dos.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DoS detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of DoS\n",
    "\n",
    "Raw data from: Bot-IoT [1] & clear data from CESNET2 network\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/dos.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Unnamed: 0.1</th>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "         Unnamed: 0.1  Unnamed: 0          DST_IP           SRC_IP  PACKETS  \\\n",
       "0                   0           0  192.168.100.7.  192.168.100.148        5   \n",
       "1                   1           1  192.168.100.7.  192.168.100.148        5   \n",
       "2                   2           2  192.168.100.7.  192.168.100.148        5   \n",
       "3                   3           3  192.168.100.7.  192.168.100.148        5   \n",
       "4                   4           4  192.168.100.7.  192.168.100.148        5   \n",
       "...               ...         ...             ...              ...      ...   \n",
       "4009815       4009815     4009815             443   192.168.0.54.5        6   \n",
       "4009816       4009816     4009816           53176   192.168.0.54.5       11   \n",
       "4009817       4009817     4009817             443   192.168.0.54.5        7   \n",
       "4009818       4009818     4009818           53189   192.168.0.54.5       13   \n",
       "4009819       4009819     4009819             443   192.168.0.54.5       10   \n",
       "\n",
       "         PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  ...  \\\n",
       "0                  0    391          0  1.528089e+09  1.528089e+09  ...   \n",
       "1                  0    389          0  1.528089e+09  1.528089e+09  ...   \n",
       "2                  0    374          0  1.528089e+09  1.528089e+09  ...   \n",
       "3                  0    507          0  1.528089e+09  1.528089e+09  ...   \n",
       "4                  0    436          0  1.528089e+09  1.528089e+09  ...   \n",
       "...              ...    ...        ...           ...           ...  ...   \n",
       "4009815            0   3349          0  1.428917e+09  1.428917e+09  ...   \n",
       "4009816            0   1492          0  1.428917e+09  1.428918e+09  ...   \n",
       "4009817            0   4646          0  1.428917e+09  1.428917e+09  ...   \n",
       "4009818            0   1891          0  1.428917e+09  1.428918e+09  ...   \n",
       "4009819            0   5532          0  1.428917e+09  1.428917e+09  ...   \n",
       "\n",
       "        SPECTRAL_SKEWNESS SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0          -313744.025253         0.878615           0.402802   \n",
       "1          -325241.042104         0.903249           0.325064   \n",
       "2          -292319.976102         0.834709          -1.611567   \n",
       "3           -42829.060112         0.783652           0.485833   \n",
       "4          -225777.997646         0.899673           0.442509   \n",
       "...                   ...              ...                ...   \n",
       "4009815       3441.134454         0.006667           0.511568   \n",
       "4009816      -4265.327353         0.276257           0.506199   \n",
       "4009817     349038.281575         0.030507          15.857747   \n",
       "4009818          0.539386         0.591939           0.566854   \n",
       "4009819       5235.183646         0.935831           0.634775   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.209271             NaN      233.222079     602.608368   \n",
       "1               0.175965             NaN      173.016423     728.468984   \n",
       "2                    NaN             NaN     2071.971558    1723.979380   \n",
       "3               0.286630             NaN      112.384700     296.101898   \n",
       "4               0.252736             NaN      187.074650     401.970775   \n",
       "...                  ...             ...             ...            ...   \n",
       "4009815         0.287497        0.027474        1.077178       0.020648   \n",
       "4009816         0.284613        0.011316        1.586032       5.450236   \n",
       "4009817              NaN             NaN  -140980.501434     176.604046   \n",
       "4009818         0.272499        0.275930        1.589183       2.146934   \n",
       "4009819         0.279933        0.395210        2.341452       0.083376   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.094367          0.029149   True  \n",
       "1                  0.068143          0.022841   True  \n",
       "2                 -0.069136          0.008521   True  \n",
       "3                 -0.017880          0.060085   True  \n",
       "4                 -0.073308          0.051124   True  \n",
       "...                     ...               ...    ...  \n",
       "4009815            0.993135          0.999670  False  \n",
       "4009816           -0.550704          0.492837  False  \n",
       "4009817           -0.000199          0.057470  False  \n",
       "4009818            0.912873          0.228825  False  \n",
       "4009819            0.993135          0.187339  False  \n",
       "\n",
       "[4009820 rows x 85 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddos: 60963\tclear: 3948857\n"
     ]
    }
   ],
   "source": [
    "print(f\"ddos: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"DIRECTIONS\",\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",
    "    \"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",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"DIRECTIONS\",\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": 6,
   "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.loc[df[F].isnull(), F] = 0.5\n",
    "    else:\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</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",
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       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
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       "      <th>BURSTINESS</th>\n",
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       "      <th>MEDIAN_DIFFTIMES</th>\n",
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       "      <th>SIG_SPACES</th>\n",
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       "      <th>CNT_ZEROS</th>\n",
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       "      <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",
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       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
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       "      <th>SPECTRAL_BANDWIDTH</th>\n",
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       "      <td>0.613386</td>\n",
       "      <td>1.709759e-07</td>\n",
       "      <td>2.548758e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.100675</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214242</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.502457</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>1.163852</td>\n",
       "      <td>16.348169</td>\n",
       "      <td>4.087042</td>\n",
       "      <td>0.581914</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>15.184317</td>\n",
       "      <td>0.269162</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-492.994408</td>\n",
       "      <td>46.269426</td>\n",
       "      <td>0.903845</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>0.022331</td>\n",
       "      <td>7.597926</td>\n",
       "      <td>0.16662</td>\n",
       "      <td>111.655553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.824497e+07</td>\n",
       "      <td>-292319.976102</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>-1.611567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2071.971558</td>\n",
       "      <td>1723.979380</td>\n",
       "      <td>-0.069136</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>3</td>\n",
       "      <td>192.168.100.7.</td>\n",
       "      <td>192.168.100.148</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>507</td>\n",
       "      <td>0</td>\n",
       "      <td>1.528089e+09</td>\n",
       "      <td>1.528089e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>52738</td>\n",
       "      <td>161</td>\n",
       "      <td>60.0</td>\n",
       "      <td>226.735529</td>\n",
       "      <td>51410</td>\n",
       "      <td>0.169537</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>567</td>\n",
       "      <td>507</td>\n",
       "      <td>60</td>\n",
       "      <td>140.829521</td>\n",
       "      <td>162</td>\n",
       "      <td>100.621118</td>\n",
       "      <td>259.186805</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.445453</td>\n",
       "      <td>1.865610</td>\n",
       "      <td>0.654226</td>\n",
       "      <td>3.431636e-08</td>\n",
       "      <td>5.115581e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.087654</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214134</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.498793</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>1.154518</td>\n",
       "      <td>16.339294</td>\n",
       "      <td>4.084823</td>\n",
       "      <td>0.577248</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>15.184776</td>\n",
       "      <td>0.269008</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>747</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.642932</td>\n",
       "      <td>17.608265</td>\n",
       "      <td>0.801532</td>\n",
       "      <td>0.783652</td>\n",
       "      <td>0.156678</td>\n",
       "      <td>0.630643</td>\n",
       "      <td>0.16664</td>\n",
       "      <td>783.392450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.829273e+06</td>\n",
       "      <td>-42829.060112</td>\n",
       "      <td>0.783652</td>\n",
       "      <td>0.485833</td>\n",
       "      <td>0.286630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.384700</td>\n",
       "      <td>296.101898</td>\n",
       "      <td>-0.017880</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>4</td>\n",
       "      <td>192.168.100.7.</td>\n",
       "      <td>192.168.100.148</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>436</td>\n",
       "      <td>0</td>\n",
       "      <td>1.528089e+09</td>\n",
       "      <td>1.528089e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>52740</td>\n",
       "      <td>147</td>\n",
       "      <td>60.0</td>\n",
       "      <td>194.984615</td>\n",
       "      <td>38019</td>\n",
       "      <td>0.140312</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>496</td>\n",
       "      <td>436</td>\n",
       "      <td>60</td>\n",
       "      <td>132.642595</td>\n",
       "      <td>139</td>\n",
       "      <td>94.557823</td>\n",
       "      <td>228.217440</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.446189</td>\n",
       "      <td>1.954000</td>\n",
       "      <td>0.625200</td>\n",
       "      <td>2.697919e-08</td>\n",
       "      <td>4.021821e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.084431</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214118</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.498479</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>1.153209</td>\n",
