doh_cic.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DoH detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of DoH\n",
    "\n",
    "Raw data from: CIC-DoHBrw-2020 [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/doh_cic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "        Unnamed: 0           DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                0         1.1.1.1.  192.168.20.144      166            0   \n",
       "1                1  192.168.20.144.         1.1.1.1      248            0   \n",
       "2                2         1.1.1.1.  192.168.20.144      239            0   \n",
       "3                3  192.168.20.144.         1.1.1.1      372            0   \n",
       "4                4         1.1.1.1.  192.168.20.144      197            0   \n",
       "...            ...              ...             ...      ...          ...   \n",
       "991355      991355   52.50.215.119.  192.168.20.191       12            0   \n",
       "991356      991356  192.168.20.191.    193.200.65.5        9            0   \n",
       "991357      991357  192.168.20.191.   52.50.215.119       11            0   \n",
       "991358      991358  85.194.242.103.  192.168.20.191        9            0   \n",
       "991359      991359  192.168.20.191.  85.194.242.103       11            0   \n",
       "\n",
       "        BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0        9009          0  1.585702e+09  1.585702e+09       443  ...   \n",
       "1       20108          0  1.585702e+09  1.585702e+09     35784  ...   \n",
       "2       13795          0  1.585702e+09  1.585702e+09       443  ...   \n",
       "3       26999          0  1.585702e+09  1.585702e+09     36080  ...   \n",
       "4       11992          0  1.585702e+09  1.585702e+09       443  ...   \n",
       "...       ...        ...           ...           ...       ...  ...   \n",
       "991355   1168          0  1.578933e+09  1.578933e+09       443  ...   \n",
       "991356   5915          0  1.578933e+09  1.578933e+09     53993  ...   \n",
       "991357   4608          0  1.578933e+09  1.578933e+09     53994  ...   \n",
       "991358   1073          0  1.578933e+09  1.578933e+09       443  ...   \n",
       "991359   5045          0  1.578933e+09  1.578933e+09     53996  ...   \n",
       "\n",
       "        SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            13630.165637          0.321156           0.647004   \n",
       "1             9850.682690          0.329699           0.520761   \n",
       "2            24168.822670          0.968015           0.688742   \n",
       "3            19910.802176          0.860933           0.521975   \n",
       "4            19832.946195          0.861529           0.674352   \n",
       "...                   ...               ...                ...   \n",
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       "991358        2156.966910          0.402808           0.541766   \n",
       "991359        6234.707529          0.431019           0.575636   \n",
       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0              0.257233        0.696817        8.236810       1.332151   \n",
       "1              0.288828        0.020163        7.383369       1.543607   \n",
       "2              0.249193        0.486452       23.280760       0.969427   \n",
       "3              0.281701        0.042755       11.823744       0.965827   \n",
       "4              0.258972        0.523305       15.915634       1.177049   \n",
       "...                 ...             ...             ...            ...   \n",
       "991355         0.279789        0.176361        1.515317       0.732219   \n",
       "991356              NaN             NaN     -841.447379   22842.921830   \n",
       "991357         0.283979        0.116189        1.402746       0.815883   \n",
       "991358         0.270578        0.158447        1.553336       0.760355   \n",
       "991359         0.277674        0.254942        2.184042       2.038912   \n",
       "\n",
       "        SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 0.428524          0.489665   True  \n",
       "1                -0.511169          0.782838   True  \n",
       "2                 0.827844          0.597161   True  \n",
       "3                 0.116021          0.749907   True  \n",
       "4                 0.197276          0.464377   True  \n",
       "...                    ...               ...    ...  \n",
       "991355            0.840360          0.168880  False  \n",
       "991356            0.403095          0.000602  False  \n",
       "991357            0.462496          0.198089  False  \n",
       "991358           -0.415611          0.138390  False  \n",
       "991359            0.992936          0.051966  False  \n",
       "\n",
       "[991360 rows x 84 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
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    "    \"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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in frequency_features: \n",
    "        df.replace([np.inf], -1, inplace=True)\n",
    "        df.replace([-np.inf], -1, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = -1\n",
    "    else:\n",
    "        df.replace([np.inf], 0, inplace=True)\n",
    "        df.replace([-np.inf], 0, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>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",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.1.1.1.</td>\n",
       "      <td>192.168.20.144</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>9009</td>\n",
       "      <td>0</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>35784</td>\n",
       "      <td>114</td>\n",
       "      <td>60.0</td>\n",
       "      <td>86.469648</td>\n",
       "      <td>7477</td>\n",
       "      <td>-0.137329</td>\n",
       "      <td>60.0</td>\n",
       "      <td>157.00</td>\n",
       "      <td>60</td>\n",
       "      <td>650</td>\n",
       "      <td>590</td>\n",
       "      <td>60</td>\n",
       "      <td>75.850568</td>\n",
       "      <td>62</td>\n",
       "      <td>54.385965</td>\n",
       "      <td>143.145326</td>\n",
       "      <td>0.578313</td>\n",
       "      <td>0.421687</td>\n",
       "      <td>0.624497</td>\n",
       "      <td>3.261260</td>\n",
       "      <td>3.282109</td>\n",
       "      <td>4.192889e-07</td>\n",
       "      <td>4.231219e-07</td>\n",
       "      <td>-0.113402</td>\n",
       "      <td>19.221764</td>\n",
       "      <td>2.343127</td>\n",
       "      <td>0.317710</td>\n",
       "      <td>0.552495</td>\n",
       "      <td>False</td>\n",
       "      <td>0.669892</td>\n",
       "      <td>0.018051</td>\n",
       "      <td>0.532485</td>\n",
       "      <td>0.169912</td>\n",
       "      <td>0.901722</td>\n",
       "      <td>49.012083</td>\n",
       "      <td>40.423646</td>\n",
       "      <td>27.949549</td>\n",
       "      <td>55.803539</td>\n",
       "      <td>288.456537</td>\n",
       "      <td>1.748221</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>2.384186e-05</td>\n",
       "      <td>15.104457</td>\n",
       "      <td>0.115742</td>\n",
       "      <td>False</td>\n",
       "      <td>0.884848</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532485</td>\n",
       "      <td>14901</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.678451e-07</td>\n",
       "      <td>0.056045</td>\n",
       "      <td>0.214869</td>\n",
       "      <td>0.643393</td>\n",
       "      <td>0.006804</td>\n",
       "      <td>0.009128</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>34.020846</td>\n",
       "      <td>214.042556</td>\n",
       "      <td>5.808420e+04</td>\n",
       "      <td>13630.165637</td>\n",
       "      <td>0.321156</td>\n",
       "      <td>0.647004</td>\n",
       "      <td>0.257233</td>\n",
       "      <td>0.696817</td>\n",
       "      <td>8.236810</td>\n",
       "      <td>1.332151</td>\n",
       "      <td>0.428524</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>192.168.20.144.</td>\n",
       "      <td>1.1.1.1</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>20108</td>\n",
       "      <td>0</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>35784</td>\n",
       "      <td>443</td>\n",
       "      <td>141</td>\n",
       "      <td>91.0</td>\n",
       "      <td>210.409125</td>\n",
       "      <td>44272</td>\n",
       "      <td>0.197517</td>\n",
       "      <td>60.0</td>\n",
       "      <td>169.00</td>\n",
       "      <td>60</td>\n",
       "      <td>3109</td>\n",
       "      <td>3049</td>\n",
       "      <td>60</td>\n",
       "      <td>149.226330</td>\n",
       "      <td>87</td>\n",
       "      <td>61.702128</td>\n",
       "      <td>252.978435</td>\n",
       "      <td>0.669355</td>\n",
       "      <td>0.330645</td>\n",
       "      <td>0.384964</td>\n",
       "      <td>1.577878</td>\n",
       "      <td>11.433725</td>\n",
       "      <td>8.657343e-09</td>\n",
       "      <td>8.710113e-09</td>\n",
       "      <td>-0.486239</td>\n",
       "      <td>159.874916</td>\n",
       "      <td>2.951888</td>\n",
       "      <td>0.371111</td>\n",
       "      <td>0.512742</td>\n",
       "      <td>False</td>\n",
       "      <td>0.751315</td>\n",
       "      <td>0.010699</td>\n",
       "      <td>0.570665</td>\n",
       "      <td>0.159045</td>\n",
       "      <td>0.890264</td>\n",
       "      <td>45.877504</td>\n",
       "      <td>40.408011</td>\n",
       "      <td>31.042470</td>\n",
       "      <td>52.690847</td>\n",
       "      <td>288.457000</td>\n",
       "      <td>1.167842</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>2.861023e-06</td>\n",
       "      <td>15.141317</td>\n",
       "      <td>0.077129</td>\n",
       "      <td>False</td>\n",
       "      <td>1.514170</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570665</td>\n",
       "      <td>26388</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.725275e-05</td>\n",
       "      <td>0.058853</td>\n",
       "      <td>0.841265</td>\n",
       "      <td>0.330096</td>\n",
       "      <td>0.007971</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.00469</td>\n",
       "      <td>39.854884</td>\n",
       "      <td>262.808003</td>\n",
       "      <td>5.089124e+04</td>\n",
       "      <td>9850.682690</td>\n",
       "      <td>0.329699</td>\n",
       "      <td>0.520761</td>\n",
       "      <td>0.288828</td>\n",
       "      <td>0.020163</td>\n",
       "      <td>7.383369</td>\n",
       "      <td>1.543607</td>\n",
       "      <td>-0.511169</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.1.1.1.</td>\n",
       "      <td>192.168.20.144</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>13795</td>\n",
       "      <td>0</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>36080</td>\n",
       "      <td>117</td>\n",
       "      <td>60.0</td>\n",
       "      <td>122.715117</td>\n",
       "      <td>15059</td>\n",
       "      <td>0.023841</td>\n",
       "      <td>60.0</td>\n",
       "      <td>157.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1271</td>\n",
       "      <td>1211</td>\n",
       "      <td>60</td>\n",
       "      <td>104.884716</td>\n",
       "      <td>69</td>\n",
       "      <td>58.974359</td>\n",
       "      <td>169.865034</td>\n",
       "      <td>0.606695</td>\n",
       "      <td>0.393305</td>\n",
       "      <td>0.464490</td>\n",
       "      <td>2.371346</td>\n",
       "      <td>5.258299</td>\n",
       "      <td>3.894362e-07</td>\n",
       "      <td>3.919001e-07</td>\n",
       "      <td>-0.175258</td>\n",
       "      <td>40.395304</td>\n",
       "      <td>2.239546</td>\n",
       "      <td>0.283456</td>\n",
       "      <td>0.505912</td>\n",
       "      <td>False</td>\n",
       "      <td>0.662955</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.658931</td>\n",
       "      <td>0.133629</td>\n",
       "      <td>0.920839</td>\n",
       "      <td>38.541851</td>\n",
       "      <td>30.936951</td>\n",
       "      <td>12.428884</td>\n",
       "      <td>49.501112</td>\n",
       "      <td>288.424106</td>\n",
       "      <td>1.211866</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>1.883507e-05</td>\n",
       "      <td>15.104759</td>\n",
       "      <td>0.080231</td>\n",
       "      <td>False</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658931</td>\n",
       "      <td>18247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.167497e-07</td>\n",
       "      <td>0.087257</td>\n",
