main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "27308531",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c2c98b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import joblib\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "pd.set_option('display.max_columns', 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "48426681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103507, 25)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('dataset.csv')\n",
    "result=list(data['pktcount']/data['dur'])\n",
    "data['pktCount/dur']=result\n",
    "result=list(data['bytecount']/data['dur'])\n",
    "data['byteCount/dur']=result\n",
    "\n",
    "# Replacing infinite with nan\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "  \n",
    "# Dropping all the rows with nan values\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d09c5a6f",
   "metadata": {
    "scrolled": true
   },
   "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>dt</th>\n",
       "      <th>switch</th>\n",
       "      <th>src</th>\n",
       "      <th>dst</th>\n",
       "      <th>pktcount</th>\n",
       "      <th>bytecount</th>\n",
       "      <th>dur</th>\n",
       "      <th>dur_nsec</th>\n",
       "      <th>tot_dur</th>\n",
       "      <th>flows</th>\n",
       "      <th>packetins</th>\n",
       "      <th>pktperflow</th>\n",
       "      <th>byteperflow</th>\n",
       "      <th>pktrate</th>\n",
       "      <th>Pairflow</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>port_no</th>\n",
       "      <th>tx_bytes</th>\n",
       "      <th>rx_bytes</th>\n",
       "      <th>tx_kbps</th>\n",
       "      <th>rx_kbps</th>\n",
       "      <th>tot_kbps</th>\n",
       "      <th>label</th>\n",
       "      <th>pktCount/dur</th>\n",
       "      <th>byteCount/dur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11425</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0.0.1</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>45304</td>\n",
       "      <td>48294064</td>\n",
       "      <td>100</td>\n",
       "      <td>716000000</td>\n",
       "      <td>1.010000e+11</td>\n",
       "      <td>3</td>\n",
       "      <td>1943</td>\n",
       "      <td>13535</td>\n",
       "      <td>14428310</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>UDP</td>\n",
       "      <td>3</td>\n",
       "      <td>143928631</td>\n",
       "      <td>3917</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>453.040000</td>\n",
       "      <td>482940.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11605</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0.0.1</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>126395</td>\n",
       "      <td>134737070</td>\n",
       "      <td>280</td>\n",
       "      <td>734000000</td>\n",
       "      <td>2.810000e+11</td>\n",
       "      <td>2</td>\n",
       "      <td>1943</td>\n",
       "      <td>13531</td>\n",
       "      <td>14424046</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>UDP</td>\n",
       "      <td>4</td>\n",
       "      <td>3842</td>\n",
       "      <td>3520</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>451.410714</td>\n",
       "      <td>481203.821429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11425</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0.0.2</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>90333</td>\n",
       "      <td>96294978</td>\n",
       "      <td>200</td>\n",
       "      <td>744000000</td>\n",
       "      <td>2.010000e+11</td>\n",
       "      <td>3</td>\n",
       "      <td>1943</td>\n",
       "      <td>13534</td>\n",
       "      <td>14427244</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>UDP</td>\n",
       "      <td>1</td>\n",
       "      <td>3795</td>\n",
       "      <td>1242</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>451.665000</td>\n",
       "      <td>481474.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11425</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0.0.2</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>90333</td>\n",
       "      <td>96294978</td>\n",
       "      <td>200</td>\n",
       "      <td>744000000</td>\n",
       "      <td>2.010000e+11</td>\n",
       "      <td>3</td>\n",
       "      <td>1943</td>\n",
       "      <td>13534</td>\n",
       "      <td>14427244</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>UDP</td>\n",
       "      <td>2</td>\n",
       "      <td>3688</td>\n",
       "      <td>1492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>451.665000</td>\n",
       "      <td>481474.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11425</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0.0.2</td>\n",
       "      <td>10.0.0.8</td>\n",
       "      <td>90333</td>\n",
       "      <td>96294978</td>\n",
       "      <td>200</td>\n",
       "      <td>744000000</td>\n",
       "      <td>2.010000e+11</td>\n",
       "      <td>3</td>\n",
       "      <td>1943</td>\n",
       "      <td>13534</td>\n",
       "      <td>14427244</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>UDP</td>\n",
       "      <td>3</td>\n",
       "      <td>3413</td>\n",
       "      <td>3665</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>451.665000</td>\n",
       "      <td>481474.890000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dt  switch       src       dst  pktcount  bytecount  dur   dur_nsec  \\\n",
       "0  11425       1  10.0.0.1  10.0.0.8     45304   48294064  100  716000000   \n",
       "1  11605       1  10.0.0.1  10.0.0.8    126395  134737070  280  734000000   \n",
       "2  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n",
       "3  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n",
       "4  11425       1  10.0.0.2  10.0.0.8     90333   96294978  200  744000000   \n",
       "\n",
       "        tot_dur  flows  packetins  pktperflow  byteperflow  pktrate  Pairflow  \\\n",
       "0  1.010000e+11      3       1943       13535     14428310      451         0   \n",
       "1  2.810000e+11      2       1943       13531     14424046      451         0   \n",
       "2  2.010000e+11      3       1943       13534     14427244      451         0   \n",
       "3  2.010000e+11      3       1943       13534     14427244      451         0   \n",
       "4  2.010000e+11      3       1943       13534     14427244      451         0   \n",
       "\n",
       "  Protocol  port_no   tx_bytes  rx_bytes  tx_kbps  rx_kbps  tot_kbps  label  \\\n",
       "0      UDP        3  143928631      3917        0      0.0       0.0      0   \n",
       "1      UDP        4       3842      3520        0      0.0       0.0      0   \n",
       "2      UDP        1       3795      1242        0      0.0       0.0      0   \n",
       "3      UDP        2       3688      1492        0      0.0       0.0      0   \n",
       "4      UDP        3       3413      3665        0      0.0       0.0      0   \n",
       "\n",
       "   pktCount/dur  byteCount/dur  \n",
       "0    453.040000  482940.640000  \n",