       "      <td>16.339031</td>\n",
       "      <td>4.084758</td>\n",
       "      <td>0.576593</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>15.185822</td>\n",
       "      <td>0.268985</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.280065</td>\n",
       "      <td>27.167804</td>\n",
       "      <td>0.972981</td>\n",
       "      <td>0.899673</td>\n",
       "      <td>0.145224</td>\n",
       "      <td>1.191793</td>\n",
       "      <td>0.16664</td>\n",
       "      <td>726.121994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.474637e+07</td>\n",
       "      <td>-225777.997646</td>\n",
       "      <td>0.899673</td>\n",
       "      <td>0.442509</td>\n",
       "      <td>0.252736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>187.074650</td>\n",
       "      <td>401.970775</td>\n",
       "      <td>-0.073308</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009815</th>\n",
       "      <td>4009815</td>\n",
       "      <td>4009815</td>\n",
       "      <td>443</td>\n",
       "      <td>192.168.0.54.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3349</td>\n",
       "      <td>0</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>53175:</td>\n",
       "      <td>23.21.159.26.4</td>\n",
       "      <td>558</td>\n",
       "      <td>228.0</td>\n",
       "      <td>651.336319</td>\n",
       "      <td>424239</td>\n",
       "      <td>0.077180</td>\n",
       "      <td>74.75</td>\n",
       "      <td>1069.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1460</td>\n",
       "      <td>1400</td>\n",
       "      <td>60</td>\n",
       "      <td>116.726939</td>\n",
       "      <td>552</td>\n",
       "      <td>98.924731</td>\n",
       "      <td>815.526313</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.764582</td>\n",
       "      <td>2.220051</td>\n",
       "      <td>0.067912</td>\n",
       "      <td>6.031605e-10</td>\n",
       "      <td>8.259115e-10</td>\n",
       "      <td>0.027910</td>\n",
       "      <td>1.064402</td>\n",
       "      <td>2.251629</td>\n",
       "      <td>0.871049</td>\n",
       "      <td>0.484439</td>\n",
       "      <td>False</td>\n",
       "      <td>0.768727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.408343</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.185961</td>\n",
       "      <td>0.132806</td>\n",
       "      <td>0.130222</td>\n",
       "      <td>0.266964</td>\n",
       "      <td>0.455405</td>\n",
       "      <td>0.091081</td>\n",
       "      <td>0.130222</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.188441</td>\n",
       "      <td>0.483340</td>\n",
       "      <td>False</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136528</td>\n",
       "      <td>0.157176</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.145915</td>\n",
       "      <td>0.004428</td>\n",
       "      <td>0.14235</td>\n",
       "      <td>729.574265</td>\n",
       "      <td>2025.401406</td>\n",
       "      <td>1.403900e+04</td>\n",
       "      <td>3441.134454</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.511568</td>\n",
       "      <td>0.287497</td>\n",
       "      <td>0.027474</td>\n",
       "      <td>1.077178</td>\n",
       "      <td>0.020648</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009816</th>\n",
       "      <td>4009816</td>\n",
       "      <td>4009816</td>\n",
       "      <td>53176</td>\n",
       "      <td>192.168.0.54.5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1492</td>\n",
       "      <td>0</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>1.428918e+09</td>\n",
       "      <td>443:</td>\n",
       "      <td>54.93.61.44.4</td>\n",
       "      <td>135</td>\n",
       "      <td>60.0</td>\n",
       "      <td>155.836453</td>\n",
       "      <td>24285</td>\n",
       "      <td>0.071643</td>\n",
       "      <td>60.00</td>\n",
       "      <td>142.50</td>\n",
       "      <td>60</td>\n",
       "      <td>561</td>\n",
       "      <td>501</td>\n",
       "      <td>60</td>\n",
       "      <td>115.434409</td>\n",
       "      <td>109</td>\n",
       "      <td>80.740741</td>\n",
       "      <td>201.183318</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.481274</td>\n",
       "      <td>2.213859</td>\n",
       "      <td>1.584614</td>\n",
       "      <td>1.681505e-07</td>\n",
       "      <td>1.959530e-07</td>\n",
       "      <td>-0.818182</td>\n",
       "      <td>5.136262</td>\n",
       "      <td>1.277613</td>\n",
       "      <td>0.369313</td>\n",
       "      <td>0.435457</td>\n",
       "      <td>False</td>\n",
       "      <td>0.573757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>1.789601</td>\n",
       "      <td>0.110076</td>\n",
       "      <td>0.053760</td>\n",
       "      <td>0.261340</td>\n",
       "      <td>18.388441</td>\n",
       "      <td>1.838844</td>\n",
       "      <td>0.053524</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>18.069654</td>\n",
       "      <td>0.101764</td>\n",
       "      <td>False</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076046</td>\n",
       "      <td>0.248211</td>\n",
       "      <td>0.992848</td>\n",
       "      <td>0.442144</td>\n",
       "      <td>0.156498</td>\n",
       "      <td>0.019121</td>\n",
       "      <td>0.15151</td>\n",
       "      <td>782.489908</td>\n",
       "      <td>2084.839909</td>\n",
       "      <td>4.346456e+04</td>\n",
       "      <td>-4265.327353</td>\n",
       "      <td>0.276257</td>\n",
       "      <td>0.506199</td>\n",
       "      <td>0.284613</td>\n",
       "      <td>0.011316</td>\n",
       "      <td>1.586032</td>\n",
       "      <td>5.450236</td>\n",
       "      <td>-0.550704</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009817</th>\n",
       "      <td>4009817</td>\n",
       "      <td>4009817</td>\n",
       "      <td>443</td>\n",
       "      <td>192.168.0.54.5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>53176:</td>\n",
       "      <td>54.93.61.44.4</td>\n",
       "      <td>663</td>\n",
       "      <td>670.0</td>\n",
       "      <td>615.725588</td>\n",
       "      <td>379118</td>\n",
       "      <td>-0.036970</td>\n",
       "      <td>105.50</td>\n",
       "      <td>1122.50</td>\n",
       "      <td>60</td>\n",
       "      <td>1460</td>\n",
       "      <td>1400</td>\n",
       "      <td>60</td>\n",
       "      <td>92.869621</td>\n",
       "      <td>491</td>\n",
       "      <td>74.057315</td>\n",
       "      <td>874.914363</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.979332</td>\n",
       "      <td>2.142188</td>\n",
       "      <td>-0.187177</td>\n",
       "      <td>3.059922e-09</td>\n",
       "      <td>3.966112e-09</td>\n",
       "      <td>-0.096362</td>\n",
       "      <td>1.133416</td>\n",
       "      <td>2.235926</td>\n",
       "      <td>0.796453</td>\n",
       "      <td>0.406730</td>\n",
       "      <td>False</td>\n",
       "      <td>0.762836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322692</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.085528</td>\n",
       "      <td>0.056286</td>\n",
       "      <td>0.050859</td>\n",
       "      <td>0.113753</td>\n",
       "      <td>0.265044</td>\n",
       "      <td>0.044174</td>\n",
       "      <td>0.028098</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.151291</td>\n",
       "      <td>0.291981</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.452389</td>\n",
       "      <td>83.867125</td>\n",
       "      <td>0.030705</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>-0.000595</td>\n",
       "      <td>1.191144</td>\n",
       "      <td>-0.01207</td>\n",
       "      <td>-2.974423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.457700e+07</td>\n",
       "      <td>349038.281575</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>15.857747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-140980.501434</td>\n",
       "      <td>176.604046</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009818</th>\n",
       "      <td>4009818</td>\n",
       "      <td>4009818</td>\n",
       "      <td>53189</td>\n",
       "      <td>192.168.0.54.5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1891</td>\n",
       "      <td>0</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>1.428918e+09</td>\n",
       "      <td>443:</td>\n",
       "      <td>70.33.182.206.4</td>\n",
       "      <td>145</td>\n",
       "      <td>60.0</td>\n",
       "      <td>187.938820</td>\n",
       "      <td>35321</td>\n",
       "      <td>0.128969</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>685</td>\n",
       "      <td>625</td>\n",
       "      <td>60</td>\n",
       "      <td>129.612979</td>\n",
       "      <td>131</td>\n",
       "      <td>90.344828</td>\n",
       "      <td>231.869492</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.452275</td>\n",
       "      <td>1.995330</td>\n",
       "      <td>1.726694</td>\n",
       "      <td>6.952779e-08</td>\n",
       "      <td>7.894562e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.448111</td>\n",
       "      <td>1.145110</td>\n",
       "      <td>0.309453</td>\n",
       "      <td>0.460809</td>\n",
       "      <td>False</td>\n",
       "      <td>0.531799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.690053</td>\n",
       "      <td>0.292666</td>\n",
       "      <td>0.290225</td>\n",
       "      <td>0.523556</td>\n",
       "      <td>17.724544</td>\n",
       "      <td>1.477045</td>\n",
       "      <td>0.040324</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>17.030356</td>\n",
       "      <td>0.086730</td>\n",
       "      <td>False</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028477</td>\n",
       "      <td>0.095561</td>\n",
       "      <td>0.050771</td>\n",
       "      <td>0.963644</td>\n",
       "      <td>0.060132</td>\n",
       "      <td>0.019177</td>\n",
       "      <td>0.03098</td>\n",
       "      <td>300.660378</td>\n",
       "      <td>1196.621611</td>\n",
       "      <td>8.771147e+03</td>\n",
       "      <td>0.539386</td>\n",
       "      <td>0.591939</td>\n",
       "      <td>0.566854</td>\n",
       "      <td>0.272499</td>\n",
       "      <td>0.275930</td>\n",
       "      <td>1.589183</td>\n",
       "      <td>2.146934</td>\n",
       "      <td>0.912873</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009819</th>\n",
       "      <td>4009819</td>\n",
       "      <td>4009819</td>\n",
       "      <td>443</td>\n",
       "      <td>192.168.0.54.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5532</td>\n",
       "      <td>0</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>1.428917e+09</td>\n",
       "      <td>53189:</td>\n",
       "      <td>70.33.182.206.4</td>\n",
       "      <td>553</td>\n",
       "      <td>388.5</td>\n",
       "      <td>568.287779</td>\n",
       "      <td>322951</td>\n",
       "      <td>0.013634</td>\n",
       "      <td>61.50</td>\n",
       "      <td>850.75</td>\n",
       "      <td>60</td>\n",
       "      <td>1440</td>\n",
       "      <td>1380</td>\n",
       "      <td>60</td>\n",
       "      <td>102.764517</td>\n",
       "      <td>483</td>\n",
       "      <td>87.341772</td>\n",