       "      <td>0.141959</td>\n",
       "      <td>0.969803</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.009796</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>18.740186</td>\n",
       "      <td>114.032099</td>\n",
       "      <td>1.507063e+05</td>\n",
       "      <td>24168.822670</td>\n",
       "      <td>0.968015</td>\n",
       "      <td>0.688742</td>\n",
       "      <td>0.249193</td>\n",
       "      <td>0.486452</td>\n",
       "      <td>23.280760</td>\n",
       "      <td>0.969427</td>\n",
       "      <td>0.827844</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>192.168.20.144.</td>\n",
       "      <td>1.1.1.1</td>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>26999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>36080</td>\n",
       "      <td>443</td>\n",
       "      <td>132</td>\n",
       "      <td>91.0</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>29929</td>\n",
       "      <td>0.134426</td>\n",
       "      <td>60.0</td>\n",
       "      <td>169.00</td>\n",
       "      <td>60</td>\n",
       "      <td>3109</td>\n",
       "      <td>3049</td>\n",
       "      <td>91</td>\n",
       "      <td>131.060606</td>\n",
       "      <td>71</td>\n",
       "      <td>53.787879</td>\n",
       "      <td>217.774576</td>\n",
       "      <td>0.647849</td>\n",
       "      <td>0.352151</td>\n",
       "      <td>0.236994</td>\n",
       "      <td>1.763006</td>\n",
       "      <td>13.814363</td>\n",
       "      <td>1.116239e-07</td>\n",
       "      <td>1.120763e-07</td>\n",
       "      <td>-0.321101</td>\n",
       "      <td>235.902877</td>\n",
       "      <td>2.665128</td>\n",
       "      <td>0.312107</td>\n",
       "      <td>0.734491</td>\n",
       "      <td>False</td>\n",
       "      <td>0.713619</td>\n",
       "      <td>0.004014</td>\n",
       "      <td>0.682523</td>\n",
       "      <td>0.124814</td>\n",
       "      <td>0.880879</td>\n",
       "      <td>35.999223</td>\n",
       "      <td>30.941632</td>\n",
       "      <td>12.478980</td>\n",
       "      <td>49.504474</td>\n",
       "      <td>288.423932</td>\n",
       "      <td>0.777423</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>1.907349e-06</td>\n",
       "      <td>15.113732</td>\n",
       "      <td>0.051438</td>\n",
       "      <td>False</td>\n",
       "      <td>1.568733</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682523</td>\n",
       "      <td>31415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.947349e-06</td>\n",
       "      <td>0.064438</td>\n",
       "      <td>0.745905</td>\n",
       "      <td>0.861927</td>\n",
       "      <td>0.005450</td>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.00259</td>\n",
       "      <td>27.249376</td>\n",
       "      <td>187.684249</td>\n",
       "      <td>1.127866e+05</td>\n",
       "      <td>19910.802176</td>\n",
       "      <td>0.860933</td>\n",
       "      <td>0.521975</td>\n",
       "      <td>0.281701</td>\n",
       "      <td>0.042755</td>\n",
       "      <td>11.823744</td>\n",
       "      <td>0.965827</td>\n",
       "      <td>0.116021</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.1.1.1.</td>\n",
       "      <td>192.168.20.144</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>11992</td>\n",
       "      <td>0</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>1.585702e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>36106</td>\n",
       "      <td>120</td>\n",
       "      <td>60.0</td>\n",
       "      <td>110.575766</td>\n",
       "      <td>12228</td>\n",
       "      <td>-0.040873</td>\n",
       "      <td>60.0</td>\n",
       "      <td>158.00</td>\n",
       "      <td>60</td>\n",
       "      <td>697</td>\n",
       "      <td>637</td>\n",
       "      <td>60</td>\n",
       "      <td>92.146472</td>\n",
       "      <td>69</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>163.634265</td>\n",
       "      <td>0.573604</td>\n",
       "      <td>0.426396</td>\n",
       "      <td>0.542614</td>\n",
       "      <td>2.713072</td>\n",
       "      <td>3.605593</td>\n",
       "      <td>6.457767e-07</td>\n",
       "      <td>6.507421e-07</td>\n",
       "      <td>-0.224490</td>\n",
       "      <td>17.918230</td>\n",
       "      <td>2.291669</td>\n",
       "      <td>0.300663</td>\n",
       "      <td>0.546144</td>\n",
       "      <td>False</td>\n",
       "      <td>0.699507</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.678108</td>\n",
       "      <td>0.139856</td>\n",
       "      <td>0.897469</td>\n",
       "      <td>40.349411</td>\n",
       "      <td>34.032359</td>\n",
       "      <td>15.470319</td>\n",
       "      <td>46.401662</td>\n",
       "      <td>288.505911</td>\n",
       "      <td>1.471969</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>3.600121e-05</td>\n",
       "      <td>15.180108</td>\n",
       "      <td>0.096967</td>\n",
       "      <td>False</td>\n",
       "      <td>0.846939</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.678108</td>\n",
       "      <td>14902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.969170e-07</td>\n",
       "      <td>0.083089</td>\n",
       "      <td>0.772129</td>\n",
       "      <td>0.969405</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.011536</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>26.102859</td>\n",
       "      <td>153.746379</td>\n",
       "      <td>1.024019e+05</td>\n",
       "      <td>19832.946195</td>\n",
       "      <td>0.861529</td>\n",
       "      <td>0.674352</td>\n",
       "      <td>0.258972</td>\n",
       "      <td>0.523305</td>\n",
       "      <td>15.915634</td>\n",
       "      <td>1.177049</td>\n",
       "      <td>0.197276</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991355</th>\n",
       "      <td>991355</td>\n",
       "      <td>52.50.215.119.</td>\n",
       "      <td>192.168.20.191</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>53994</td>\n",
       "      <td>157</td>\n",
       "      <td>60.0</td>\n",
       "      <td>174.462030</td>\n",
       "      <td>30437</td>\n",
       "      <td>0.052682</td>\n",
       "      <td>60.0</td>\n",
       "      <td>161.25</td>\n",
       "      <td>60</td>\n",
       "      <td>577</td>\n",
       "      <td>517</td>\n",
       "      <td>60</td>\n",
       "      <td>111.122312</td>\n",
       "      <td>124</td>\n",
       "      <td>78.980892</td>\n",
       "      <td>229.465684</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.555995</td>\n",
       "      <td>2.355813</td>\n",
       "      <td>1.250411</td>\n",
       "      <td>6.277345e-08</td>\n",
       "      <td>7.212120e-08</td>\n",
       "      <td>-0.916049</td>\n",
       "      <td>3.555866</td>\n",
       "      <td>1.947339</td>\n",
       "      <td>0.543196</td>\n",
       "      <td>0.529795</td>\n",
       "      <td>False</td>\n",
       "      <td>0.697158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222636</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.604286</td>\n",
       "      <td>0.205629</td>\n",
       "      <td>0.198467</td>\n",
       "      <td>0.305524</td>\n",
       "      <td>2.714241</td>\n",
       "      <td>0.246749</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>1.499653e-04</td>\n",
       "      <td>2.305230</td>\n",
       "      <td>0.107039</td>\n",
       "      <td>False</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036682e-01</td>\n",
       "      <td>0.220444</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.847027</td>\n",
       "      <td>0.145477</td>\n",
       "      <td>0.034521</td>\n",
       "      <td>0.10367</td>\n",
       "      <td>727.387232</td>\n",
       "      <td>1994.168554</td>\n",
       "      <td>1.026022e+04</td>\n",
       "      <td>2686.423343</td>\n",
       "      <td>0.357115</td>\n",
       "      <td>0.549690</td>\n",
       "      <td>0.279789</td>\n",
       "      <td>0.176361</td>\n",
       "      <td>1.515317</td>\n",
       "      <td>0.732219</td>\n",
       "      <td>0.840360</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991356</th>\n",
       "      <td>991356</td>\n",
       "      <td>192.168.20.191.</td>\n",
       "      <td>193.200.65.5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5915</td>\n",
       "      <td>0</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>53993</td>\n",
       "      <td>443</td>\n",
       "      <td>717</td>\n",
       "      <td>504.0</td>\n",
       "      <td>634.621147</td>\n",
       "      <td>402744</td>\n",
       "      <td>-0.060948</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1236.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>88.510620</td>\n",
       "      <td>567</td>\n",
       "      <td>79.079498</td>\n",
       "      <td>934.025041</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.035263</td>\n",
       "      <td>2.595249</td>\n",
       "      <td>-0.225570</td>\n",
       "      <td>8.694485e-10</td>\n",
       "      <td>1.053931e-09</td>\n",
       "      <td>-0.117347</td>\n",
       "      <td>1.040113</td>\n",
       "      <td>2.419382</td>\n",
       "      <td>0.763230</td>\n",
       "      <td>0.644126</td>\n",
       "      <td>True</td>\n",
       "      <td>0.831270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.155874</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.419432</td>\n",
       "      <td>0.089800</td>\n",
       "      <td>0.089799</td>\n",
       "      <td>0.273100</td>\n",
       "      <td>2.690845</td>\n",
       "      <td>0.336356</td>\n",
       "      <td>0.044471</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.416939</td>\n",
       "      <td>0.139166</td>\n",
       "      <td>False</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6455</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.065506e+03</td>\n",
       "      <td>939.990021</td>\n",
       "      <td>0.399232</td>\n",
       "      <td>0.802327</td>\n",
       "      <td>-1.117111</td>\n",
       "      <td>115.745464</td>\n",
       "      <td>0.15693</td>\n",
       "      <td>-5585.554388</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.357086e+07</td>\n",
       "      <td>-337596.929634</td>\n",
       "      <td>0.802327</td>\n",
       "      <td>0.217884</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-841.447379</td>\n",
       "      <td>22842.921830</td>\n",
       "      <td>0.403095</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991357</th>\n",
       "      <td>991357</td>\n",
       "      <td>192.168.20.191.</td>\n",
       "      <td>52.50.215.119</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4608</td>\n",
       "      <td>0</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>53994</td>\n",
       "      <td>443</td>\n",
       "      <td>478</td>\n",
       "      <td>138.0</td>\n",
       "      <td>612.249132</td>\n",
       "      <td>374850</td>\n",
       "      <td>0.123136</td>\n",
       "      <td>60.0</td>\n",
       "      <td>762.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>128.085593</td>\n",
       "      <td>510</td>\n",
       "      <td>106.694561</td>\n",
       "      <td>755.067246</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.682729</td>\n",
       "      <td>2.116785</td>\n",
       "      <td>0.678100</td>\n",
       "      <td>3.961164e-09</td>\n",
       "      <td>4.616115e-09</td>\n",
       "      <td>-0.190883</td>\n",
       "      <td>1.811262</td>\n",
       "      <td>2.550341</td>\n",
       "      <td>0.737214</td>\n",
       "      <td>0.650127</td>\n",
       "      <td>False</td>\n",
       "      <td>0.863059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145677</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.381092</td>\n",
       "      <td>0.206271</td>\n",
       "      <td>0.100230</td>\n",
       "      <td>0.207283</td>\n",
       "      <td>2.616012</td>\n",
       "      <td>0.261601</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>2.266145</td>\n",
       "      <td>0.115439</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>5268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.680560e-01</td>\n",
       "      <td>0.308658</td>\n",
       "      <td>0.397643</td>\n",
       "      <td>0.860139</td>\n",
       "      <td>0.220039</td>\n",
       "      <td>0.037392</td>\n",
       "      <td>0.17840</td>\n",
       "      <td>1100.193858</td>\n",
       "      <td>2380.650114</td>\n",
       "      <td>1.117312e+04</td>\n",
       "      <td>2992.511286</td>\n",
       "      <td>0.360095</td>\n",
       "      <td>0.536066</td>\n",