       "1    451.410714  481203.821429  \n",
       "2    451.665000  481474.890000  \n",
       "3    451.665000  481474.890000  \n",
       "4    451.665000  481474.890000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c212a92b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>switch</th>\n",
       "      <th>pktcount</th>\n",
       "      <th>bytecount</th>\n",
       "      <th>dur</th>\n",
       "      <th>dur_nsec</th>\n",
       "      <th>tot_dur</th>\n",
       "      <th>flows</th>\n",
       "      <th>packetins</th>\n",
       "      <th>pktperflow</th>\n",
       "      <th>byteperflow</th>\n",
       "      <th>pktrate</th>\n",
       "      <th>Pairflow</th>\n",
       "      <th>port_no</th>\n",
       "      <th>tx_bytes</th>\n",
       "      <th>rx_bytes</th>\n",
       "      <th>tx_kbps</th>\n",
       "      <th>rx_kbps</th>\n",
       "      <th>tot_kbps</th>\n",
       "      <th>label</th>\n",
       "      <th>pktCount/dur</th>\n",
       "      <th>byteCount/dur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>1.035070e+05</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "      <td>103507.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17997.346943</td>\n",
       "      <td>4.214652</td>\n",
       "      <td>52949.769842</td>\n",
       "      <td>3.813851e+07</td>\n",
       "      <td>323.159342</td>\n",
       "      <td>4.613735e+08</td>\n",
       "      <td>3.235481e+11</td>\n",
       "      <td>5.668631</td>\n",
       "      <td>5200.534292</td>\n",
       "      <td>6414.635744</td>\n",
       "      <td>4.729172e+06</td>\n",
       "      <td>213.306259</td>\n",
       "      <td>0.604181</td>\n",
       "      <td>2.337455</td>\n",
       "      <td>9.388806e+07</td>\n",
       "      <td>9.391601e+07</td>\n",
       "      <td>1004.058885</td>\n",
       "      <td>1004.098428</td>\n",
       "      <td>2008.157313</td>\n",
       "      <td>0.389375</td>\n",
       "      <td>252.017792</td>\n",
       "      <td>188156.047010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11963.141732</td>\n",
       "      <td>1.957204</td>\n",
       "      <td>52060.047726</td>\n",
       "      <td>4.877646e+07</td>\n",
       "      <td>283.793460</td>\n",
       "      <td>2.768589e+08</td>\n",
       "      <td>2.836776e+11</td>\n",
       "      <td>2.951304</td>\n",
       "      <td>5268.963828</td>\n",
       "      <td>7187.717686</td>\n",
       "      <td>7.367902e+06</td>\n",
       "      <td>239.622206</td>\n",
       "      <td>0.489028</td>\n",
       "      <td>1.082924</td>\n",
       "      <td>1.522918e+08</td>\n",
       "      <td>1.332648e+08</td>\n",
       "      <td>2428.584644</td>\n",
       "      <td>2054.970744</td>\n",
       "      <td>3144.913299</td>\n",
       "      <td>0.487611</td>\n",
       "      <td>188.222092</td>\n",
       "      <td>205067.884489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2488.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.024000e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-130933.000000</td>\n",
       "      <td>-1.464426e+08</td>\n",
       "      <td>-4365.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.527000e+03</td>\n",
       "      <td>8.560000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7128.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>7.947800e+04</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>2.340000e+08</td>\n",
       "      <td>1.300000e+11</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1943.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.842000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.812000e+03</td>\n",
       "      <td>3.539000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.977528</td>\n",
       "      <td>95.797753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11965.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42895.000000</td>\n",
       "      <td>6.443094e+06</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>4.180000e+08</td>\n",
       "      <td>2.530000e+11</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3024.000000</td>\n",
       "      <td>8314.000000</td>\n",
       "      <td>5.478100e+05</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.609657e+06</td>\n",
       "      <td>1.421994e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>298.455556</td>\n",
       "      <td>29671.707809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29982.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>94814.000000</td>\n",
       "      <td>7.600244e+07</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>7.020000e+08</td>\n",
       "      <td>4.130000e+11</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7385.000000</td>\n",
       "      <td>10015.000000</td>\n",
       "      <td>9.727070e+06</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.356555e+08</td>\n",
       "      <td>1.439286e+08</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>557.000000</td>\n",
       "      <td>3838.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>434.847403</td>\n",
       "      <td>352350.357675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42935.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>260006.000000</td>\n",
       "      <td>1.471280e+08</td>\n",
       "      <td>1881.000000</td>\n",
       "      <td>9.990000e+08</td>\n",
       "      <td>1.880000e+12</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>25224.000000</td>\n",
       "      <td>19190.000000</td>\n",
       "      <td>1.495387e+07</td>\n",
       "      <td>639.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.269982e+09</td>\n",
       "      <td>9.905962e+08</td>\n",
       "      <td>20580.000000</td>\n",
       "      <td>16577.000000</td>\n",
       "      <td>20580.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2383.000000</td>\n",
       "      <td>673712.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dt         switch       pktcount     bytecount  \\\n",
       "count  103507.000000  103507.000000  103507.000000  1.035070e+05   \n",
       "mean    17997.346943       4.214652   52949.769842  3.813851e+07   \n",
       "std     11963.141732       1.957204   52060.047726  4.877646e+07   \n",
       "min      2488.000000       1.000000       0.000000  0.000000e+00   \n",
       "25%      7128.000000       3.000000     810.000000  7.947800e+04   \n",
       "50%     11965.000000       4.000000   42895.000000  6.443094e+06   \n",
       "75%     29982.000000       5.000000   94814.000000  7.600244e+07   \n",
       "max     42935.000000      10.000000  260006.000000  1.471280e+08   \n",
       "\n",
       "                 dur      dur_nsec       tot_dur          flows  \\\n",
       "count  103507.000000  1.035070e+05  1.035070e+05  103507.000000   \n",
       "mean      323.159342  4.613735e+08  3.235481e+11       5.668631   \n",
       "std       283.793460  2.768589e+08  2.836776e+11       2.951304   \n",
       "min         1.000000  0.000000e+00  1.024000e+09       2.000000   \n",
       "25%       129.000000  2.340000e+08  1.300000e+11       3.000000   \n",
