MaliciousDetection.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MALICIOUS WEBPAGE DETECTION USING SUPERVISED LEARNING METHODS**\n",
    "\n",
    "We analyze the data about webpages that contains attributes such as url and ip address and classify webpages as malicious or benign.\n",
    "\n",
    "We use these methods:\n",
    "- Logistic Regression\n",
    "- Decision Tree\n",
    "- Naive Bayes\n",
    "- Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Miracetdin\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# maps\n",
    "import nbformat\n",
    "import base64\n",
    "from base64 import encodestring\n",
    "from base64 import encodebytes\n",
    "\n",
    "import re   # regular expression\n",
    "import string\n",
    "import nltk   # natural language processing toolkit\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from tqdm import tqdm   # porgress bar\n",
    "from scipy import stats   # scientific computing\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, LabelEncoder\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import recall_score, f1_score, accuracy_score, precision_score\n",
    "\n",
    "# methods\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Load Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   index       50000 non-null  int64  \n",
      " 1   index       50000 non-null  int64  \n",
      " 2   url         50000 non-null  object \n",
      " 3   url_len     50000 non-null  int64  \n",
      " 4   ip_add      50000 non-null  object \n",
      " 5   geo_loc     50000 non-null  object \n",
      " 6   tld         50000 non-null  object \n",
      " 7   who_is      50000 non-null  object \n",
      " 8   https       50000 non-null  object \n",
      " 9   js_len      50000 non-null  float64\n",
      " 10  js_obf_len  50000 non-null  float64\n",
      " 11  content     50000 non-null  object \n",
      " 12  label       50000 non-null  object \n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# load the dataset and general information about the dataset\n",
    "df_dataset = pd.read_csv('datasets/small_dataset.csv').rename(columns={\"Unnamed: 0\": \"index\"})\n",
    "df_dataset.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write a small dataset\n",
    "# df_dataset.iloc[0:50000].to_csv('small_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>url</th>\n",
       "      <th>url_len</th>\n",
       "      <th>ip_add</th>\n",
       "      <th>geo_loc</th>\n",
       "      <th>tld</th>\n",
       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
       "      <th>js_len</th>\n",
       "      <th>js_obf_len</th>\n",
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       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Named themselves charged particles in a manly ...</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://www.naef-usa.com/</td>\n",
       "      <td>24</td>\n",
       "      <td>24.232.54.41</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>no</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete   yes    58.0         0.0   \n",
       "1  United States  com    complete   yes    52.5         0.0   \n",
       "2      Argentina  com    complete   yes   103.5         0.0   \n",
