96.122.52      114            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0           78          0  1.540469e+09  1.540469e+09       443     37653   \n",
       "1           70          0  1.540469e+09  1.540469e+09     37653       443   \n",
       "2        11304          0  1.540469e+09  1.540469e+09      1900      1900   \n",
       "3          117          0  1.540469e+09  1.540470e+09       443     37653   \n",
       "4          105          0  1.540469e+09  1.540470e+09     37653       443   \n",
       "...        ...        ...           ...           ...       ...       ...   \n",
       "4060793    185          0  1.532593e+09  1.532593e+09        81     58000   \n",
       "4060794      0          0  1.532595e+09  1.532595e+09        23     33206   \n",
       "4060795      0          0  1.532595e+09  1.532595e+09        23     44152   \n",
       "4060796    258          0  1.532595e+09  1.532595e+09        23     55398   \n",
       "4060797    630          0  1.532595e+09  1.532595e+09     55398        23   \n",
       "\n",
       "         MEAN  MEDIAN      STDEV   VAR  BURSTINESS     Q1     Q3  MIN  MAX  \\\n",
       "0          66    60.0  15.165751   230   -0.626302   60.0   60.0   60   99   \n",
       "1          67    60.0  14.730920   217   -0.639526   60.0   60.0   60   95   \n",
       "2         374   373.0   3.741657    14   -0.980189  370.0  379.0  370  379   \n",
       "3          68    60.0  16.583124   275   -0.607886   60.0   60.0   60   99   \n",
       "4          69    60.0  16.340135   267   -0.617059   60.0   77.5   60   95   \n",
       "...       ...     ...        ...   ...         ...    ...    ...  ...  ...   \n",
       "4060793    90    60.0  75.524830  5705   -0.087450   60.0   60.0   60  245   \n",
       "4060794    60    60.0   0.000000     0   -1.000000   60.0   60.0   60   60   \n",
       "4060795    60    60.0   0.000000     0   -1.000000   60.0   60.0   60   60   \n",
       "4060796    62    63.0   2.828427     8   -0.912741   60.0   63.0   60   82   \n",
       "4060797    65    63.0   9.327379    88   -0.749019   62.0   63.0   60  123   \n",
       "\n",
       "         MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                   39    60                 22.978410                  10   \n",
       "1                   35    60                 21.986448                  11   \n",
       "2                    9   370                  1.000443                   3   \n",
       "3                   39    60                 24.386947                  12   \n",
       "4                   35    60                 23.681355                  13   \n",
       "...                ...   ...                       ...                 ...   \n",
       "4060793            185    60                 83.916478                  50   \n",
       "4060794              0    60                  0.000000                   0   \n",
       "4060795              0    60                  0.000000                   0   \n",
       "4060796             22    63                  4.561979                   1   \n",
       "4060797             63    63                 14.349814                   5   \n",
       "\n",
       "         PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0                15.151515         68.069817            0.833333   \n",
       "1                16.417910         68.447060            0.800000   \n",
       "2                 0.802139        374.018716            0.666667   \n",
       "3                17.647059         70.205311            0.785714   \n",
       "4                18.840580         71.270930            0.727273   \n",
       "...                    ...               ...                 ...   \n",
       "4060793          55.555556        114.035813            0.833333   \n",
       "4060794           0.000000         60.000000            0.000000   \n",
       "4060795           0.000000         60.000000            0.000000   \n",
       "4060796           1.612903         62.782498            0.263158   \n",
       "4060797           7.692308         66.186472            0.842105   \n",
       "\n",
       "         PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                  0.166667              0.395628              9.099451   \n",
       "1                  0.200000              0.475191              9.571704   \n",
       "2                  0.333333              1.069045            200.178670   \n",
       "3                  0.214286              0.482418              8.683527   \n",
       "4                  0.272727              0.550791              8.996254   \n",
       "...                     ...                   ...                   ...   \n",
       "4060793            0.166667              0.397220              2.780542   \n",