       "50%       252.000000  4.180000e+08  2.530000e+11       5.000000   \n",
       "75%       413.000000  7.020000e+08  4.130000e+11       7.000000   \n",
       "max      1881.000000  9.990000e+08  1.880000e+12      17.000000   \n",
       "\n",
       "           packetins     pktperflow   byteperflow        pktrate  \\\n",
       "count  103507.000000  103507.000000  1.035070e+05  103507.000000   \n",
       "mean     5200.534292    6414.635744  4.729172e+06     213.306259   \n",
       "std      5268.963828    7187.717686  7.367902e+06     239.622206   \n",
       "min         4.000000 -130933.000000 -1.464426e+08   -4365.000000   \n",
       "25%      1943.000000      29.000000  2.842000e+03       0.000000   \n",
       "50%      3024.000000    8314.000000  5.478100e+05     277.000000   \n",
       "75%      7385.000000   10015.000000  9.727070e+06     333.000000   \n",
       "max     25224.000000   19190.000000  1.495387e+07     639.000000   \n",
       "\n",
       "            Pairflow        port_no      tx_bytes      rx_bytes  \\\n",
       "count  103507.000000  103507.000000  1.035070e+05  1.035070e+05   \n",
       "mean        0.604181       2.337455  9.388806e+07  9.391601e+07   \n",
       "std         0.489028       1.082924  1.522918e+08  1.332648e+08   \n",
       "min         0.000000       1.000000  2.527000e+03  8.560000e+02   \n",
       "25%         0.000000       1.000000  4.812000e+03  3.539000e+03   \n",
       "50%         1.000000       2.000000  4.609657e+06  1.421994e+07   \n",
       "75%         1.000000       3.000000  1.356555e+08  1.439286e+08   \n",
       "max         1.000000       5.000000  1.269982e+09  9.905962e+08   \n",
       "\n",
       "             tx_kbps        rx_kbps       tot_kbps          label  \\\n",
       "count  103507.000000  103507.000000  103507.000000  103507.000000   \n",
       "mean     1004.058885    1004.098428    2008.157313       0.389375   \n",
       "std      2428.584644    2054.970744    3144.913299       0.487611   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       4.000000       0.000000   \n",
       "75%       255.000000     557.000000    3838.000000       1.000000   \n",
       "max     20580.000000   16577.000000   20580.000000       1.000000   \n",
       "\n",
       "        pktCount/dur  byteCount/dur  \n",
       "count  103507.000000  103507.000000  \n",
       "mean      252.017792  188156.047010  \n",
       "std       188.222092  205067.884489  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.977528      95.797753  \n",
       "50%       298.455556   29671.707809  \n",
       "75%       434.847403  352350.357675  \n",
       "max      2383.000000  673712.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cb1eccd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    63204\n",
       "1    40303\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0-Benign  1-Malicious\n",
    "data.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "22dc70e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Benign\",'Malicious']\n",
    "sizes = [dict(data.label.value_counts())[0], dict(data.label.value_counts())[1]]\n",
    "plot.figure(figsize = (5,5))\n",
    "plot.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "plot.legend([\"Benign\",\"Malicious\"])\n",
    "plot.title('The percentage of Benign and Malicious Requests')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "76879732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of requests from different IP address')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 7))\n",
    "#for All\n",
    "plot.barh(list(dict(data.src.value_counts()).keys()), dict(data.src.value_counts()).values())\n",
    "\n",
    "for idx, val in enumerate(dict(data.src.value_counts()).values()):\n",
    "    plot.text(x = val, y = idx-0.2, s = str(val), size = 13)\n",
    "\n",
    "plot.xlabel('Number of Requests')\n",
    "plot.ylabel('IP address of sender')\n",
    "plot.legend(['All'])\n",
    "plot.title('Number of requests from different IP address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d7370109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Malicious requests from different IP address')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uaNiqVatSt25dQkJC6NatG/Hx8UXGzp49m1atWlG9enWqVKlC1apVWbdundXvzvr166lVqxaPP/64Vez139WNPHfX35el+Zw5OTnRuHFj3n77baKjo/n555+tll8QEZHyTYk/ERGREnj66adp1aoVY8aMIScnp1TavH7H0GrVqmFra0v16tXzlF+8eDFPvIuLS75ludO0/vzzTwBeeeUV44/a3Nfw4cMBOHnypFV8cabhApw6dSrfuq6ursbxm/XUU09RvXp13n33Xf7zn//wwgsvFFg3ICCA2NhYBg4cyH//+1+2bNnC1q1bqVu3bomTZSdOnAAo8YYfp06dokqVKnmmBppMJqvvpSTef/99XnvtNVauXEmnTp1wcnKiV69e7N27t1jx139HufdEmzZt8twTy5YtM+6H3L4WdI/diJLcjzdz3R06dGDlypVGgrN+/fo0a9asyHURi3L9Z5n7GRX0HFy9epUzZ85Yld/sM3/9+atUqULt2rWtym/0+ynMn3/+icViwdnZOc9398MPPxTrd+TPP//k119/zRNfq1YtLBZLnjauvy4As9lc4ue5OGxsbNi6dStbt27l119/5ezZs3z55ZdFbuoRHR3NsGHD8Pb2Zvny5fzwww9s3bqVrl27WvXz1KlTVlOhc13/Xd3Ic1fQM14az5nJZGLdunX4+/szdepUWrVqRd26dXnxxRc5d+5coZ+NiIiUPa3xJyIiUgImk4kpU6bg5+fHhx9+mOd47h/u12+GURoJsILktzB8Wlqa8Qdz7rp2YWFhBa4Vdvfdd1u9L+6Or7Vr1+bYsWN5ylNTU63OfTNsbW15+umniYqKws7OrsBrSE9PZ/Xq1UyYMIHXX3/dKL906ZKxBldJ5Cbujhw5QoMGDYodV7t2bS5fvsyJEyeskn8Wi4W0tDRjJBpcS2Dkt3HK9fdLjRo1mDhxIhMnTuTPP/80Ruf06NGD3bt3F9mn67/P3O/l888/p1GjRoVeCxR8j/1VQff+9YmcktyPN3vdPXv2pGfPnly6dIkffviBqKgoAgICcHd3p127dkXG5+f6zzL3MyroOahUqRKOjo43dK7iyL3fTp06ZZUkK2jDiJtRp04dTCYT//vf//Jdp/L6svx+R+rUqYONjQ3z5s0r8BxlpVKlSrRu3brEcZ988gkdO3Zk1qxZVuXXJ8Vq167Nli1b8sRf/12V5LnLVdAzXlrPWaNGjYxNjf744w8+/fRTIiIiyM7OZvbs2fm2LyIi5YNG/ImIiJTQv/71L/z8/Jg0aRKZmZlWx5ydnalevTq//vqrVfmqVatuWX+WLFmCxWIx3h88eJBNmzYZC8rffffdNGnShF9++YXWrVvn+/rrVLqS6Ny5M7///js//fSTVfnHH3+MyWSiU6dON3xdfzVs2DB69OjB+PHj84yKymUymbBYLHmSDx999BFXrlwp8Tm7dOlC5cqV8/wxX5TOnTsD15IBf7V8+XLOnz9vHIdru6pef698++23ee6rv3J2diY4OJhnnnmGPXv25JlGWhz+/v5UqVKF/fv3F3hPwLV7p169egXeY3+VO/36+uv54osvrN7f6P1Y0HXnft+FjQAzm834+voyZcoUgHx3Lr5Rd999N25ubixevNjqMzp//jzLly83dvq9VXKfsdyNSHItXry41M/VvXt3LBYLR48ezfd7u++++4rVxv79+6ldu3a+bRQ0jb8wxbkHbiWTyZTnd+fXX3/Ns6tzp06dOHfuXJ5n4vrvqiTPXUFK+zn7q7vuuouxY8dy33335fntFxGR8kcj/kRERG7AlClTeOCBBzh+/Dj33nuvUZ67/tW8efNo3LgxLVq0YMuWLbfkj/Bcx48f54knnmDQoEGkp6czYcIEqlevTlhYmFHngw8+oFu3bvj7+xMcHIybmxunT59m165d/PTTT3z22Wc3dO6XXnqJjz/+mMcee4xJkybRqFEjvvzyS2bOnMmwYcO46667SuUa77//flauXFloHTs7Ozp06MDbb79NnTp1cHd3JzExkblz5+Lg4FDic7q7uxMeHs4bb7zBhQsXeOaZZ7C3t+f333/n5MmTTJw4Md84Pz8//P39ee2118jIyKB9+/bGrr4tW7YkMDDQqBsYGMi4ceMYP348vr6+/P7778TGxmJvb2/Vpre3N927d6d58+Y4Ojqya9cuFi5ceMNJJXd3dyZNmsSYMWM4cOAAXbt2xdHRkT///JMtW7YYI4AqVarEG2+8wcCBA4177OzZs0REROSZcuji4sK//vUvoqKicHR0pFGjRqxbt44VK1bkOX9x78fiXHdusmnKlCl069aNypUr07x5c958802OHDlC586dqV+/PmfPnuW9996jatWq+Pr6lvgzK0ilSpWYOnUq/fv3p3v37gwZMoRLly7x9ttvc/bsWd56661SO1d+unTpQocOHRg9ejTnz5+ndevWfP/99yxcuLDUz9W+fXsGDx7Mc889x7Zt2+jQoQM1atTg2LFjbNy4kfvuu49hw4YV2kZISAjLly+nQ4cOvPTSSzRv3pyrV69y6NAh1qxZw8svv4y3t3eJ+pV7D7z33nsMGDCAqlWrcvfdd9/wP2iUVPfu3XnjjTeYMGECvr6+7Nmzh0mTJuHh4cHly5eNekFBQbz77rsEBQUxefJkmjRpwldffcV///tfq/ZK8twVprSes19//ZWRI0fSp08fmjRpQrVq1fj222/59ddfrUZXi4hI+aTEn4iIyA1o2bIlzzzzTL4JvXfeeQeAqVOnkpmZySOPPMLq1atvaCRLcURGRrJ161aee+45MjIyaNu2LUuXLqVx48ZGnU6dOrFlyxYmT55MSEgIZ86coXbt2nh5edG3b98bPnfdunXZtGkTYWFhhIWFkZGRgaenJ1OnTiU0NLQ0Lq9EFi9ezKhRoxg9ejSXL1+mffv2JCQk8Nhjj91Qe5MmTaJJkyZMnz6d/v37U6VKFZo0acKLL75YYIzJZGLlypVEREQwf/58Jk+eTJ06dQgMDCQyMtJqZNCrr75KRkYGcXFxTJs2jbZt2/Lpp5/Ss2dPqzYfeeQRvvjiC959912ysrJwc3MjKCiIMWPG3NB1wbUpgF5eXrz33nssWbKES5cu4eLiQps2bRg6dKhRL3dNxSlTptC7d28jIZqYmJhnI4aFCxfy73//m9dee40rV67Qo0cPlixZkmf6ZHHvx+Jcd0BAAN9//z0zZ85k0qRJWCwWkpOT8fb2Ztu2bbz22mucOHECBwcHWrduzbfffmuVrC8NAQEB1KhRg6ioKPr160flypV58MEHWb9+/Q1vglJclSpV4osvviA0NJSpU6eSnZ1N+/bt+eqrr7jnnntK/XwffPABDz74IB988AEzZ87k6tWruLq60r59+zyb/OSnRo0a/O9//+Ott97iww8/JDk5GRsbGxo2bMi//vWvG/qd7NixI2FhYSxYsIA5c+Zw9epV1q9fb4x6vtXGjBlDVlYWc+fOZerUqXh5eTF79mzi4+OtnhFbW1u+/fZbRo0axeuvv47JZKJLly4sXbo0z31SkueuIKX1nLm4uNC4cWNmzpzJ4cOHMZlMeHp68s477/Dvf//75j48ERG55UyWv44fFxEREREphuDgYL777jtSUlLKuisiIiIiUgCt8SciIiIiIiIiIlIBKfEnIiIiIiIiIiJSAWmqr4iIiIiIiIiISAWkEX8iIiIiIiIiIiIVkBJ/IiIiIiIiIiIiFZASfyIiIiIiIiIiIhVQlbLugBTt6tWrpKamUqtWLUwmU1l3R0REREREREREyojFYuHcuXO4urpSqVLhY/qU+LsNpKam0qBBg7LuhoiIiIiIiIiIlBOHDx+mfv36hdZR4u82UKtWLeDaF2pnZ1fGvRERERERERERkbKSkZFBgwYNjHxRYZT4uw3kTu+1s7NT4k9ERERERERERIq1HJw29xAREREREREREamAlPgTERERERERERGpgJT4ExERERERERERqYC0xp+IiIiIiIiISAVjsVi4fPkyV65cKeuuSAlVrlyZKlWqFGsNv6Io8SciIiIiIiIiUoFkZ2dz7NgxsrKyyrorcoNsbW2pV68e1apVu6l2lPgTEREREREREakgrl69SnJyMpUrV8bV1ZVq1aqVysgx+XtYLBays7M5ceIEycnJNGnShEqVbnylPiX+REREREREREQqiOzsbK5evUqDBg2wtbUt6+7IDbCxsaFq1aocPHiQ7OxsqlevfsNtaXMPEREREREREZEK5mZGiUnZK63vT3eBiIiIiIiIiIhIBaTEn4iIiIiIiIiISAWkxJ+IiIiIiIiIiFQoKSkpmEwmtm/fDsB3332HyWTi7NmzxYrv2LEjISEht6x/fxdt7iEiIiIiIiIi8k+w+G/c3TfAUuKQ4OBgFixYwJAhQ5g9e7bVseHDhzNr1iwGDBhAXFxcidv28fHh2LFj2NvbF6v+ihUrqFq1aonPU95oxJ+IiIiIiIiIiJQLDRo0YOnSpVy4cMEou3jxIkuWLKFhw4Y33G61atVwcXHBZCpe8tPJyYlatWrd8PnKCyX+RERERERERESkXGjVqhUNGzZkxYoVRtmKFSto0KABLVu2NMq++eYbHnroIRwcHKhduzbdu3dn//79Bbab31Tf77//Hl9fX2xtbXF0dMTf358zZ84Aeaf6njlzhqCgIBwdHbG1taVbt27s3bvXOB4REcH9999vdc6YmBjc3d2t+tC2bVtq1KiBg4MD7du35+DBgyX8hEpGiT8RERERERERESk3nnvuOebPn2+8nzdvHs8//7xVnfPnzxMaGsrWrVtZt24dlSpV4oknnuDq1avFOsf27dvp3Lkz9957L0lJSWzcuJEePXpw5cqVfOsHBwezbds2vvjiC5KSkrBYLDz66KPk5OQU63yXL1+mV69e+Pr68uuvv5KUlMTgwYOLPQLxRmmNPxERERERERERKTcCAwMJCwszNuj4/vvvWbp0Kd99951R58knn7SKmTt3LnfccQe///47zZo1K/IcU6dOpXXr1sycOdMou/fee/Otu3fvXr744gu+//57fHx8AFi0aBENGjRg5cqV9OnTp8jzZWRkkJ6eTvfu3WncuDEATZs2LTLuZmnEn4iIiIiIiIiIlBt16tThscceY8GCBcyfP5/HHnuMOnXqWNXZv38/AQEBeHp6Ymdnh4eHBwCHDh0q1jlyR/wVx65du6hSpQre3t5GWe3atbn77rvZtWtXsdpwcnIiODgYf39/evTowXvvvcexY8eKFXszlPgTEREREREREZFy5fnnnycuLo4FCxbkmeYL0KNHD06dOsWcOXPYvHkzmzdvBiA7O7tY7dvY2BS7LxZL/jsUWywWY6pupUqV8tS7fhrw/PnzSUpKwsfHh2XLlnHXXXfxww8/FLsfN0KJPxERERERERERKVe6du1KdnY22dnZ+Pv7Wx07deoUu3btYuzYsXTu3JmmTZsam3IUV/PmzVm3bl2x6np5eXH58mUjuZjbhz/++MOYrlu3bl3S0tKskn/bt2/P01bLli0JCwtj06ZNNGvWjMWLF5eo3yWlxJ+IiIiIiIiIiJQrlStXZteuXezatYvKlStbHXN0dKR27dp8+OGH7Nu3j2+//ZbQ0NAStR8WFsbWrVsZPnw4v/76K7t372bWrFmcPHkyT90mTZrQs2dPBg0axMaNG/nll1949tlncXNzo2fPnsC1XYBPnDjB1KlT2b9/PzNmzODrr7822khOTiYsLIykpCQOHjzImjVrrBKHt4o29xARERERERER+ScIyH/KanllZ2eXb3mlSpVYunQpL774Is2aNePuu+/m/fffp2PHjsVu+6677mLNmjWEh4fTtm1bbGxs8Pb25plnnsm3/vz58xk1ahTdu3cnOzubDh068NVXX1G1alXg2kYdM2fOJDIykjfeeIMnn3ySV155hQ8//BAAW1tbdu/ezYIFCzh16hT16tVj5MiRDBkypGQfSgmZLAVNVJZyIyMjA3t7e9LngJ1tWfemnLnNfrREREREREREbqWLFy+SnJyMh4cH1atXL+vuyA0q7Hs08kTp6QUmR3Npqq+IiIiIiIiIiEgFpMSfiIiIiIiIiIhIBaTEn1RIWVlZeHp6YjKZGDp0aKF1Z86ciclkwmQykZaWlud47rH8XmfPnr1FVyAiIiIiIiIicnO0uYdUSOPHj+fEiRNF1ktNTSUsLIyaNWuSmZlZYL2HH36YwYMH5ymvUaPGTfVTRERERERERORWUeJPKpyff/6ZmJgYpkyZwiuvvFJo3ZEjR+Lp6UmzZs345JNPCqzn6enJs88+W9pdFREREREREbkltJfr7a20vj9N9ZUK5cqVKwwaNAh/f3+efPLJQuuuXLmSVatWMXv2bCpXrlxk29nZ2Zw7d660uioiIiIiIiJS6qpWrQpcWwJLbl+531/u93mjyjTxt2HDBnr06IGrqysmk4mVK1daHbdYLERERODq6oqNjQ0dO3Zk586dRba7fPlyvLy8MJvNeHl5ER8fX2TMjh078PX1xcbGBjc3NyZNmlRkdnXy5Mn4+Phga2uLg4NDnuNxcXEFrg13/PjxIvskJRcTE8Pvv/9ObGxsofUyMjIYOXIkgwcPxtvbu8h2P//8c2xtbbGzs6N27doMHDgw3/UARURERERERMpS5cqVcXBw4Pjx45w6dYoLFy5w8eJFvW6T14ULFzh16hTHjx/HwcGhWAOVClOmU33Pnz9PixYteO655/IdnTV16lSio6OJi4vjrrvu4s0338TPz489e/ZQq1atfNtMSkqiX79+vPHGGzzxxBPEx8fTt29fNm7cWGCCJyMjAz8/Pzp16sTWrVv5448/CA4OpkaNGrz88ssF9j87O5s+ffrQrl075s6dm+d4v3796Nq1q1VZcHAwFy9e5I477ijso5EbcPDgQSZMmMC4cePw8PAgJSWlwLphYWFcvnyZqKioIttt06YNTz31FE2aNCErK4v169czf/581qxZw+bNm6lXr14pXoWIiIiIiIjIzXFxcQHQoKPbmIODg/E93owyTfx169aNbt265XvMYrEQExPDmDFj6N27NwALFizA2dmZxYsXM2TIkHzjYmJi8PPzIywsDLiW4ElMTCQmJoYlS5bkG7No0SIuXrxIXFwcZrOZZs2a8ccffxAdHU1oaCgmkynfuIkTJwLXRvblx8bGBhsbG+P9iRMn+Pbbb/NNEsrNGzZsGI0aNSpyXb+kpCRmz57Nxx9/nO9Izett2bLF6n3//v3x9fUlKCiICRMm8OGHH95Mt0VERERERERKlclkol69etxxxx3k5OSUdXekhKpWrXrTI/1yldvNPZKTk0lLS6NLly5GmdlsxtfXl02bNhWY+EtKSuKll16yKvP39ycmJqbAcyUlJeHr64vZbLaKCQsLIyUlBQ8Pj5u7mP/v448/xtbWlqeeeqrQepcuXeLSpUvG+4yMjFI5f0W2ePFivv76axITEwud/56Tk8OgQYPo1KkT/fv3v+HzBQYGMn78eL788ssbbkNERERERETkVqpcuXKpJZDk9lRuN/fIXT/N2dnZqtzZ2bnQtdXS0tJKLeav/SgN8+bNIyAgwGoUYH6ioqKwt7c3Xg0aNCi1PlRE2dnZvPTSS3Tv3p2GDRuSkpJCSkoKR44cAeDcuXOkpKSQnp7OjBkz2LVrF6NHjzbqpaSkkJmZCcDhw4c5ePBgsc7r7u7OiRMnbtl1iYiIiIiIiIjcjHI74i/X9dNsLRZLgVNvSzsmv/IblZSUxO+//87HH39cZN2wsDBCQ0ON9xkZGUr+FSIrK4vjx4+zevVqVq9enef44sWLWbx4MVFRUaSlpXH16lX8/f3zbatt27aYzWYuXrxY6DktFgv79u0rlfn2IiIiIiIiIiK3QrlN/OUmVNLS0qw2Tzh+/Hie0XnXx10/Su9GYyDviMMb9dFHH3H//ffzwAMPFFnXbDZbTTuWwtWoUSPfnZuPHz/OkCFD8Pf3Z+jQodx7771cvHiRjh075qn7/vvvG5t21K5d2yj/888/870Hpk+fzpEjRxgxYkSpXouIiIiIiIiISGkpt4k/Dw8PXFxcSEhIoGXLlsC1KZ2JiYlMmTKlwLh27dqRkJBgtc7fmjVr8PHxKTQmPDyc7OxsqlWrZsS4urri7u5+09eSmZnJp59+WqwdZKXkqlatSq9evfKU5+7q6+7ubnX8vvvuy1N35cqVAHTt2tVqFF9UVBRr166le/fuNGrUiAsXLvDdd9/xn//8hyZNmhAREVGKVyIiIiIiIiIiUnrKdI2/zMxMtm/fzvbt24FrG3ps376dQ4cOYTKZCAkJITIykvj4eH777TeCg4OxtbUlICDAaCMoKMjYwRdg1KhRrFmzhilTprB7926mTJnC2rVrCQkJMerExsbSuXNn431AQABms5ng4GB+++034uPjiYyMtNrRd8uWLdxzzz0cPXrUiDt06JDR3ytXrhjXkrteXK5ly5Zx+fLlm9pMQsrGI488Qv369fnkk0946aWXGDNmDAcOHGDMmDFs3bqVOnXqlHUXRURERERERETyVaYj/rZt20anTp2M97nr2g0YMIC4uDhGjx7NhQsXGD58OGfOnMHb25s1a9ZQq1YtI+bQoUNUqvR/+UsfHx+WLl3K2LFjGTduHI0bN2bZsmV4e3sbdU6ePMn+/fuN9/b29iQkJDBixAhat26No6MjoaGhVuvsZWVlsWfPHqttsMePH8+CBQuM97kjE9evX281nXTu3Ln07t0bR0fHm/m4pITc3d2NtRqLEhcXR1xcXJ7yxx9/nMcff7yUeyYiIiIiIiIicuuZLMXNjEiZycjIwN7envQ5YGdb1r0pZwJ0+4qIiIiIiIjIP4eRJ0pPx87OrtC6ZTrVV0RERERERERERG4NJf5EREREREREREQqICX+REREREREREREKqAy3dxDSqhvOhQxd1tERERERERERAQ04k9ERERERERERKRCUuJPRERERERERESkAlLiT0REREREREREpAJS4k9ERERERERERKQC0uYet5NP7cG2rDshhgBLWfdARERERERERKRAGvEnIiIiIiIiIiJSASnxJyIiIiIiIiIiUgEp8SciIiIiIiIiIlIBKfEnUgrS09MJCwvj7rvvpnr16jg5OeHj40N8fHyBMTNnzsRkMmEymUhLS7M69scffzB+/HgefPBB6tatS61atbj//vuZPHky58+fv9WXIyIiIiIiIiIVgDb3ELlJhw8fplOnTpw+fZrnnnsOLy8vsrKy2L17N4cOHco3JjU1lbCwMGrWrElmZmae4/PmzSM2NpYePXoQEBBAtWrVWL9+PWPHjuXTTz/lhx9+wMbG5lZfmoiIiIiIiIjcxpT4E7lJgYGBnD9/nl9++YUGDRoUK2bkyJF4enrSrFkzPvnkkzzHn3rqKV5//XUcHByMsqFDh9KkSRMmT57MvHnzGDFiRGldgoiIiIiIiIhUQGU61XfDhg306NEDV1dXTCYTK1eutDpusViIiIjA1dUVGxsbOnbsyM6dO4tsd/ny5Xh5eWE2m/Hy8ip0umWuHTt24Ovri42NDW5ubkyaNAmLxVJozOTJk/Hx8cHW1tYqQXO9uLg4mjdvTvXq1XFxcWHkyJFF9kduD//73/9ITEzktddeo0GDBly+fLnIqbgrV65k1apVzJ49m8qVK+dbp3Xr1vneU3379gWu3a8iIiIiIiIiIoUp08Tf+fPnadGiBbGxsfkenzp1KtHR0cTGxrJ161ZcXFzw8/Pj3LlzBbaZlJREv379CAwM5JdffiEwMJC+ffuyefPmAmMyMjLw8/PD1dWVrVu3Mn36dKZNm0Z0dHSh/c/OzqZPnz4MGzaswDrR0dGMGTOG119/nZ07d7Ju3Tr8/f0LbVduH1999RUAnp6e9O7dGxsbG2rWrIm7u3u+93VGRgYjR45k8ODBeHt7l/h8R48eBeCOO+64