       "      <td>0.283979</td>\n",
       "      <td>0.116189</td>\n",
       "      <td>1.402746</td>\n",
       "      <td>0.815883</td>\n",
       "      <td>0.462496</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991358</th>\n",
       "      <td>991358</td>\n",
       "      <td>85.194.242.103.</td>\n",
       "      <td>192.168.20.191</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1073</td>\n",
       "      <td>0</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>53996</td>\n",
       "      <td>179</td>\n",
       "      <td>60.0</td>\n",
       "      <td>219.522208</td>\n",
       "      <td>48190</td>\n",
       "      <td>0.101681</td>\n",
       "      <td>60.0</td>\n",
       "      <td>124.00</td>\n",
       "      <td>60</td>\n",
       "      <td>577</td>\n",
       "      <td>517</td>\n",
       "      <td>60</td>\n",
       "      <td>122.638105</td>\n",
       "      <td>171</td>\n",
       "      <td>95.530726</td>\n",
       "      <td>273.781949</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.542086</td>\n",
       "      <td>2.172901</td>\n",
       "      <td>0.829533</td>\n",
       "      <td>2.100642e-08</td>\n",
       "      <td>2.546363e-08</td>\n",
       "      <td>-2.718750</td>\n",
       "      <td>2.184689</td>\n",
       "      <td>1.446617</td>\n",
       "      <td>0.456357</td>\n",
       "      <td>0.403785</td>\n",
       "      <td>False</td>\n",
       "      <td>0.620162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285566</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.687360</td>\n",
       "      <td>0.272049</td>\n",
       "      <td>0.131318</td>\n",
       "      <td>0.408135</td>\n",
       "      <td>2.407005</td>\n",
       "      <td>0.300876</td>\n",
       "      <td>0.118417</td>\n",
       "      <td>2.529621e-04</td>\n",
       "      <td>1.893101</td>\n",
       "      <td>0.158933</td>\n",
       "      <td>False</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.253678e-02</td>\n",
       "      <td>0.207564</td>\n",
       "      <td>0.926295</td>\n",
       "      <td>0.510684</td>\n",
       "      <td>0.133625</td>\n",
       "      <td>0.037330</td>\n",
       "      <td>0.09662</td>\n",
       "      <td>668.124556</td>\n",
       "      <td>1903.065003</td>\n",
       "      <td>1.001464e+04</td>\n",
       "      <td>2156.966910</td>\n",
       "      <td>0.402808</td>\n",
       "      <td>0.541766</td>\n",
       "      <td>0.270578</td>\n",
       "      <td>0.158447</td>\n",
       "      <td>1.553336</td>\n",
       "      <td>0.760355</td>\n",
       "      <td>-0.415611</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991359</th>\n",
       "      <td>991359</td>\n",
       "      <td>192.168.20.191.</td>\n",
       "      <td>85.194.242.103</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5045</td>\n",
       "      <td>0</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>1.578933e+09</td>\n",
       "      <td>53996</td>\n",
       "      <td>443</td>\n",
       "      <td>518</td>\n",
       "      <td>69.0</td>\n",
       "      <td>655.968749</td>\n",
       "      <td>430296</td>\n",
       "      <td>0.117523</td>\n",
       "      <td>60.0</td>\n",
       "      <td>983.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1520</td>\n",
       "      <td>1460</td>\n",
       "      <td>60</td>\n",
       "      <td>126.634894</td>\n",
       "      <td>545</td>\n",
       "      <td>105.212355</td>\n",
       "      <td>812.503259</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.698204</td>\n",
       "      <td>2.263827</td>\n",
       "      <td>0.572365</td>\n",
       "      <td>2.254529e-09</td>\n",
       "      <td>2.627300e-09</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>1.613399</td>\n",
       "      <td>1.971747</td>\n",
       "      <td>0.569963</td>\n",
       "      <td>0.648488</td>\n",
       "      <td>False</td>\n",
       "      <td>0.738121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.239715</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.551626</td>\n",
       "      <td>0.132079</td>\n",
       "      <td>0.132011</td>\n",
       "      <td>0.277084</td>\n",
       "      <td>2.301176</td>\n",
       "      <td>0.230118</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.998857</td>\n",
       "      <td>0.115125</td>\n",
       "      <td>False</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.335771e-01</td>\n",
       "      <td>0.475899</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999603</td>\n",
       "      <td>0.217898</td>\n",
       "      <td>0.094434</td>\n",
       "      <td>0.13358</td>\n",
       "      <td>1089.491950</td>\n",
       "      <td>2263.525018</td>\n",
       "      <td>1.673457e+04</td>\n",
       "      <td>6234.707529</td>\n",
       "      <td>0.431019</td>\n",
       "      <td>0.575636</td>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.254942</td>\n",
       "      <td>2.184042</td>\n",
       "      <td>2.038912</td>\n",
       "      <td>0.992936</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>991360 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0           DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                0         1.1.1.1.  192.168.20.144      166            0   \n",
       "1                1  192.168.20.144.         1.1.1.1      248            0   \n",
       "2                2         1.1.1.1.  192.168.20.144      239            0   \n",
       "3                3  192.168.20.144.         1.1.1.1      372            0   \n",
       "4                4         1.1.1.1.  192.168.20.144      197            0   \n",
       "...            ...              ...             ...      ...          ...   \n",
       "991355      991355   52.50.215.119.  192.168.20.191       12            0   \n",
       "991356      991356  192.168.20.191.    193.200.65.5        9            0   \n",
       "991357      991357  192.168.20.191.   52.50.215.119       11            0   \n",
       "991358      991358  85.194.242.103.  192.168.20.191        9            0   \n",
       "991359      991359  192.168.20.191.  85.194.242.103       11            0   \n",
       "\n",
       "        BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0        9009          0  1.585702e+09  1.585702e+09       443     35784   \n",
       "1       20108          0  1.585702e+09  1.585702e+09     35784       443   \n",
       "2       13795          0  1.585702e+09  1.585702e+09       443     36080   \n",
       "3       26999          0  1.585702e+09  1.585702e+09     36080       443   \n",
       "4       11992          0  1.585702e+09  1.585702e+09       443     36106   \n",
       "...       ...        ...           ...           ...       ...       ...   \n",
       "991355   1168          0  1.578933e+09  1.578933e+09       443     53994   \n",
       "991356   5915          0  1.578933e+09  1.578933e+09     53993       443   \n",
       "991357   4608          0  1.578933e+09  1.578933e+09     53994       443   \n",
       "991358   1073          0  1.578933e+09  1.578933e+09       443     53996   \n",
       "991359   5045          0  1.578933e+09  1.578933e+09     53996       443   \n",
       "\n",
       "        MEAN  MEDIAN       STDEV     VAR  BURSTINESS    Q1       Q3  MIN  \\\n",
       "0        114    60.0   86.469648    7477   -0.137329  60.0   157.00   60   \n",
       "1        141    91.0  210.409125   44272    0.197517  60.0   169.00   60   \n",
       "2        117    60.0  122.715117   15059    0.023841  60.0   157.00   60   \n",
       "3        132    91.0  173.000000   29929    0.134426  60.0   169.00   60   \n",
       "4        120    60.0  110.575766   12228   -0.040873  60.0   158.00   60   \n",
       "...      ...     ...         ...     ...         ...   ...      ...  ...   \n",
       "991355   157    60.0  174.462030   30437    0.052682  60.0   161.25   60   \n",
       "991356   717   504.0  634.621147  402744   -0.060948  60.0  1236.00   60   \n",
       "991357   478   138.0  612.249132  374850    0.123136  60.0   762.00   60   \n",
       "991358   179    60.0  219.522208   48190    0.101681  60.0   124.00   60   \n",
       "991359   518    69.0  655.968749  430296    0.117523  60.0   983.00   60   \n",
       "\n",
       "         MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0        650            590    60                 75.850568   \n",
       "1       3109           3049    60                149.226330   \n",
       "2       1271           1211    60                104.884716   \n",
       "3       3109           3049    91                131.060606   \n",
       "4        697            637    60                 92.146472   \n",
       "...      ...            ...   ...                       ...   \n",
       "991355   577            517    60                111.122312   \n",
       "991356  1520           1460    60                 88.510620   \n",
       "991357  1520           1460    60                128.085593   \n",
       "991358   577            517    60                122.638105   \n",
       "991359  1520           1460    60                126.634894   \n",
       "\n",
       "        AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                       62          54.385965        143.145326   \n",
       "1                       87          61.702128        252.978435   \n",
       "2                       69          58.974359        169.865034   \n",
       "3                       71          53.787879        217.774576   \n",
       "4                       69          57.500000        163.634265   \n",
       "...                    ...                ...               ...   \n",
       "991355                 124          78.980892        229.465684   \n",
       "991356                 567          79.079498        934.025041   \n",
       "991357                 510         106.694561        755.067246   \n",
       "991358                 171          95.530726        273.781949   \n",
       "991359                 545         105.212355        812.503259   \n",
       "\n",
       "        PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                 0.578313            0.421687              0.624497   \n",
       "1                 0.669355            0.330645              0.384964   \n",
       "2                 0.606695            0.393305              0.464490   \n",
       "3                 0.647849            0.352151              0.236994   \n",
       "4                 0.573604            0.426396              0.542614   \n",
       "...                    ...                 ...                   ...   \n",
       "991355            0.750000            0.250000              0.555995   \n",
       "991356            0.555556            0.444444              1.035263   \n",
       "991357            0.727273            0.272727              0.682729   \n",
       "991358            0.777778            0.222222              0.542086   \n",
       "991359            0.727273            0.272727              0.698204   \n",
       "\n",
       "        PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   3.261260              3.282109   \n",
       "1                   1.577878             11.433725   \n",
       "2                   2.371346              5.258299   \n",
       "3                   1.763006             13.814363   \n",
       "4                   2.713072              3.605593   \n",
       "...                      ...                   ...   \n",
       "991355              2.355813              1.250411   \n",
       "991356              2.595249             -0.225570   \n",
       "991357              2.116785              0.678100   \n",
       "991358              2.172901              0.829533   \n",
       "991359              2.263827              0.572365   \n",
       "\n",
       "        FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                     4.192889e-07                4.231219e-07   \n",
       "1                     8.657343e-09                8.710113e-09   \n",
       "2                     3.894362e-07                3.919001e-07   \n",
       "3                     1.116239e-07                1.120763e-07   \n",
       "4                     6.457767e-07                6.507421e-07   \n",
       "...                            ...                         ...   \n",
       "991355                6.277345e-08                7.212120e-08   \n",
       "991356                8.694485e-10                1.053931e-09   \n",
       "991357                3.961164e-09                4.616115e-09   \n",