       "4060794            0.000000              0.000000              0.000000   \n",
       "4060795            0.000000              0.000000              0.000000   \n",
       "4060796            0.736842             -0.353553             43.487067   \n",
       "4060797            0.157895              0.214423             14.151885   \n",
       "\n",
       "         FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                    2.488588                1.433436e-04   \n",
       "1                   -0.032847                6.946253e-20   \n",
       "2                    0.381802                3.179138e-18   \n",
       "3                    1.557662                7.831463e-05   \n",
       "4                    1.637120                1.250235e-04   \n",
       "...                       ...                         ...   \n",
       "4060793              0.840172                1.934414e-06   \n",
       "4060794              0.000000                0.000000e+00   \n",
       "4060795              0.000000                0.000000e+00   \n",
       "4060796            377.023753                3.163372e-02   \n",
       "4060797             11.904795                6.485859e-04   \n",
       "\n",
       "         FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS   KURTOSIS   ENTROPY  \\\n",
       "0                      1.646892e-04         0.000000   3.756777  0.650022   \n",
       "1                      8.237243e-20         0.000000   2.651405  0.721928   \n",
       "2                      3.319065e-18         0.111111   1.500000  1.584963   \n",
       "3                      8.804352e-05         0.000000   2.659380  0.749595   \n",
       "4                      1.456953e-04        -0.028571   1.815167  0.845351   \n",
       "...                             ...              ...        ...       ...   \n",
       "4060793                2.648806e-06         0.000000   2.977509  0.650022   \n",
       "4060794                0.000000e+00         0.000000   0.000000  0.000000   \n",
       "4060795                0.000000e+00         0.000000   0.000000  0.000000   \n",
       "4060796                3.214350e-02        -0.333333  31.611184  1.255631   \n",
       "4060797                6.572660e-04        -5.000000  17.514511  2.163352   \n",
       "\n",
       "         SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0              0.181319        0.460941                  False   0.467697   \n",
       "1              0.217322        0.456583                  False   0.477121   \n",
       "2              0.306574        0.562542                  False   0.602060   \n",
       "3              0.196881        0.566243                  False   0.477121   \n",
       "4              0.244361        0.545990                  False   0.477121   \n",
       "...                 ...             ...                    ...        ...   \n",
       "4060793        0.251463        0.285759                  False   0.467697   \n",
       "4060794        0.000000        1.000000                  False   0.301030   \n",
       "4060795        0.000000        1.000000                  False   0.301030   \n",
       "4060796        0.191120        0.512283                  False   0.540279   \n",
       "4060797        0.316609        0.576896                  False   0.708925   \n",
       "\n",
       "         NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                   0.375751          0.721174           0.509234   \n",
       "1                   0.344039          0.725146           0.500002   \n",
       "2                        NaN          0.000000           0.502374   \n",
       "3                   0.078883          0.748428           0.500969   \n",
       "4                   0.206162          0.578947           0.501120   \n",
       "...                      ...               ...                ...   \n",
       "4060793             0.000000          0.000000           0.662328   \n",
       "4060794             0.000000          0.000000           0.605209   \n",
       "4060795             0.000000          0.000000           0.386663   \n",
       "4060796             0.000000          0.000000           0.470660   \n",
       "4060797             0.000000          0.000000           0.468941   \n",
       "\n",
       "         AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                        0.825000        137.697131          150.240088   \n",
       "1                        0.812500        135.200560          150.240088   \n",
       "2                        0.517857        137.177365          160.116974   \n",
       "3                        0.806818        149.890171          150.058877   \n",
