vpn_vnat_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VPN detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of VPN\n",
    "\n",
    "Raw data from: VNAT [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "import lightgbm as lgb\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/vpn_vnat_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "       Unnamed: 0      DST_IP      SRC_IP  PACKETS  PACKETS_REV    BYTES  \\\n",
       "0               0  10.123.1.1  10.123.1.2    20340        49299  2733120   \n",
       "1               1  10.123.1.1  10.123.1.2    15616        35671  1995168   \n",
       "2               2  10.123.1.1  10.123.1.2    14389        34094  1806592   \n",
       "3               3  10.123.1.1  10.123.1.2    16712        39599  2089552   \n",
       "4               4  10.123.1.1  10.123.1.2    16558        37833  2033712   \n",
       "...           ...         ...         ...      ...          ...      ...   \n",
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       "\n",
       "       BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0       65450128  1.563293e+09  1.563293e+09      1195  ...   \n",
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       "...          ...           ...           ...       ...  ...   \n",
       "43395         59  1.563289e+09  1.563289e+09        53  ...   \n",
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       "\n",
       "       SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            6256.588624          0.020375           0.371542   \n",
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       "...                  ...               ...                ...   \n",
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       "\n",
       "       SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0             0.285401       -0.593465        4.118970       0.369593   \n",
       "1             0.282941        0.047512        6.678297       0.563229   \n",
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       "3             0.280458       -0.167617        5.556799       0.400651   \n",
       "4             0.288300        0.065759        5.049888       0.367567   \n",
       "...                ...             ...             ...            ...   \n",
       "43395              NaN             NaN             NaN            NaN   \n",
       "43396         0.148478             NaN    -1507.729344    3333.140856   \n",
       "43397              NaN             NaN     5691.469545    2379.736237   \n",
       "43398              NaN             NaN     6431.166398    1889.856018   \n",
       "43399              NaN             NaN             NaN            NaN   \n",
       "\n",
       "       SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0               -0.699704          0.993715   True  \n",
       "1               -0.777184          0.997639   True  \n",
       "2               -0.657587          0.998530   True  \n",
       "3               -0.395545          0.999731   True  \n",
       "4               -0.638117          0.999996   True  \n",
       "...                   ...               ...    ...  \n",
       "43395            0.000000               NaN  False  \n",
       "43396           -0.000199          0.003284  False  \n",
       "43397           -0.000199          0.004592  False  \n",
       "43398           -0.000199          0.005661  False  \n",
       "43399            0.000000               NaN  False  \n",
       "\n",
       "[43400 rows x 84 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
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    "    \"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",
    "    \"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",
    "    \"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",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>10.123.1.1</td>\n",
       "      <td>10.123.1.2</td>\n",
       "      <td>20340</td>\n",
       "      <td>49299</td>\n",
       "      <td>2733120</td>\n",
       "      <td>65450128</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1195</td>\n",
       "      <td>1195</td>\n",
       "      <td>1039</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>611.207821</td>\n",
       "      <td>373576</td>\n",
       "      <td>-0.259235</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1500</td>\n",
       "      <td>1360</td>\n",
       "      <td>1500</td>\n",
       "      <td>58.826547</td>\n",
       "      <td>578</td>\n",
       "      <td>55.630414</td>\n",
       "      <td>1205.525024</td>\n",
       "      <td>0.366619</td>\n",
       "      <td>0.633381</td>\n",
       "      <td>-0.754244</td>\n",
       "      <td>2.645581</td>\n",
       "      <td>-0.623882</td>\n",
       "      <td>4.192868e-10</td>\n",
       "      <td>4.192959e-10</td>\n",
       "      <td>-0.305723</td>\n",
       "      <td>1.448732</td>\n",
       "      <td>1.710696</td>\n",
       "      <td>0.106336</td>\n",
       "      <td>0.497854</td>\n",
       "      <td>False</td>\n",
       "      <td>0.547175</td>\n",
       "      <td>0.033936</td>\n",
       "      <td>0.457244</td>\n",
       "      <td>0.342489</td>\n",
       "      <td>0.973871</td>\n",
       "      <td>101.523006</td>\n",
       "      <td>58.287464</td>\n",
       "      <td>48.530557</td>\n",
       "      <td>159.588072</td>\n",
       "      <td>296.427280</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>-9.536743e-07</td>\n",
       "      <td>28.475746</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>True</td>\n",
       "      <td>0.739338</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.457244</td>\n",
       "      <td>38293912</td>\n",
       "      <td>0.292078</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.391264e-07</td>\n",
       "      <td>0.004747</td>\n",
       "      <td>0.860139</td>\n",
       "      <td>0.160435</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.00031</td>\n",
       "      <td>5.762327</td>\n",
       "      <td>53.506410</td>\n",
       "      <td>2.120952e+04</td>\n",
       "      <td>6256.588624</td>\n",
       "      <td>0.020375</td>\n",
       "      <td>0.371542</td>\n",
       "      <td>0.285401</td>\n",
       "      <td>-0.593465</td>\n",
       "      <td>4.118970</td>\n",
       "      <td>0.369593</td>\n",
       "      <td>-0.699704</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>10.123.1.1</td>\n",
       "      <td>10.123.1.2</td>\n",