       "991358                2.100642e-08                2.546363e-08   \n",
       "991359                2.254529e-09                2.627300e-09   \n",
       "\n",
       "        GALTON_SKEWNESS    KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0             -0.113402   19.221764  2.343127        0.317710        0.552495   \n",
       "1             -0.486239  159.874916  2.951888        0.371111        0.512742   \n",
       "2             -0.175258   40.395304  2.239546        0.283456        0.505912   \n",
       "3             -0.321101  235.902877  2.665128        0.312107        0.734491   \n",
       "4             -0.224490   17.918230  2.291669        0.300663        0.546144   \n",
       "...                 ...         ...       ...             ...             ...   \n",
       "991355        -0.916049    3.555866  1.947339        0.543196        0.529795   \n",
       "991356        -0.117347    1.040113  2.419382        0.763230        0.644126   \n",
       "991357        -0.190883    1.811262  2.550341        0.737214        0.650127   \n",
       "991358        -2.718750    2.184689  1.446617        0.456357        0.403785   \n",
       "991359         0.007584    1.613399  1.971747        0.569963        0.648488   \n",
       "\n",
       "        BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                       False   0.669892             0.018051   \n",
       "1                       False   0.751315             0.010699   \n",
       "2                       False   0.662955             0.004694   \n",
       "3                       False   0.713619             0.004014   \n",
       "4                       False   0.699507             0.004128   \n",
       "...                       ...        ...                  ...   \n",
       "991355                  False   0.697158             0.000000   \n",
       "991356                   True   0.831270             0.000000   \n",
       "991357                  False   0.863059             0.000000   \n",
       "991358                  False   0.620162             0.000000   \n",
       "991359                  False   0.738121             0.000000   \n",
       "\n",
       "        CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0               0.532485           0.169912                  0.901722   \n",
       "1               0.570665           0.159045                  0.890264   \n",
       "2               0.658931           0.133629                  0.920839   \n",
       "3               0.682523           0.124814                  0.880879   \n",
       "4               0.678108           0.139856                  0.897469   \n",
       "...                  ...                ...                       ...   \n",
       "991355          0.000000           0.222636                  0.696429   \n",
       "991356          0.000000           0.155874                  0.375000   \n",
       "991357          0.000000           0.145677                  0.437500   \n",
       "991358          0.000000           0.285566                  0.750000   \n",
       "991359          0.000000           0.239715                  0.575000   \n",
       "\n",
       "        MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0              49.012083           40.423646       27.949549       55.803539   \n",
       "1              45.877504           40.408011       31.042470       52.690847   \n",
       "2              38.541851           30.936951       12.428884       49.501112   \n",
       "3              35.999223           30.941632       12.478980       49.504474   \n",
       "4              40.349411           34.032359       15.470319       46.401662   \n",
       "...                  ...                 ...             ...             ...   \n",
       "991355          0.604286            0.205629        0.198467        0.305524   \n",
       "991356          0.419432            0.089800        0.089799        0.273100   \n",
       "991357          0.381092            0.206271        0.100230        0.207283   \n",
       "991358          0.687360            0.272049        0.131318        0.408135   \n",
       "991359          0.551626            0.132079        0.132011        0.277084   \n",
       "\n",
       "          DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0       288.456537        1.748221          0.001355   2.384186e-05   \n",
       "1       288.457000        1.167842          0.000199   2.861023e-06   \n",
       "2       288.424106        1.211866          0.000764   1.883507e-05   \n",
       "3       288.423932        0.777423          0.000084   1.907349e-06   \n",
       "4       288.505911        1.471969          0.000840   3.600121e-05   \n",
       "...            ...             ...               ...            ...   \n",
       "991355    2.714241        0.246749          0.006826   1.499653e-04   \n",
       "991356    2.690845        0.336356          0.044471   0.000000e+00   \n",
       "991357    2.616012        0.261601          0.001455   9.536743e-07   \n",
       "991358    2.407005        0.300876          0.118417   2.529621e-04   \n",
       "991359    2.301176        0.230118          0.000402   0.000000e+00   \n",
       "\n",
       "        MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0           15.104457               0.115742       False          0.884848   \n",
       "1           15.141317               0.077129       False          1.514170   \n",
       "2           15.104759               0.080231       False          0.764706   \n",
       "3           15.113732               0.051438       False          1.568733   \n",
       "4           15.180108               0.096967       False          0.846939   \n",
       "...               ...                    ...         ...               ...   \n",
       "991355       2.305230               0.107039       False          1.454545   \n",
       "991356       2.416939               0.139166       False          1.500000   \n",
       "991357       2.266145               0.115439       False          1.400000   \n",
       "991358       1.893101               0.158933       False          1.250000   \n",
       "991359       1.998857               0.115125       False          1.200000   \n",
       "\n",
       "        TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0            False        0.0             0.532485              14901   \n",
       "1             True        0.0             0.570665              26388   \n",
       "2            False        0.0             0.658931              18247   \n",
       "3             True        0.0             0.682523              31415   \n",
       "4            False        0.0             0.678108              14902   \n",
       "...            ...        ...                  ...                ...   \n",
       "991355        True        0.0             0.000000               1888   \n",
       "991356        True        0.0             0.000000               6455   \n",
       "991357        True        0.0             0.000000               5268   \n",
       "991358       False        0.0             0.000000               1613   \n",
       "991359        True        0.0             0.000000               5705   \n",
       "\n",
       "        DIRECTIONS  PERIODICITY  VAL  TIME     MIN_POWER   MAX_POWER  \\\n",
       "0              1.0        False    0   0.0  5.678451e-07    0.056045   \n",
       "1              1.0        False    0   0.0  1.725275e-05    0.058853   \n",
       "2              1.0        False    0   0.0  4.167497e-07    0.087257   \n",
       "3              1.0        False    0   0.0  6.947349e-06    0.064438   \n",
       "4              1.0        False    0   0.0  5.969170e-07    0.083089   \n",
       "...            ...          ...  ...   ...           ...         ...   \n",
       "991355         1.0        False    0   0.0  1.036682e-01    0.220444   \n",
       "991356         1.0        False    0   0.0 -8.065506e+03  939.990021   \n",
       "991357         1.0        False    0   0.0  1.680560e-01    0.308658   \n",
       "991358         1.0        False    0   0.0  7.253678e-02    0.207564   \n",
       "991359         1.0        False    0   0.0  1.335771e-01    0.475899   \n",
       "\n",
       "        MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN   POWER_STD  POWER_MODE  \\\n",
       "0             0.214869        0.643393    0.006804    0.009128     0.00032   \n",
       "1             0.841265        0.330096    0.007971    0.006208     0.00469   \n",
       "2             0.141959        0.969803    0.003748    0.009796     0.00004   \n",
       "3             0.745905        0.861927    0.005450    0.006819     0.00259   \n",
       "4             0.772129        0.969405    0.005221    0.011536     0.00007   \n",
       "...                ...             ...         ...         ...         ...   \n",
       "991355        0.006667        0.847027    0.145477    0.034521     0.10367   \n",
       "991356        0.399232        0.802327   -1.117111  115.745464     0.15693   \n",
       "991357        0.397643        0.860139    0.220039    0.037392     0.17840   \n",
       "991358        0.926295        0.510684    0.133625    0.037330     0.09662   \n",
       "991359        0.006667        0.999603    0.217898    0.094434     0.13358   \n",
       "\n",
       "        SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0             34.020846        214.042556       5.808420e+04   \n",
       "1             39.854884        262.808003       5.089124e+04   \n",
       "2             18.740186        114.032099       1.507063e+05   \n",
       "3             27.249376        187.684249       1.127866e+05   \n",
       "4             26.102859        153.746379       1.024019e+05   \n",
       "...                 ...               ...                ...   \n",
       "991355       727.387232       1994.168554       1.026022e+04   \n",
       "991356     -5585.554388         -1.000000       2.357086e+07   \n",
       "991357      1100.193858       2380.650114       1.117312e+04   \n",
       "991358       668.124556       1903.065003       1.001464e+04   \n",
       "991359      1089.491950       2263.525018       1.673457e+04   \n",
       "\n",
       "        SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            13630.165637          0.321156           0.647004   \n",
       "1             9850.682690          0.329699           0.520761   \n",
       "2            24168.822670          0.968015           0.688742   \n",
       "3            19910.802176          0.860933           0.521975   \n",
       "4            19832.946195          0.861529           0.674352   \n",
       "...                   ...               ...                ...   \n",
       "991355        2686.423343          0.357115           0.549690   \n",
       "991356     -337596.929634          0.802327           0.217884   \n",
       "991357        2992.511286          0.360095           0.536066   \n",
       "991358        2156.966910          0.402808           0.541766   \n",
       "991359        6234.707529          0.431019           0.575636   \n",
       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0              0.257233        0.696817        8.236810       1.332151   \n",
       "1              0.288828        0.020163        7.383369       1.543607   \n",
       "2              0.249193        0.486452       23.280760       0.969427   \n",
       "3              0.281701        0.042755       11.823744       0.965827   \n",
       "4              0.258972        0.523305       15.915634       1.177049   \n",
       "...                 ...             ...             ...            ...   \n",
       "991355         0.279789        0.176361        1.515317       0.732219   \n",
       "991356        -1.000000       -1.000000     -841.447379   22842.921830   \n",
       "991357         0.283979        0.116189        1.402746       0.815883   \n",
       "991358         0.270578        0.158447        1.553336       0.760355   \n",
       "991359         0.277674        0.254942        2.184042       2.038912   \n",
       "\n",
       "        SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0                 0.428524              True   True           1  \n",
       "1                -0.511169              True   True           1  \n",
       "2                 0.827844              True   True           1  \n",
       "3                 0.116021              True   True           1  \n",
       "4                 0.197276              True   True           1  \n",