uY6LiIiIiIiISIVnshQ1rO1vYjKZiI+Pp1evXsC10X6urq6EhITw2muvAXDp0iWcnZ2ZMmUKQ4YMybedfv36kZGRwddff22Ude3aFUdHR5YsWZJvzKxZswgLC+PPP//EbDYD8NZbbzF9+nSOHDmCyWQqtO9xcXGEhIRw9uxZq/IzZ87g5ubGf/7zHzp37lycjyFfGRkZ2Nvbkz4H7GxvuBkpbQEWnnjiCVauXEndunXx8PBgxIgRmEwmZs6cyQ8//MCkSZMYN26cETJixAiWL1/O7t27cXBwIDg4mAULFnDs2DFcXFwKPd2VK1d46KGH2LZtG7/99ht33333rb5CERERERERESlnjDxRejp2dnaF1i23u/omJyeTlpZGly5djDKz2Yyvry+bNm0qMC4pKckqBsDf37/IGF9fXyPplxuTmppKSkrKDV9DQkICV69e5ejRozRt2pT69evTt29fDh8+XGjcpUuXyMjIsHpJ+ZQ7+rRGjRps2LCBoKAgAgMDSUxMpHHjxkRFRXHmzBng2n02e/Zs3nnnnUKnhhfkxRdf5IcffiAiIkJJPxEREREREREpUrlN/KWlpQHg7OxsVe7s7GwcKyiutGL+2o8bceDAAa5evUpkZCQxMTF8/vnnnD59Gj8/P7KzswuMi4qKwt7e3ngVd8MI+fvl7qwbEBBglTiuVq0a/fv358KFC2zevJmcnBwGDRpEp06d6N+/f4nPM3bsWGbOnMnAgQMJDw8vtf6LiIiIiIiISMVVbhN/ua6fZmuxWIqceltaMfmVl8TVq1fJycnh/fffx9/fnwcffJAlS5awd+9e1q9fX2BcWFgY6enpxquoEYJSdurXrw9AvXr18hzLLTt9+jQzZsxg165djB49mpSUFOOVmZkJwOHDhzl48GC+54iIiGDy5MkEBQXxwQcf3NQ9KSIiIiIiIiL/HFXKugMFyV3vLC0tzSqpcvz48Tyj866Pu36U3o3GQN4RhyWR228vLy+jrG7dutSpU4dDhw4VGGc2m61Gj0n59eCDDzJ79ux8k7O537GzszNbtmzh6tWrBW7s0rZtW8xmMxcvXrQqnzhxIhMnTuTZZ59l/vz5VKpU7nP1IiIiIiIiIlJOlNssgoeHBy4uLiQkJBhl2dnZJCYm4uPjU2Bcu3btrGIA1qxZU2TMhg0brKbfrlmzBldXV9zd3W/4Gtq3bw/Anj17jLLTp09z8uRJGjVqdMPtSvnRs2dP7Ozs+Pjjj0lPTzfKz507x4IFC3B0dKRdu3a88MILxMfH53l16tQJgPnz5/PZZ59ZtT1p0iQiIiLo378/cXFxSvqJiIiIiIiISImU6Yi/zMxM9u3bZ7xPTk5m+/btODk50bBhQ0JCQoiMjKRJkyY0adKEyMhIbG1tCQgIMGKCgoJwc3MjKioKgFGjRtGhQwemTJlCz549WbVqFWvXrmXjxo1GTGxsLPHx8axbtw64tj7bxIkTCQ4OJjw8nL179xIZGcn48eONaZVbtmwhKCiIdevW4ebmBlwb0XX69GkOHTrElStX2L59OwB33nknNWvW5K677qJnz56MGjWKDz/8EDs7O8LCwrjnnnuMhI/c3hwcHHj33Xd54YUXaNu2LQMHDsRkMjF37lyOHTtGXFwctra23Hfffdx333154leuXAlc23n6r7v6zpgxgwkTJtCwYUP8/Pzy7Ejt7OyMn5/fLb02EREREREREbm9lWnib9u2bVYJsNDQUAAGDBhAXFwco0eP5sKFCwwfPpwzZ87g7e3NmjVrqFWrlhFz6NAhq5FQPj4+LF26lLFjxzJu3DgaN27MsmXL8Pb2NuqcPHmS/fv3G+/t7e1JSEhgxIgRtG7dGkdHR0JDQ43+AGRlZbFnzx5ycnKMsvHjx7NgwQLjfcuWLQFYv349HTt2BODjjz/mpZde4rHHHqNSpUr4+vryzTffULVq1Zv9+KSceP7556lbty5Tpkxh4sSJWCwWHnjgAaKjo+nWrdsNtbl161bg2v0dHByc57ivr68SfyIiIiIiIiJSKJMldxcLKbcyMjKwt7cnfQ7Y2ZZ1b8QQoEdHRERERERERP5eRp4oPR07O7tC62rRMBERERERERERkQpIiT8REREREREREZEKSIk/ERERERERERGRCqhMN/eQEuqbDkXM3RYREREREREREQGN+BMREREREREREamQlPgTERERERERERGpgJT4ExERERERERERqYCU+BMREREREREREamAtLnH7eRTe7At607IP16Apax7ICIiIiIiIiLFoBF/IiIiIiIiIiIiFZASfyIiIiIiIiIiIhWQEn8iIiIiIiIiIiIVkBJ/InJDoqKi6NOnD56enphMJtzd3Qut/+eff/L888/j7OxM9erVad68OXPmzCnyPFlZWcY5hg4dWqpti4iIiIiIiFRk2txDRG5IeHg4Tk5OtGrVirNnzxZa9+zZszz00EMcPXqUkJAQPDw8WLVqFYMHDyY1NZUJEyYUGDt+/HhOnDhxS9oWERERERERqcg04k9Ebsj+/fs5deoUCQkJuLq6Flp3ypQp7Nu3j08++YTIyEgGDRrE6tWr6dGjB5MnTyY5OTnfuJ9//pmYmBgiIiJKvW0RERERERGRiq5ME38bNmygR48euLq6YjKZWLlypdVxi8VCREQErq6u2NjY0LFjR3bu3Flku8uXL8fLywuz2YyXlxfx8fFFxuzYsQNfX19sbGxwc3Nj0qRJWCyWAuunpKTwwgsv4OHhgY2NDY0bN2bChAlkZ2db1Tt06BA9evSgRo0a1KlThxdffDFPHZHbkaenZ7HrLlq0CA8PD3r37m1VHhoaSk5ODsuWLcsTc+XKFQYNGoS/vz9PPvlkqbYtIiIiIiIi8k9Qpom/8+fP06JFC2JjY/M9PnXqVKKjo4mNjWXr1q24uLjg5+fHuXPnCmwzKSmJfv36ERgYyC+//EJgYCB9+/Zl8+bNBcZkZGTg5+eHq6srW7duZfr06UybNo3o6OgCY3bv3s3Vq1f54IMP2LlzJ++++y6zZ88mPDzcqHPlyhUee+wxzp8/z8aNG1m6dCnLly/n5ZdfLsanI1IxpKWlcfjwYdq1a5fnWLt27TCZTGzZsiXPsZiYGH7//fcCfx9upm0RERERERGRf4IyXeOvW7dudOvWLd9jFouFmJgYxowZY4zkWbBgAc7OzixevJghQ4bkGxcTE4Ofnx9hYWEAhIWFkZiYSExMDEuWLMk3ZtGiRVy8eJG4uDjMZjPNmjXjjz/+IDo6mtDQUEwmU56Yrl270rVrV+O9p6cne/bsYdasWUybNg2ANWvW8Pvvv3P48GFjKuQ777xDcHAwkydPxs7OrpiflMjt6+jRowDUr18/zzGz2UydOnU4cuSIVfnBgweZMGEC48aNw8PDg5SUlFJrW0REREREROSfotyu8ZecnExaWhpdunQxysxmM76+vmzatKnAuKSkJKsYAH9//yJjfH19MZvNVjGpqakFJhzyk56ejpOTk1W7zZo1s1r/zN/fn0uXLvHjjz8W2M6lS5fIyMiweoncrrKysgCsnq+/ql69ulEn17Bhw2jUqBGvvPJKqbctIiIiIiIi8k9RbhN/aWlpADg7O1uVOzs7G8cKiiutmL/2oyj79+9n+vTpDB06tNB2HR0dqVatWqHtRkVFYW9vb7waNGhQrD6IlEe2trbAtYR2fi5cuGDUAVi8eDFff/01s2bNomrVqqXatoiIiIiIiMg/SblN/OW6fpqtxWLJd+rtrYjJrzw/qampdO3alT59+jBw4MBC2y1Of8LCwkhPTzdehw8fLrIPIuWVm5sbQL5Tbi9evMipU6eMqbrZ2dm89NJLdO/enYYNG5KSkkJKSooRe+7cOVJSUkhPTy9x2yIiIiIiIiL/NOU28efi4gLkHXF3/PjxPKPoro8rrRjIO+LweqmpqXTq1Il27drx4YcfFtnumTNnyMnJKbRds9mMnZ2d1UvkduXi4kL9+vVJSkrKc+yHH37AYrHQpk0b4NrU3ePHj7N69Wo8PDyM18MPPwxcGw3o4eHBrFmzSty2iIiIiIiIyD9NuU38eXh44OLiQkJCglGWnZ1NYmIiPj4+Bca1a9fOKgaubbJRVMyGDRvIzs62inF1dcXd3b3AuKNHj9KxY0datWrF/PnzqVTJ+uNs164dv/32G8eOHbNq12w288ADDxTYrkhFExAQQHJyMitWrLAqj46OpkqVKvTr1w+AGjVqEB8fn+f1wQcfANfWyIyPj+fJJ58scdsiIiIiIiIi/zRluqtvZmYm+/btM94nJyezfft2nJycaNiwISEhIURGRtKkSROaNGlCZGQktra2BAQEGDFBQUG4ubkRFRUFwKhRo+jQoQNTpkyhZ8+erFq1irVr17Jx40YjJjY2lvj4eNatWwdcSxxMnDiR4OBgwsPD2bt3L5GRkYwfP96YkrtlyxaCgoJYt24dbm5upKam0rFjRxo2bMi0adM4ceKE0X7uaMUuXbrg5eVFYGAgb7/9NqdPn+aVV15h0KBBGsUnt72FCxdy8OBBAE6cOEF2djZvvvkmAA4ODowcOdKo+/rrr/P5558TGBjIjz/+iIeHB6tWrWL16tWMGzcOT09PAKpWrUqvXr3ynCt3kx13d/c8x4vbtoiIiIiIiMg/TZkm/rZt20anTp2M96GhoQAMGDCAuLg4Ro8ezYULFxg+fDhnzpzB29ubNWvWUKtWLSPm0KFDViPtfHx8WLp0KWPHjmXcuHE0btyYZcuW4e3tbdQ5efIk+/fvN97b29uTkJDAiBEjaN26NY6OjoSGhhr9gWtTEPfs2UNOTg5wbeTevn372LdvX541xHLXB6xcuTJffvklw4cPp3379tjY2BAQEMC0adNK4+MTKVNz584lMTHRqmzcuHEANGrUyCrx5+joyMaNGwkPD2fOnDlkZGRw5513MmvWLKsNcW7ErWxbRERERERE5HZmsuRmqaTcysjIwN7envQ5YKcNSqWsBegnQ0RERERERKSsGHmi9PQiZ5SW2zX+RERERERERERE5MYp8SciIiIiIiIiIlIBKfEnIiIiIiIiIiJSAZXp5h5SQn3TQbsBi4iIiIiIiIhIMWjEn4iIiIiIiIiISAWkxJ+IiIiIiIiIiEgFpMSfiIiIiIiIiIhIBaTEn4iIiIiIiIiISAWkzT1uJ5/ag21Zd0LkOgGWsu6BiIiIiIiIiORDI/5EREREREREREQqICX+REREREREREREKiAl/kRERERERERERCogJf5EpFRERUXRp08fPD09MZlMuLu7F1r/zz//5Pnnn8fZ2Znq1avTvHlz5syZU2D99PR0wsLCuPvuu6levTpOTk74+PgQHx9vVe+dd96hY8eO1KtXD7PZTL169XjkkUdYtWpVaVymiIiIiIiIyG1Dm3uISKkIDw/HycmJVq1acfbs2ULrnj17loceeoijR48SEhKCh4cHq1atYvDgwaSmpjJhwgSr+ocPH6ZTp06cPn2a5557Di8vL7Kysti9ezeHDh2yqrtlyxY8PDx47LHHqF27NqdPn+azzz6jV69eTJw4kfHjx5f2pYuIiIiIiIiUSyaLxaItOcu5jIwM7O3tSZ8DdtrVV8qb/7+r74EDB/D09ASgWbNmZGZmkpKSkm9IWFgYb731FsuXL6d3795G+eOPP84333zDnj178PDwMMo7duzInj172LJlCw0aNChxFy9fvkyrVq3Yv38/Z8+epWrVqiVuQ0RERERERKQ8MPJE6enY2dkVWrfcT/XdsGEDPXr0wNXVFZPJxMqVK62OWywWIiIicHV1xcbGho4dO7Jz584i212+fDleXl6YzWa8vLzyTBfMz44dO/D19cXGxgY3NzcmTZpEcfKmX375Jd7e3tjY2FCnTh2rRIdIRZGb9CuORYsW4eHhkedZCA0NJScnh2XLlhll//vf/0hMTOS1116jQYMGXL58mfPnz5eob1WqVKFBgwZkZWWRnZ1dolgRERERERGR21W5T/ydP3+eFi1aEBsbm+/xqVOnEh0dTWxsLFu3bsXFxQU/Pz/OnTtXYJtJSUn069ePwMBAfvnlFwIDA+nbty+bN28uMCYjIwM/Pz9cXV3ZunUr06dPZ9q0aURHRxfa/+XLlxMYGMhzzz3HL7/8wvfff09AQEDxLl6kAkpLS+Pw4cO0a9cuz7F27dphMpnYsmWLUfbVV18B1xKLvXv3xsbGhpo1a+Lu7l7g7wLA6dOnOXHiBLt372by5Ml88803+Pr6UqNGjdK/KBEREREREZFyqNyv8detWze6deuW7zGLxUJMTAxjxowxRg4tWLAAZ2dnFi9ezJAhQ/KNi4mJwc/Pj7CwMODatMPExERiYmJYsmRJvjGLFi3i4sWLxMXFYTabadasGX/88QfR0dGEhoZiMpnyxFy+fJlRo0bx9ttv88ILLxjld999d4k+A5GK5OjRowDUr18/zzGz2UydOnU4cuSIUbZ7924ABg4ciIeHB3PnzsVkMjFz5kz+/e9/c+bMGcaNG5enrbvuuotTp04BULlyZXr16sWsWbNuxSWJiIiIiIiIlEvlfsRfYZKTk0lLS6NLly5GmdlsxtfXl02bNhUYl5SUZBUD4O/vX2SMr68vZrPZKiY1NbXAdcx++uknjh49SqVKlWjZsiX16tWjW7duRU5FvnTpEhkZGVYvkYoiKysLwOpZ+qvq1asbdQBj9G6NGjXYsGEDQUFBBAYGkpiYSOPGjYmKiuLMmTN52lmxYgX//e9/mTdvHv7+/uTk5JCZmXkLrkhERERERESkfLqtE39paWkAODs7W5U7OzsbxwqKK62Yv/bjegcOHAAgIiKCsWPHsnr1ahwdHfH19eX06dMFnisqKgp7e3vjdSObGYiUV7a213aouXTpUr7HL1y4YNQBsLGxASAgIMAqWVitWjX69+/PhQsX8p2m36FDB7p06cJzzz3Hl19+SY0aNWjfvn2+SUIRERERERGRiui2Tvzlun6arcViyXfq7a2Iya8819WrVwEYM2YMTz75JA888ADz58/HZDLx2WefFXiesLAw0tPTjdfhw4cL7ZfI7cTNzQ3AajpvrosXL3Lq1CmracC5/12vXr089XPLCkuk5xowYABpaWmsWLHihvotIiIiIiIicru