       "      <td>15616</td>\n",
       "      <td>35671</td>\n",
       "      <td>1995168</td>\n",
       "      <td>47781200</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1195</td>\n",
       "      <td>1195</td>\n",
       "      <td>1030</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>615.832770</td>\n",
       "      <td>379250</td>\n",
       "      <td>-0.251646</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1500</td>\n",
       "      <td>1360</td>\n",
       "      <td>1500</td>\n",
       "      <td>59.789589</td>\n",
       "      <td>585</td>\n",
       "      <td>56.796117</td>\n",
       "      <td>1200.527766</td>\n",
       "      <td>0.369002</td>\n",
       "      <td>0.630998</td>\n",
       "      <td>-0.763194</td>\n",
       "      <td>2.581870</td>\n",
       "      <td>-0.588274</td>\n",
       "      <td>2.335721e-09</td>\n",
       "      <td>2.335789e-09</td>\n",
       "      <td>-0.292169</td>\n",
       "      <td>1.408053</td>\n",
       "      <td>1.638017</td>\n",
       "      <td>0.104690</td>\n",
       "      <td>0.501046</td>\n",
       "      <td>False</td>\n",
       "      <td>0.540783</td>\n",
       "      <td>0.476536</td>\n",
       "      <td>0.419301</td>\n",
       "      <td>0.437683</td>\n",
       "      <td>0.973569</td>\n",
       "      <td>131.171766</td>\n",
       "      <td>127.396369</td>\n",
       "      <td>29.031121</td>\n",
       "      <td>207.886434</td>\n",
       "      <td>299.695530</td>\n",
       "      <td>0.005844</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>-9.536743e-07</td>\n",
       "      <td>10.289603</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>False</td>\n",
       "      <td>0.738252</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.419301</td>\n",
       "      <td>17815924</td>\n",
       "      <td>0.304483</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.315279e-07</td>\n",
       "      <td>0.006404</td>\n",
       "      <td>0.909209</td>\n",
       "      <td>0.132025</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.001029</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>4.794477</td>\n",
       "      <td>44.764359</td>\n",
       "      <td>3.430100e+04</td>\n",
       "      <td>9371.830350</td>\n",
       "      <td>0.131231</td>\n",
       "      <td>0.497228</td>\n",
       "      <td>0.282941</td>\n",
       "      <td>0.047512</td>\n",
       "      <td>6.678297</td>\n",
       "      <td>0.563229</td>\n",
       "      <td>-0.777184</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>10.123.1.1</td>\n",
       "      <td>10.123.1.2</td>\n",
       "      <td>14389</td>\n",
       "      <td>34094</td>\n",
       "      <td>1806592</td>\n",
       "      <td>46132816</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1195</td>\n",
       "      <td>1195</td>\n",
       "      <td>1048</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>611.685377</td>\n",
       "      <td>374159</td>\n",
       "      <td>-0.262890</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1500</td>\n",
       "      <td>1360</td>\n",
       "      <td>1500</td>\n",
       "      <td>58.366925</td>\n",
       "      <td>576</td>\n",
       "      <td>54.961832</td>\n",
       "      <td>1214.128380</td>\n",
       "      <td>0.355424</td>\n",
       "      <td>0.644576</td>\n",
       "      <td>-0.738942</td>\n",
       "      <td>2.687656</td>\n",
       "      <td>-0.648382</td>\n",
       "      <td>3.442984e-09</td>\n",
       "      <td>3.443091e-09</td>\n",
       "      <td>-0.319277</td>\n",
       "      <td>1.484499</td>\n",
       "      <td>1.625543</td>\n",
       "      <td>0.104434</td>\n",
       "      <td>0.512041</td>\n",
       "      <td>False</td>\n",
       "      <td>0.541793</td>\n",
       "      <td>0.585190</td>\n",
       "      <td>0.629192</td>\n",
       "      <td>0.552158</td>\n",
       "      <td>0.974407</td>\n",
       "      <td>161.081312</td>\n",
       "      <td>163.015106</td>\n",
       "      <td>98.884294</td>\n",
       "      <td>243.510338</td>\n",
       "      <td>291.730501</td>\n",
       "      <td>0.006017</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>-9.536743e-07</td>\n",
       "      <td>10.383657</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>False</td>\n",
       "      <td>0.736232</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.629192</td>\n",
       "      <td>13804304</td>\n",
       "      <td>0.296784</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.634018e-08</td>\n",
       "      <td>0.005335</td>\n",
       "      <td>0.781864</td>\n",
       "      <td>0.124277</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.00050</td>\n",
       "      <td>4.797325</td>\n",
       "      <td>45.311590</td>\n",
       "      <td>2.111820e+04</td>\n",
       "      <td>6677.506001</td>\n",
       "      <td>0.123880</td>\n",
       "      <td>0.483015</td>\n",
       "      <td>0.284942</td>\n",
       "      <td>-0.101350</td>\n",
       "      <td>5.560733</td>\n",
       "      <td>0.590775</td>\n",
       "      <td>-0.657587</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>10.123.1.1</td>\n",
       "      <td>10.123.1.2</td>\n",
       "      <td>16712</td>\n",
       "      <td>39599</td>\n",
       "      <td>2089552</td>\n",
       "      <td>55411600</td>\n",
       "      <td>1.563293e+09</td>\n",
       "      <td>1.563294e+09</td>\n",
       "      <td>1195</td>\n",
       "      <td>1195</td>\n",
       "      <td>1081</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>607.953123</td>\n",
       "      <td>369607</td>\n",
       "      <td>-0.280083</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1500</td>\n",
       "      <td>1360</td>\n",
       "      <td>1500</td>\n",
       "      <td>56.239882</td>\n",
       "      <td>563</td>\n",
       "      <td>52.081406</td>\n",
       "      <td>1240.344396</td>\n",
       "      <td>0.324590</td>\n",
       "      <td>0.675410</td>\n",
       "      <td>-0.689198</td>\n",
       "      <td>2.866997</td>\n",
       "      <td>-0.786883</td>\n",
       "      <td>6.022939e-10</td>\n",
       "      <td>6.023099e-10</td>\n",
       "      <td>-0.368976</td>\n",
       "      <td>1.650572</td>\n",
       "      <td>1.328474</td>\n",
       "      <td>0.084181</td>\n",
       "      <td>0.496996</td>\n",
       "      <td>False</td>\n",
       "      <td>0.512558</td>\n",
       "      <td>0.907419</td>\n",
       "      <td>0.643604</td>\n",