       "...                    ...               ...    ...         ...  \n",
       "991355            0.840360              True  False           0  \n",
       "991356            0.403095              True  False           0  \n",
       "991357            0.462496              True  False           0  \n",
       "991358           -0.415611              True  False           0  \n",
       "991359            0.992936              True  False           0  \n",
       "\n",
       "[991360 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hod: 302627\tclear: 688733\n"
     ]
    }
   ],
   "source": [
    "print(f\"hod: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace([np.inf], 0, inplace=True)\n",
    "df.replace([-np.inf], 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doh: 302627\tclear: 688733\n"
     ]
    }
   ],
   "source": [
    "print(f\"doh: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=100000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891360"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "623952.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_features)*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267408.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_features)*0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABEL_code</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</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>DIRECTIONS</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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9009.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>86.469648</td>\n",
       "      <td>7477.0</td>\n",
       "      <td>-0.137329</td>\n",
       "      <td>60.0</td>\n",
       "      <td>157.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>75.850568</td>\n",
       "      <td>62.0</td>\n",
       "      <td>54.385965</td>\n",
       "      <td>143.145326</td>\n",
       "      <td>0.578313</td>\n",
       "      <td>0.421687</td>\n",
       "      <td>0.624497</td>\n",
       "      <td>3.261260</td>\n",
       "      <td>3.282109</td>\n",
       "      <td>4.192889e-07</td>\n",
       "      <td>4.231219e-07</td>\n",
       "      <td>-0.113402</td>\n",
       "      <td>19.221764</td>\n",
       "      <td>2.343127</td>\n",
       "      <td>0.317710</td>\n",
       "      <td>0.552495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.669892</td>\n",
       "      <td>0.018051</td>\n",
       "      <td>0.532485</td>\n",
       "      <td>0.169912</td>\n",
       "      <td>0.901722</td>\n",
       "      <td>49.012083</td>\n",
       "      <td>40.423646</td>\n",
       "      <td>27.949549</td>\n",
       "      <td>55.803539</td>\n",
       "      <td>288.456537</td>\n",
       "      <td>1.748221</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>2.384186e-05</td>\n",
       "      <td>15.104457</td>\n",
       "      <td>0.115742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.884848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.532485</td>\n",
       "      <td>14901.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.678451e-07</td>\n",
       "      <td>0.056045</td>\n",
       "      <td>0.214869</td>\n",
       "      <td>0.643393</td>\n",
       "      <td>0.006804</td>\n",
       "      <td>0.009128</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>34.020846</td>\n",
       "      <td>214.042556</td>\n",
       "      <td>5.808420e+04</td>\n",
       "      <td>13630.165637</td>\n",
       "      <td>0.321156</td>\n",
       "      <td>0.647004</td>\n",
       "      <td>0.257233</td>\n",
       "      <td>0.696817</td>\n",
       "      <td>8.236810</td>\n",
       "      <td>1.332151</td>\n",
       "      <td>0.428524</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>210.409125</td>\n",
       "      <td>44272.0</td>\n",
       "      <td>0.197517</td>\n",
       "      <td>60.0</td>\n",
       "      <td>169.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3109.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>149.226330</td>\n",
       "      <td>87.0</td>\n",
       "      <td>61.702128</td>\n",
       "      <td>252.978435</td>\n",
       "      <td>0.669355</td>\n",
       "      <td>0.330645</td>\n",
       "      <td>0.384964</td>\n",
       "      <td>1.577878</td>\n",
       "      <td>11.433725</td>\n",
       "      <td>8.657343e-09</td>\n",
       "      <td>8.710113e-09</td>\n",
       "      <td>-0.486239</td>\n",
       "      <td>159.874916</td>\n",
       "      <td>2.951888</td>\n",
       "      <td>0.371111</td>\n",
       "      <td>0.512742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.751315</td>\n",
       "      <td>0.010699</td>\n",
       "      <td>0.570665</td>\n",
       "      <td>0.159045</td>\n",
       "      <td>0.890264</td>\n",
       "      <td>45.877504</td>\n",
       "      <td>40.408011</td>\n",
       "      <td>31.042470</td>\n",
       "      <td>52.690847</td>\n",
       "      <td>288.457000</td>\n",
       "      <td>1.167842</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>2.861023e-06</td>\n",
       "      <td>15.141317</td>\n",
       "      <td>0.077129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.514170</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.570665</td>\n",
       "      <td>26388.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.725275e-05</td>\n",
       "      <td>0.058853</td>\n",
       "      <td>0.841265</td>\n",
       "      <td>0.330096</td>\n",
       "      <td>0.007971</td>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.00469</td>\n",
       "      <td>39.854884</td>\n",
       "      <td>262.808003</td>\n",
       "      <td>5.089124e+04</td>\n",
       "      <td>9850.682690</td>\n",
       "      <td>0.329699</td>\n",
       "      <td>0.520761</td>\n",
       "      <td>0.288828</td>\n",
       "      <td>0.020163</td>\n",
       "      <td>7.383369</td>\n",
       "      <td>1.543607</td>\n",
       "      <td>-0.511169</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13795.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>122.715117</td>\n",
       "      <td>15059.0</td>\n",
       "      <td>0.023841</td>\n",
       "      <td>60.0</td>\n",
       "      <td>157.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>104.884716</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.974359</td>\n",
       "      <td>169.865034</td>\n",
       "      <td>0.606695</td>\n",
       "      <td>0.393305</td>\n",
       "      <td>0.464490</td>\n",
       "      <td>2.371346</td>\n",
       "      <td>5.258299</td>\n",
       "      <td>3.894362e-07</td>\n",
       "      <td>3.919001e-07</td>\n",
       "      <td>-0.175258</td>\n",
       "      <td>40.395304</td>\n",
       "      <td>2.239546</td>\n",
       "      <td>0.283456</td>\n",
       "      <td>0.505912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.662955</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.658931</td>\n",
       "      <td>0.133629</td>\n",
       "      <td>0.920839</td>\n",
       "      <td>38.541851</td>\n",
       "      <td>30.936951</td>\n",
       "      <td>12.428884</td>\n",
       "      <td>49.501112</td>\n",
       "      <td>288.424106</td>\n",
       "      <td>1.211866</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>1.883507e-05</td>\n",
       "      <td>15.104759</td>\n",
       "      <td>0.080231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658931</td>\n",
       "      <td>18247.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.167497e-07</td>\n",
       "      <td>0.087257</td>\n",
       "      <td>0.141959</td>\n",
       "      <td>0.969803</td>\n",
       "      <td>0.003748</td>\n",
       "      <td>0.009796</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>18.740186</td>\n",
       "      <td>114.032099</td>\n",
       "      <td>1.507063e+05</td>\n",
       "      <td>24168.822670</td>\n",
       "      <td>0.968015</td>\n",
       "      <td>0.688742</td>\n",
       "      <td>0.249193</td>\n",
       "      <td>0.486452</td>\n",
       "      <td>23.280760</td>\n",
       "      <td>0.969427</td>\n",
       "      <td>0.827844</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>29929.0</td>\n",
       "      <td>0.134426</td>\n",
       "      <td>60.0</td>\n",
       "      <td>169.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3109.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>131.060606</td>\n",
       "      <td>71.0</td>\n",
       "      <td>53.787879</td>\n",
       "      <td>217.774576</td>\n",
       "      <td>0.647849</td>\n",
       "      <td>0.352151</td>\n",
       "      <td>0.236994</td>\n",
       "      <td>1.763006</td>\n",
       "      <td>13.814363</td>\n",
       "      <td>1.116239e-07</td>\n",
       "      <td>1.120763e-07</td>\n",
       "      <td>-0.321101</td>\n",
       "      <td>235.902877</td>\n",
       "      <td>2.665128</td>\n",
       "      <td>0.312107</td>\n",
       "      <td>0.734491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.713619</td>\n",
       "      <td>0.004014</td>\n",
       "      <td>0.682523</td>\n",
       "      <td>0.124814</td>\n",
       "      <td>0.880879</td>\n",
       "      <td>35.999223</td>\n",
       "      <td>30.941632</td>\n",
       "      <td>12.478980</td>\n",
       "      <td>49.504474</td>\n",
       "      <td>288.423932</td>\n",
       "      <td>0.777423</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>1.907349e-06</td>\n",
       "      <td>15.113732</td>\n",
       "      <td>0.051438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.568733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682523</td>\n",
       "      <td>31415.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.947349e-06</td>\n",
       "      <td>0.064438</td>\n",
       "      <td>0.745905</td>\n",
       "      <td>0.861927</td>\n",
       "      <td>0.005450</td>\n",
       "      <td>0.006819</td>\n",
       "      <td>0.00259</td>\n",
       "      <td>27.249376</td>\n",
       "      <td>187.684249</td>\n",
       "      <td>1.127866e+05</td>\n",
       "      <td>19910.802176</td>\n",
       "      <td>0.860933</td>\n",
       "      <td>0.521975</td>\n",
       "      <td>0.281701</td>\n",
       "      <td>0.042755</td>\n",
       "      <td>11.823744</td>\n",
       "      <td>0.965827</td>\n",
       "      <td>0.116021</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>110.575766</td>\n",
       "      <td>12228.0</td>\n",
       "      <td>-0.040873</td>\n",
       "      <td>60.0</td>\n",
       "      <td>158.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>637.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>92.146472</td>\n",
       "      <td>69.0</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>163.634265</td>\n",
       "      <td>0.573604</td>\n",
       "      <td>0.426396</td>\n",
       "      <td>0.542614</td>\n",
       "      <td>2.713072</td>\n",
       "      <td>3.605593</td>\n",
       "      <td>6.457767e-07</td>\n",
       "      <td>6.507421e-07</td>\n",
       "      <td>-0.224490</td>\n",
       "      <td>17.918230</td>\n",
       "      <td>2.291669</td>\n",
       "      <td>0.300663</td>\n",
       "      <td>0.546144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.699507</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.678108</td>\n",
       "      <td>0.139856</td>\n",
       "      <td>0.897469</td>\n",
       "      <td>40.349411</td>\n",
       "      <td>34.032359</td>\n",
       "      <td>15.470319</td>\n",
       "      <td>46.401662</td>\n",
       "      <td>288.505911</td>\n",
       "      <td>1.471969</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>3.600121e-05</td>\n",
       "      <td>15.180108</td>\n",
       "      <td>0.096967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.846939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.678108</td>\n",
       "      <td>14902.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.969170e-07</td>\n",
       "      <td>0.083089</td>\n",
       "      <td>0.772129</td>\n",
       "      <td>0.969405</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.011536</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>26.102859</td>\n",
       "      <td>153.746379</td>\n",
       "      <td>1.024019e+05</td>\n",
       "      <td>19832.946195</td>\n",
       "      <td>0.861529</td>\n",
       "      <td>0.674352</td>\n",
       "      <td>0.258972</td>\n",
       "      <td>0.523305</td>\n",
       "      <td>15.915634</td>\n",
       "      <td>1.177049</td>\n",
       "      <td>0.197276</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>991355</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>174.462030</td>\n",