       "      <td>772.454918</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.867518</td>\n",
       "      <td>2.235663</td>\n",
       "      <td>0.185448</td>\n",
       "      <td>1.089745e-09</td>\n",
       "      <td>1.292279e-09</td>\n",
       "      <td>-0.245486</td>\n",
       "      <td>1.464245</td>\n",
       "      <td>2.646439</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.558617</td>\n",
       "      <td>True</td>\n",
       "      <td>0.877121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540905</td>\n",
       "      <td>0.313889</td>\n",
       "      <td>0.299543</td>\n",
       "      <td>0.303346</td>\n",
       "      <td>0.149818</td>\n",
       "      <td>0.528792</td>\n",
       "      <td>0.553782</td>\n",
       "      <td>0.061531</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.225442</td>\n",
       "      <td>0.272936</td>\n",
       "      <td>False</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.107618</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.045962</td>\n",
       "      <td>0.022777</td>\n",
       "      <td>0.02424</td>\n",
       "      <td>229.811108</td>\n",
       "      <td>984.128846</td>\n",
       "      <td>1.487078e+04</td>\n",
       "      <td>5235.183646</td>\n",
       "      <td>0.935831</td>\n",
       "      <td>0.634775</td>\n",
       "      <td>0.279933</td>\n",
       "      <td>0.395210</td>\n",
       "      <td>2.341452</td>\n",
       "      <td>0.083376</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4009820 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0.1  Unnamed: 0          DST_IP           SRC_IP  PACKETS  \\\n",
       "0                   0           0  192.168.100.7.  192.168.100.148        5   \n",
       "1                   1           1  192.168.100.7.  192.168.100.148        5   \n",
       "2                   2           2  192.168.100.7.  192.168.100.148        5   \n",
       "3                   3           3  192.168.100.7.  192.168.100.148        5   \n",
       "4                   4           4  192.168.100.7.  192.168.100.148        5   \n",
       "...               ...         ...             ...              ...      ...   \n",
       "4009815       4009815     4009815             443   192.168.0.54.5        6   \n",
       "4009816       4009816     4009816           53176   192.168.0.54.5       11   \n",
       "4009817       4009817     4009817             443   192.168.0.54.5        7   \n",
       "4009818       4009818     4009818           53189   192.168.0.54.5       13   \n",
       "4009819       4009819     4009819             443   192.168.0.54.5       10   \n",
       "\n",
       "         PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST DST_PORT  \\\n",
       "0                  0    391          0  1.528089e+09  1.528089e+09       80   \n",
       "1                  0    389          0  1.528089e+09  1.528089e+09       80   \n",
       "2                  0    374          0  1.528089e+09  1.528089e+09       80   \n",
       "3                  0    507          0  1.528089e+09  1.528089e+09       80   \n",
       "4                  0    436          0  1.528089e+09  1.528089e+09       80   \n",
       "...              ...    ...        ...           ...           ...      ...   \n",
       "4009815            0   3349          0  1.428917e+09  1.428917e+09   53175:   \n",
       "4009816            0   1492          0  1.428917e+09  1.428918e+09     443:   \n",
       "4009817            0   4646          0  1.428917e+09  1.428917e+09   53176:   \n",
       "4009818            0   1891          0  1.428917e+09  1.428918e+09     443:   \n",
       "4009819            0   5532          0  1.428917e+09  1.428917e+09   53189:   \n",
       "\n",
       "                SRC_PORT  MEAN  MEDIAN       STDEV     VAR  BURSTINESS  \\\n",
       "0                  52732   138    60.0  174.859944   30576    0.117816   \n",
       "1                  52734   137    60.0  173.965514   30265    0.118873   \n",
       "2                  52736   134    60.0  167.257287   27976    0.110395   \n",
       "3                  52738   161    60.0  226.735529   51410    0.169537   \n",
       "4                  52740   147    60.0  194.984615   38019    0.140312   \n",
       "...                  ...   ...     ...         ...     ...         ...   \n",
       "4009815   23.21.159.26.4   558   228.0  651.336319  424239    0.077180   \n",
       "4009816    54.93.61.44.4   135    60.0  155.836453   24285    0.071643   \n",
       "4009817    54.93.61.44.4   663   670.0  615.725588  379118   -0.036970   \n",
       "4009818  70.33.182.206.4   145    60.0  187.938820   35321    0.128969   \n",
       "4009819  70.33.182.206.4   553   388.5  568.287779  322951    0.013634   \n",
       "\n",
       "             Q1       Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0         60.00    60.00   60   451            391    60   \n",
       "1         60.00    60.00   60   449            389    60   \n",
       "2         60.00    60.00   60   434            374    60   \n",
       "3         60.00    60.00   60   567            507    60   \n",
       "4         60.00    60.00   60   496            436    60   \n",
       "...         ...      ...  ...   ...            ...   ...   \n",
       "4009815   74.75  1069.00   60  1460           1400    60   \n",
       "4009816   60.00   142.50   60   561            501    60   \n",
       "4009817  105.50  1122.50   60  1460           1400    60   \n",
       "4009818   60.00    60.00   60   685            625    60   \n",
       "4009819   61.50   850.75   60  1440           1380    60   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                      126.710104                 125          90.579710   \n",
       "1                      126.982127                 124          90.510949   \n",
       "2                      124.818871                 119          88.805970   \n",
       "3                      140.829521                 162         100.621118   \n",
       "4                      132.642595                 139          94.557823   \n",
       "...                           ...                 ...                ...   \n",
       "4009815                116.726939                 552          98.924731   \n",
       "4009816                115.434409                 109          80.740741   \n",
       "4009817                 92.869621                 491          74.057315   \n",
       "4009818                129.612979                 131          90.344828   \n",
       "4009819                102.764517                 483          87.341772   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              208.710805            0.800000            0.200000   \n",
       "1              207.846578            0.800000            0.200000   \n",
       "2              201.373285            0.800000            0.200000   \n",
       "3              259.186805            0.800000            0.200000   \n",
       "4              228.217440            0.800000            0.200000   \n",
       "...                   ...                 ...                 ...   \n",
       "4009815        815.526313            0.666667            0.333333   \n",
       "4009816        201.183318            0.727273            0.272727   \n",
       "4009817        874.914363            0.428571            0.571429   \n",
       "4009818        231.869492            0.769231            0.230769   \n",
       "4009819        772.454918            0.500000            0.500000   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    0.446071              2.024477              0.604699   \n",
       "1                    0.442616              2.017641              0.620471   \n",
       "2                    0.442432              2.044754              0.613386   \n",
       "3                    0.445453              1.865610              0.654226   \n",
       "4                    0.446189              1.954000              0.625200   \n",
       "...                       ...                   ...                   ...   \n",
       "4009815              0.764582              2.220051              0.067912   \n",
       "4009816              0.481274              2.213859              1.584614   \n",
       "4009817              0.979332              2.142188             -0.187177   \n",
       "4009818              0.452275              1.995330              1.726694   \n",
       "4009819              0.867518              2.235663              0.185448   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      3.740753e-08                5.576385e-08   \n",
       "1                      1.519499e-07                2.265136e-07   \n",
       "2                      1.709759e-07                2.548758e-07   \n",
       "3                      3.431636e-08                5.115581e-08   \n",
       "4                      2.697919e-08                4.021821e-08   \n",
       "...                             ...                         ...   \n",
       "4009815                6.031605e-10                8.259115e-10   \n",
       "4009816                1.681505e-07                1.959530e-07   \n",
       "4009817                3.059922e-09                3.966112e-09   \n",
       "4009818                6.952779e-08                7.894562e-08   \n",
       "4009819                1.089745e-09                1.292279e-09   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000  2.084944  0.721928        0.310918        0.301789   \n",
       "1               0.000000  2.099872  0.721928        0.310918        0.301789   \n",
       "2               0.000000  2.100675  0.721928        0.310918        0.301789   \n",
       "3               0.000000  2.087654  0.721928        0.310918        0.301789   \n",
       "4               0.000000  2.084431  0.721928        0.310918        0.301789   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "4009815         0.027910  1.064402  2.251629        0.871049        0.484439   \n",
       "4009816        -0.818182  5.136262  1.277613        0.369313        0.435457   \n",
       "4009817        -0.096362  1.133416  2.235926        0.796453        0.406730   \n",
       "4009818         0.000000  5.448111  1.145110        0.309453        0.460809   \n",
       "4009819        -0.245486  1.464245  2.646439        0.796658        0.558617   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.477121                  0.0   \n",
       "1                        False   0.477121                  0.0   \n",
       "2                        False   0.477121                  0.0   \n",
       "3                        False   0.477121                  0.0   \n",
       "4                        False   0.477121                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "4009815                  False   0.768727                  0.0   \n",
       "4009816                  False   0.573757                  0.0   \n",
       "4009817                  False   0.762836                  0.0   \n",
       "4009818                  False   0.531799                  0.0   \n",
       "4009819                   True   0.877121                  0.0   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                     1.0           0.214227                  0.812500   \n",