       "      <td>0.556384</td>\n",
       "      <td>0.976422</td>\n",
       "      <td>163.392884</td>\n",
       "      <td>165.215944</td>\n",
       "      <td>101.826513</td>\n",
       "      <td>245.729782</td>\n",
       "      <td>293.669508</td>\n",
       "      <td>0.005215</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>-1.192093e-06</td>\n",
       "      <td>10.865248</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>False</td>\n",
       "      <td>0.769135</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.643604</td>\n",
       "      <td>17049160</td>\n",
       "      <td>0.296780</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.009582e-07</td>\n",
       "      <td>0.005148</td>\n",
       "      <td>0.458633</td>\n",
       "      <td>0.063088</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>4.632428</td>\n",
       "      <td>44.445726</td>\n",
       "      <td>2.632063e+04</td>\n",
       "      <td>7216.671906</td>\n",
       "      <td>0.062889</td>\n",
       "      <td>0.466304</td>\n",
       "      <td>0.280458</td>\n",
       "      <td>-0.167617</td>\n",
       "      <td>5.556799</td>\n",
       "      <td>0.400651</td>\n",
       "      <td>-0.395545</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>10.123.1.1</td>\n",
       "      <td>10.123.1.2</td>\n",
       "      <td>16558</td>\n",
       "      <td>37833</td>\n",
       "      <td>2033712</td>\n",
       "      <td>51659328</td>\n",
       "      <td>1.563294e+09</td>\n",
       "      <td>1.563294e+09</td>\n",
       "      <td>1195</td>\n",
       "      <td>1195</td>\n",
       "      <td>1047</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>614.565700</td>\n",
       "      <td>377691</td>\n",
       "      <td>-0.260257</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>140</td>\n",
       "      <td>1500</td>\n",
       "      <td>1360</td>\n",
       "      <td>1500</td>\n",
       "      <td>58.697775</td>\n",
       "      <td>579</td>\n",
       "      <td>55.300860</td>\n",
       "      <td>1214.184918</td>\n",
       "      <td>0.354857</td>\n",
       "      <td>0.645143</td>\n",
       "      <td>-0.737106</td>\n",
       "      <td>2.670178</td>\n",
       "      <td>-0.657892</td>\n",
       "      <td>7.226128e-10</td>\n",
       "      <td>7.226328e-10</td>\n",
       "      <td>-0.317771</td>\n",
       "      <td>1.478362</td>\n",
       "      <td>1.533098</td>\n",
       "      <td>0.097457</td>\n",
       "      <td>0.516165</td>\n",
       "      <td>False</td>\n",
       "      <td>0.528030</td>\n",
       "      <td>0.348421</td>\n",
       "      <td>0.577223</td>\n",
       "      <td>0.435664</td>\n",
       "      <td>0.974506</td>\n",
       "      <td>128.586145</td>\n",
       "      <td>86.481691</td>\n",
       "      <td>55.056774</td>\n",
       "      <td>230.865272</td>\n",
       "      <td>295.150082</td>\n",
       "      <td>0.005427</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>-3.099442e-06</td>\n",
       "      <td>11.857728</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>False</td>\n",
       "      <td>0.694760</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.577223</td>\n",
       "      <td>20698796</td>\n",
       "      <td>0.304425</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.216495e-08</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.701007</td>\n",
       "      <td>0.062889</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.00021</td>\n",
       "      <td>2.924631</td>\n",
       "      <td>30.041069</td>\n",
       "      <td>2.367935e+04</td>\n",
       "      <td>6541.407987</td>\n",
       "      <td>0.061697</td>\n",
       "      <td>0.504052</td>\n",
       "      <td>0.288300</td>\n",
       "      <td>0.065759</td>\n",
       "      <td>5.049888</td>\n",
       "      <td>0.367567</td>\n",
       "      <td>-0.638117</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>43395</th>\n",
       "      <td>41922</td>\n",
       "      <td>10.124.1.2</td>\n",
       "      <td>10.122.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>43629</td>\n",
       "      <td>119</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>2.138615e-04</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>238</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43396</th>\n",
       "      <td>41923</td>\n",
       "      <td>10.124.1.2</td>\n",
       "      <td>10.122.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>75</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>18920</td>\n",
       "      <td>127</td>\n",
       "      <td>127.0</td>\n",
       "      <td>11.313708</td>\n",
       "      <td>128</td>\n",
       "      <td>-0.836405</td>\n",
       "      <td>123.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>119</td>\n",
       "      <td>135</td>\n",
       "      <td>16</td>\n",
       "      <td>119</td>\n",
       "      <td>8.908432</td>\n",
       "      <td>8</td>\n",
       "      <td>6.299213</td>\n",
       "      <td>127.251719</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>22.450640</td>\n",
       "      <td>-16.837980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563329</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>7.801056e-04</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>254</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.328084e+03</td>\n",
       "      <td>339.002102</td>\n",
       "      <td>0.118913</td>\n",
       "      <td>0.118715</td>\n",
       "      <td>-0.224843</td>\n",
       "      <td>20.120869</td>\n",
       "      <td>-0.03793</td>\n",
       "      <td>-1124.214048</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.906996e+07</td>\n",
       "      <td>-283738.615816</td>\n",
       "      <td>0.118715</td>\n",
       "      <td>0.272398</td>\n",
       "      <td>0.148478</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1507.729344</td>\n",
       "      <td>3333.140856</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43397</th>\n",
       "      <td>41924</td>\n",
       "      <td>10.124.1.2</td>\n",
       "      <td>10.122.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>75</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>20509</td>\n",
       "      <td>127</td>\n",
       "      <td>127.0</td>\n",
       "      <td>11.313708</td>\n",
       "      <td>128</td>\n",
       "      <td>-0.836405</td>\n",
       "      <td>123.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>119</td>\n",
       "      <td>135</td>\n",