       "3  United States   de  incomplete    no   720.0       532.8   \n",
       "4  United States  com    complete   yes    46.5         0.0   \n",
       "\n",
       "                                             content label  \n",
       "0  Named themselves charged particles in a manly ...  good  \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...  good  \n",
       "2  Took in cognitivism, whose adherents argue for...  good  \n",
       "3  fire cumshot sodomize footaction tortur failed...   bad  \n",
       "4  Levant, also monsignor georges. In 1800, lists...  good  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# samples datas\n",
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1200000\n",
       "unique          2\n",
       "top          good\n",
       "freq      1172747\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label distribution\n",
    "df_dataset.label.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Preprocessing of Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Alpha-2 code</th>\n",
       "      <th>Alpha-3 code</th>\n",
       "      <th>Numeric code</th>\n",
       "      <th>ISO 3166-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ISO 3166-2:AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Åland Islands</td>\n",
       "      <td>AX</td>\n",
       "      <td>ALA</td>\n",
       "      <td>248.0</td>\n",
       "      <td>ISO 3166-2:AX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>8.0</td>\n",
       "      <td>ISO 3166-2:AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>ISO 3166-2:DZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16.0</td>\n",
       "      <td>ISO 3166-2:AS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Country Alpha-2 code Alpha-3 code  Numeric code     ISO 3166-2\n",
       "0     Afghanistan           AF          AFG           4.0  ISO 3166-2:AF\n",
       "1   Åland Islands           AX          ALA         248.0  ISO 3166-2:AX\n",
       "2         Albania           AL          ALB           8.0  ISO 3166-2:AL\n",
       "3         Algeria           DZ          DZA          12.0  ISO 3166-2:DZ\n",
       "4  American Samoa           AS          ASM          16.0  ISO 3166-2:AS"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country codes dataset\n",
    "df_country = pd.read_csv('datasets\\iso_country_codes.csv').rename(columns={'English short name lower case': 'Country'})\n",
    "df_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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       "      <th>js_obf_len</th>\n",
       "      <th>content</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Named themselves charged particles in a manly ...</td>\n",
       "      <td>good</td>\n",
       "      <td>TWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...</td>\n",
       "      <td>good</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://www.naef-usa.com/</td>\n",
       "      <td>24</td>\n",
       "      <td>24.232.54.41</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>good</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>no</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>bad</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>good</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete   yes    58.0         0.0   \n",