       "      <td>30437.0</td>\n",
       "      <td>0.052682</td>\n",
       "      <td>60.0</td>\n",
       "      <td>161.25</td>\n",
       "      <td>60.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>111.122312</td>\n",
       "      <td>124.0</td>\n",
       "      <td>78.980892</td>\n",
       "      <td>229.465684</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.555995</td>\n",
       "      <td>2.355813</td>\n",
       "      <td>1.250411</td>\n",
       "      <td>6.277345e-08</td>\n",
       "      <td>7.212120e-08</td>\n",
       "      <td>-0.916049</td>\n",
       "      <td>3.555866</td>\n",
       "      <td>1.947339</td>\n",
       "      <td>0.543196</td>\n",
       "      <td>0.529795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.697158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222636</td>\n",
       "      <td>0.696429</td>\n",
       "      <td>0.604286</td>\n",
       "      <td>0.205629</td>\n",
       "      <td>0.198467</td>\n",
       "      <td>0.305524</td>\n",
       "      <td>2.714241</td>\n",
       "      <td>0.246749</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>1.499653e-04</td>\n",
       "      <td>2.305230</td>\n",
       "      <td>0.107039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036682e-01</td>\n",
       "      <td>0.220444</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.847027</td>\n",
       "      <td>0.145477</td>\n",
       "      <td>0.034521</td>\n",
       "      <td>0.10367</td>\n",
       "      <td>727.387232</td>\n",
       "      <td>1994.168554</td>\n",
       "      <td>1.026022e+04</td>\n",
       "      <td>2686.423343</td>\n",
       "      <td>0.357115</td>\n",
       "      <td>0.549690</td>\n",
       "      <td>0.279789</td>\n",
       "      <td>0.176361</td>\n",
       "      <td>1.515317</td>\n",
       "      <td>0.732219</td>\n",
       "      <td>0.840360</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991356</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5915.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>634.621147</td>\n",
       "      <td>402744.0</td>\n",
       "      <td>-0.060948</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1236.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>88.510620</td>\n",
       "      <td>567.0</td>\n",
       "      <td>79.079498</td>\n",
       "      <td>934.025041</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.035263</td>\n",
       "      <td>2.595249</td>\n",
       "      <td>-0.225570</td>\n",
       "      <td>8.694485e-10</td>\n",
       "      <td>1.053931e-09</td>\n",
       "      <td>-0.117347</td>\n",
       "      <td>1.040113</td>\n",
       "      <td>2.419382</td>\n",
       "      <td>0.763230</td>\n",
       "      <td>0.644126</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.831270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.155874</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.419432</td>\n",
       "      <td>0.089800</td>\n",
       "      <td>0.089799</td>\n",
       "      <td>0.273100</td>\n",
       "      <td>2.690845</td>\n",
       "      <td>0.336356</td>\n",
       "      <td>0.044471</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.416939</td>\n",
       "      <td>0.139166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6455.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.065506e+03</td>\n",
       "      <td>939.990021</td>\n",
       "      <td>0.399232</td>\n",
       "      <td>0.802327</td>\n",
       "      <td>-1.117111</td>\n",
       "      <td>115.745464</td>\n",
       "      <td>0.15693</td>\n",
       "      <td>-5585.554388</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.357086e+07</td>\n",
       "      <td>-337596.929634</td>\n",
       "      <td>0.802327</td>\n",
       "      <td>0.217884</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-841.447379</td>\n",
       "      <td>22842.921830</td>\n",
       "      <td>0.403095</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991357</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4608.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>612.249132</td>\n",
       "      <td>374850.0</td>\n",
       "      <td>0.123136</td>\n",
       "      <td>60.0</td>\n",
       "      <td>762.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>128.085593</td>\n",
       "      <td>510.0</td>\n",
       "      <td>106.694561</td>\n",
       "      <td>755.067246</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.682729</td>\n",
       "      <td>2.116785</td>\n",
       "      <td>0.678100</td>\n",
       "      <td>3.961164e-09</td>\n",
       "      <td>4.616115e-09</td>\n",
       "      <td>-0.190883</td>\n",
       "      <td>1.811262</td>\n",
       "      <td>2.550341</td>\n",
       "      <td>0.737214</td>\n",
       "      <td>0.650127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.863059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145677</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.381092</td>\n",
       "      <td>0.206271</td>\n",
       "      <td>0.100230</td>\n",
       "      <td>0.207283</td>\n",
       "      <td>2.616012</td>\n",
       "      <td>0.261601</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>2.266145</td>\n",
       "      <td>0.115439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5268.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.680560e-01</td>\n",
       "      <td>0.308658</td>\n",
       "      <td>0.397643</td>\n",
       "      <td>0.860139</td>\n",
       "      <td>0.220039</td>\n",
       "      <td>0.037392</td>\n",
       "      <td>0.17840</td>\n",
       "      <td>1100.193858</td>\n",
       "      <td>2380.650114</td>\n",
       "      <td>1.117312e+04</td>\n",
       "      <td>2992.511286</td>\n",
       "      <td>0.360095</td>\n",
       "      <td>0.536066</td>\n",
       "      <td>0.283979</td>\n",
       "      <td>0.116189</td>\n",
       "      <td>1.402746</td>\n",
       "      <td>0.815883</td>\n",
       "      <td>0.462496</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991358</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>219.522208</td>\n",
       "      <td>48190.0</td>\n",
       "      <td>0.101681</td>\n",
       "      <td>60.0</td>\n",
       "      <td>124.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>122.638105</td>\n",
       "      <td>171.0</td>\n",
       "      <td>95.530726</td>\n",
       "      <td>273.781949</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.542086</td>\n",
       "      <td>2.172901</td>\n",
       "      <td>0.829533</td>\n",
       "      <td>2.100642e-08</td>\n",
       "      <td>2.546363e-08</td>\n",
       "      <td>-2.718750</td>\n",
       "      <td>2.184689</td>\n",
       "      <td>1.446617</td>\n",
       "      <td>0.456357</td>\n",
       "      <td>0.403785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.620162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285566</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.687360</td>\n",
       "      <td>0.272049</td>\n",
       "      <td>0.131318</td>\n",
       "      <td>0.408135</td>\n",
       "      <td>2.407005</td>\n",
       "      <td>0.300876</td>\n",
       "      <td>0.118417</td>\n",
       "      <td>2.529621e-04</td>\n",
       "      <td>1.893101</td>\n",
       "      <td>0.158933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.253678e-02</td>\n",
       "      <td>0.207564</td>\n",
       "      <td>0.926295</td>\n",
       "      <td>0.510684</td>\n",
       "      <td>0.133625</td>\n",
       "      <td>0.037330</td>\n",
       "      <td>0.09662</td>\n",
       "      <td>668.124556</td>\n",
       "      <td>1903.065003</td>\n",
       "      <td>1.001464e+04</td>\n",
       "      <td>2156.966910</td>\n",
       "      <td>0.402808</td>\n",
       "      <td>0.541766</td>\n",
       "      <td>0.270578</td>\n",
       "      <td>0.158447</td>\n",
       "      <td>1.553336</td>\n",
       "      <td>0.760355</td>\n",
       "      <td>-0.415611</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991359</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5045.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>655.968749</td>\n",
       "      <td>430296.0</td>\n",
       "      <td>0.117523</td>\n",
       "      <td>60.0</td>\n",
       "      <td>983.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>126.634894</td>\n",
       "      <td>545.0</td>\n",
       "      <td>105.212355</td>\n",
       "      <td>812.503259</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.698204</td>\n",
       "      <td>2.263827</td>\n",
       "      <td>0.572365</td>\n",
       "      <td>2.254529e-09</td>\n",
       "      <td>2.627300e-09</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>1.613399</td>\n",
       "      <td>1.971747</td>\n",
       "      <td>0.569963</td>\n",
       "      <td>0.648488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.738121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.239715</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.551626</td>\n",
       "      <td>0.132079</td>\n",
       "      <td>0.132011</td>\n",
       "      <td>0.277084</td>\n",
       "      <td>2.301176</td>\n",
       "      <td>0.230118</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.998857</td>\n",
       "      <td>0.115125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5705.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.335771e-01</td>\n",
       "      <td>0.475899</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999603</td>\n",
       "      <td>0.217898</td>\n",
       "      <td>0.094434</td>\n",
       "      <td>0.13358</td>\n",
       "      <td>1089.491950</td>\n",
       "      <td>2263.525018</td>\n",
       "      <td>1.673457e+04</td>\n",
       "      <td>6234.707529</td>\n",
       "      <td>0.431019</td>\n",
       "      <td>0.575636</td>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.254942</td>\n",
       "      <td>2.184042</td>\n",
       "      <td>2.038912</td>\n",
       "      <td>0.992936</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891360 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LABEL_code  PACKETS  PACKETS_REV    BYTES  BYTES_REV   MEAN  MEDIAN  \\\n",
       "0              1.0    166.0          0.0   9009.0        0.0  114.0    60.0   \n",
       "1              1.0    248.0          0.0  20108.0        0.0  141.0    91.0   \n",
       "2              1.0    239.0          0.0  13795.0        0.0  117.0    60.0   \n",
       "3              1.0    372.0          0.0  26999.0        0.0  132.0    91.0   \n",
       "4              1.0    197.0          0.0  11992.0        0.0  120.0    60.0   \n",
       "...            ...      ...          ...      ...        ...    ...     ...   \n",
       "991355         0.0     12.0          0.0   1168.0        0.0  157.0    60.0   \n",
       "991356         0.0      9.0          0.0   5915.0        0.0  717.0   504.0   \n",
       "991357         0.0     11.0          0.0   4608.0        0.0  478.0   138.0   \n",
       "991358         0.0      9.0          0.0   1073.0        0.0  179.0    60.0   \n",
       "991359         0.0     11.0          0.0   5045.0        0.0  518.0    69.0   \n",
       "\n",
       "             STDEV       VAR  BURSTINESS    Q1       Q3   MIN     MAX  \\\n",
       "0        86.469648    7477.0   -0.137329  60.0   157.00  60.0   650.0   \n",
       "1       210.409125   44272.0    0.197517  60.0   169.00  60.0  3109.0   \n",
       "2       122.715117   15059.0    0.023841  60.0   157.00  60.0  1271.0   \n",
       "3       173.000000   29929.0    0.134426  60.0   169.00  60.0  3109.0   \n",
       "4       110.575766   12228.0   -0.040873  60.0   158.00  60.0   697.0   \n",
       "...            ...       ...         ...   ...      ...   ...     ...   \n",
       "991355  174.462030   30437.0    0.052682  60.0   161.25  60.0   577.0   \n",
       "991356  634.621147  402744.0   -0.060948  60.0  1236.00  60.0  1520.0   \n",
       "991357  612.249132  374850.0    0.123136  60.0   762.00  60.0  1520.0   \n",
       "991358  219.522208   48190.0    0.101681  60.0   124.00  60.0   577.0   \n",
       "991359  655.968749  430296.0    0.117523  60.0   983.00  60.0  1520.0   \n",
       "\n",
       "        MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0               590.0  60.0                 75.850568                62.0   \n",
       "1              3049.0  60.0                149.226330                87.0   \n",
       "2              1211.0  60.0                104.884716                69.0   \n",