       "      <td>16</td>\n",
       "      <td>119</td>\n",
       "      <td>8.908432</td>\n",
       "      <td>8</td>\n",
       "      <td>6.299213</td>\n",
       "      <td>127.251719</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>22.450640</td>\n",
       "      <td>-16.837980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563329</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>4.210472e-04</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>254</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.482656e+02</td>\n",
       "      <td>942.110978</td>\n",
       "      <td>0.090305</td>\n",
       "      <td>0.090107</td>\n",
       "      <td>0.165530</td>\n",
       "      <td>14.327156</td>\n",
       "      <td>-0.02007</td>\n",
       "      <td>827.651778</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.879716e+07</td>\n",
       "      <td>280164.200134</td>\n",
       "      <td>0.090107</td>\n",
       "      <td>-0.033354</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>5691.469545</td>\n",
       "      <td>2379.736237</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43398</th>\n",
       "      <td>41925</td>\n",
       "      <td>10.124.1.2</td>\n",
       "      <td>10.122.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>75</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>5279</td>\n",
       "      <td>127</td>\n",
       "      <td>127.0</td>\n",
       "      <td>11.313708</td>\n",
       "      <td>128</td>\n",
       "      <td>-0.836405</td>\n",
       "      <td>123.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>119</td>\n",
       "      <td>135</td>\n",
       "      <td>16</td>\n",
       "      <td>119</td>\n",
       "      <td>8.908432</td>\n",
       "      <td>8</td>\n",
       "      <td>6.299213</td>\n",
       "      <td>127.251719</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>22.450640</td>\n",
       "      <td>-16.837980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563329</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>3.838539e-04</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>254</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.578179e+02</td>\n",
       "      <td>687.617836</td>\n",
       "      <td>0.086531</td>\n",
       "      <td>0.086332</td>\n",
       "      <td>0.106920</td>\n",
       "      <td>11.026202</td>\n",
       "      <td>-0.01826</td>\n",
       "      <td>534.491156</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.546277e+07</td>\n",
       "      <td>231326.137416</td>\n",
       "      <td>0.086332</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>6431.166398</td>\n",
       "      <td>1889.856018</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43399</th>\n",
       "      <td>41926</td>\n",
       "      <td>10.124.1.2</td>\n",
       "      <td>10.122.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>1.563289e+09</td>\n",
       "      <td>53</td>\n",
       "      <td>63585</td>\n",
       "      <td>119</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>3.499985e-04</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>238</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43400 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0      DST_IP      SRC_IP  PACKETS  PACKETS_REV    BYTES  \\\n",
       "0               0  10.123.1.1  10.123.1.2    20340        49299  2733120   \n",
       "1               1  10.123.1.1  10.123.1.2    15616        35671  1995168   \n",
       "2               2  10.123.1.1  10.123.1.2    14389        34094  1806592   \n",
       "3               3  10.123.1.1  10.123.1.2    16712        39599  2089552   \n",
       "4               4  10.123.1.1  10.123.1.2    16558        37833  2033712   \n",
       "...           ...         ...         ...      ...          ...      ...   \n",
       "43395       41922  10.124.1.2  10.122.1.2        1            1       59   \n",
       "43396       41923  10.124.1.2  10.122.1.2        1            1       59   \n",
       "43397       41924  10.124.1.2  10.122.1.2        1            1       59   \n",
       "43398       41925  10.124.1.2  10.122.1.2        1            1       59   \n",
       "43399       41926  10.124.1.2  10.122.1.2        1            1       59   \n",
       "\n",
       "       BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  MEAN  \\\n",
       "0       65450128  1.563293e+09  1.563293e+09      1195      1195  1039   \n",
       "1       47781200  1.563293e+09  1.563293e+09      1195      1195  1030   \n",
       "2       46132816  1.563293e+09  1.563293e+09      1195      1195  1048   \n",
       "3       55411600  1.563293e+09  1.563294e+09      1195      1195  1081   \n",
       "4       51659328  1.563294e+09  1.563294e+09      1195      1195  1047   \n",
       "...          ...           ...           ...       ...       ...   ...   \n",
       "43395         59  1.563289e+09  1.563289e+09        53     43629   119   \n",
       "43396         75  1.563289e+09  1.563289e+09        53     18920   127   \n",
       "43397         75  1.563289e+09  1.563289e+09        53     20509   127   \n",
       "43398         75  1.563289e+09  1.563289e+09        53      5279   127   \n",
       "43399         59  1.563289e+09  1.563289e+09        53     63585   119   \n",
       "\n",
       "       MEDIAN       STDEV     VAR  BURSTINESS     Q1      Q3  MIN   MAX  \\\n",
       "0      1500.0  611.207821  373576   -0.259235  172.0  1500.0  140  1500   \n",
       "1      1500.0  615.832770  379250   -0.251646  172.0  1500.0  140  1500   \n",
       "2      1500.0  611.685377  374159   -0.262890  172.0  1500.0  140  1500   \n",
       "3      1500.0  607.953123  369607   -0.280083  172.0  1500.0  140  1500   \n",
       "4      1500.0  614.565700  377691   -0.260257  172.0  1500.0  140  1500   \n",
       "...       ...         ...     ...         ...    ...     ...  ...   ...   \n",
       "43395   119.0    0.000000       0   -1.000000  119.0   119.0  119   119   \n",
       "43396   127.0   11.313708     128   -0.836405  123.0   131.0  119   135   \n",
       "43397   127.0   11.313708     128   -0.836405  123.0   131.0  119   135   \n",
       "43398   127.0   11.313708     128   -0.836405  123.0   131.0  119   135   \n",
       "43399   119.0    0.000000       0   -1.000000  119.0   119.0  119   119   \n",
       "\n",
       "       MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0               1360  1500                 58.826547                 578   \n",