       "4                        0.781250        149.935146          150.041420   \n",
       "...                           ...               ...                 ...   \n",
       "4060793                  0.825000          2.396589            3.295116   \n",
       "4060794                  0.875000          2.200344            3.080492   \n",
       "4060795                  0.875000         11.657316            7.453995   \n",
       "4060796                  0.753906          8.841241            7.870761   \n",
       "4060797                  0.805594          8.815622            7.659130   \n",
       "\n",
       "         Q1_SCALED_TIME  Q3_SCALED_TIME    DURATION  MEAN_DIFFTIMES  \\\n",
       "0             90.094787      210.247711  270.400254       24.581841   \n",
       "1             60.160052      210.230226  270.400258       30.044473   \n",
       "2             59.061678      221.129277  273.058496        7.801671   \n",
       "3             60.000115      240.320280  299.200274       23.015406   \n",
       "4             60.000127      240.320292  299.200297       29.920030   \n",
       "...                 ...             ...         ...             ...   \n",
       "4060793        1.045401        3.295119    3.618435        0.723687   \n",
       "4060794        1.000679        3.284874    3.635673        0.908918   \n",
       "4060795        3.108478       23.668489   30.148543        4.306935   \n",
       "4060796        3.325352       14.148180   18.784777        0.199838   \n",
       "4060797        3.113725       14.400031   18.799019        0.166363   \n",
       "\n",
       "         MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  \\\n",
       "0               30.072400       0.017732      30.080149   \n",
       "1               30.079904      29.910242      30.089888   \n",
       "2                0.050465       0.049466      60.611790   \n",
       "3               30.004427       0.017738      30.101376   \n",
       "4               30.079890      28.858330      30.118849   \n",
       "...                   ...            ...            ...   \n",
       "4060793          0.323316       0.000003       2.080064   \n",
       "4060794          0.675739       0.204382       2.079813   \n",
       "4060795          3.006775       0.265346      13.207719   \n",
       "4060796          0.231122       0.000002       0.432504   \n",
       "4060797          0.231113       0.000002       0.432505   \n",
       "\n",
       "         MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  \\\n",
       "0                     0.817211       False          0.727273       False   \n",
       "1                     0.998491       False          0.888889       False   \n",
       "2                     0.128715       False          0.971429       False   \n",
       "3                     0.764596       False          0.923077       False   \n",
       "4                     0.993399       False          1.200000       False   \n",
       "...                        ...         ...               ...         ...   \n",
       "4060793               0.347916       False          0.800000       False   \n",
       "4060794               0.437019       False          0.000000       False   \n",
       "4060795               0.326092       False          0.000000       False   \n",
       "4060796               0.462049       False          0.531915       False   \n",
       "4060797               0.384650       False          0.814159       False   \n",
       "\n",
       "         CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  \\\n",
       "0              0.0             0.721174                219         1.0   \n",
       "1              0.0             0.725146                155         1.0   \n",
       "2              0.0             0.000000               2244         1.0   \n",
       "3              0.0             0.748428                219         1.0   \n",
       "4              0.0             0.578947                155         1.0   \n",
       "...            ...                  ...                ...         ...   \n",
       "4060793        0.0             0.000000                545         1.0   \n",
       "4060794        0.0             0.000000                300         1.0   \n",
       "4060795        0.0             0.000000                480         1.0   \n",
       "4060796        0.0             0.000000               5958         1.0   \n",
       "4060797        0.0             0.000000               7470         1.0   \n",
       "\n",
       "         PERIODICITY  VAL      TIME     MIN_POWER  MAX_POWER  MIN_POWER_FREQ  \\\n",
       "0              False    0  0.000000 -1.463006e+00   4.013201        0.566509   \n",