       "1                     1.0           0.214224                  0.812500   \n",
       "2                     1.0           0.214242                  0.812500   \n",
       "3                     1.0           0.214134                  0.812500   \n",
       "4                     1.0           0.214118                  0.812500   \n",
       "...                   ...                ...                       ...   \n",
       "4009815               0.0           0.408343                  0.375000   \n",
       "4009816               0.0           0.097322                  0.781250   \n",
       "4009817               0.0           0.322692                  0.291667   \n",
       "4009818               0.0           0.095351                  0.800000   \n",
       "4009819               0.0           0.540905                  0.313889   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0                3.502377            0.000132        0.000082        1.162747   \n",
       "1                3.502252            0.000373        0.000089        1.162257   \n",
       "2                3.502457            0.000143        0.000119        1.163852   \n",
       "3                3.498793            0.000088        0.000066        1.154518   \n",
       "4                3.498479            0.000089        0.000066        1.153209   \n",
       "...                   ...                 ...             ...             ...   \n",
       "4009815          0.185961            0.132806        0.130222        0.266964   \n",
       "4009816          1.789601            0.110076        0.053760        0.261340   \n",
       "4009817          0.085528            0.056286        0.050859        0.113753   \n",
       "4009818          1.690053            0.292666        0.290225        0.523556   \n",
       "4009819          0.299543            0.303346        0.149818        0.528792   \n",
       "\n",
       "          DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        16.348923        4.087231          0.581348       0.000050   \n",
       "1        16.348542        4.087135          0.581084       0.000089   \n",
       "2        16.348169        4.087042          0.581914       0.000024   \n",
       "3        16.339294        4.084823          0.577248       0.000022   \n",
       "4        16.339031        4.084758          0.576593       0.000023   \n",
       "...            ...             ...               ...            ...   \n",
       "4009815   0.455405        0.091081          0.130222       0.000149   \n",
       "4009816  18.388441        1.838844          0.053524       0.000002   \n",
       "4009817   0.265044        0.044174          0.028098       0.000089   \n",
       "4009818  17.724544        1.477045          0.040324       0.000002   \n",
       "4009819   0.553782        0.061531          0.000224       0.000002   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            15.186176               0.269142       False          1.000000   \n",
       "1            15.186285               0.269133       False          1.000000   \n",
       "2            15.184317               0.269162       False          1.000000   \n",
       "3            15.184776               0.269008       False          1.000000   \n",
       "4            15.185822               0.268985       False          1.000000   \n",
       "...                ...                    ...         ...               ...   \n",
       "4009815       0.188441               0.483340       False          1.600000   \n",
       "4009816      18.069654               0.101764       False          1.200000   \n",
       "4009817       0.151291               0.291981       False          1.333333   \n",
       "4009818      17.030356               0.086730       False          1.000000   \n",
       "4009819       0.225442               0.272936       False          1.777778   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0                  1.0                631   \n",
       "1             False        0.0                  1.0                629   \n",
       "2             False        0.0                  1.0                614   \n",
       "3             False        0.0                  1.0                747   \n",
       "4             False        0.0                  1.0                676   \n",
       "...             ...        ...                  ...                ...   \n",
       "4009815        True        0.0                  0.0               3349   \n",
       "4009816       False        0.0                  0.0               1492   \n",
       "4009817       False        0.0                  0.0               4646   \n",
       "4009818       False        0.0                  0.0               1891   \n",
       "4009819        True        0.0                  0.0               5532   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL  TIME   MIN_POWER  MAX_POWER  \\\n",
       "0               1.0        False    0   0.0 -157.771646  31.720354   \n",
       "1               1.0        False    0   0.0 -204.860903  19.097306   \n",
       "2               1.0        False    0   0.0 -492.994408  46.269426   \n",
       "3               1.0        False    0   0.0  -23.642932  17.608265   \n",
       "4               1.0        False    0   0.0  -73.280065  27.167804   \n",
       "...             ...          ...  ...   ...         ...        ...   \n",
       "4009815         0.0        False    0   0.0    0.136528   0.157176   \n",
       "4009816         0.0        False    0   0.0    0.076046   0.248211   \n",
       "4009817         0.0        False    0   0.0   -4.452389  83.867125   \n",
       "4009818         0.0        False    0   0.0    0.028477   0.095561   \n",
       "4009819         0.0        False    0   0.0    0.024242   0.107618   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  \\\n",
       "0              0.972981        0.878615    0.136009   2.317015     0.16662   \n",
       "1              0.835107        0.903249    0.110379   2.985574     0.16650   \n",
       "2              0.903845        0.834709    0.022331   7.597926     0.16662   \n",
       "3              0.801532        0.783652    0.156678   0.630643     0.16664   \n",
       "4              0.972981        0.899673    0.145224   1.191793     0.16664   \n",
       "...                 ...             ...         ...        ...         ...   \n",
       "4009815        0.006667        0.999801    0.145915   0.004428     0.14235   \n",
       "4009816        0.992848        0.442144    0.156498   0.019121     0.15151   \n",
       "4009817        0.030705        0.030507   -0.000595   1.191144    -0.01207   \n",
       "4009818        0.050771        0.963644    0.060132   0.019177     0.03098   \n",
       "4009819        0.006667        0.999801    0.045962   0.022777     0.02424   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0             680.046116          0.000000       2.161064e+07   \n",
       "1             551.892856          0.000000       2.223578e+07   \n",
       "2             111.655553          0.000000       1.824497e+07   \n",
       "3             783.392450          0.000000       3.829273e+06   \n",
       "4             726.121994          0.000000       1.474637e+07   \n",
       "...                  ...               ...                ...   \n",
       "4009815       729.574265       2025.401406       1.403900e+04   \n",
       "4009816       782.489908       2084.839909       4.346456e+04   \n",
       "4009817        -2.974423          0.000000       2.457700e+07   \n",
       "4009818       300.660378       1196.621611       8.771147e+03   \n",
       "4009819       229.811108        984.128846       1.487078e+04   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -313744.025253          0.878615           0.402802   \n",
       "1           -325241.042104          0.903249           0.325064   \n",
       "2           -292319.976102          0.834709          -1.611567   \n",
       "3            -42829.060112          0.783652           0.485833   \n",
       "4           -225777.997646          0.899673           0.442509   \n",
       "...                    ...               ...                ...   \n",
       "4009815        3441.134454          0.006667           0.511568   \n",
       "4009816       -4265.327353          0.276257           0.506199   \n",
       "4009817      349038.281575          0.030507          15.857747   \n",
       "4009818           0.539386          0.591939           0.566854   \n",
       "4009819        5235.183646          0.935831           0.634775   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.209271        0.000000      233.222079     602.608368   \n",
       "1               0.175965        0.000000      173.016423     728.468984   \n",
       "2               0.000000        0.000000     2071.971558    1723.979380   \n",
       "3               0.286630        0.000000      112.384700     296.101898   \n",
       "4               0.252736        0.000000      187.074650     401.970775   \n",
       "...                  ...             ...             ...            ...   \n",
       "4009815         0.287497        0.027474        1.077178       0.020648   \n",
       "4009816         0.284613        0.011316        1.586032       5.450236   \n",
       "4009817         0.000000        0.000000  -140980.501434     176.604046   \n",
       "4009818         0.272499        0.275930        1.589183       2.146934   \n",
       "4009819         0.279933        0.395210        2.341452       0.083376   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0                 -0.094367              True   True           1  \n",
       "1                  0.068143              True   True           1  \n",
       "2                 -0.069136              True   True           1  \n",
       "3                 -0.017880              True   True           1  \n",
       "4                 -0.073308              True   True           1  \n",
       "...                     ...               ...    ...         ...  \n",
       "4009815            0.993135              True  False           0  \n",
       "4009816           -0.550704              True  False           0  \n",
       "4009817           -0.000199              True  False           0  \n",
       "4009818            0.912873              True  False           0  \n",
       "4009819            0.993135              True  False           0  \n",
       "\n",
       "[4009820 rows x 86 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddos: 60963\tclear: 3948857\n"
     ]
    }
   ],
   "source": [
    "print(f\"ddos: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace([np.inf], 0, inplace=True)\n",
    "df.replace([-np.inf], 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4009820, 70)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddos: 60963\tclear: 3948857\n"
     ]
    }
   ],
   "source": [
    "print(f\"ddos: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 100000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABEL_code</th>\n",
       "      <th>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>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>174.859944</td>\n",