       "1               1360  1500                 59.789589                 585   \n",
       "2               1360  1500                 58.366925                 576   \n",
       "3               1360  1500                 56.239882                 563   \n",
       "4               1360  1500                 58.697775                 579   \n",
       "...              ...   ...                       ...                 ...   \n",
       "43395              0   119                  0.000000                   0   \n",
       "43396             16   119                  8.908432                   8   \n",
       "43397             16   119                  8.908432                   8   \n",
       "43398             16   119                  8.908432                   8   \n",
       "43399              0   119                  0.000000                   0   \n",
       "\n",
       "       PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0              55.630414       1205.525024            0.366619   \n",
       "1              56.796117       1200.527766            0.369002   \n",
       "2              54.961832       1214.128380            0.355424   \n",
       "3              52.081406       1240.344396            0.324590   \n",
       "4              55.300860       1214.184918            0.354857   \n",
       "...                  ...               ...                 ...   \n",
       "43395           0.000000        119.000000            0.000000   \n",
       "43396           6.299213        127.251719            0.500000   \n",
       "43397           6.299213        127.251719            0.500000   \n",
       "43398           6.299213        127.251719            0.500000   \n",
       "43399           0.000000        119.000000            0.000000   \n",
       "\n",
       "       PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                0.633381             -0.754244              2.645581   \n",
       "1                0.630998             -0.763194              2.581870   \n",
       "2                0.644576             -0.738942              2.687656   \n",
       "3                0.675410             -0.689198              2.866997   \n",
       "4                0.645143             -0.737106              2.670178   \n",
       "...                   ...                   ...                   ...   \n",
       "43395            0.000000              0.000000              0.000000   \n",
       "43396            0.500000              0.707107             22.450640   \n",
       "43397            0.500000              0.707107             22.450640   \n",
       "43398            0.500000              0.707107             22.450640   \n",
       "43399            0.000000              0.000000              0.000000   \n",
       "\n",
       "       FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                 -0.623882                4.192868e-10   \n",
       "1                 -0.588274                2.335721e-09   \n",
       "2                 -0.648382                3.442984e-09   \n",
       "3                 -0.786883                6.022939e-10   \n",
       "4                 -0.657892                7.226128e-10   \n",
       "...                     ...                         ...   \n",
       "43395              0.000000                0.000000e+00   \n",
       "43396            -16.837980                0.000000e+00   \n",
       "43397            -16.837980                0.000000e+00   \n",
       "43398            -16.837980                0.000000e+00   \n",
       "43399              0.000000                0.000000e+00   \n",
       "\n",
       "       FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                    4.192959e-10        -0.305723  1.448732  1.710696   \n",
       "1                    2.335789e-09        -0.292169  1.408053  1.638017   \n",
       "2                    3.443091e-09        -0.319277  1.484499  1.625543   \n",
       "3                    6.023099e-10        -0.368976  1.650572  1.328474   \n",
       "4                    7.226328e-10        -0.317771  1.478362  1.533098   \n",
       "...                           ...              ...       ...       ...   \n",
       "43395                0.000000e+00         0.000000  0.000000  0.000000   \n",
       "43396                0.000000e+00         0.000000  0.250000  1.000000   \n",
       "43397                0.000000e+00         0.000000  0.250000  1.000000   \n",
       "43398                0.000000e+00         0.000000  0.250000  1.000000   \n",
       "43399                0.000000e+00         0.000000  0.000000  0.000000   \n",
       "\n",
       "       SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0            0.106336        0.497854                  False   0.547175   \n",
       "1            0.104690        0.501046                  False   0.540783   \n",
       "2            0.104434        0.512041                  False   0.541793   \n",
       "3            0.084181        0.496996                  False   0.512558   \n",
       "4            0.097457        0.516165                  False   0.528030   \n",
       "...               ...             ...                    ...        ...   \n",
       "43395        0.000000        1.000000                  False   0.301030   \n",
       "43396        1.000000        0.563329                  False   0.477121   \n",
       "43397        1.000000        0.563329                  False   0.477121   \n",
       "43398        1.000000        0.563329                  False   0.477121   \n",
       "43399        0.000000        1.000000                  False   0.301030   \n",
       "\n",
       "       NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                 0.033936          0.457244           0.342489   \n",
       "1                 0.476536          0.419301           0.437683   \n",
       "2                 0.585190          0.629192           0.552158   \n",
       "3                 0.907419          0.643604           0.556384   \n",
       "4                 0.348421          0.577223           0.435664   \n",
       "...                    ...               ...                ...   \n",
       "43395             0.000000          0.000000           0.500000   \n",
       "43396             0.000000          0.000000           0.500000   \n",
       "43397             0.000000          0.000000           0.500000   \n",
       "43398             0.000000          0.000000           0.500000   \n",