5rRN/Li4uQN4Rd8ePH88zOu/6uNKKgbwjDnPlJiW8vLyMMrPZjKenJ4cOHSrwXGazGTs7O6uXSEXh4uJC/fr1SUpKynPshx9+wGKx0KZNG6PswQcfBMg3AZ77HBX27Oa6cOECULwkoYiIiIiIiEhFcFsn/jw8PHBxcSEhIcEoy87OJjExER8fnwLj2rVrZxUDsGbNmiJjNmzYQHZ2tlWMq6sr7u7u+cY88MADmM1m9uzZY5Tl5OSQkpJCo0aNiro8kQorICCA5OTkPKPvoqOjqVKlCv369TPKevbsiZ2dHR9//DHp6elG+blz51iwYAGOjo7GDsHnz5/Pdx2/K1euMGPGDOD/EokiIiIiIiIiFV2539U3MzOTffv2Ge+Tk5PZvn07Tk5ONGzYkJCQECIjI2nSpAlNmjQhMjISW1tbAgICjJigoCDc3NyIiooCYNSoUXTo0IEpU6bQs2dPVq1axdq1a9m4caMRExsbS3x8POvWrQOuJSomTpxIcHAw4eHh7N27l8jISMaPH29M9d2yZQtBQUGsW7cONzc37OzsGDp0KBMmTKBBgwY0atSIt99+G4A+ffrc8s9O5O+0cOFCDh48CMCJEyfIzs7mzTffBMDBwYGRI0cadV9//XU+//xzAgMD+fHHH/Hw8GDVqlWsXr2acePG4enpadR1cHDg3Xff5YUXXqBt27YMHDgQk8nE3LlzOXbsGHFxccaagHv37sXX15ennnqKu+++GycnJ44cOcLSpUvZs2cPAwYM4OGHH/4bPxURERERERGRslPuE3/btm2jU6dOxvvQ0FDg2npdcXFxjB49mgsXLjB8+HDOnDmDt7c3a9asoVatWkbMoUOHqFTp/wY3+vj4sHTpUsaOHcu4ceNo3Lgxy5Ytw9vb26hz8uRJ9u/fb7y3t7cnISGBESNG0Lp1axwdHQkNDTX6A9d2K92zZw85OTlG2dtvv02VKlUIDAzkwoULeHt78+233+Lo6Fi6H5RIGZs7dy6JiYlWZePGjQOgUaNGVok/R0dHNm7cSHh4OHPmzCEjI4M777yTWbNmMXTo0DxtP//889StW5cpU6YwceJELBYLDzzwANHR0XTr1s2oV79+fZ599lk2btxIfHw8586dw97enpYtWzJu3DirfxAQERERERERqehMltwdKqTcysjIwN7envQ5YGdbdH2Rv1WAfkJERERERERE/i5Gnig9vch9IW7rNf5EREREREREREQkf0r8iYiIiIiIiIiIVEBK/ImIiIiIiIiIiFRA5X5zD/mLvulQxNxtERERERERERER0Ig/ERERERERERGRCkmJPxERERERERERkQpIiT8REREREREREZEKSIk/ERERERERERGRCkibe9xOPrUH27LuhMhNCLCUdQ9ERERERERE/jE04k9ERERERERERKQCUuJPRERERERERESkAlLiT0REREREREREpAJS4k9E/nZRUVH06dMHT09PTCYT7u7uBdbdsmULL774Iu3bt6dGjRqYTCbi4uLyrbtnzx769+9P06ZNsbe3p0aNGjRt2pSXX36ZtLS0PPUjIiIwmUz5vkJCQkrnYkVERERERETKiDb3EJG/XXh4OE5OTrRq1YqzZ88WWverr75ixowZ3HPPPdx///1s2rSpwLpHjhwhLS2NJ554gvr161OlShV27NjBBx98wJIlS/j5559xdnbOE/fuu+9Sp04dq7KmTZve0LWJiIiIiIiIlBdK/InI327//v14enoC0KxZMzIzMwusO2zYMF599VVq1KjB559/Xmjir3PnznTu3DlP+cMPP0y/fv2YO3cu4eHheY736tWr0FGHIiIiIiIiIrejMp3qu2HDBnr06IGrqysmk4mVK1daHbdYLERERODq6oqNjQ0dO3Zk586dRba7fPlyvLy8MJvNeHl5ER8fX2TMjh078PX1xcbGBjc3NyZNmoTFYik0ZvLkyfj4+GBra4uDg0O+dfKbQjh79uwi+yNSkeUm/YrD2dmZGjVq3NT5PDw8ADhz5kyBdc6dO0dOTs5NnUdERERERESkPCnTxN/58+dp0aIFsbGx+R6fOnUq0dHRxMbGsnXrVlxcXPDz8+PcuXMFtpmUlES/fv0IDAzkl19+ITAwkL59+7J58+YCYzIyMvDz88PV1ZWtW7cyffp0pk2bRnR0dKH9z87Opk+fPgwbNqzQevPnz+fYsWPGa8CAAYXWF5Gbc/HiRU6ePMmRI0dYu3at8Yw++uij+dZv0aIFdnZ2VK9endatW7Ns2bK/s7siIiIiIiIit0SZTvXt1q0b3bp1y/eYxWIhJiaGMWPG0Lt3bwAWLFiAs7MzixcvZsiQIfnGxcTE4OfnR1hYGABhYWEkJiYSExPDkiVL8o1ZtGgRFy9eJC4uDrPZTLNmzfjjjz+Ijo4mNDQUk8mUb9zEiRMBCtxoIJeDgwMuLi6F1hGR0vPRRx/x73//23jfoEEDFixYQKdOnazqOTg4MHDgQNq3b0/t2rXZv38/sbGxPP300+zdu5exY8f+3V0XERERERERKTXldlff5ORk0tLS6NKli1FmNpvx9fUtdI2vpKQkqxgAf3//ImN8fX0xm81WMampqaSkpNz4Rfx/I0eOpE6dOrRp04bZs2dz9erVQutfunSJjIwMq5eIFF+vXr1ISEggPj6e8ePHU6dOnXyn+YaEhDBnzhyCg4Pp0aMHISEh7Nixg3vvvZeJEydy8ODBMui9iIiIiIiISOkot5t7pKWlAeTZgdPZ2bnQP8bT0tLyjcltr6CY6xf2z20jLS3NWB/sRrzxxht07twZGxsb1q1bx8svv8zJkycLHUkUFRVljCYUkZKrX78+9evXB64lAZ988knatGlDVlaWMRq4IDY2Nrz66qsEBwezZs0aBg0a9Hd0WURERERERKTUldsRf7mun2ZrsVgKnHpb2jH5lZfU2LFjadeuHffffz8vv/wykyZN4u233y40JiwsjPT0dON1+PDhm+qDyD9d8+bNadmyJTNnzixW/dx/CDhx4sQt7JWIiIiIiIjIrVVuR/zlromXlpZGvXr1jPLjx4/nGdF3fdz1o/tuNAbyjji8WQ8++CAZGRn8+eefBbZtNputph2LyM27cOECp0+fLlbdvXv3AmhtThEREREREbmtldsRfx4eHri4uJCQkGCUZWdnk5iYiI+PT4Fx7dq1s4oBWLNmTZExGzZsIDs72yrG1dU1zxTgm/Xzzz9TvXp1HBwcSrVdEaHAKf3r16/nt99+48EHHzTKLl++zKlTp/LUPXv2LFFRUVSrVg1/f/9b1lcRERERERGRW61MR/xlZmayb98+431ycjLbt2/HycmJhg0bEhISQmRkJE2aNKFJkyZERkZia2tLQECAERMUFISbmxtRUVEAjBo1ig4dOjBlyhR69uzJqlWrWLt2LRs3bjRiYmNjiY+PZ926dQAEBAQwceJEgoODCQ8PZ+/evURGRjJ+/Hhjqu+WLVsICgpi3bp1uLm5AXDo0CFOnz7NoUOHuHLlCtu3bwfgzjvvpGbNmvznP/8hLS2Ndu3aYWNjw/r16xkzZgyDBw/WiD75R1u4cKGxVueJEyfIzs7mzTffBK7ttDty5Eij7sGDB1m4cCEAO3fuBOA///kPR44cAeDxxx+nefPmAAwbNoxjx47xyCOP0KhRIy5evMiPP/7I0qVLqVWrFu+8847RbmZmJvXr16d3797cd9991K5dmwMHDjBv3jyOHz9OTEyM8ayLiIiIiIiI3I5MltzF7MrAd999R6dOnfKUDxgwgLi4OCwWCxMnTuSDDz7gzJkzeHt7M2PGDJo1a2bU7dixI+7u7sTFxRlln3/+OWPHjuXAgQM0btyYyZMn07t3b+N4REQEcXFxVjv27tixgxEjRrBlyxYcHR0ZOnSoVeIvt6/JycnGKMDg4GAWLFiQp//r16+nY8eOfPPNN4SFhbFv3z6uXr2Kp6cnAwcOZMSIEVSpUvyca0ZGBvb29qTPATvbYoeJlD8B135uOnbsSGJiYr5VGjVqZPVsFvQ7kWv+/PkEBwcD8Omnn7JgwQJ+/fVXTpw4gclkolGjRvj5+fHqq6/SsGFDI+7SpUvGM3/48GEyMzNxdHTE29ubkJAQOnfufPPXKyIiIiIiIlLKjDxRejp2dnaF1i3TxJ8UjxJ/UmEE6OdGRERERERE5GaUJPFXbtf4ExERERERERERkRunxJ+IiIiIiIiIiEgFpMSfiIiIiIiIiIhIBVSmu/pKCfVNhyLmbouIiIiIiIiIiIBG/ImIiIiIiIiIiFRISvyJiIiIiIiIiIhUQEr8iYiIiIiIiIiIVEBK/ImIiIiIiIiIiFRASvyJiIiIiIiIiIhUQNrV93byqT3YlnUnRP7BAixl3QMRERERERGRYtOIPxERERERERERkQpIiT8REREREREREZEKSIk/EZESioqKok+fPnh6emIymXB3dy927MyZMzGZTJhMJtLS0qyO7dmzh/79+9O0aVPs7e2pUaMGTZs25eWXX85TN9dnn32Gj48PNWrUoFatWjz88MN89dVXN3N5IiIiIiIiUkFojT8RkRIKDw/HycmJVq1acfbs2WLHpaamEhYWRs2aNcnMzMxz/MiRI6SlpfHEE09Qv359qlSpwo4dO/jggw9YsmQJP//8M87Ozkb9KVOm8Prrr9OyZUsmTZqEyWTik08+oXv37ixcuJD+/fuXxuWKiIiIiIjIbcpksVi0Wn05l5GRgb29PelzwE6be4iUnf+/uceBAwfw9PQEoFmzZmRmZpKSklJkeO/evUlOTqZZs2Z88sknHDt2DBcXlyLjPv30U/r168fkyZMJDw8H4Pjx4zRo0IC77rqLn376iapVqwKQk5NDq1atSE1NJTk5GTs7uxu8WBERERERESmPjDxRenqRf/OV+6m+GzZsoEePHri6umIymVi5cqXVcYvFQkREBK6urtjY2NCxY0d27txZZLvLly/Hy8sLs9mMl5cX8fHxRcbs2LEDX19fbGxscHNzY9KkSRSWN01JSeGFF17Aw8MDGxsbGjduzIQJE8jOzi7yXCJSfuUm/Upi5cqVrFq1itmzZ1O5cuUSxXp4eABw5swZo2zTpk1kZ2fTv39/I+kHULVqVQICAjh9+jSrVq0qcT9FRERERESk4ij3ib/z58/TokULYmNj8z0+depUoqOjiY2NZevWrbi4uODn58e5c+cKbDMpKYl+/foRGBjIL7/8QmBgIH379mXz5s0FxmRkZODn54erqytbt25l+vTpTJs2jejo6AJjdu/ezdWrV/nggw/YuXMn7777LrNnzzZG7IjIP0NGRgYjR45k8ODBeHt7F1n/4sWLnDx5kiNHjrB27VqGDRsGwKOPPmpVB8DWNu8w4Nyywn7TREREREREpOIr92v8devWjW7duuV7zGKxEBMTw5gxY+jduzcACxYswNnZmcWLFzNkyJB842JiYvDz8yMsLAyAsLAwEhMTiYmJYcmSJfnGLFq0iIsXLxIXF4fZbKZZs2b88ccfREdHExoaislkyhPTtWtXunbtarz39PRkz549zJo1i2nTppXocxCR21dYWBiXL18mKiqqWPU/+ugj/v3vfxvvGzRowIIFC+jUqZNR5uXlBcC3337Liy++aBW/fv16AA4dOnSzXRcREREREZHbWLkf8VeY5ORk0tLS6NKli1FmNpvx9fVl06ZNBcYlJSVZxQD4+/sXGePr64vZbLaKSU1NLdbaXrnS09NxcnIqtM6lS5fIyMiweonI7SkpKYnZs2fzzjvv4ODgUKyYXr16kZCQQHx8POPHj6dOnTpW03wBmjdvTufOnVm1ahWjR49m165d7Nq1i9dee83Y1TcrK6u0L0dERERERERuI+V+xF9h0tLSAKx2ucx9f/DgwULj8ovJba+gGHd39zwxucdy1+AqzP79+5k+fTrvvPNOofWioqKYOHFike2JSPmWk5PDoEGD6NSpU4l22K1fvz7169cHriUBn3zySdq0aUNWVpYxUhlg2bJlDBo0iLfffpu3334buDY6MDY2liFDhmhjDxERERERkX+423rEX67rp9laLJZ8p97eipj8yvOTmppK165d6dOnDwMHDiy0blhYGOnp6cbr8OHDRbYvIuXPjBkz2LVrF6NHjyYlJcV4ZWZmAnD48OFC/5EiV/PmzWnZsiUzZ860Kq9duzYrVqwgLS2NDRs28NNPP5GcnGwkDe+5557SvygRERERERG5bdzWI/5cXFyAayPu6tWrZ5QfP348z4i+6+OuH913ozGQd8Th9VJTU+nUqRPt2rXjww8/LLQuXJuu/NcpxSJye0pJSeHq1av4+/vne7xt27aYzWZjo47CXLhwgdOnT+d7zNnZ2ep3KHeq7183AxEREREREZF/ntt6xJ+HhwcuLi4kJCQYZdnZ2SQmJuLj41NgXLt27axiANasWVNkzIYNG8jOzraKcXV1zTMF+K+OHj1Kx44dadWqFfPnz6dSpdv6IxeREnjhhReIj4/P88rdpGP+/Pl89tlnRv2ClhtYv349v/32Gw8++GCR59y2bRsfffQRvr6+PPTQQ6VzISIiIiIiInJbKvcj/jIzM9m3b5/xPjk5me3bt+Pk5ETDhg0JCQkhMjKSJk2a0KRJEyIjI7G1tSUgIMCICQoKws3NzdhRc9SoUXTo0IEpU6bQs2dPVq1axdq1a9m4caMRExsbS3x8POvWrQMgICCAiRMnEhwcTHh4OHv37iUyMpLx48cbU323bNlCUFAQ69atw83NjdTUVDp27EjDhg2ZNm0aJ06cMNrPHa0oIrefhQsXGlN0T5w4QXZ2Nm+++SYADg4OjBw5EoD77ruP++67L0/8ypUrgWs7f//1t2DYsGEcO3aMRx55hEaNGnHx4kV+/PFHli5dSq1atfKsDzpu3Dj27t1L27Ztsbe356effmLevHm4ubmxcOHCW3HpIiIiIiIichsp94m/bdu2GaNjAEJDQwEYMGAAcXFxjB49mgsXLjB8+HDOnDmDt7c3a9asoVatWkbMoUOHrEba+fj4sHTpUsaOHcu4ceNo3Lgxy5Ytw9vb26hz8uRJ9u/fb7y3t7cnISGBESNG0Lp1axwdHQkNDTX6A9d20NyzZw85OTnAtRGB+/btY9++fcaaW7