       "      <td>30576.0</td>\n",
       "      <td>0.117816</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>126.710104</td>\n",
       "      <td>125.0</td>\n",
       "      <td>90.579710</td>\n",
       "      <td>208.710805</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.446071</td>\n",
       "      <td>2.024477</td>\n",
       "      <td>0.604699</td>\n",
       "      <td>3.740753e-08</td>\n",
       "      <td>5.576385e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.084944</td>\n",
       "      <td>0.721928</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.000132</td>\n",
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       "      <td>4.087231</td>\n",
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       "      <td>0.000050</td>\n",
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       "      <td>0.269142</td>\n",
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       "      <td>2.161064e+07</td>\n",
       "      <td>-313744.025253</td>\n",
       "      <td>0.878615</td>\n",
       "      <td>0.402802</td>\n",
       "      <td>0.209271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>233.222079</td>\n",
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       "      <td>-0.094367</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>126.982127</td>\n",
       "      <td>124.0</td>\n",
       "      <td>90.510949</td>\n",
       "      <td>207.846578</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.442616</td>\n",
       "      <td>2.017641</td>\n",
       "      <td>0.620471</td>\n",
       "      <td>1.519499e-07</td>\n",
       "      <td>2.265136e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.099872</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214224</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.502252</td>\n",
       "      <td>0.000373</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>1.162257</td>\n",
       "      <td>16.348542</td>\n",
       "      <td>4.087135</td>\n",
       "      <td>0.581084</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>15.186285</td>\n",
       "      <td>0.269133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>629.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-204.860903</td>\n",
       "      <td>19.097306</td>\n",
       "      <td>0.835107</td>\n",
       "      <td>0.903249</td>\n",
       "      <td>551.892856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.223578e+07</td>\n",
       "      <td>-325241.042104</td>\n",
       "      <td>0.903249</td>\n",
       "      <td>0.325064</td>\n",
       "      <td>0.175965</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>173.016423</td>\n",
       "      <td>728.468984</td>\n",
       "      <td>0.068143</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>167.257287</td>\n",
       "      <td>27976.0</td>\n",
       "      <td>0.110395</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>124.818871</td>\n",
       "      <td>119.0</td>\n",
       "      <td>88.805970</td>\n",
       "      <td>201.373285</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.442432</td>\n",
       "      <td>2.044754</td>\n",
       "      <td>0.613386</td>\n",
       "      <td>1.709759e-07</td>\n",
       "      <td>2.548758e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.100675</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214242</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.502457</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>1.163852</td>\n",
       "      <td>16.348169</td>\n",
       "      <td>4.087042</td>\n",
       "      <td>0.581914</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>15.184317</td>\n",
       "      <td>0.269162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-492.994408</td>\n",
       "      <td>46.269426</td>\n",
       "      <td>0.903845</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>111.655553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.824497e+07</td>\n",
       "      <td>-292319.976102</td>\n",
       "      <td>0.834709</td>\n",
       "      <td>-1.611567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2071.971558</td>\n",
       "      <td>1723.979380</td>\n",
       "      <td>-0.069136</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>226.735529</td>\n",
       "      <td>51410.0</td>\n",
       "      <td>0.169537</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>140.829521</td>\n",
       "      <td>162.0</td>\n",
       "      <td>100.621118</td>\n",
       "      <td>259.186805</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.445453</td>\n",
       "      <td>1.865610</td>\n",
       "      <td>0.654226</td>\n",
       "      <td>3.431636e-08</td>\n",
       "      <td>5.115581e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.087654</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214134</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.498793</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>1.154518</td>\n",
       "      <td>16.339294</td>\n",
       "      <td>4.084823</td>\n",
       "      <td>0.577248</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>15.184776</td>\n",
       "      <td>0.269008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.642932</td>\n",
       "      <td>17.608265</td>\n",
       "      <td>0.801532</td>\n",
       "      <td>0.783652</td>\n",
       "      <td>783.392450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.829273e+06</td>\n",
       "      <td>-42829.060112</td>\n",
       "      <td>0.783652</td>\n",
       "      <td>0.485833</td>\n",
       "      <td>0.286630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.384700</td>\n",
       "      <td>296.101898</td>\n",
       "      <td>-0.017880</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>194.984615</td>\n",
       "      <td>38019.0</td>\n",
       "      <td>0.140312</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>132.642595</td>\n",
       "      <td>139.0</td>\n",
       "      <td>94.557823</td>\n",
       "      <td>228.217440</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.446189</td>\n",
       "      <td>1.954000</td>\n",
       "      <td>0.625200</td>\n",
       "      <td>2.697919e-08</td>\n",
       "      <td>4.021821e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.084431</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.310918</td>\n",
       "      <td>0.301789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.214118</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>3.498479</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>1.153209</td>\n",
       "      <td>16.339031</td>\n",
       "      <td>4.084758</td>\n",
       "      <td>0.576593</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>15.185822</td>\n",
       "      <td>0.268985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.280065</td>\n",
       "      <td>27.167804</td>\n",
       "      <td>0.972981</td>\n",
       "      <td>0.899673</td>\n",
       "      <td>726.121994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.474637e+07</td>\n",
       "      <td>-225777.997646</td>\n",
       "      <td>0.899673</td>\n",
       "      <td>0.442509</td>\n",
       "      <td>0.252736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>187.074650</td>\n",
       "      <td>401.970775</td>\n",
       "      <td>-0.073308</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009815</th>\n",
       "      <td>0.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>651.336319</td>\n",
       "      <td>424239.0</td>\n",
       "      <td>0.077180</td>\n",
       "      <td>74.75</td>\n",
       "      <td>1069.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>116.726939</td>\n",
       "      <td>552.0</td>\n",
       "      <td>98.924731</td>\n",
       "      <td>815.526313</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.764582</td>\n",
       "      <td>2.220051</td>\n",
       "      <td>0.067912</td>\n",
       "      <td>6.031605e-10</td>\n",
       "      <td>8.259115e-10</td>\n",
       "      <td>0.027910</td>\n",
       "      <td>1.064402</td>\n",
       "      <td>2.251629</td>\n",
       "      <td>0.871049</td>\n",
       "      <td>0.484439</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.768727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.408343</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.185961</td>\n",
       "      <td>0.132806</td>\n",
       "      <td>0.130222</td>\n",
       "      <td>0.266964</td>\n",
       "      <td>0.455405</td>\n",
       "      <td>0.091081</td>\n",
       "      <td>0.130222</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.188441</td>\n",
       "      <td>0.483340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3349.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136528</td>\n",
       "      <td>0.157176</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>729.574265</td>\n",
       "      <td>2025.401406</td>\n",
       "      <td>1.403900e+04</td>\n",
       "      <td>3441.134454</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.511568</td>\n",
       "      <td>0.287497</td>\n",
       "      <td>0.027474</td>\n",
       "      <td>1.077178</td>\n",
       "      <td>0.020648</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009816</th>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>155.836453</td>\n",
       "      <td>24285.0</td>\n",
       "      <td>0.071643</td>\n",
       "      <td>60.00</td>\n",
       "      <td>142.50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>115.434409</td>\n",
       "      <td>109.0</td>\n",
       "      <td>80.740741</td>\n",
       "      <td>201.183318</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.481274</td>\n",
       "      <td>2.213859</td>\n",
       "      <td>1.584614</td>\n",
       "      <td>1.681505e-07</td>\n",
       "      <td>1.959530e-07</td>\n",
       "      <td>-0.818182</td>\n",
       "      <td>5.136262</td>\n",
       "      <td>1.277613</td>\n",
       "      <td>0.369313</td>\n",
       "      <td>0.435457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>1.789601</td>\n",
       "      <td>0.110076</td>\n",
       "      <td>0.053760</td>\n",
       "      <td>0.261340</td>\n",
       "      <td>18.388441</td>\n",
       "      <td>1.838844</td>\n",
       "      <td>0.053524</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>18.069654</td>\n",
       "      <td>0.101764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076046</td>\n",
       "      <td>0.248211</td>\n",
       "      <td>0.992848</td>\n",
       "      <td>0.442144</td>\n",
       "      <td>782.489908</td>\n",
       "      <td>2084.839909</td>\n",
       "      <td>4.346456e+04</td>\n",
       "      <td>-4265.327353</td>\n",
       "      <td>0.276257</td>\n",
       "      <td>0.506199</td>\n",
       "      <td>0.284613</td>\n",
       "      <td>0.011316</td>\n",
       "      <td>1.586032</td>\n",
       "      <td>5.450236</td>\n",
       "      <td>-0.550704</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009817</th>\n",
       "      <td>0.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>615.725588</td>\n",
       "      <td>379118.0</td>\n",
       "      <td>-0.036970</td>\n",
       "      <td>105.50</td>\n",