       "43399             0.000000          0.000000           0.500000   \n",
       "\n",
       "       AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                      0.973871        101.523006           58.287464   \n",
       "1                      0.973569        131.171766          127.396369   \n",
       "2                      0.974407        161.081312          163.015106   \n",
       "3                      0.976422        163.392884          165.215944   \n",
       "4                      0.974506        128.586145           86.481691   \n",
       "...                         ...               ...                 ...   \n",
       "43395                  0.875000          0.000107            0.000214   \n",
       "43396                  0.660714          0.000390            0.000780   \n",
       "43397                  0.660714          0.000211            0.000421   \n",
       "43398                  0.660714          0.000192            0.000384   \n",
       "43399                  0.875000          0.000175            0.000350   \n",
       "\n",
       "       Q1_SCALED_TIME  Q3_SCALED_TIME    DURATION  MEAN_DIFFTIMES  \\\n",
       "0           48.530557      159.588072  296.427280        0.004257   \n",
       "1           29.031121      207.886434  299.695530        0.005844   \n",
       "2           98.884294      243.510338  291.730501        0.006017   \n",
       "3          101.826513      245.729782  293.669508        0.005215   \n",
       "4           55.056774      230.865272  295.150082        0.005427   \n",
       "...               ...             ...         ...             ...   \n",
       "43395        0.000000        0.000214    0.000214        0.000214   \n",
       "43396        0.000000        0.000780    0.000780        0.000780   \n",
       "43397        0.000000        0.000421    0.000421        0.000421   \n",
       "43398        0.000000        0.000384    0.000384        0.000384   \n",
       "43399        0.000000        0.000350    0.000350        0.000350   \n",
       "\n",
       "       MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0              0.000035  -9.536743e-07      28.475746               0.000149   \n",
       "1              0.000037  -9.536743e-07      10.289603               0.000568   \n",
       "2              0.000035  -9.536743e-07      10.383657               0.000579   \n",
       "3              0.000040  -1.192093e-06      10.865248               0.000480   \n",
       "4              0.000033  -3.099442e-06      11.857728               0.000458   \n",
       "...                 ...            ...            ...                    ...   \n",
       "43395          0.000214   2.138615e-04       0.000214               1.000000   \n",
       "43396          0.000780   7.801056e-04       0.000780               1.000000   \n",
       "43397          0.000421   4.210472e-04       0.000421               1.000000   \n",
       "43398          0.000384   3.838539e-04       0.000384               1.000000   \n",
       "43399          0.000350   3.499985e-04       0.000350               1.000000   \n",
       "\n",
       "       SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0            True          0.739338       False        0.0   \n",
       "1           False          0.738252       False        0.0   \n",
       "2           False          0.736232       False        0.0   \n",
       "3           False          0.769135       False        0.0   \n",
       "4           False          0.694760       False        0.0   \n",
       "...           ...               ...         ...        ...   \n",
       "43395       False          0.000000       False        0.0   \n",
       "43396       False          2.000000       False        0.0   \n",
       "43397       False          2.000000       False        0.0   \n",
       "43398       False          2.000000       False        0.0   \n",
       "43399       False          0.000000       False        0.0   \n",
       "\n",
       "       CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                 0.457244           38293912    0.292078        False    0   \n",
       "1                 0.419301           17815924    0.304483        False    0   \n",
       "2                 0.629192           13804304    0.296784        False    0   \n",
       "3                 0.643604           17049160    0.296780        False    0   \n",
       "4                 0.577223           20698796    0.304425        False    0   \n",
       "...                    ...                ...         ...          ...  ...   \n",
       "43395             0.000000                238    0.500000        False    0   \n",
       "43396             0.000000                254    0.500000        False    0   \n",
       "43397             0.000000                254    0.500000        False    0   \n",
       "43398             0.000000                254    0.500000        False    0   \n",
       "43399             0.000000                238    0.500000        False    0   \n",
       "\n",
       "       TIME     MIN_POWER   MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0       0.0  2.391264e-07    0.004747        0.860139        0.160435   \n",
       "1       0.0  3.315279e-07    0.006404        0.909209        0.132025   \n",
       "2       0.0  7.634018e-08    0.005335        0.781864        0.124277   \n",
       "3       0.0  1.009582e-07    0.005148        0.458633        0.063088   \n",
       "4       0.0  6.216495e-08    0.002954        0.701007        0.062889   \n",
       "...     ...           ...         ...             ...             ...   \n",
       "43395   0.0 -1.000000e+00   -1.000000       -1.000000       -1.000000   \n",
       "43396   0.0 -1.328084e+03  339.002102        0.118913        0.118715   \n",
       "43397   0.0 -2.482656e+02  942.110978        0.090305        0.090107   \n",
       "43398   0.0 -2.578179e+02  687.617836        0.086531        0.086332   \n",
       "43399   0.0 -1.000000e+00   -1.000000       -1.000000       -1.000000   \n",
       "\n",
       "       POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0        0.001152   0.000968     0.00031         5.762327         53.506410   \n",
       "1        0.000959   0.001029     0.00002         4.794477         44.764359   \n",
       "2        0.000959   0.000904     0.00050         4.797325         45.311590   \n",
       "3        0.000926   0.000791     0.00002         4.632428         44.445726   \n",