       "1  United States  com    complete   yes    52.5         0.0   \n",
       "2      Argentina  com    complete   yes   103.5         0.0   \n",
       "3  United States   de  incomplete    no   720.0       532.8   \n",
       "4  United States  com    complete   yes    46.5         0.0   \n",
       "\n",
       "                                             content label country  \n",
       "0  Named themselves charged particles in a manly ...  good     TWN  \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...  good     USA  \n",
       "2  Took in cognitivism, whose adherents argue for...  good     ARG  \n",
       "3  fire cumshot sodomize footaction tortur failed...   bad     USA  \n",
       "4  Levant, also monsignor georges. In 1800, lists...  good     USA  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding a feature which iso alpha 3 code for countries\n",
    "countries = dict(zip(df_country['Country'], df_country['Alpha-3 code']))\n",
    "\n",
    "df_dataset['country'] = df_dataset['geo_loc']\n",
    "df_dataset['country'].replace(countries, inplace=True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>url</th>\n",
       "      <th>url_len</th>\n",
       "      <th>ip_add</th>\n",
       "      <th>geo_loc</th>\n",
       "      <th>tld</th>\n",
       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
       "      <th>js_len</th>\n",
       "      <th>js_obf_len</th>\n",
       "      <th>content</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "      <th>net_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Named themselves charged particles in a manly ...</td>\n",
       "      <td>good</td>\n",
       "      <td>TWN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...</td>\n",
       "      <td>good</td>\n",
       "      <td>USA</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://www.naef-usa.com/</td>\n",
       "      <td>24</td>\n",
       "      <td>24.232.54.41</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>good</td>\n",
       "      <td>ARG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>no</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>bad</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>good</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete   yes    58.0         0.0   \n",
       "1  United States  com    complete   yes    52.5         0.0   \n",
       "2      Argentina  com    complete   yes   103.5         0.0   \n",
       "3  United States   de  incomplete    no   720.0       532.8   \n",
       "4  United States  com    complete   yes    46.5         0.0   \n",
       "\n",
       "                                             content label country net_type  \n",
       "0  Named themselves charged particles in a manly ...  good     TWN        A  \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...  good     USA        A  \n",
       "2  Took in cognitivism, whose adherents argue for...  good     ARG        A  \n",
       "3  fire cumshot sodomize footaction tortur failed...   bad     USA        B  \n",
       "4  Levant, also monsignor georges. In 1800, lists...  good     USA        C  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding network type feature\n",