       "      <td>1122.50</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>92.869621</td>\n",
       "      <td>491.0</td>\n",
       "      <td>74.057315</td>\n",
       "      <td>874.914363</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.979332</td>\n",
       "      <td>2.142188</td>\n",
       "      <td>-0.187177</td>\n",
       "      <td>3.059922e-09</td>\n",
       "      <td>3.966112e-09</td>\n",
       "      <td>-0.096362</td>\n",
       "      <td>1.133416</td>\n",
       "      <td>2.235926</td>\n",
       "      <td>0.796453</td>\n",
       "      <td>0.406730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.762836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.322692</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.085528</td>\n",
       "      <td>0.056286</td>\n",
       "      <td>0.050859</td>\n",
       "      <td>0.113753</td>\n",
       "      <td>0.265044</td>\n",
       "      <td>0.044174</td>\n",
       "      <td>0.028098</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.151291</td>\n",
       "      <td>0.291981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4646.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.452389</td>\n",
       "      <td>83.867125</td>\n",
       "      <td>0.030705</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>-2.974423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.457700e+07</td>\n",
       "      <td>349038.281575</td>\n",
       "      <td>0.030507</td>\n",
       "      <td>15.857747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-140980.501434</td>\n",
       "      <td>176.604046</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009818</th>\n",
       "      <td>0.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>187.938820</td>\n",
       "      <td>35321.0</td>\n",
       "      <td>0.128969</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>129.612979</td>\n",
       "      <td>131.0</td>\n",
       "      <td>90.344828</td>\n",
       "      <td>231.869492</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.452275</td>\n",
       "      <td>1.995330</td>\n",
       "      <td>1.726694</td>\n",
       "      <td>6.952779e-08</td>\n",
       "      <td>7.894562e-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.448111</td>\n",
       "      <td>1.145110</td>\n",
       "      <td>0.309453</td>\n",
       "      <td>0.460809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.531799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.690053</td>\n",
       "      <td>0.292666</td>\n",
       "      <td>0.290225</td>\n",
       "      <td>0.523556</td>\n",
       "      <td>17.724544</td>\n",
       "      <td>1.477045</td>\n",
       "      <td>0.040324</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>17.030356</td>\n",
       "      <td>0.086730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028477</td>\n",
       "      <td>0.095561</td>\n",
       "      <td>0.050771</td>\n",
       "      <td>0.963644</td>\n",
       "      <td>300.660378</td>\n",
       "      <td>1196.621611</td>\n",
       "      <td>8.771147e+03</td>\n",
       "      <td>0.539386</td>\n",
       "      <td>0.591939</td>\n",
       "      <td>0.566854</td>\n",
       "      <td>0.272499</td>\n",
       "      <td>0.275930</td>\n",
       "      <td>1.589183</td>\n",
       "      <td>2.146934</td>\n",
       "      <td>0.912873</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009819</th>\n",
       "      <td>0.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>388.5</td>\n",
       "      <td>568.287779</td>\n",
       "      <td>322951.0</td>\n",
       "      <td>0.013634</td>\n",
       "      <td>61.50</td>\n",
       "      <td>850.75</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>102.764517</td>\n",
       "      <td>483.0</td>\n",
       "      <td>87.341772</td>\n",
       "      <td>772.454918</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.867518</td>\n",
       "      <td>2.235663</td>\n",
       "      <td>0.185448</td>\n",
       "      <td>1.089745e-09</td>\n",
       "      <td>1.292279e-09</td>\n",
       "      <td>-0.245486</td>\n",
       "      <td>1.464245</td>\n",
       "      <td>2.646439</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.558617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.877121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540905</td>\n",
       "      <td>0.313889</td>\n",
       "      <td>0.299543</td>\n",
       "      <td>0.303346</td>\n",
       "      <td>0.149818</td>\n",
       "      <td>0.528792</td>\n",
       "      <td>0.553782</td>\n",
       "      <td>0.061531</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.225442</td>\n",
       "      <td>0.272936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5532.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.107618</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>229.811108</td>\n",
       "      <td>984.128846</td>\n",
       "      <td>1.487078e+04</td>\n",
       "      <td>5235.183646</td>\n",
       "      <td>0.935831</td>\n",
       "      <td>0.634775</td>\n",
       "      <td>0.279933</td>\n",
       "      <td>0.395210</td>\n",
       "      <td>2.341452</td>\n",
       "      <td>0.083376</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3909820 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         LABEL_code   MEAN  MEDIAN       STDEV       VAR  BURSTINESS      Q1  \\\n",
       "0               1.0  138.0    60.0  174.859944   30576.0    0.117816   60.00   \n",
       "1               1.0  137.0    60.0  173.965514   30265.0    0.118873   60.00   \n",
       "2               1.0  134.0    60.0  167.257287   27976.0    0.110395   60.00   \n",
       "3               1.0  161.0    60.0  226.735529   51410.0    0.169537   60.00   \n",
       "4               1.0  147.0    60.0  194.984615   38019.0    0.140312   60.00   \n",
       "...             ...    ...     ...         ...       ...         ...     ...   \n",
       "4009815         0.0  558.0   228.0  651.336319  424239.0    0.077180   74.75   \n",
       "4009816         0.0  135.0    60.0  155.836453   24285.0    0.071643   60.00   \n",
       "4009817         0.0  663.0   670.0  615.725588  379118.0   -0.036970  105.50   \n",
       "4009818         0.0  145.0    60.0  187.938820   35321.0    0.128969   60.00   \n",
       "4009819         0.0  553.0   388.5  568.287779  322951.0    0.013634   61.50   \n",
       "\n",
       "              Q3   MIN     MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0          60.00  60.0   451.0          391.0  60.0                126.710104   \n",
       "1          60.00  60.0   449.0          389.0  60.0                126.982127   \n",
       "2          60.00  60.0   434.0          374.0  60.0                124.818871   \n",
       "3          60.00  60.0   567.0          507.0  60.0                140.829521   \n",
       "4          60.00  60.0   496.0          436.0  60.0                132.642595   \n",
       "...          ...   ...     ...            ...   ...                       ...   \n",
       "4009815  1069.00  60.0  1460.0         1400.0  60.0                116.726939   \n",
       "4009816   142.50  60.0   561.0          501.0  60.0                115.434409   \n",
       "4009817  1122.50  60.0  1460.0         1400.0  60.0                 92.869621   \n",
       "4009818    60.00  60.0   685.0          625.0  60.0                129.612979   \n",
       "4009819   850.75  60.0  1440.0         1380.0  60.0                102.764517   \n",
       "\n",
       "         AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                     125.0          90.579710        208.710805   \n",
       "1                     124.0          90.510949        207.846578   \n",
       "2                     119.0          88.805970        201.373285   \n",
       "3                     162.0         100.621118        259.186805   \n",
       "4                     139.0          94.557823        228.217440   \n",
       "...                     ...                ...               ...   \n",
       "4009815               552.0          98.924731        815.526313   \n",
       "4009816               109.0          80.740741        201.183318   \n",
       "4009817               491.0          74.057315        874.914363   \n",
       "4009818               131.0          90.344828        231.869492   \n",
       "4009819               483.0          87.341772        772.454918   \n",
       "\n",
       "         PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                  0.800000            0.200000              0.446071   \n",
       "1                  0.800000            0.200000              0.442616   \n",
       "2                  0.800000            0.200000              0.442432   \n",
       "3                  0.800000            0.200000              0.445453   \n",
       "4                  0.800000            0.200000              0.446189   \n",
       "...                     ...                 ...                   ...   \n",
       "4009815            0.666667            0.333333              0.764582   \n",
       "4009816            0.727273            0.272727              0.481274   \n",
       "4009817            0.428571            0.571429              0.979332   \n",
       "4009818            0.769231            0.230769              0.452275   \n",
       "4009819            0.500000            0.500000              0.867518   \n",
       "\n",
       "         PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    2.024477              0.604699   \n",
       "1                    2.017641              0.620471   \n",
       "2                    2.044754              0.613386   \n",
       "3                    1.865610              0.654226   \n",
       "4                    1.954000              0.625200   \n",
       "...                       ...                   ...   \n",
       "4009815              2.220051              0.067912   \n",
       "4009816              2.213859              1.584614   \n",
       "4009817              2.142188             -0.187177   \n",
       "4009818              1.995330              1.726694   \n",
       "4009819              2.235663              0.185448   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      3.740753e-08                5.576385e-08   \n",
       "1                      1.519499e-07                2.265136e-07   \n",
       "2                      1.709759e-07                2.548758e-07   \n",
       "3                      3.431636e-08                5.115581e-08   \n",
       "4                      2.697919e-08                4.021821e-08   \n",
       "...                             ...                         ...   \n",
       "4009815                6.031605e-10                8.259115e-10   \n",
       "4009816                1.681505e-07                1.959530e-07   \n",
       "4009817                3.059922e-09                3.966112e-09   \n",
       "4009818                6.952779e-08                7.894562e-08   \n",
       "4009819                1.089745e-09                1.292279e-09   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000  2.084944  0.721928        0.310918        0.301789   \n",
       "1               0.000000  2.099872  0.721928        0.310918        0.301789   \n",