       "4        0.000585   0.000489     0.00021         2.924631         30.041069   \n",
       "...           ...        ...         ...              ...               ...   \n",
       "43395         NaN        NaN     0.00000        -1.000000         -1.000000   \n",
       "43396   -0.224843  20.120869    -0.03793     -1124.214048         -1.000000   \n",
       "43397    0.165530  14.327156    -0.02007       827.651778         -1.000000   \n",
       "43398    0.106920  11.026202    -0.01826       534.491156         -1.000000   \n",
       "43399         NaN        NaN     0.00000        -1.000000         -1.000000   \n",
       "\n",
       "       SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0           2.120952e+04        6256.588624          0.020375   \n",
       "1           3.430100e+04        9371.830350          0.131231   \n",
       "2           2.111820e+04        6677.506001          0.123880   \n",
       "3           2.632063e+04        7216.671906          0.062889   \n",
       "4           2.367935e+04        6541.407987          0.061697   \n",
       "...                  ...                ...               ...   \n",
       "43395      -1.000000e+00          -1.000000          0.006667   \n",
       "43396       1.906996e+07     -283738.615816          0.118715   \n",
       "43397       1.879716e+07      280164.200134          0.090107   \n",
       "43398       1.546277e+07      231326.137416          0.086332   \n",
       "43399      -1.000000e+00          -1.000000          0.006667   \n",
       "\n",
       "       SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0               0.371542         0.285401       -0.593465        4.118970   \n",
       "1               0.497228         0.282941        0.047512        6.678297   \n",
       "2               0.483015         0.284942       -0.101350        5.560733   \n",
       "3               0.466304         0.280458       -0.167617        5.556799   \n",
       "4               0.504052         0.288300        0.065759        5.049888   \n",
       "...                  ...              ...             ...             ...   \n",
       "43395          -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "43396           0.272398         0.148478       -1.000000    -1507.729344   \n",
       "43397          -0.033354        -1.000000       -1.000000     5691.469545   \n",
       "43398          -1.000000        -1.000000       -1.000000     6431.166398   \n",
       "43399          -1.000000        -1.000000       -1.000000       -1.000000   \n",
       "\n",
       "       SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0           0.369593           -0.699704              True   True           1  \n",
       "1           0.563229           -0.777184              True   True           1  \n",
       "2           0.590775           -0.657587              True   True           1  \n",
       "3           0.400651           -0.395545              True   True           1  \n",
       "4           0.367567           -0.638117              True   True           1  \n",
       "...              ...                 ...               ...    ...         ...  \n",
       "43395      -1.000000            0.000000             False  False           0  \n",
       "43396    3333.140856           -0.000199              True  False           0  \n",
       "43397    2379.736237           -0.000199              True  False           0  \n",
       "43398    1889.856018           -0.000199              True  False           0  \n",
       "43399      -1.000000            0.000000             False  False           0  \n",
       "\n",
       "[43400 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": [
      "vpn: 1473\tclear: 41927\n"
     ]
    }
   ],
   "source": [
    "print(f\"vpn: {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": [
    "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43400, 74)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vpn: 1473\tclear: 41927\n"
     ]
    }
   ],
   "source": [
    "print(f\"vpn: {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 = 5000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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 = 42, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), \n",
    "                        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\",\"error\",\"logloss\"],\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:02<00:00,  1.59trial/s, best loss: -0.9922680412371134]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.5300351226869394, 'gamma': 2.0417057228901965, 'max_depth': 24.0, 'min_child_weight': 0.0, 'n_estimators': 340.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6457604200823447}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), \n",
    "            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",
    "      eval_set=[(X_train, y_train), (X_test, y_test)]\n",
    "      model.fit(X_train, y_train, eval_metric=[\"auc\",\"error\",\"logloss\"], eval_set=eval_set, verbose=False)\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.99 & 100.00 & 99.74 & 99.87 \\\\\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": 18,
   "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']), \n",
    "                        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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "\n",
      "\n",
      "100.0\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}100.00 & 100.00 & 100.00 & 100.00 \\\\\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=25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}100.00 & 100.00 & 100.00 & 100.00 \\\\\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"VPN-VNAT-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.38116494, 0.2575011, 0.06332256, 0.107187636, 0.09746248]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PACKETS                       0.000000\n",
       "FISHER_PEARSON_G1_SKEWNESS    0.000000\n",
       "GALTON_SKEWNESS               0.000000\n",
       "BENFORD_LAW_PRESENTED         0.000000\n",
       "P_BENFORD                     0.000000\n",
       "                                ...   \n",
       "MIN_DIFFTIMES                 0.061452\n",
       "MEAN_DIFFTIMES                0.069991\n",
       "Q3                            0.080065\n",
       "PACKETS_REV                   0.080657\n",
       "MEDIAN                        0.107831\n",