       "1              False    0  0.000000 -7.021285e-01   8.752327        0.766368   \n",
       "2              False    0  0.000000  1.138821e-09   0.312913        0.009249   \n",
       "3              False    0  0.000000 -8.266439e+00   1.167456        0.799148   \n",
       "4              False    0  0.000000 -8.537466e+02   5.213503        0.999404   \n",
       "...              ...  ...       ...           ...        ...             ...   \n",
       "4060793        False    0  0.000000  5.791260e-02   0.431002        0.304071   \n",
       "4060794         True   60  1.000679           NaN        NaN             NaN   \n",
       "4060795         True   60  1.028664           NaN        NaN             NaN   \n",
       "4060796        False    0  0.000000  1.629717e-05   0.012329        0.338837   \n",
       "4060797        False    0  0.000000  2.801399e-05   0.151968        0.564125   \n",
       "\n",
       "         MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  \\\n",
       "0              0.799744    0.116564   0.116184     0.04762       582.822003   \n",
       "1              0.666439    0.216484   0.231095     0.00959      1082.421593   \n",
       "2              0.968015    0.005881   0.016882     0.00000        29.404827   \n",
       "3              0.832723    0.086849   0.151707     0.00997       434.247249   \n",
       "4              0.832723    0.024404  12.077722     0.05369       122.021539   \n",
       "...                 ...         ...        ...         ...              ...   \n",
       "4060793        0.957883    0.164291   0.085767     0.22377       821.456120   \n",
       "4060794             NaN         NaN        NaN     0.00000              NaN   \n",
       "4060795             NaN         NaN        NaN     0.00000              NaN   \n",
       "4060796        0.264337    0.004042   0.003049     0.00090        20.211983   \n",
       "4060797        0.989471    0.033182   0.033495     0.02663       165.907746   \n",
       "\n",
       "         SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  \\\n",
       "0                     NaN       1.336520e+06       34090.440487   \n",
       "1                     NaN       1.870985e+06       53068.485072   \n",
       "2              161.169066       5.215497e+05       42537.728442   \n",
       "3                     NaN       9.256952e+06     -170402.665012   \n",
       "4                     NaN       2.497040e+07     -353239.359292   \n",
       "...                   ...                ...                ...   \n",
       "4060793       1996.693665       2.247969e+04        6673.865606   \n",
       "4060794               NaN                NaN                NaN   \n",
       "4060795               NaN                NaN                NaN   \n",
       "4060796        152.424197       1.466892e+04        4254.083695   \n",
       "4060797        711.290334       2.672506e+04        8302.213474   \n",
       "\n",
       "         SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  \\\n",
       "0                0.799744           0.514825         0.287951             NaN   \n",
       "1                0.666439           0.514491         0.287656             NaN   \n",
       "2                0.968015           0.760893         0.195900        2.038808   \n",
       "3                0.832723           0.504827         0.280721             NaN   \n",
       "4                0.832723          -2.906110              NaN             NaN   \n",
       "...                   ...                ...              ...             ...   \n",
       "4060793          0.937420           0.610721         0.282812        0.323292   \n",
       "4060794          0.006667                NaN              NaN             NaN   \n",
       "4060795          0.006667                NaN              NaN             NaN   \n",
       "4060796          0.089312           0.452776         0.250489       -0.018027   \n",
       "4060797          0.871463           0.612411         0.304072        0.059420   \n",
       "\n",
       "         SPECTRAL_CREST  SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  \\\n",
       "0             34.429048      97.558887            0.233235          0.264240   \n",
       "1             40.429382     176.315430           -0.099929          0.152260   \n",
       "2             53.207736       5.686098            0.958765          0.666428   \n",
       "3             13.442301     104.245067            0.033575          0.313974   \n",
       "4            213.630434    1922.317814           -0.166681          0.005841   \n",
       "...                 ...            ...                 ...               ...   \n",
       "4060793        2.623404       1.426229            0.653812          0.056909   \n",