    "# Identifying the type of network [A, B, C]\n",
    "def network_type(ip):\n",
    "    ip_str = ip.split(\".\")\n",
    "    ip = [int(x) for x in ip_str]\n",
    "\n",
    "    if ip[0] >= 0 and ip[0] <= 127:\n",
    "        return (ip_str[0], \"A\")\n",
    "    elif ip[0] >= 128 and ip[0] <= 191:\n",
    "        return (\".\".join(ip_str[0:2]), \"B\")\n",
    "    else:\n",
    "        return (\".\".join(ip_str[0:3]), \"C\")\n",
    "\n",
    "\n",
    "df_dataset['Network'] = df_dataset['ip_add'].apply(lambda x: network_type(x))\n",
    "# df_dataset['net_part'], df_dataset['net_type'] = zip(*df_dataset.Network)\n",
    "_, df_dataset['net_type'] = zip(*df_dataset.Network)\n",
    "df_dataset.drop(columns=['Network'], inplace=True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>url</th>\n",
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       "      <th>ip_add</th>\n",
       "      <th>geo_loc</th>\n",
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       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
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       "      <th>js_obf_len</th>\n",
       "      <th>content</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "      <th>net_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Named themselves charged particles in a manly ...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>TWN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://www.naef-usa.com/</td>\n",
       "      <td>24</td>\n",
       "      <td>24.232.54.41</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>ARG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>no</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>Malicious</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>yes</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete   yes    58.0         0.0   \n",
       "1  United States  com    complete   yes    52.5         0.0   \n",
       "2      Argentina  com    complete   yes   103.5         0.0   \n",
       "3  United States   de  incomplete    no   720.0       532.8   \n",
       "4  United States  com    complete   yes    46.5         0.0   \n",
       "\n",
       "                                             content      label country  \\\n",
       "0  Named themselves charged particles in a manly ...     Benign     TWN   \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...     Benign     USA   \n",
       "2  Took in cognitivism, whose adherents argue for...     Benign     ARG   \n",
       "3  fire cumshot sodomize footaction tortur failed...  Malicious     USA   \n",
       "4  Levant, also monsignor georges. In 1800, lists...     Benign     USA   \n",
       "\n",
       "  net_type  \n",
       "0        A  \n",
       "1        A  \n",
       "2        A  \n",
       "3        B  \n",
       "4        C  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label attribute changes -> bad:Malicious, good:Benign\n",
    "df_dataset.label.replace({'bad': 'Malicious', 'good': 'Benign'}, inplace=True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>index</th>\n",
       "      <th>url</th>\n",
       "      <th>url_len</th>\n",