       "Length: 73, dtype: float32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PACKETS': 0.0,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'P_BENFORD': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'Q1_SCALED_TIME': 0.0,\n",
       " 'Q3_SCALED_TIME': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MAX_POWER': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'SPECTRAL_ENERGY': 0.0,\n",
       " 'SPECTRAL_SPREAD': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'STDEV': 0.0,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0,\n",
       " 'MODE': 0.0,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0,\n",
       " 'PERIODICITY_SCDF': 0.0,\n",
       " 'ROOT_MEAN_SQUARE': 0.0030605352949351072,\n",
       " 'MEAN_SCALED_TIME': 0.003193481359630823,\n",
       " 'SPECTRAL_ROLLOFF': 0.004449560772627592,\n",
       " 'BURSTINESS': 0.005328008905053139,\n",
       " 'BYTES': 0.005378311965614557,\n",
       " 'SPECTRAL_SKEWNESS': 0.005471420474350452,\n",
       " 'MAX_DIFFTIMES': 0.005807398818433285,\n",
       " 'MIN_POWER_FREQ': 0.005870400927960873,\n",
       " 'MIN': 0.006258394103497267,\n",
       " 'TIME_DISTRIBUTION': 0.006876025348901749,\n",
       " 'BIGGEST_CNT_1_SEC': 0.00708144111558795,\n",
       " 'Q1': 0.007234536577016115,\n",
       " 'BYTES_REV': 0.007325677666813135,\n",
       " 'KURTOSIS': 0.007512533105909824,\n",
       " 'NORMAL_DISTRIBUTION': 0.007532013580203056,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.007850142195820808,\n",
       " 'SPECTRAL_CENTROID': 0.008515636436641216,\n",
       " 'AVERAGE_DISPERSION': 0.009615961462259293,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.009780846536159515,\n",
       " 'SPECTRAL_ENTROPY': 0.010047481395304203,\n",
       " 'MIN_MINUS_MAX': 0.013391654007136822,\n",
       " 'DIRECTIONS': 0.014986459165811539,\n",
       " 'MAX': 0.015459947288036346,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.01606215536594391,\n",
       " 'VAR': 0.01665181666612625,\n",
       " 'MIN_POWER': 0.01726238615810871,\n",
       " 'SPECTRAL_FLUX': 0.01752893067896366,\n",
       " 'SPECTRAL_SLOPE': 0.018299387767910957,\n",
       " 'SWITCHING_METRIC': 0.018797827884554863,\n",
       " 'SPECTRAL_KURTOSIS': 0.019742431119084358,\n",
       " 'CNT_DISTRIBUTION': 0.019757278263568878,\n",
       " 'PERCENT_DEVIATION': 0.022254865616559982,\n",
       " 'DURATION': 0.024054640904068947,\n",
       " 'VAL': 0.0243833065032959,\n",
       " 'ENTROPY': 0.027093330398201942,\n",
       " 'HURST_EXPONENT': 0.029157238081097603,\n",
       " 'TRANSIENTS': 0.032213445752859116,\n",
       " 'SCALED_ENTROPY': 0.03549528121948242,\n",
       " 'MEDIAN_DIFFTIMES': 0.03965545445680618,\n",
       " 'MEDIAN_SCALED_TIME': 0.04356541112065315,\n",
       " 'MIN_DIFFTIMES': 0.06145242601633072,\n",
       " 'MEAN_DIFFTIMES': 0.06999143213033676,\n",
       " 'Q3': 0.08006467670202255,\n",
       " 'PACKETS_REV': 0.08065733313560486,\n",
       " 'MEDIAN': 0.10783109068870544}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=25):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features, 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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:32<00:00,  3.09trial/s, best loss: -0.9807938540332907]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2769178918322269, 'gamma': 6.714666696185831, 'max_depth': 30.0, 'min_child_weight': 1.0, 'n_estimators': 140.0, 'reg_alpha': 1.0, 'reg_lambda': 0.19853369866202156}\n",
      "100%|██████████| 100/100 [00:32<00:00,  3.10trial/s, best loss: -0.9935483870967742]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2203988011108299, 'gamma': 3.265384416323076, 'max_depth': 20.0, 'min_child_weight': 0.0, 'n_estimators': 240.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3013520483078699}\n",
      "100%|██████████| 100/100 [00:27<00:00,  3.67trial/s, best loss: -0.9922680412371134]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9346402796904334, 'gamma': 2.974412966661644, 'max_depth': 24.0, 'min_child_weight': 8.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.48884181034394325}\n",
      "100%|██████████| 100/100 [00:28<00:00,  3.56trial/s, best loss: -0.9896907216494846]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.211976232026595, 'gamma': 4.97784091422183, 'max_depth': 16.0, 'min_child_weight': 18.0, 'n_estimators': 240.0, 'reg_alpha': 1.0, 'reg_lambda': 0.14246413974238126}\n",
      "100%|██████████| 100/100 [00:33<00:00,  2.95trial/s, best loss: -0.9909443725743854]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.36026983247980693, 'gamma': 1.8110652211509914, 'max_depth': 8.0, 'min_child_weight': 0.0, 'n_estimators': 360.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7029280334820297}\n",
      "100%|██████████| 100/100 [00:36<00:00,  2.73trial/s, best loss: -0.9935483870967742]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4657976760519182, 'gamma': 8.772003306572044, 'max_depth': 24.0, 'min_child_weight': 1.0, 'n_estimators': 400.0, 'reg_alpha': 0.0, 'reg_lambda': 0.006377202949943925}\n",
      "100%|██████████| 100/100 [00:30<00:00,  3.31trial/s, best loss: -0.9922480620155039]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8398238219268503, 'gamma': 7.507893313910519, 'max_depth': 28.0, 'min_child_weight': 20.0, 'n_estimators': 80.0, 'reg_alpha': 5.0, 'reg_lambda': 0.9011493891914941}\n",
      "100%|██████████| 100/100 [00:28<00:00,  3.45trial/s, best loss: -0.9922680412371134]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.22087134686725307, 'gamma': 6.681359606436369, 'max_depth': 30.0, 'min_child_weight': 19.0, 'n_estimators': 380.0, 'reg_alpha': 2.0, 'reg_lambda': 0.5410929450808701}\n",
      "100%|██████████| 100/100 [00:30<00:00,  3.32trial/s, best loss: -0.9922680412371134]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.39102165142797696, 'gamma': 8.536866328381917, 'max_depth': 16.0, 'min_child_weight': 0.0, 'n_estimators': 260.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5099512291297184}\n",
      "100%|██████████| 100/100 [00:33<00:00,  3.01trial/s, best loss: -0.9922680412371134]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6179534329575151, 'gamma': 4.4287720464001605, 'max_depth': 10.0, 'min_child_weight': 8.0, 'n_estimators': 100.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9483134364624876}\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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"VPN-VNAT-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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