       "4060794             NaN            NaN            0.000000               NaN   \n",
       "4060795             NaN            NaN            0.000000               NaN   \n",
       "4060796        3.049933       0.167340           -0.074500          0.734515   \n",
       "4060797        4.579908       1.606854            0.425345          0.126679   \n",
       "\n",
       "         LABEL  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3        False  \n",
       "4        False  \n",
       "...        ...  \n",
       "4060793  False  \n",
       "4060794  False  \n",
       "4060795  False  \n",
       "4060796  False  \n",
       "4060797  False  \n",
       "\n",
       "[4060798 rows x 84 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df = df[[\"LABEL\"] + features].copy()\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    df.replace([np.inf], 0, inplace=True)\n",
    "    df.replace([-np.inf], 0, inplace=True)\n",
    "    df.loc[df[F].isnull(), F] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iot: 3063723\tclear: 997075\n"
     ]
    }
   ],
   "source": [
    "print(f\"iot: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4060798, 77)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iot: 3063723\tclear: 997075\n"
     ]
    }
   ],
   "source": [
    "print(f\"iot: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=500000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [02:36<00:00,  1.56s/trial, best loss: -0.9989391561871505]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.28451446941943037, 'gamma': 7.52593179775905, 'max_depth': 18.0, 'min_child_weight': 2.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7216231214056903}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(200000), features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.85 & 99.95 & 99.86 & 99.90 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # 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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "\n",
      "\n",
      "99.90722405050487\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.86 & 99.96 & 99.86 & 99.91 \\\\\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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.86 & 99.96 & 99.85 & 99.90 \\\\\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_code']  # 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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IoT-23\"\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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 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",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'POWER_MEAN': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'SPECTRAL_CENTROID': 0.0002030708856182173,\n",
       " 'MAX_POWER': 0.00020900132949464023,\n",
       " 'P_BENFORD': 0.0002534535888116807,\n",
       " 'Q3_SCALED_TIME': 0.00027934377430938184,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0003126506635453552,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0003294299531262368,\n",
       " 'BYTES_REV': 0.0003423714661039412,\n",
       " 'MODE': 0.00036787628778256476,\n",
       " 'MIN': 0.0004032755387015641,\n",
       " 'SPECTRAL_ENERGY': 0.0004113254835829139,\n",
       " 'MIN_POWER_FREQ': 0.00042390276212245226,\n",
       " 'ROOT_MEAN_SQUARE': 0.00045464656432159245,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.00048453256022185087,\n",
       " 'BURSTINESS': 0.000488949241116643,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0004934155731461942,\n",
       " 'SCALED_ENTROPY': 0.0005247239023447037,\n",
       " 'MAX_POWER_FREQ': 0.0005401521339081228,\n",
       " 'SPECTRAL_FLUX': 0.0005651786341331899,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0005964144947938621,\n",
       " 'SPECTRAL_SLOPE': 0.0006141375051811337,\n",
       " 'TIME': 0.0006465254118666053,\n",
       " 'STDEV': 0.000705599959474057,\n",
       " 'BYTES': 0.0007073265733197331,\n",
       " 'Q1': 0.0007670497871004045,\n",
       " 'KURTOSIS': 0.0007720707217231393,\n",
       " 'PERCENT_DEVIATION': 0.0007775276899337769,\n",
       " 'POWER_STD': 0.0008456873474642634,\n",
       " 'MIN_MINUS_MAX': 0.0008533906075172126,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.0009169687982648611,\n",
       " 'SPECTRAL_ENTROPY': 0.0009692041203379631,\n",
       " 'NORMAL_DISTRIBUTION': 0.0010295804822817445,\n",
       " 'AVERAGE_DISPERSION': 0.0011783423833549023,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0011936323717236519,\n",
       " 'SPECTRAL_KURTOSIS': 0.0011959256371483207,\n",
       " 'MAX': 0.0012027807533740997,\n",
       " 'Q1_SCALED_TIME': 0.0012081076856702566,\n",
       " 'SPECTRAL_ROLLOFF': 0.001234730938449502,\n",