       "3              3049.0  91.0                131.060606                71.0   \n",
       "4               637.0  60.0                 92.146472                69.0   \n",
       "...               ...   ...                       ...                 ...   \n",
       "991355          517.0  60.0                111.122312               124.0   \n",
       "991356         1460.0  60.0                 88.510620               567.0   \n",
       "991357         1460.0  60.0                128.085593               510.0   \n",
       "991358          517.0  60.0                122.638105               171.0   \n",
       "991359         1460.0  60.0                126.634894               545.0   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               54.385965        143.145326            0.578313   \n",
       "1               61.702128        252.978435            0.669355   \n",
       "2               58.974359        169.865034            0.606695   \n",
       "3               53.787879        217.774576            0.647849   \n",
       "4               57.500000        163.634265            0.573604   \n",
       "...                   ...               ...                 ...   \n",
       "991355          78.980892        229.465684            0.750000   \n",
       "991356          79.079498        934.025041            0.555556   \n",
       "991357         106.694561        755.067246            0.727273   \n",
       "991358          95.530726        273.781949            0.777778   \n",
       "991359         105.212355        812.503259            0.727273   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.421687              0.624497              3.261260   \n",
       "1                 0.330645              0.384964              1.577878   \n",
       "2                 0.393305              0.464490              2.371346   \n",
       "3                 0.352151              0.236994              1.763006   \n",
       "4                 0.426396              0.542614              2.713072   \n",
       "...                    ...                   ...                   ...   \n",
       "991355            0.250000              0.555995              2.355813   \n",
       "991356            0.444444              1.035263              2.595249   \n",
       "991357            0.272727              0.682729              2.116785   \n",
       "991358            0.222222              0.542086              2.172901   \n",
       "991359            0.272727              0.698204              2.263827   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                   3.282109                4.192889e-07   \n",
       "1                  11.433725                8.657343e-09   \n",
       "2                   5.258299                3.894362e-07   \n",
       "3                  13.814363                1.116239e-07   \n",
       "4                   3.605593                6.457767e-07   \n",
       "...                      ...                         ...   \n",
       "991355              1.250411                6.277345e-08   \n",
       "991356             -0.225570                8.694485e-10   \n",
       "991357              0.678100                3.961164e-09   \n",
       "991358              0.829533                2.100642e-08   \n",
       "991359              0.572365                2.254529e-09   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS    KURTOSIS   ENTROPY  \\\n",
       "0                     4.231219e-07        -0.113402   19.221764  2.343127   \n",
       "1                     8.710113e-09        -0.486239  159.874916  2.951888   \n",
       "2                     3.919001e-07        -0.175258   40.395304  2.239546   \n",
       "3                     1.120763e-07        -0.321101  235.902877  2.665128   \n",
       "4                     6.507421e-07        -0.224490   17.918230  2.291669   \n",
       "...                            ...              ...         ...       ...   \n",
       "991355                7.212120e-08        -0.916049    3.555866  1.947339   \n",
       "991356                1.053931e-09        -0.117347    1.040113  2.419382   \n",
       "991357                4.616115e-09        -0.190883    1.811262  2.550341   \n",
       "991358                2.546363e-08        -2.718750    2.184689  1.446617   \n",
       "991359                2.627300e-09         0.007584    1.613399  1.971747   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.317710        0.552495                    0.0   0.669892   \n",
       "1             0.371111        0.512742                    0.0   0.751315   \n",
       "2             0.283456        0.505912                    0.0   0.662955   \n",
       "3             0.312107        0.734491                    0.0   0.713619   \n",
       "4             0.300663        0.546144                    0.0   0.699507   \n",
       "...                ...             ...                    ...        ...   \n",
       "991355        0.543196        0.529795                    0.0   0.697158   \n",
       "991356        0.763230        0.644126                    1.0   0.831270   \n",
       "991357        0.737214        0.650127                    0.0   0.863059   \n",
       "991358        0.456357        0.403785                    0.0   0.620162   \n",
       "991359        0.569963        0.648488                    0.0   0.738121   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                  0.018051          0.532485           0.169912   \n",
       "1                  0.010699          0.570665           0.159045   \n",
       "2                  0.004694          0.658931           0.133629   \n",
       "3                  0.004014          0.682523           0.124814   \n",
       "4                  0.004128          0.678108           0.139856   \n",
       "...                     ...               ...                ...   \n",
       "991355             0.000000          0.000000           0.222636   \n",
       "991356             0.000000          0.000000           0.155874   \n",
       "991357             0.000000          0.000000           0.145677   \n",
       "991358             0.000000          0.000000           0.285566   \n",
       "991359             0.000000          0.000000           0.239715   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.901722         49.012083           40.423646   \n",
       "1                       0.890264         45.877504           40.408011   \n",
       "2                       0.920839         38.541851           30.936951   \n",
       "3                       0.880879         35.999223           30.941632   \n",
       "4                       0.897469         40.349411           34.032359   \n",
       "...                          ...               ...                 ...   \n",
       "991355                  0.696429          0.604286            0.205629   \n",
       "991356                  0.375000          0.419432            0.089800   \n",
       "991357                  0.437500          0.381092            0.206271   \n",
       "991358                  0.750000          0.687360            0.272049   \n",
       "991359                  0.575000          0.551626            0.132079   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME    DURATION  MEAN_DIFFTIMES  \\\n",
       "0            27.949549       55.803539  288.456537        1.748221   \n",
       "1            31.042470       52.690847  288.457000        1.167842   \n",
       "2            12.428884       49.501112  288.424106        1.211866   \n",
       "3            12.478980       49.504474  288.423932        0.777423   \n",
       "4            15.470319       46.401662  288.505911        1.471969   \n",
       "...                ...             ...         ...             ...   \n",
       "991355        0.198467        0.305524    2.714241        0.246749   \n",
       "991356        0.089799        0.273100    2.690845        0.336356   \n",
       "991357        0.100230        0.207283    2.616012        0.261601   \n",
       "991358        0.131318        0.408135    2.407005        0.300876   \n",
       "991359        0.132011        0.277084    2.301176        0.230118   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0               0.001355   2.384186e-05      15.104457               0.115742   \n",
       "1               0.000199   2.861023e-06      15.141317               0.077129   \n",
       "2               0.000764   1.883507e-05      15.104759               0.080231   \n",
       "3               0.000084   1.907349e-06      15.113732               0.051438   \n",
       "4               0.000840   3.600121e-05      15.180108               0.096967   \n",
       "...                  ...            ...            ...                    ...   \n",
       "991355          0.006826   1.499653e-04       2.305230               0.107039   \n",
       "991356          0.044471   0.000000e+00       2.416939               0.139166   \n",
       "991357          0.001455   9.536743e-07       2.266145               0.115439   \n",
       "991358          0.118417   2.529621e-04       1.893101               0.158933   \n",
       "991359          0.000402   0.000000e+00       1.998857               0.115125   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  DIRECTIONS  TRANSIENTS  CNT_ZEROS  \\\n",
       "0              0.0          0.884848         1.0         0.0        0.0   \n",
       "1              0.0          1.514170         1.0         1.0        0.0   \n",
       "2              0.0          0.764706         1.0         0.0        0.0   \n",
       "3              0.0          1.568733         1.0         1.0        0.0   \n",
       "4              0.0          0.846939         1.0         0.0        0.0   \n",
       "...            ...               ...         ...         ...        ...   \n",
       "991355         0.0          1.454545         1.0         1.0        0.0   \n",
       "991356         0.0          1.500000         1.0         1.0        0.0   \n",
       "991357         0.0          1.400000         1.0         1.0        0.0   \n",
       "991358         0.0          1.250000         1.0         0.0        0.0   \n",
       "991359         0.0          1.200000         1.0         1.0        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  PERIODICITY  VAL  TIME  \\\n",
       "0                  0.532485            14901.0          0.0  0.0   0.0   \n",
       "1                  0.570665            26388.0          0.0  0.0   0.0   \n",
       "2                  0.658931            18247.0          0.0  0.0   0.0   \n",
       "3                  0.682523            31415.0          0.0  0.0   0.0   \n",
       "4                  0.678108            14902.0          0.0  0.0   0.0   \n",
       "...                     ...                ...          ...  ...   ...   \n",
       "991355             0.000000             1888.0          0.0  0.0   0.0   \n",
       "991356             0.000000             6455.0          0.0  0.0   0.0   \n",
       "991357             0.000000             5268.0          0.0  0.0   0.0   \n",
       "991358             0.000000             1613.0          0.0  0.0   0.0   \n",
       "991359             0.000000             5705.0          0.0  0.0   0.0   \n",
       "\n",
       "           MIN_POWER   MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  \\\n",
       "0       5.678451e-07    0.056045        0.214869        0.643393    0.006804   \n",
       "1       1.725275e-05    0.058853        0.841265        0.330096    0.007971   \n",
       "2       4.167497e-07    0.087257        0.141959        0.969803    0.003748   \n",
       "3       6.947349e-06    0.064438        0.745905        0.861927    0.005450   \n",
       "4       5.969170e-07    0.083089        0.772129        0.969405    0.005221   \n",
       "...              ...         ...             ...             ...         ...   \n",
       "991355  1.036682e-01    0.220444        0.006667        0.847027    0.145477   \n",
       "991356 -8.065506e+03  939.990021        0.399232        0.802327   -1.117111   \n",
       "991357  1.680560e-01    0.308658        0.397643        0.860139    0.220039   \n",
       "991358  7.253678e-02    0.207564        0.926295        0.510684    0.133625   \n",
       "991359  1.335771e-01    0.475899        0.006667        0.999603    0.217898   \n",
       "\n",
       "         POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0         0.009128     0.00032        34.020846        214.042556   \n",
       "1         0.006208     0.00469        39.854884        262.808003   \n",