       "2               0.000000  2.100675  0.721928        0.310918        0.301789   \n",
       "3               0.000000  2.087654  0.721928        0.310918        0.301789   \n",
       "4               0.000000  2.084431  0.721928        0.310918        0.301789   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "4009815         0.027910  1.064402  2.251629        0.871049        0.484439   \n",
       "4009816        -0.818182  5.136262  1.277613        0.369313        0.435457   \n",
       "4009817        -0.096362  1.133416  2.235926        0.796453        0.406730   \n",
       "4009818         0.000000  5.448111  1.145110        0.309453        0.460809   \n",
       "4009819        -0.245486  1.464245  2.646439        0.796658        0.558617   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                          0.0   0.477121                  0.0   \n",
       "1                          0.0   0.477121                  0.0   \n",
       "2                          0.0   0.477121                  0.0   \n",
       "3                          0.0   0.477121                  0.0   \n",
       "4                          0.0   0.477121                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "4009815                    0.0   0.768727                  0.0   \n",
       "4009816                    0.0   0.573757                  0.0   \n",
       "4009817                    0.0   0.762836                  0.0   \n",
       "4009818                    0.0   0.531799                  0.0   \n",
       "4009819                    1.0   0.877121                  0.0   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                     1.0           0.214227                  0.812500   \n",
       "1                     1.0           0.214224                  0.812500   \n",
       "2                     1.0           0.214242                  0.812500   \n",
       "3                     1.0           0.214134                  0.812500   \n",
       "4                     1.0           0.214118                  0.812500   \n",
       "...                   ...                ...                       ...   \n",
       "4009815               0.0           0.408343                  0.375000   \n",
       "4009816               0.0           0.097322                  0.781250   \n",
       "4009817               0.0           0.322692                  0.291667   \n",
       "4009818               0.0           0.095351                  0.800000   \n",
       "4009819               0.0           0.540905                  0.313889   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0                3.502377            0.000132        0.000082        1.162747   \n",
       "1                3.502252            0.000373        0.000089        1.162257   \n",
       "2                3.502457            0.000143        0.000119        1.163852   \n",
       "3                3.498793            0.000088        0.000066        1.154518   \n",
       "4                3.498479            0.000089        0.000066        1.153209   \n",
       "...                   ...                 ...             ...             ...   \n",
       "4009815          0.185961            0.132806        0.130222        0.266964   \n",
       "4009816          1.789601            0.110076        0.053760        0.261340   \n",
       "4009817          0.085528            0.056286        0.050859        0.113753   \n",
       "4009818          1.690053            0.292666        0.290225        0.523556   \n",
       "4009819          0.299543            0.303346        0.149818        0.528792   \n",
       "\n",
       "          DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        16.348923        4.087231          0.581348       0.000050   \n",
       "1        16.348542        4.087135          0.581084       0.000089   \n",
       "2        16.348169        4.087042          0.581914       0.000024   \n",
       "3        16.339294        4.084823          0.577248       0.000022   \n",
       "4        16.339031        4.084758          0.576593       0.000023   \n",
       "...            ...             ...               ...            ...   \n",
       "4009815   0.455405        0.091081          0.130222       0.000149   \n",
       "4009816  18.388441        1.838844          0.053524       0.000002   \n",
       "4009817   0.265044        0.044174          0.028098       0.000089   \n",
       "4009818  17.724544        1.477045          0.040324       0.000002   \n",
       "4009819   0.553782        0.061531          0.000224       0.000002   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0            15.186176               0.269142         0.0          1.000000   \n",
       "1            15.186285               0.269133         0.0          1.000000   \n",
       "2            15.184317               0.269162         0.0          1.000000   \n",
       "3            15.184776               0.269008         0.0          1.000000   \n",
       "4            15.185822               0.268985         0.0          1.000000   \n",
       "...                ...                    ...         ...               ...   \n",
       "4009815       0.188441               0.483340         0.0          1.600000   \n",
       "4009816      18.069654               0.101764         0.0          1.200000   \n",
       "4009817       0.151291               0.291981         0.0          1.333333   \n",
       "4009818      17.030356               0.086730         0.0          1.000000   \n",
       "4009819       0.225442               0.272936         0.0          1.777778   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0               0.0        0.0                  1.0              631.0   \n",
       "1               0.0        0.0                  1.0              629.0   \n",
       "2               0.0        0.0                  1.0              614.0   \n",
       "3               0.0        0.0                  1.0              747.0   \n",
       "4               0.0        0.0                  1.0              676.0   \n",
       "...             ...        ...                  ...                ...   \n",
       "4009815         1.0        0.0                  0.0             3349.0   \n",
       "4009816         0.0        0.0                  0.0             1492.0   \n",
       "4009817         0.0        0.0                  0.0             4646.0   \n",
       "4009818         0.0        0.0                  0.0             1891.0   \n",
       "4009819         1.0        0.0                  0.0             5532.0   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL  TIME   MIN_POWER  MAX_POWER  \\\n",
       "0               1.0          0.0  0.0   0.0 -157.771646  31.720354   \n",
       "1               1.0          0.0  0.0   0.0 -204.860903  19.097306   \n",
       "2               1.0          0.0  0.0   0.0 -492.994408  46.269426   \n",
       "3               1.0          0.0  0.0   0.0  -23.642932  17.608265   \n",
       "4               1.0          0.0  0.0   0.0  -73.280065  27.167804   \n",
       "...             ...          ...  ...   ...         ...        ...   \n",
       "4009815         0.0          0.0  0.0   0.0    0.136528   0.157176   \n",
       "4009816         0.0          0.0  0.0   0.0    0.076046   0.248211   \n",
       "4009817         0.0          0.0  0.0   0.0   -4.452389  83.867125   \n",
       "4009818         0.0          0.0  0.0   0.0    0.028477   0.095561   \n",
       "4009819         0.0          0.0  0.0   0.0    0.024242   0.107618   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0              0.972981        0.878615       680.046116          0.000000   \n",
       "1              0.835107        0.903249       551.892856          0.000000   \n",
       "2              0.903845        0.834709       111.655553          0.000000   \n",
       "3              0.801532        0.783652       783.392450          0.000000   \n",
       "4              0.972981        0.899673       726.121994          0.000000   \n",
       "...                 ...             ...              ...               ...   \n",
       "4009815        0.006667        0.999801       729.574265       2025.401406   \n",
       "4009816        0.992848        0.442144       782.489908       2084.839909   \n",
       "4009817        0.030705        0.030507        -2.974423          0.000000   \n",
       "4009818        0.050771        0.963644       300.660378       1196.621611   \n",
       "4009819        0.006667        0.999801       229.811108        984.128846   \n",
       "\n",
       "         SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0             2.161064e+07     -313744.025253          0.878615   \n",
       "1             2.223578e+07     -325241.042104          0.903249   \n",
       "2             1.824497e+07     -292319.976102          0.834709   \n",
       "3             3.829273e+06      -42829.060112          0.783652   \n",
       "4             1.474637e+07     -225777.997646          0.899673   \n",
       "...                    ...                ...               ...   \n",
       "4009815       1.403900e+04        3441.134454          0.006667   \n",
       "4009816       4.346456e+04       -4265.327353          0.276257   \n",
       "4009817       2.457700e+07      349038.281575          0.030507   \n",
       "4009818       8.771147e+03           0.539386          0.591939   \n",
       "4009819       1.487078e+04        5235.183646          0.935831   \n",
       "\n",
       "         SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0                 0.402802         0.209271        0.000000      233.222079   \n",
       "1                 0.325064         0.175965        0.000000      173.016423   \n",
       "2                -1.611567         0.000000        0.000000     2071.971558   \n",
       "3                 0.485833         0.286630        0.000000      112.384700   \n",
       "4                 0.442509         0.252736        0.000000      187.074650   \n",
       "...                    ...              ...             ...             ...   \n",
       "4009815           0.511568         0.287497        0.027474        1.077178   \n",
       "4009816           0.506199         0.284613        0.011316        1.586032   \n",
       "4009817          15.857747         0.000000        0.000000  -140980.501434   \n",
       "4009818           0.566854         0.272499        0.275930        1.589183   \n",
       "4009819           0.634775         0.279933        0.395210        2.341452   \n",
       "\n",
       "         SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  \n",
       "0           602.608368           -0.094367               1.0  \n",
       "1           728.468984            0.068143               1.0  \n",
       "2          1723.979380           -0.069136               1.0  \n",
       "3           296.101898           -0.017880               1.0  \n",
       "4           401.970775           -0.073308               1.0  \n",
       "...                ...                 ...               ...  \n",
       "4009815       0.020648            0.993135               1.0  \n",