       "      <th>ip_add</th>\n",
       "      <th>geo_loc</th>\n",
       "      <th>tld</th>\n",
       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
       "      <th>js_len</th>\n",
       "      <th>js_obf_len</th>\n",
       "      <th>content</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "      <th>net_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Named themselves charged particles in a manly ...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>TWN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>http://www.naef-usa.com/</td>\n",
       "      <td>24</td>\n",
       "      <td>24.232.54.41</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>ARG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>Malicious</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is  https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete  HTTPS    58.0         0.0   \n",
       "1  United States  com    complete  HTTPS    52.5         0.0   \n",
       "2      Argentina  com    complete  HTTPS   103.5         0.0   \n",
       "3  United States   de  incomplete   HTTP   720.0       532.8   \n",
       "4  United States  com    complete  HTTPS    46.5         0.0   \n",
       "\n",
       "                                             content      label country  \\\n",
       "0  Named themselves charged particles in a manly ...     Benign     TWN   \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...     Benign     USA   \n",
       "2  Took in cognitivism, whose adherents argue for...     Benign     ARG   \n",
       "3  fire cumshot sodomize footaction tortur failed...  Malicious     USA   \n",
       "4  Levant, also monsignor georges. In 1800, lists...     Benign     USA   \n",
       "\n",
       "  net_type  \n",
       "0        A  \n",
       "1        A  \n",
       "2        A  \n",
       "3        B  \n",
       "4        C  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HTTPS column -> yes: HTTPS, no: HTTP\n",
    "df_dataset.https.replace({'yes' : 'HTTPS', 'no' : 'HTTP'}, inplace = True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Data Exploration**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Distribution of Webpage Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YGo2G+fPn+9UAF1573X+bNTU1WK1WJkyY4PN3Fxoa6vflayDex0VgSEsMnhBz8R+UWq3GYDCQkJDAhAkTLjuNuHtwoM1m4+jRo36Xq9VqzGYzZrO5x2ZiuDDotTvMXI1LW3HUarXfrBuTyYTJZMLhcNDY2EhHRwcdHR00NTXR0dHR44yjwRj02traCtDj2J6oqCh0Oh0tLS0+54eEhPS7taH7etHR0VRWVjJ79mzvdFKn09nrWiEhISGMGzcOt9vtbXXp7OykpaXF+ybdn906uh9vT90+6enpvV6vo6MDl8tFbGys35gA8HyQVVRU0NLS0u/Bl9nZ2VRWVrJz506OHj1KYmIiSUlJxMfHX3E8jEql8gbArq4uWltb6ezspK2tzTs4tKfn59LX6MXTn8HTQud0OnscW2Eymfr9+794rJaiKN7X/aFDh9ixY4dPa09zczMajYazZ8/63U57ezvgaSm4+AOup5bTSx/TpZqbm1EUpcfu0Ojo6B5/z9DzdH2dTofD4ejx+IuFhIQQHh7uDawtLS3YbDafgBIfH49KpaK+vp7s7OweB/VezXPU02u++7Ff+jceExPj9zu+9FiDwUBGRgaKotDa2nrZv83ux9vTc31pXQPxPi4CQ0IM+LyZ9Vd38GhqavIbPHfpcb29+LvfaDs6Oq54fx0dHYSGhvr9sfdlIKbL5aKgoIDCwkJcLhfg6YaKi4sjKCjI263V39vtr+433t6+7YeEhPg9F9dSR2pqKgUFBTQ2NhIbG0t5eTnh4eGXnaZ+7tw5jh8/7n1OdDodMTExREREXPb33JOrbWnry/MEeH+X/ZGUlMSSJUu8/f1nz57l7Nmz6PV6pk6detkB5uAJZt3jl8DzJh8REYHJZOo1EF/6O+z+wO4+9nLPk1qtvqaWSpVKhV6vJykpiZCQEDZs2MCRI0e8f/d2u907CLU3l37JuJrXZHcLVU9j7NRqda8tvj0FuN4CT0+6x8V0D2jvPq9b90KA3