ly1wcUkdvP3LlzSUxMtCobN24cAI0aNTISfyX1zDPPsGDBAhYuXMiJEycwmUw0atSIIUOG8Oqrr9KwYUOr+i1btmTt2rWsWbOGrKwsGjZsyIsvvkhYWFixdxAWERERERGRistkUQaq3MvIyMDe3p70OWBnW9a9EfkHC9DPpYiIiIiIiJQtI0+Uno6dnV2hdbXgnIiIiIiIiIiISAWkxJ+IiIiIiIiIiEgFpMSfiIiIiIiIiIhIBVTuN/eQv+ibDkXM3RYREREREREREQGN+BMREREREREREamQlPgTERERERERERGpgJT4ExERERERERERqYCU+BMREREREREREamAlPgTERERERERERGpgLSr7+3kU3uwLetOiPyDBVjKugciIiIiIiIixaYRfyIiIiIiIiIiIhWQEn8iIiIiIiIiIiIVkBJ/IiIlFBUVRZ8+ffD09MRkMuHu7l7s2JkzZ2IymTCZTKSlpRVaNysryzjH0KFD8xzfsmULL774Iu3bt6dGjRqYTCbi4uJKeDUiIiIiIiJSUSnxJyJSQuHh4Xz77bc0btwYR0fHYselpqYSFhZGzZo1i1V//PjxnDhxosDjX331FTNmzODs2bPcf//9xe6HiIiIiIiI/DMo8SciUkL79+/n1KlTJCQk4OrqWuy4kSNH4unpSa9evYqs+/PPPxMTE0NERESBdYYNG0ZGRgY7d+7kpZdeKnY/RERERERE5J+hTBN/GzZsoEePHri6umIymVi5cqXVcYvFQkREBK6urtjY2NCxY0d27txZZLvLly/Hy8sLs9mMl5cX8fHxRcbs2LEDX19fbGxscHNzY9KkSVgshe/gOXnyZHx8fLC1tcXBwSHfOqNGjeKBBx7AbDZrRI5IBeHp6VnimJUrV7Jq1Spmz55N5cqVC6175coVBg0ahL+/P08++WSB9ZydnalRo0aJ+yIiIiIiIiL/DGWa+Dt//jwtWrQgNjY23+NTp04lOjqa2NhYtm7diouLC35+fpw7d67ANpOSkujXrx+BgYH88ssvBAYG0rdvXzZv3lxgTEZGBn5+fri6urJ161amT5/OtGnTiI6OLrT/2dnZ9OnTh2HDhhVYx2Kx8Pzzz9OvX79C2xKRiisjI4ORI0cyePBgvL29i6wfExPD77//XuBvo4iIiIiIiEhxVCnLk3fr1o1u3brle8xisRATE8OYMWPo3bs3AAsWLMDZ2ZnFixczZMiQfONiYmLw8/MjLCwMgLCwMBITE4mJiWHJkiX5xixatIiLFy8SFxeH2WymWbNm/PHHH0RHRxMaGorJZMo3buLEiQCFLqb//vvvA3DixAl+/fXXAuuJSMUVFhbG5cuXiYqKKrLuwYMHmTBhAuPGjcPDw4OUlJRb30ERERERERGpkMrtGn/JycmkpaXRpUsXo8xsNuPr68umTZsKjEtKSrKKAfD39y8yxtfXF7PZbBWTmppaJn90X7p0iYyMDKuXiNyekpKSmD17Nu+8806BSwL81bBhw2jUqBGvvPLKre+ciIiIiIiIVGjlNvGXlpYGXFvD6q+cnZ2NYwXFlVbMX/vxd4qKisLe3t54NWjQ4G/vg4jcvJycHAYNGkSnTp3o379/kfUXL17M119/zaxZs6haterf0EMRERERERGpyMpt4i/X9dNsLRZLgVNvSzsmv/K/Q1hYGOnp6cbr8OHDf3sfROTmzZgxg127djF69GhSUlKMV2ZmJgCHDx/m4MGDwLU1Q1966SW6d+9Ow4YNjbpHjhwB4Ny5c6SkpJCenl5m1yMiIiIiIiK3lzJd468wLi4uwLURd/Xq1TPKjx8/nmd03vVx14/Su9EYyDvi8O9gNputph2LyO0pJSWFq1ev4u/vn+/xtm3bYjabuXjxIllZWRw/fpzVq1ezevXqPHUXL17M4sWLiYqK4vXXX7/VXRcREREREZEKoNwm/jw8PHBxcSEhIYGWLVsC10bEJCYmMmXKlALj2rVrR0JCAi+99JJRtmbNGnx8fAqNCQ8PJzs7m2rVqhkxrq6uuLu7l84Ficg/zgsvvEDHjh3zlL///vusX7+e+fPnU7t2bQBq1KhBfHx8nrrHjx9nyJAh+Pv7M3ToUO69995b3W0RERERERGpIMo08ZeZmcm+ffuM98nJyWzfvh0nJycaNmxISEgIkZGRNGnShCZNmhAZGYmtrS0BAQFGTFBQEG5ubsZumaNGjaJDhw5MmTKFnj17smrVKtauXcvGjRuNmNjYWOLj41m3bh0AAQEBTJw4keDgYMLDw9m7dy+RkZGMHz/emOq7ZcsWgoKCWLduHW5ubgAcOnSI06dPc+jQIa5cucL27dsBuPPOO6lZsyYA+/btIzMzk7S0NC5cuGDU8fLyMpKMInJ7WbhwoTFF98SJE2RnZ/Pmm28C4ODgwMiRIwG47777uO+++/LEr1y5EoCuXbsao5urVq1Kr1698tTN3WDI3d09z/GDBw+ycOFCAHbu3AnAf/7zH2N68OOPP07z5s1v/EJFRERERETktlamib9t27bRqVMn431oaCgAAwYMIC4ujtGjR3PhwgWGDx/OmTNn8Pb2Zs2aNdSqVcuIOXToEJUq/d9ShT4+PixdupSxY8cybtw4GjduzLJly/D29jbqnDx5kv379xvv7e3tSUhIYMSIEbRu3RpHR0dCQ0ON/gBkZWWxZ88ecnJyjLLx48ezYMEC433uyMT169cbo3wGDhxIYmJinjrJyckaTShym5o7d67Vcw0wbtw4ABo1amQk/m615ORk47y5VqxYwYoVKwCoX7++En8iIiIiIiL/YCZL7i4WUm5lZGRgb29P+hywsy3r3oj8gwXo51JERERERETKlpEnSk/Hzs6u0LrlfldfERERERERERERKbkSJf6uXLlCYmIiZ86cuVX9ERERERERERERkVJQosRf5cqV8ff35+zZs7eoOyIiIiIiIiIiIlIaSry5x3333ceBAwfw8PC4Ff2RwvRNhyLmbouIiIiIiIiIiMANrPE3efJkXnnlFVavXs2xY8fIyMiweomIiIiIiIiIiEjZK/GuvpUq/V+u0GQyGf9tsVgwmUxcuXKl9HonQMl2axERERERERERkYqrJHmiEk/1Xb9+/Q13TERERERERERERP4eJU78+fr63op+iIiIiIiIiIiISCkq8Rp/AP/73/949tln8fHx4ejRowAsXLiQjRs3lmrnRERERERERERE5MaUeMTf8uXLCQwMpH///vz0009cunQJgHPnzhEZGclXX31V6p2U/+9Te7At606ISIECSrRkqoiIiIiIiMgtVeIRf2+++SazZ89mzpw5VK1a1Sj38fHhp59+KtXOiYiIiIiIiIiIyI0pceJvz549dOjQIU+5nZ0dZ8+eLY0+iYiIiIiIiIiIyE0qceKvXr167Nu3L0/5xo0b8fT0LJVOiYjczqKioujTpw+enp6YTCbc3d2LHTtz5kxMJhMmk4m0tLQ8x//880+GDh1KgwYNqFatGg0bNmTUqFEF/sPLn3/+yfPPP4+zszPVq1enefPmzJkz5wavTERERERERG4nJV7jb8iQIYwaNYp58+ZhMplITU0lKSmJV155hfHjx9+KPoqI3FbCw8NxcnKiVatWJRoJnZqaSlhYGDVr1iQzMzPP8ePHj+Pt7U1qaipDhgyhWbNm/Pbbb8yaNYsNGzbw/fffY2v7fwuBnj17loceeoijR48SEhKCh4cHq1atYvDgwaSmpjJhwoTSuFwREREREREpp0qc+Bs9ejTp6el06tSJixcv0qFDB8xmM6+88gojR468FX0UEbmt7N+/3xgB3axZs3yTePkZOXIknp6eNGvWjE8++STP8aioKA4ePMjixYt55plnjHIfHx8CAgKIjo5m7NixRvmUKVPYt28fy5cvp3fv3gAMGjSIxx9/nMmTJxMUFISHh8fNXKqIiIiIiIiUYyWe6gswefJkTp48yZYtW/jhhx84ceIEb7zxRmn3DYANGzbQo0cPXF1dMZlMrFy50uq4xWIhIiICV1dXbGxs6NixIzt37iyy3eXLl+Pl5YXZbMbLy4v4+PgiY3bs2IGvry82Nja4ubkxadIkLJbCd/H86aef8PPzw8HBgdq1azN48OBiJwFE5PZ0I8serFy5klWrVjF79mwqV66cb53169djY2PD008/bVXer18/qlevzvz5863KFy1ahIeHh5H0yxUaGkpOTg7Lli0rcT9FRERERETk9nFDiT8AW1tbWrduTdu2balZs2Zp9snK+fPnadGiBbGxsfkenzp1KtHR0cTGxrJ161ZcXFzw8/Pj3LlzBbaZlJREv379CAwM5JdffiEwMJC+ffuyefPmAmMyMjLw8/PD1dWVrVu3Mn36dKZNm0Z0dHSBMampqfzrX//izjvvZPPmzXzzzTfs3LmT4ODgYl+/iFR8GRkZjBw5ksGDB+Pt7V1gvYsXL1K9enVMJpNVeaVKlbCxseHAgQOcPHkSgLS0NA4fPky7du3ytNOuXTtMJhNbtmwp3QsRERERERGRcqVYU32vHy1SmBUrVtxwZ/LTrVs3unXrlu8xi8VCTEwMY8aMMfq4YMECnJ2dWbx4MUOGDMk3LiYmBj8/P8LCwgAICwsjMTGRmJgYlixZkm/MokWLuHjxInFxcZjNZpo1a8Yff/xBdHQ0oaGhef4QB1i9ejVVq1ZlxowZVKp0Lcc6Y8YMWrZsyb59+7jzzjtL/HmISMUTFhbG5cuXiYqKKrSel5cXe/bsYfv27dx///1G+fbt2zlz5gwAhw4dok6dOhw9ehSA+vXr52nHbDZTp04djhw5UnoXISIiIiIiIuVOsUb82dvbGy87OzvWrVvHtm3bjOM//vgj69atw97e/pZ1ND/JycmkpaXRpUsXo8xsNuPr68umTZsKjEtKSrKKAfD39y8yxtfXF7PZbBWTmppKSkpKvjGXLl2iWrVqRtIPwMbGBri2C3JBLl26REZGhtVLRCqmpKQkZs+ezTvvvIODg0OhdUeNGkWlSpXo27cvX331FYcOHeLrr7+mX79+VK1aFYCsrCyr//3rb9ZfVa9e3agjIiIiIiIiFVOxEn/z5883Xs7OzvTt25fk5GRWrFjBihUrOHDgAE8//TR16tS51f21kpaWBoCzs7NVubOzs3GsoLjSivlrP673yCOPkJaWxttvv012djZnzpwhPDwcgGPHjhV4rqioKKtka4MGDQqsKyK3r5ycHAYNGkSnTp3o379/kfV9fX1ZtGgRGRkZPPbYYzRq1Iju3bvToUMHunfvDoCdnR2AsbvvpUuX8m3rwoULVjsAi4iIiIiISMVT4jX+5s2bxyuvvGK1+HzlypUJDQ1l3rx5pdq54rp+mq3FYsl36u2tiMmvPNe9997LggULeOedd7C1tcXFxQVPT0+cnZ0LXLwfrk37S09PN16HDx8utF8icnuaMWMGu3btYvTo0aSkpBiv3A2ADh8+zMGDB61inn76aY4cOcLPP//Mhg0bSE1NZc6cORw5coQqVaoYSwi4ubkB5Dud9+LFi5w6dSrfacAiIiIiIiJScRRrjb+/unz5Mrt27eLuu++2Kt+1axdXr14ttY4Vh4uLC3BtxF29evWM8uPHj+cZnXd93PWj9G40BvKOOPyrgIAAAgIC+PPPP6lRowYmk4no6Gg8PDwKjDGbzQVOzxORiiMlJYWrV6/i7++f7/G2bdtiNpu5ePGiVXmVKlWs1vhLS0vj559/xtfX1xjF5+LiQv369UlKSsrT7g8//IDFYqFNmzaldzEiIiIiIiJS7pQ48ffcc8/x/PPPs2/fPh588EHg2h+Rb731Fs8991ypd7AwHh4euLi4kJCQQMuWLQHIzs4mMTGRKVOmFBjXrl07EhISeOmll4yyNWvW4OPjU2hMeHg42dnZVKtWzYhxdXXF3d29yL7mJgfnzZtH9erV8fPzK84likgF9sILL9CxY8c85e+//z7r169n/vz51K5du9A2rl69yosvvsiVK1cYM2aM1bGAgACmTp3KihUrrDZpio6OpkqVKvTr169UrkNERERERETKpxIn/qZNm4aLiwvvvvuusU5dvXr1GD16NC+//HKpdzAzM5N9+/YZ75OTk9m+fTtOTk40bNiQkJAQIiMjadKkCU2aNCEyMhJbW1sCAgKMmKCgINzc3IwdM0eNGkWHDh2YMmUKPXv2ZNWqVaxdu9Zqw43Y2Fji4+NZt24dcO0P6IkTJxIcHEx4eDh79+4lMjKS8ePHG1N9t2zZQlBQEOvWrTOm2cXGxuLj40PNmjVJSEjg1Vdf5a233ipyEX8RuX0tXLjQmKJ74sQJsrOzefPNNwFwcHBg5MiRANx3333cd999eeJXrlwJQNeuXY2RzXDt97Bt27Y88cQTeHh4kJ6ezpIlS/jxxx+ZPHkynTp1smrn9ddf5/PPPycwMJAff/wRDw8PVq1axerVqxk3bhyenp634vJFRERERESknChx4q9SpUqMHj2a0aNHG7vN5i4mfyts27bN6o/Z0NBQAAYMGEBcXByjR4/mwoULDB8+nDNnzuDt7c2aNWuoVauWEXPo0CGrnXV9fHxYunQpY8eOZdy4cTRu3Jhly5bh7e1t1Dl58iT79+833tvb25OQkMCIESNo3bo1jo6OhIaGGv2Ba7to7tmzh5ycHKNsy5YtTJgwgczMTO655x4++OADAgMDS/dDEpFyZe7cuSQmJlqVjRs3DoBGjRoZib+SqlatGs2bN2fx4sUcO3YMW1tb2rRpwzfffJPvdGFHR0c2btxIeHg4c+bMISMjgzvvvJNZs2YxdOjQG+qDiIiIiIiI3D5MltwdKqTcysjIwN7envQ5YKdNOEXKrwD9nIqIiIiIiMitZeSJ0tOLHIxX4l19//zzTwIDA3F1daVKlSpUrlzZ6iUiIiIiIiIiIiJlr8RTfYODgzl06BDjxo2jXr16xvp2IiIiIiIiIiIiUn6UOPG3ceNG/ve//3H//fffgu6IiIiIiIiIiIhIaSjxVN8GDRqgZQFFRERERERERETKtxKP+IuJieH111/ngw8+wN3d/RZ0SQrUNx1u4Q7KIiIiIiIiIiJScZQ48devXz+ysrJo3Lgxtra2VK1a1er46dOnS61zIiIiIiIiIiIicmNuaMSfiIiIiIiIiIiIlG8lTvwNGDDgVvRDRERERERERERESlGJN/cA2L9/P2PHjuWZZ57h+PHjAHzzzTfs3LmzVDsnIiIiIiIiIiIiN6bEI/4SExPp1q0b7du3Z8OGDUyePJk77riDX3/9lY8++ojPP//8VvRTAD61B9uy7oSIFEuAdj8XERERERGRslXiEX+vv/46b775JgkJCVSrVs0o79SpE0lJSaXaOREREREREREREbkxJU787dixgyeeeCJPed26dTl16lSpdEpERERERERERERuTokTfw4ODhw7dixP+c8//4ybm1updEpEpKKIioqiT58+eHp6YjKZcHd3L7BuREQEJpMp31dISEi+MUuWLOGBBx7AxsaGOnXq8Mwzz3Dw4MF863722Wf4+PhQo0YNatWqxcMPP8xXX31VClcpIiIiIiIi5VGJ1/gLCAjgtdde47PPPsNkMnH16lW+//57XnnlFYKCgm5FH0VEblvh4eE4OTnRqlUrzp49W6yYd999lzp16liVNW3aNE+92NhY/v3vf9O+fXveffddTp48SUxMDBs2bGDr1q24uroadadMmcLrr79Oy5YtmTRpEiaTiU8++YTu3buzcOFC+vfvf1PXKSIiIiIiIuWPyWKxlGgF+pycHIKDg1m6dCkWi4UqVapw5coVAgICiIuLo3Llyreqr/9YGRkZ2Nvbkz4H7LS5h8jt4f9v7nHgwAE8PT0BaNasGZmZmaSkpOQbEhERwcSJE0lOTi50ZCDAqVOncHd356677mLz5s1UqXLt33G2bdtG27Ztef755/noo48AOH78OA0aNOCuu+7ip59+omrVqsC13/NWrVqRmppKcnIydnZ2pXDhIiIiIiIicisZeaL09CL/jivxVN+qVauyaNEi9u7dy6effsonn3zC7t27Wbhw4S1J+m3YsIEePXrg6uqKyWRi5cqVVsctFgsRERG4urpiY2NDx44d2blzZ5HtLl++HC8vL8xmM15eXsTHxxcZs2PHDnx9fbGxscHNzY1JkyZRVN70jz/+oGfPntSpUwc7Ozvat2/P+vXrizyXiFQMuUm/kjp37hw5OTkFHl+1ahWZmZm8+OKLRtIPoHXr1nTo0IFPP/2U7OxsADZt2kR2djb9+/c3kn5w7fc8ICCA06dPs2rVqhvqp4iIiIiIiJRfJU785fL09OSpp57iySef5Pz585w5c6Y0+2U4f/48LVq0IDY2Nt/jU6dOJTo6mtjYWLZu3YqLiwt+fn6cO3euwDaTkpLo168fgYGB/PLLLwQGBtK3b182b95cYExGRgZ+fn64urqydetWpk+fzrRp04iOji60/4899hiXL1/m22+/5ccff+T++++ne/fupKWlFe8DEJF/nBYtWmBnZ0f16tVp3bo1y5Yty1Nny5YtAPj4+OQ55uPjw7lz59i9ezcAFy9eBMDWNu+Q4dyywn7/RERERERE5PZU4sRfSEgIc+fOBeDKlSv4+vrSqlUrGjRowHfffVfa/aNbt268+eab9O7dO88xi8VCTEwMY8aMoXfv3jRr1owFCxaQlZXF4sWLC2wzJiYGPz8/wsLCuOeeewgLC6Nz587ExMQUGLNo0SIuXrxIXFwczZo1o3fv3oSHhxMdHV3gqL+TJ0+yb98+Xn/9dZo3b06TJk146623yMrKKtaoRBH5Z3FwcGDgwIG89957fPHFF7zzzjucPXuWp59+mjfffNOq7tGjRwGoX79+nnZyy44cOQKAl5cXAN9++22eurkjkA8dOlR6FyIiIiIiIiLlQokTf59//jktWrQA4D//+Q8HDhxg9+7dhISEMGbMmFLvYGGSk5NJS0ujS5cuRpnZbMbX15dNmzYVGJeUlGQVA+Dv719kjK+vL2az2SomNTW1wPW6ateuTdOmTfn44485f/48ly9f5oMPPsDZ2ZkHHnigwHNdunSJjIwMq5eIVHwhISHMmTOH4OBgevToQUhICDt27ODee+9l4sSJVrv1ZmVlAVj9JuWqXr26VZ3mzZvTuXNnVq1axejRo9m1axe7du3itddeM3b1za0rIiIiIiIiFUeJE38nT57ExcUFgK+++oq+ffty11138cILL7Bjx45S72BhcqfLOjs7W5U7OzsXOpU2LS2t1GL+2o/rmUwmEhIS+Pnnn6lVqxbVq1fn3Xff5ZtvvsHBwaHAc0VFRWFvb2+8GjRoUGBdEanYbGxsePXVV7l8+TJr1qwxynOn6F66dClPzIULF6zqACxbtownnniCt99+Gy8vL7y8vFiyZImxjII29hAREREREal4Spz4c3Z25vfff+fKlSt88803/Otf/wKujRYpqx19TSaT1XuLxZKn7FbF5Ff+1+PDhw/njjvu4H//+x9btmyhZ8+edO/enWPHjhV4nrCwMNLT043X4cOHC+2XiFRsuTv8njhxwihzc3MD/m8671/lNw24du3arFixgrS0NDZs2MBPP/1EcnKyUeeee+65Vd0XERERERGRMlLixN9zzz1H3759adasGSaTCT8/P+DawvB/9x+OuSMPrx9xd/z48Tyj866PK60YyDviMNe3337L6tWrWbp0Ke3bt6dVq1bMnDkTGxsbFixYUOC5zGYzdnZ2Vi8R+efau3cv8H+/eQBt2rQByHeJgk2bNlGzZs18f5OdnZ15+OGHadmyJZUrVzam+j766KO3ousiIiIiIiJShkqc+IuIiOCjjz5i8ODBfP/998b6UpUrV+b1118v9Q4WxsPDAxcXFxISEoyy7OxsEhMT893pMle7du2sYgDWrFlTZMyGDRvIzs62inF1dTVG41wvd82sSpWsP+ZKlSpx9erVAs8lIv88ly9f5tSpU3nKz549S1RUFNWqVcPf398o79mzJ7a2trz//vtcvnzZKN+2bRsbNmygb9++VKtWrdBzbtu2jY8++ghfX18eeuih0rsYERERERERKReq3EjQU089ladswIABN92Z/GRmZrJv3z7jfXJyMtu3b8fJyYmGDRsSEhJCZGQkTZo0oUmTJkRGRmJra0tAQIARExQUhJubG1FRUQCMGjWKDh06MGXKFHr27MmqVatYu3YtGzduNGJiY2OJj49n3bp1AAQEBDBx4kSCg4MJDw9n7969REZGMn78eGOq75YtWwgKCmLdunW4ubnRrl07HB0dGTBgAOPHj8fGxoY5c+aQnJzMY489dks+LxEpXxYuXGhsynHixAmys7ONHXodHBwYOXIkcO23rn79+vTu3Zv77ruP2rVrc+DAAebNm8fx48eJiYkxpvcC1KlTh8jISEJCQujYsSOBgYGcPHmSd999F2dnZyZNmmTVj3HjxrF3717atm2Lvb09P/30E/PmzcPNzY2FCxf+TZ+GiIiIiIiI/J1uKPH3d9q2bRudOnUy3oeGhgLXEo1xcXGMHj2aCxcuMHz4cM6cOYO3tzdr1qyhVq1aRsyhQ4esRt35+PiwdOlSxo4dy7hx42jcuDHLli3D29vbqHPy5En2799vvLe3tychIYERI0bQunVrHB0dCQ0NNfoD10b47dmzh5ycHODaH+bffPMNY8aM4ZFHHiEnJ4d7772XVatWGTsji0jFNnfuXBITE63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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 7))\n",
    "#for Malicious\n",
    "plot.barh(list(dict(data[data.label == 1].src.value_counts()).keys()), dict(data[data.label == 1].src.value_counts()).values(), color='orange')\n",
    "\n",
    "for idx, val in enumerate(dict(data[data.label == 1].src.value_counts()).values()):\n",
    "    plot.text(x = val, y = idx-0.2, s = str(val), size = 13)\n",
    "\n",
    "plot.xlabel('Number of Malicious Requests')\n",
    "plot.ylabel('IP address of sender')\n",
    "plot.legend(['Malicious'])\n",
    "plot.title('Number of Malicious requests from different IP address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "aa7bfb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.copy()\n",
    "df = df.dropna()\n",
    "X = df.drop(['dt','src','dst','label'], axis=1)\n",
    "y = df.label\n",
    "X = pd.get_dummies(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "158b92df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.75      0.71     19066\n",
      "           1       0.50      0.40      0.45     11987\n",
      "\n",
      "    accuracy                           0.62     31053\n",
      "   macro avg       0.58      0.58      0.58     31053\n",
      "weighted avg       0.60      0.62      0.61     31053\n",
      "\n"
     ]
    }
   ],
   "source": [
    "M = LogisticRegression(solver='liblinear')\n",
    "M.fit(X_train, y_train)\n",
    "joblib.dump(M, 'logisticr.joblib')\n",
    "prediction = M.predict(X_test)\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7a3cf08a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     19066\n",
      "           1       1.00      1.00      1.00     11987\n",
      "\n",
      "    accuracy                           1.00     31053\n",
      "   macro avg       1.00      1.00      1.00     31053\n",
      "weighted avg       1.00      1.00      1.00     31053\n",
      "\n"
     ]
    }
   ],
   "source": [
    "W = RandomForestClassifier()\n",
    "W.fit(X_train, y_train)\n",
    "joblib.dump(W, 'randomf.joblib')\n",
    "prediction = W.predict(X_test)\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "de674cfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     19066\n",
      "           1       0.96      1.00      0.98     11987\n",
      "\n",
      "    accuracy                           0.98     31053\n",
      "   macro avg       0.98      0.98      0.98     31053\n",
      "weighted avg       0.98      0.98      0.98     31053\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Z = DecisionTreeClassifier(criterion='entropy', max_depth=7, max_leaf_nodes=11)\n",
    "Z.fit(X_train, y_train)\n",
    "joblib.dump(Z, 'decisiont.joblib')\n",
    "prediction = Z.predict(X_test)\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6fbd3d33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.82      0.73     19066\n",
      "           1       0.54      0.35      0.42     11987\n",
      "\n",
      "    accuracy                           0.63     31053\n",
      "   macro avg       0.60      0.58      0.58     31053\n",
      "weighted avg       0.62      0.63      0.61     31053\n",
      "\n"
     ]
    }
   ],
   "source": [
    "A = GaussianNB()\n",
    "A.fit(X_train, y_train)\n",
    "joblib.dump(A, 'naiveb.joblib')\n",
    "prediction = A.predict(X_test)\n",
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d6177e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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