       " 'SWITCHING_METRIC': 0.0012920061126351357,\n",
       " 'HURST_EXPONENT': 0.0015123274642974138,\n",
       " 'ENTROPY': 0.0015777720836922526,\n",
       " 'MEAN_SCALED_TIME': 0.001658815424889326,\n",
       " 'MAX_DIFFTIMES': 0.0017091869376599789,\n",
       " 'VAL': 0.0019592861644923687,\n",
       " 'VAR': 0.0031172840390354395,\n",
       " 'TIME_DISTRIBUTION': 0.003511811373755336,\n",
       " 'TRANSIENTS': 0.005351657513529062,\n",
       " 'DIRECTIONS': 0.0058152684941887856,\n",
       " 'PACKETS': 0.0067015644162893295,\n",
       " 'Q3': 0.007736694999039173,\n",
       " 'POWER_MODE': 0.00866390299052,\n",
       " 'MIN_POWER': 0.009818515740334988,\n",
       " 'DURATION': 0.01136182714253664,\n",
       " 'SPECTRAL_SPREAD': 0.012242822907865047,\n",
       " 'PACKETS_REV': 0.012271478772163391,\n",
       " 'MEDIAN': 0.015119529329240322,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.022367417812347412,\n",
       " 'CNT_DISTRIBUTION': 0.026572370901703835,\n",
       " 'BIGGEST_CNT_1_SEC': 0.027041293680667877,\n",
       " 'MEDIAN_SCALED_TIME': 0.04225041717290878,\n",
       " 'MEDIAN_DIFFTIMES': 0.19248861074447632,\n",
       " 'MEAN_DIFFTIMES': 0.2624861001968384,\n",
       " 'MIN_DIFFTIMES': 0.29086965322494507}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, 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_code']  # 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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:41<00:00,  2.38trial/s, best loss: -0.9978106548132423]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.028716981351798856, 'gamma': 3.9745827168797043, 'max_depth': 16.0, 'min_child_weight': 0.0, 'n_estimators': 260.0, 'reg_alpha': 4.0, 'reg_lambda': 0.3369872469179803}\n",
      "100%|██████████| 100/100 [00:51<00:00,  1.93trial/s, best loss: -0.9989615784008307]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.10865274775444425, 'gamma': 1.347423698554034, 'max_depth': 14.0, 'min_child_weight': 1.0, 'n_estimators': 80.0, 'reg_alpha': 0.0, 'reg_lambda': 0.08466995401961928}\n",
      "100%|██████████| 100/100 [00:51<00:00,  1.95trial/s, best loss: -0.9986720375370723]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7544670540225322, 'gamma': 4.487030622672707, 'max_depth': 26.0, 'min_child_weight': 3.0, 'n_estimators': 400.0, 'reg_alpha': 3.0, 'reg_lambda': 0.8957837830339233}\n",
      "100%|██████████| 100/100 [00:27<00:00,  3.63trial/s, best loss: -0.9983891965842142]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.510934266044205, 'gamma': 6.978540455461742, 'max_depth': 16.0, 'min_child_weight': 0.0, 'n_estimators': 280.0, 'reg_alpha': 1.0, 'reg_lambda': 0.005968276080734858}\n",
      "100%|██████████| 100/100 [01:06<00:00,  1.50trial/s, best loss: -0.9986313465783664]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2758220512321732, 'gamma': 5.100548220481222, 'max_depth': 10.0, 'min_child_weight': 0.0, 'n_estimators': 380.0, 'reg_alpha': 1.0, 'reg_lambda': 0.35945005320078594}\n",
      "100%|██████████| 100/100 [00:29<00:00,  3.43trial/s, best loss: -0.9982958192240445]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7885054729744466, 'gamma': 7.463831108956702, 'max_depth': 16.0, 'min_child_weight': 3.0, 'n_estimators': 240.0, 'reg_alpha': 0.0, 'reg_lambda': 0.34680013179472297}\n",
      "100%|██████████| 100/100 [00:56<00:00,  1.76trial/s, best loss: -0.9983167220376522]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9959087594305525, 'gamma': 4.727555340154632, 'max_depth': 12.0, 'min_child_weight': 1.0, 'n_estimators': 140.0, 'reg_alpha': 0.0, 'reg_lambda': 0.0782335113905743}\n",
      "100%|██████████| 100/100 [00:40<00:00,  2.45trial/s, best loss: -0.9979206300048665]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7761666246887418, 'gamma': 5.472795059638907, 'max_depth': 28.0, 'min_child_weight': 0.0, 'n_estimators': 380.0, 'reg_alpha': 2.0, 'reg_lambda': 0.6217606998292363}\n",
      "100%|██████████| 100/100 [00:37<00:00,  2.69trial/s, best loss: -0.9981226671378405]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2843319092907563, 'gamma': 8.827986814867765, 'max_depth': 12.0, 'min_child_weight': 1.0, 'n_estimators': 120.0, 'reg_alpha': 2.0, 'reg_lambda': 0.003773700294924298}\n",
      "100%|██████████| 100/100 [00:41<00:00,  2.41trial/s, best loss: -0.9980827291359059]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6883706838805148, 'gamma': 1.7646457778763536, 'max_depth': 22.0, 'min_child_weight': 13.0, 'n_estimators': 100.0, 'reg_alpha': 4.0, 'reg_lambda': 0.6784853917625295}\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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IoT-23\"\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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