       "2         0.009796     0.00004        18.740186        114.032099   \n",
       "3         0.006819     0.00259        27.249376        187.684249   \n",
       "4         0.011536     0.00007        26.102859        153.746379   \n",
       "...            ...         ...              ...               ...   \n",
       "991355    0.034521     0.10367       727.387232       1994.168554   \n",
       "991356  115.745464     0.15693     -5585.554388         -1.000000   \n",
       "991357    0.037392     0.17840      1100.193858       2380.650114   \n",
       "991358    0.037330     0.09662       668.124556       1903.065003   \n",
       "991359    0.094434     0.13358      1089.491950       2263.525018   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0            5.808420e+04       13630.165637          0.321156   \n",
       "1            5.089124e+04        9850.682690          0.329699   \n",
       "2            1.507063e+05       24168.822670          0.968015   \n",
       "3            1.127866e+05       19910.802176          0.860933   \n",
       "4            1.024019e+05       19832.946195          0.861529   \n",
       "...                   ...                ...               ...   \n",
       "991355       1.026022e+04        2686.423343          0.357115   \n",
       "991356       2.357086e+07     -337596.929634          0.802327   \n",
       "991357       1.117312e+04        2992.511286          0.360095   \n",
       "991358       1.001464e+04        2156.966910          0.402808   \n",
       "991359       1.673457e+04        6234.707529          0.431019   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0                0.647004         0.257233        0.696817        8.236810   \n",
       "1                0.520761         0.288828        0.020163        7.383369   \n",
       "2                0.688742         0.249193        0.486452       23.280760   \n",
       "3                0.521975         0.281701        0.042755       11.823744   \n",
       "4                0.674352         0.258972        0.523305       15.915634   \n",
       "...                   ...              ...             ...             ...   \n",
       "991355           0.549690         0.279789        0.176361        1.515317   \n",
       "991356           0.217884        -1.000000       -1.000000     -841.447379   \n",
       "991357           0.536066         0.283979        0.116189        1.402746   \n",
       "991358           0.541766         0.270578        0.158447        1.553336   \n",
       "991359           0.575636         0.277674        0.254942        2.184042   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  \n",
       "0            1.332151            0.428524               1.0  \n",
       "1            1.543607           -0.511169               1.0  \n",
       "2            0.969427            0.827844               1.0  \n",
       "3            0.965827            0.116021               1.0  \n",
       "4            1.177049            0.197276               1.0  \n",
       "...               ...                 ...               ...  \n",
       "991355       0.732219            0.840360               1.0  \n",
       "991356   22842.921830            0.403095               1.0  \n",
       "991357       0.815883            0.462496               1.0  \n",
       "991358       0.760355           -0.415611               1.0  \n",
       "991359       2.038912            0.992936               1.0  \n",
       "\n",
       "[891360 rows x 77 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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\", 50, 120, 20),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,90,1),\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', 100, 600, 100),\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [05:34<00:00,  3.35s/trial, best loss: -0.9958973797932281]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9833634335183691, 'gamma': 3.783047673499423, 'max_depth': 80.0, 'min_child_weight': 5.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6505004858745966}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42) # 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",
    "            random_state=42,\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.86 & 99.91 & 99.62 & 99.76 \\\\\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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "\n",
      "\n",
      "99.82295029743125\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.89 & 99.90 & 99.75 & 99.82 \\\\\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.90 & 99.91 & 99.76 & 99.84 \\\\\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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DoH_CIC\"\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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "f_def = feat_importances[default_faetures].sum() \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf, f_def]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \"Default\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3501379, 0.20885396, 0.06929518, 0.19694567, 0.1680329, 0.0067344187]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF            0.000000\n",
       "GALTON_SKEWNESS             0.000000\n",
       "BENFORD_LAW_PRESENTED       0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000000\n",
       "SPECTRAL_BANDWIDTH          0.000000\n",
       "                              ...   \n",
       "Q3                          0.048114\n",
       "POWER_MODE                  0.073138\n",
       "MIN_DIFFTIMES               0.076912\n",
       "MEDIAN                      0.082266\n",
       "SIG_SPACES                  0.101164\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'DIRECTIONS': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'BYTES_REV': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'PACKETS_REV': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0009570932015776634,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0011525291483849287,\n",
       " 'PACKETS': 0.0014948856551200151,\n",
       " 'SPECTRAL_CREST': 0.0015564670320600271,\n",
       " 'Q3_SCALED_TIME': 0.002080689650028944,\n",
       " 'SPECTRAL_SPREAD': 0.0023118439130485058,\n",
       " 'SPECTRAL_CENTROID': 0.002343512140214443,\n",
       " 'POWER_MEAN': 0.0023528942838311195,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.002365656429901719,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.002377285622060299,\n",
       " 'ROOT_MEAN_SQUARE': 0.002655280288308859,\n",
       " 'SPECTRAL_SLOPE': 0.002663404680788517,\n",
       " 'STDEV': 0.0028518997132778168,\n",
       " 'Q1': 0.0031339642591774464,\n",
       " 'BURSTINESS': 0.003254928393289447,\n",
       " 'P_BENFORD': 0.0033154089469462633,\n",
       " 'BIGGEST_CNT_1_SEC': 0.003659876761958003,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0041832830756902695,\n",
       " 'Q1_SCALED_TIME': 0.004675517790019512,\n",
       " 'PERCENT_DEVIATION': 0.004758875817060471,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.004829412791877985,\n",
       " 'SCALED_ENTROPY': 0.00509501900523901,\n",
       " 'BYTES': 0.005239532794803381,\n",
       " 'MODE': 0.005322854034602642,\n",
       " 'KURTOSIS': 0.006425015162676573,\n",
       " 'SPECTRAL_FLUX': 0.007068042177706957,\n",
       " 'SPECTRAL_ENERGY': 0.007075833156704903,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0075034466572105885,\n",
       " 'MIN_POWER_FREQ': 0.00789683312177658,\n",
       " 'MEAN_SCALED_TIME': 0.008089595474302769,\n",
       " 'TIME_DISTRIBUTION': 0.008376922458410263,\n",
       " 'MIN': 0.009353579953312874,\n",
       " 'MAX_POWER': 0.00991846527904272,\n",
       " 'MIN_POWER': 0.010038702748715878,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.010858070105314255,\n",
       " 'VAL': 0.01217921357601881,\n",
       " 'NORMAL_DISTRIBUTION': 0.013535870239138603,\n",
       " 'POWER_STD': 0.014090229757130146,\n",
       " 'ENTROPY': 0.01414267998188734,\n",
       " 'SPECTRAL_KURTOSIS': 0.014903824776411057,\n",
       " 'SPECTRAL_ROLLOFF': 0.015505684539675713,\n",
       " 'DURATION': 0.01596827618777752,\n",
       " 'MEDIAN_SCALED_TIME': 0.01791059412062168,\n",
       " 'SWITCHING_METRIC': 0.01948179490864277,\n",
       " 'MAX_DIFFTIMES': 0.021437348797917366,\n",
       " 'MEAN_DIFFTIMES': 0.02163267321884632,\n",
       " 'HURST_EXPONENT': 0.02178824134171009,\n",
       " 'CNT_DISTRIBUTION': 0.022278741002082825,\n",
       " 'SPECTRAL_ENTROPY': 0.025125009939074516,\n",
       " 'MAX': 0.025823304429650307,\n",
       " 'AVERAGE_DISPERSION': 0.027230100706219673,\n",
       " 'TRANSIENTS': 0.03154829144477844,\n",
       " 'MIN_MINUS_MAX': 0.03515636548399925,\n",
       " 'MEDIAN_DIFFTIMES': 0.03531762212514877,\n",
       " 'VAR': 0.046113237738609314,\n",
       " 'Q3': 0.04811427742242813,\n",
       " 'POWER_MODE': 0.07313782721757889,\n",
       " 'MIN_DIFFTIMES': 0.07691223174333572,\n",
       " 'MEDIAN': 0.08226623386144638,\n",
       " 'SIG_SPACES': 0.10116373002529144}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=10):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), 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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [04:47<00:00,  2.88s/trial, best loss: -0.982527249821986]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.21607465727366026, 'gamma': 2.7635835534533304, 'max_depth': 100.0, 'min_child_weight': 14.0, 'n_estimators': 300.0, 'reg_alpha': 1.0, 'reg_lambda': 0.9255959011571568}\n",
      "100%|██████████| 100/100 [03:37<00:00,  2.17s/trial, best loss: -0.9855263157894737]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.16612474531961913, 'gamma': 3.585627759961905, 'max_depth': 80.0, 'min_child_weight': 17.0, 'n_estimators': 200.0, 'reg_alpha': 16.0, 'reg_lambda': 0.5999371006815093}\n",
      "100%|██████████| 100/100 [02:49<00:00,  1.70s/trial, best loss: -0.9918921880135861]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3574925961771054, 'gamma': 3.879422348016748, 'max_depth': 80.0, 'min_child_weight': 18.0, 'n_estimators': 200.0, 'reg_alpha': 4.0, 'reg_lambda': 0.338868429875885}\n",
      "100%|██████████| 100/100 [05:05<00:00,  3.06s/trial, best loss: -0.990960575038118]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7785291445123907, 'gamma': 3.4162770960307256, 'max_depth': 80.0, 'min_child_weight': 2.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3388953077281458}\n",
      "100%|██████████| 100/100 [02:12<00:00,  1.32s/trial, best loss: -0.992459840454595]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.24133563546172354, 'gamma': 1.3860268748904403, 'max_depth': 80.0, 'min_child_weight': 8.0, 'n_estimators': 100.0, 'reg_alpha': 5.0, 'reg_lambda': 0.2661399772028715}\n",
      "100%|██████████| 100/100 [01:23<00:00,  1.19trial/s, best loss: -0.9716970578364471]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6512619513837679, 'gamma': 4.107087391436919, 'max_depth': 100.0, 'min_child_weight': 3.0, 'n_estimators': 500.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7426237568020082}\n",
      "100%|██████████| 100/100 [02:44<00:00,  1.65s/trial, best loss: -0.9886060077413727]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.0858650602089076, 'gamma': 3.7963208052680164, 'max_depth': 120.0, 'min_child_weight': 9.0, 'n_estimators': 600.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9794197177191257}\n",
      "100%|██████████| 100/100 [00:40<00:00,  2.47trial/s, best loss: -0.9007985803016859]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8221956187712027, 'gamma': 1.1766457791467693, 'max_depth': 120.0, 'min_child_weight': 3.0, 'n_estimators': 100.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8223730227615583}\n",
      "100%|██████████| 100/100 [02:09<00:00,  1.30s/trial, best loss: -0.9893327528028736]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7330960360834191, 'gamma': 4.061903927159342, 'max_depth': 60.0, 'min_child_weight': 1.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8118323049871361}\n",
      "100%|██████████| 100/100 [02:46<00:00,  1.67s/trial, best loss: -0.9852152009637498]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5068550970823504, 'gamma': 2.5702570903993776, 'max_depth': 60.0, 'min_child_weight': 4.0, 'n_estimators': 100.0, 'reg_alpha': 6.0, 'reg_lambda': 0.8240413054647582}\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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DoH_CIC\"\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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