       "4009816       5.450236           -0.550704               1.0  \n",
       "4009817     176.604046           -0.000199               1.0  \n",
       "4009818       2.146934            0.912873               1.0  \n",
       "4009819       0.083376            0.993135               1.0  \n",
       "\n",
       "[3909820 rows x 70 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:37<00:00,  2.64trial/s, best loss: -0.5302013422818792]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2639071318500841, 'gamma': 7.135696061068099, 'max_depth': 28.0, 'min_child_weight': 2.0, 'n_estimators': 260.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4563473228001519}\n"
     ]
    }
   ],
   "source": [
    "df_to_tunne = df_features.sample(100000)\n",
    "best_hyperparams = tunne_together_XGBoost_classification(df_to_tunne, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "        for F, N in zip(\n",
    "                [ features], # statistics_features, time_features, distribution_features, frequency_features, behavior_features, \n",
    "                [ \"All\"], # \"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \n",
    "        ):\n",
    "                X_train = X_train_a[F]\n",
    "                X_test = X_test_a[F] \n",
    "                model = XGBClassifier(\n",
    "                        n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "                        reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "                        colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "                        # subsample=int(best_hyperparams['subsample']),\n",
    "                        # eta=best_hyperparams['eta'],\n",
    "                )\n",
    "                model.fit(X_train, y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "                precision = precision_score(y_test, y_pred)  * 100\n",
    "                recall = recall_score(y_test, y_pred)  * 100\n",
    "                F1 = f1_score(y_test, y_pred)  * 100\n",
    "                if N == \"All\":\n",
    "                    tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\hline\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_f1 = F1\n",
    "                elif N in [\"Statistical-based\", \"Distribution-based\", \"Behavior-based\"]:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                else:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \\\\rowcolor{LigthGray} \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "        if best_results[0] < tmp_f1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (tmp_f1, tmp_str, matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "\n",
      "\n",
      "99.95793016407235\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}100.00 & 99.99 & 99.92 & 99.96 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 & 99.87 & 99.93 \\\\\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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DoS\"\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": 24,
   "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",
    "\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]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\"]\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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.34191766, 0.024488715, 0.10490872, 0.080934174, 0.44775066]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEAN                0.000000\n",
       "TIME                0.000000\n",
       "CNT_ZEROS           0.000000\n",
       "MAX_POWER_FREQ      0.000000\n",
       "BURSTINESS          0.000000\n",
       "                      ...   \n",
       "CNT_DISTRIBUTION    0.042615\n",
       "P_BENFORD           0.057364\n",
       "MIN                 0.068645\n",
       "Q1                  0.154302\n",
       "DIRECTIONS          0.379166\n",
       "Length: 69, dtype: float32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MEAN': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'BURSTINESS': 0.0,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.0,\n",
       " 'Q3_SCALED_TIME': 0.0,\n",
       " 'MAX_POWER': 0.00022561535297427326,\n",
       " 'MIN_MINUS_MAX': 0.0002872903423849493,\n",
       " 'PERCENT_BELOW_MEAN': 0.00028834654949605465,\n",
       " 'HURST_EXPONENT': 0.00031865498749539256,\n",
       " 'ROOT_MEAN_SQUARE': 0.00042035794467665255,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0004552518075797707,\n",
       " 'SPECTRAL_ROLLOFF': 0.00046552307321690023,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0004739263094961643,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.000503810471855104,\n",
       " 'TRANSIENTS': 0.0006507473299279809,\n",
       " 'VAR': 0.0007091192528605461,\n",
       " 'TIME_DISTRIBUTION': 0.0007223737775348127,\n",
       " 'DURATION': 0.0007495176396332681,\n",
       " 'SPECTRAL_ENTROPY': 0.0007908997940830886,\n",
       " 'MIN_POWER': 0.0009815796511247754,\n",
       " 'MIN_DIFFTIMES': 0.0010421995539218187,\n",
       " 'MEDIAN_DIFFTIMES': 0.0010627275332808495,\n",
       " 'MEAN_SCALED_TIME': 0.0011828802525997162,\n",
       " 'MIN_POWER_FREQ': 0.0012802471173927188,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0013025092193856835,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.00162114214617759,\n",
       " 'ENTROPY': 0.0016409424133598804,\n",
       " 'SIG_SPACES': 0.0017406517872586846,\n",
       " 'SPECTRAL_SLOPE': 0.0017883855616673827,\n",
       " 'GALTON_SKEWNESS': 0.0017994934460148215,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0019778574351221323,\n",
       " 'SPECTRAL_KURTOSIS': 0.00218449579551816,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0025956921745091677,\n",
       " 'MAX_DIFFTIMES': 0.002668248023837805,\n",
       " 'PERCENT_DEVIATION': 0.0026685320772230625,\n",
       " 'AVERAGE_DISPERSION': 0.0028234669007360935,\n",
       " 'STDEV': 0.00289007811807096,\n",
       " 'NORMAL_DISTRIBUTION': 0.002959145000204444,\n",
       " 'SPECTRAL_ENERGY': 0.0030119302682578564,\n",
       " 'SCALED_ENTROPY': 0.0035748605150729418,\n",
       " 'Q1_SCALED_TIME': 0.00366981397382915,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.004307453986257315,\n",
       " 'VAL': 0.005269053392112255,\n",
       " 'MEAN_DIFFTIMES': 0.005419556051492691,\n",
       " 'SPECTRAL_FLUX': 0.005904065445065498,\n",
       " 'MEDIAN_SCALED_TIME': 0.006098078563809395,\n",
       " 'SPECTRAL_CENTROID': 0.0069570462219417095,\n",
       " 'MAX': 0.007897238247096539,\n",
       " 'PERIODICITY': 0.00909491814672947,\n",
       " 'Q3': 0.010982056148350239,\n",
       " 'KURTOSIS': 0.012920503504574299,\n",
       " 'SPECTRAL_SPREAD': 0.013890516944229603,\n",
       " 'MEDIAN': 0.01512926071882248,\n",
       " 'SPECTRAL_CREST': 0.016632886603474617,\n",
       " 'PERCENT_ABOVE_MEAN': 0.022215766832232475,\n",
       " 'BIGGEST_CNT_1_SEC': 0.022413350641727448,\n",
       " 'MODE': 0.024313760921359062,\n",
       " 'PERIODICITY_SCDF': 0.02682098187506199,\n",
       " 'SWITCHING_METRIC': 0.02811354771256447,\n",
       " 'CNT_DISTRIBUTION': 0.0426148846745491,\n",
       " 'P_BENFORD': 0.057364486157894135,\n",
       " 'MIN': 0.06864478439092636,\n",
       " 'Q1': 0.15430155396461487,\n",
       " 'DIRECTIONS': 0.3791658878326416}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=10):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:39<00:00,  2.52trial/s, best loss: -0.9663409337676439]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.015018147458956216, 'gamma': 1.1882122634590884, 'max_depth': 10.0, 'min_child_weight': 20.0, 'n_estimators': 140.0, 'reg_alpha': 1.0, 'reg_lambda': 0.11183740718596905}\n",
      "100%|██████████| 100/100 [00:20<00:00,  4.88trial/s, best loss: -0.7362637362637362]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.1765897313091065, 'gamma': 3.3731328276309545, 'max_depth': 16.0, 'min_child_weight': 10.0, 'n_estimators': 180.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3281057119053379}\n",
      "100%|██████████| 100/100 [00:39<00:00,  2.56trial/s, best loss: -0.9271676300578033]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7022828306619356, 'gamma': 4.0978281686169105, 'max_depth': 10.0, 'min_child_weight': 11.0, 'n_estimators': 260.0, 'reg_alpha': 2.0, 'reg_lambda': 0.7989693325334363}\n",
      "100%|██████████| 100/100 [00:41<00:00,  2.39trial/s, best loss: -0.9440559440559442]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3571947874628909, 'gamma': 6.026288528761174, 'max_depth': 16.0, 'min_child_weight': 7.0, 'n_estimators': 360.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5764833571112791}\n",
      "100%|██████████| 100/100 [00:27<00:00,  3.60trial/s, best loss: -0.7156462585034014]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6452111044783672, 'gamma': 1.4986891124908173, 'max_depth': 24.0, 'min_child_weight': 3.0, 'n_estimators': 400.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8682316454146011}\n",
      "100%|██████████| 100/100 [01:26<00:00,  1.16trial/s, best loss: -0.9536423841059603]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4664497972692183, 'gamma': 3.4339569900306777, 'max_depth': 28.0, 'min_child_weight': 11.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.5820295456311807}\n",
      "100%|██████████| 100/100 [00:50<00:00,  1.96trial/s, best loss: -0.9664429530201343]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.606790607024243, 'gamma': 3.909700104689977, 'max_depth': 14.0, 'min_child_weight': 13.0, 'n_estimators': 360.0, 'reg_alpha': 19.0, 'reg_lambda': 0.5871051072966477}\n",
      "100%|██████████| 100/100 [01:41<00:00,  1.01s/trial, best loss: -0.9397874852420307]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8685116317461612, 'gamma': 2.9321007861047823, 'max_depth': 16.0, 'min_child_weight': 3.0, 'n_estimators': 280.0, 'reg_alpha': 1.0, 'reg_lambda': 0.5029209519338268}\n",
      "100%|██████████| 100/100 [01:52<00:00,  1.12s/trial, best loss: -0.9446902654867256]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7531346727268375, 'gamma': 7.185875076250563, 'max_depth': 24.0, 'min_child_weight': 4.0, 'n_estimators': 120.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4060229197584918}\n",
      "100%|██████████| 100/100 [00:16<00:00,  6.04trial/s, best loss: -0.9967707212055974]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.1420498382540165, 'gamma': 3.175942180000594, 'max_depth': 20.0, 'min_child_weight': 0.0, 'n_estimators': 380.0, 'reg_alpha': 6.0, 'reg_lambda': 0.7184810680979276}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.without_flow_header(FR.botnet_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.doh_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.dos_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.dns_malware_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.tor_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.vpn_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.all_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.combo_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.combo_2_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.crypto_features))\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"DoS\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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