b/PhoYGDAaDT3fr1TxHPT2e7t/jpa2PPb0/6nQ6NBqNT1irqKigoKDA+x6h1WoxmUwYjUaflqDu57qnGi59/gfifVwEhoSYa9T9B9nbipd9ERsbi1arpaGhodc1TcDzYfXZZ5+hKAqrVq3qdUBtbw4dOsS5c+e8o/OjoqK8rUDr16/vMcQMhu7HZ7FYenyDsdvtAzolOy0tjYKCAioqKjCZTFRVVV32Q7q8vJz9+/cTFRXF7NmzMRqN3gX69u3b1+8Q0/14e/rW7HK5UKlUPX5IXfw89aT7jbf7ubrch1pP3VXd69k4nU7q6+upqqqipKSEgwcPEh4e3mv3ncPhYOvWrTgcDvLz80lISCAiIgKNRkNjYyNlZWW91nE53a/nnlokFUUZsLV8ul/3FosFq9VKUFAQWq0WnU7HV7/61QG5j950v1/01oLSl5aVqxEXF+cdlF5XV4fBYPAL8QkJCZw8eZKuri4aGxtJTEz0eU1dzXPU0+Ppfj1f+v7VUxh3uVy4XC7va7yxsZEdO3YQHBzMggULiI6OJiwsDJVKxcmTJ31CzOWe60tfSwPxPi4CQ8bEXKPubyq9fbAdPXqUEydOXPbbskajIT09HZfLxenTp3s9rqSkBLvdjslk6neAAU+/clBQEIsWLSI+Pt77xuB0Or1960OxqXn3m2dPS8N3dHRgsViIjIwcsPsLCwvDaDRSWVnpnbZ7uWXnu/vfFyxYQEpKis8Kw93N5v1xudfIqVOnePvtt/3W6AC8waCpqanH10/389f9XHUHoUvfoO12u18wOH36NEeOHAE8H05JSUnMnj2b2bNn+9x2T+rq6rxjDSZNmoTRaPS2SlzN89MtPDwcnU7X45ombW1tV9Xi1BO3243T6USlUnmDotFoxGw29xgYq6qqOHLkiF/3x9Xo7irr6bXQ1tY2oIsuXqy71aWpqYnGxkZv99HFuruXysrKcDgcfjONruY56ulxdv9+L+027OnYS7uEysrKUBSF2bNnk5GRQXh4uPdxtLW1ARfewy73XF/6GhuI93ERGBJirlFYWBhxcXHU1NRQXl7uc1lJSQnHjx+npqbmik3PeXl56HQ6jh07RmFhoV+YqK6u5uDBg6hUqqte+Vaj0eByuXy+mbjdbg4ePOj94+ytL/9i3R+WfTm2J5mZmd5vThcPPO6eTdB9zEBKTU2ls7OT48ePe0NNb7p/V5cO3i0uLvZ+uPfnsaekpKDVajlz5ozPQEybzUZhYaG3q6qnOtLT07FYLBw7dsznsurqasrKyggLC/Net3vcRPdeUN1OnDjh93qqqanhxIkTfm/m3b+Py20N0f37v/TDrKury1vn1bw21Go1GRkZdHZ2curUKe/5LpfLG7gGwpkzZ3C5XCQkJHhDTPfr7cCBAz4fVBaLhf3793Py5MkBWSckJCSEhIQEamtrfX5PLpeLw4cPX/Pt9yY4OJiIiAiKi4u9a+9cqntMSvcA1kuPuZrnqKSkxKdruK2tjbNnz2IwGPwG0DY1Nfm04jkcDgoKClCpVN5uv97+Nmtra73X7X6tp6SkoNfrOXv2rM/7jNls9nl9wcC9j4uhJ91JA2DOnDls2rSJHTt2kJiYSFRUFO3t7VRXV6PX673fbi8nODiYxYsXs337dvbt2+edRqhWq2lpaaG+vh61Ws28efOuet+mzMxMTp06xaeffkpKSgput5va2lra29sxGAzYbDbsdvsV9+7pvvzcuXPY7Xbv6P2+CgsLY8aMGRw8eJBPPvnE+yFfU1NDZ2cn6enpgxJijh49SlNTE5MnT77ssRkZGZSVlfHll1+Snp6OTqejqanJu+Cf1WrtcfZNbwwGA7NmzWLPnj0+j7eiogKLxcJ1113X65vj9OnTaWho4OTJk9TV1REbG0tnZydVVVVotVoWLFjg/SaalJREcHAw5eXlbN26FaPRSGNjI62trURFRfkEqLy8POrr69myZQupqamEhITQ1tZGVVUVERERl33+4+LiCA0NpbS0FJvN5v2G3r0cAfQ8O6kvpk2bRm1tLYcPH6ampobIyEhqa2u9i+n1x8VTrMETrOrr62lsbESn0/lMlx03bhxVVVVUVFSwYcMGEhMTURSF8vJybDYb06dPH7BB7rNmzeKzzz7jiy++8FkLp/s5689Yl/6Ij4/n3LlzgH9AAU+LXGxsLHV1dd4Zixe7mufIYDDw2WefeVuaKyoqcLlcLFy40K/bXKfTsXPnTsrLywkODqa6uprOzk5yc3O9XzrS09M5ffo0+/fvp76+nuDgYO+gXoPB4PO3qdVqmTVrFrt27eLTTz/1rrFz8UzFi5/rgXgfF0NPQswAiIiIYOXKlRw/fty72FdwcDAZGRl+04gvJyYmhltvvZXCwkIqKyu9q+8GBweTlZXFxIkTr6mbpXsl0ZKSEs6dO4fBYCAyMpKZM2fS1tbGoUOHqK6u7nXhtW5xcXGMHz+e0tJSzp49S0JCQr8Hu+Xk5BAeHs6pU6e8byoRERHefaMGWmRkJBEREbS3t19xB+Pk5GQWLlzIyZMnKS0tRaPREBYWxqxZs4iJieHTTz+lurqajIyMPt//uHHjCAkJ4cSJE5SXl6MoCkajkTlz5vh9I71YUFAQK1as4Pjx41RUVHi/xWZmZjJlyhSfGTIajYYlS5ZQUFBAXV0djY2NxMXFsXz5co4cOeITYqKjo1m6dCnHjx+nrq7OuzppTk4Oubm5l51Wr9VquemmmygoKKChoYGGhgZCQkK8r/fPP/+choYGHA5Hv1sv9Ho9y5Yt4+jRo1RVVdHQ0EBsbCyLFi1i48aN/bqtM2fO+Pys0Wi8q8BOnjzZ5+9SpVKxaNEizp07R3FxMUVFRWg0GiIjI5k4cWK/dyO/nIiICJYvX05BQQG1tbW43W7i4+NZtGgRGzZsGLRv+90hJiwsrNf3pISEBOrq6nqc7XY1z9G0adNoaWmhuLgYh8NBdHQ0eXl5Pc5MjI+PJzk5mZMnT1JdXU1ERITfLvRGo5Ebb7yRo0ePUllZ6d1QdurUqWRlZfHBBx9QXV3NlClTAM8XEq1Wy4kTJygtLUWr1ZKWlkZcXBw7d+70eZ0P1Pu4GFoqZSgGQQwTv//973tcqVEIIYaCoih0dnb2OMOwe/n98ePHj6lv/b1tzXCtHA6H90vgpa1bRUVF7N27l4ULF152mYO+SEhI4J//+Z+v6TbE1ZMxMUIIMYQ++eQTNmzY4DdItHuchmy4ODDa29v54IMP2Lt3r8/5TqeTc+fOoVKpfBYrFCOTdCcJIcQQUalUjB8/nlOnTvHJJ594pzE3NDTQ1NREYmLigHZdjWUmk4no6GiKi4vp7OwkOjoal8vl3fx22rRpsubLKCAhRgghhtD06dOJiIigsLCQ4uJiFEUhLCyM6dOn97hjs7g6KpWKxYsXc/r0acrLyzl79ixqtRqj0Uh+fv4Vx8aJkUHGxAghhBBXScbEBJaMiRFCCCHEiCQhRgghhBAj0pgaEyMj0YUQQgwk+VwJrDEzJsbtdve4yZ4QQghxLeTzJXDGTIgRQgghxOgi0VEIIYQQI5KEGCGEEEKMSBJihBBCCDEiSYgRQgghxIgkIUYIIYQQI5KEGCGEEEKMSBJihBBCCDEijakVe4UQYiC4XC4cDkegyxDDnFarRaPRyM7kg0hCjBBC9JGiKNTW1tLa2hroUsQIodFoiIuLIzIyUsLMIJAVe4UQoo9qampobW0lLi6OkJAQ+VASvVIUBafTSXt7O+3t7URFRZGYmBjoskYdaYkRQog+cLlc3gATHR0d6HLECBEeHo7BYKCxsZG4uDg0Gk2gSxpVZGCvEEL0QfcYmJCQkABXIkaa0NBQFEWRcVSDQEKMEEL0g3Qhif6S18zgkRAjhBBCiBFJQowQQgghRiQJMUIIIXr0k5/8hJycHJ9Tbm4uN9xwA//2b/9GXV3doN13ZWUlOTk5/PKXvxy0+xAjn8xOEkKIgeB2g3qYfS8coJqefPJJjEYjAHa7nZKSEt555x3279/P+++/T1hY2DXfx6VMJhPPP/88EyZMGPDbFqOHhBghhBgIajW892torAx0JR4xKXDXDwfkppYuXUpKSorPefn5+XznO9/hgw8+4KGHHhqQ+7lYSEgIq1atGvDbFaOLhBghhBgojZVQUxzoKobE3LlzASgsLAxwJWIsG2Ztn0IIIUaC6upqANLT073n1dXV8eSTT7JgwQJyc3O57bbb+Nvf/uZzvbVr15KTk8OxY8d48sknmTt3LtOmTeMb3/gGp0+f9h7X25iYv/3tb9x8883k5eVx++23s3HjRh5++GHWrFnjPWbNmjWsWbOGPXv2cO+995KXl8fChQt5+umnsVqtg/F0iACRlhghhBCX1d7eTnNzMwBOp5PS0lKee+45kpOTueuuuwBoaGjgnnvuwW63c//99xMdHc3OnTv52c9+RklJCU899ZTPbX7/+98nNTWV733ve9TX1/PKK6/w6KOPsm3bNrTanj+afvWrX/H73/+eRYsW8dBDD3H69Gl+8IMfEBYWRk5Ojs+xJSUlPP7449x5553cddddbN68mddffx2dTse//Mu/DMKzJAJBQowQQojLuuOOO/zO02g0/Nd//RcRERGAJ2B0dnby4YcfesfPPPjggzzzzDP85S9/YfXq1UycONF7/aysLP74xz96f9Zqtbz88svs3buXhQsX+t1fZWUlf/7zn1m6dCkvv/yydwG5cePG8dxzz/kd39DQwK9//WtuueUWAFavXs3y5cv56KOPJMSMItKdJIQQ4rJ+8Ytf8Oqrr/Lqq6/yhz/8gZ///Ofk5uby2GOP8cEHH+B2u9m0aRP5+fmEhITQ3NzsPS1fvhyAzz//3Oc2b775Zp+fJ02aBHjCR0+2bt2K0+nkm9/8ps8KuA8++GCPs6N0Oh3Lli3z/qxWq8nJyaGxsfGqngMxPElLjBBCiMuaMWOG3+ykVatWcfvtt/Pss88ye/ZsOjo6+PLLL5k/f36Pt9E9hqbbpZto6vV6ANxud4/XLysrAyAzM9PveqmpqX7Hh4eHo9Pp/I7t7fbFyCQhRgghRL8ZDAYWL17Ma6+9Rn19PQA33XSTzwDbi8XFxfn83N/9hLo3T+wOO5fWcin1cFuzRwwKCTFCCCGuSnerRnBwMMHBwdjtdhYsWOBzTHNzM/v37/eZxXQ1uq9fUlLC1KlTvecrikJZWRnjx4+/ptsXI5NEVSGEEP1msVjYsmULJpOJ7OxsbrjhBnbt2kVBQYHPcS+99BLf+973rnk9mWXLlqFWq3nzzTd9zl+/fj0tLS3XdNti5JKWGCGEEJe1efNm77YDiqLQ1NTEe++9R1VVFU8//TRarZYnnniCvXv38vDDD3P//feTkZHBnj172LBhAzfeeCPXXXfdNdWQlpbGww8/zCuvvEJzczPXX389xcXFvPPOO35jX8TYISFGCCEGSkzKlY8ZKgNYy7PPPuv9v1qtJiIigkmTJvGjH/2IpUuXApCamsq7777LSy+9xIcffkhHRwdJSUl897vf5Vvf+taAjFH58Y9/jNFo5J133mHnzp1kZmby0ksv8R//8R89jpURo59KURQl0EUIIcRwZ7VaKSkpITMzk6CgIP8DRvEGkMOB2WxGURRCQ0N9zlcUhenTp7NixQqef/75AFV3eVd87YirNjpe3UIIEWjDMSwMx5qu0smTJ5kxYwbvvfeez/lbt27FarWSl5cXoMpEIEl3khBCiGFv2rRpZGRk8Mwzz1BWVkZqaiplZWW89dZbZGVlebc/EGOLhBghhBDDnk6n4/XXX+d3v/sdH330EY2NjURHR3PHHXfw3e9+l+Dg4ECXKAJAQowQQogRIT4+np/97GeBLkMMI6Onw1QIIYQQY4qEGCGEEEKMSBJihBBCCDEiSYgRQgghxIgkIUYIIYQQI5KEGCGEEEKMSBJihBBCCDEiSYgRQgghxIgkIUaIsURxg8sJTqfnX+/JBW6XZ8NAxQ3d+8Iqiuc8t+uiYx2ef92uC8eJUeknP/kJOTk55OTkUFZW1utxP/3pT8nJyWHhwoX9uv21a9eSk5PDF198AUBlZSU5OTn88pe/7NftrFmzpt/3LUYHWbFXiJFMUTxhAkCtAZXqwmVuF9itYDWDtQssnWDt9Pzf2gU2C7id50PK+fDidoNyUZhBBVodaHSef7V60BtAF+T5Vx8MYVEQGgkhEaDvYYdelwtQQDO6324Ut4JKrbrygUNoIGvavHkzjzzyiP99KAqbNm0akPswmUw8//zzTJgwoV/Xe+yxx+js7ByQGsTIMrrfVYQYDbqDiloNqvONpw4btDVCawO0N0B7M7Q3nT81ev61dg19rRrthUATGuk5RUSDMR5MiZ5TuOnC7srex3ZJABuBVGoVx16qoKvKFuhSAAhNNjD1e6kDclupqam9hphDhw7R0NCAyWS65vsJCQlh1apV/b6etMKMXRJihBhOXE7fD/TOFqivgIYKaKyCxkrPqaMlsHX2xuW8EKZ6o9ZAZIwn2BjjwZQE8RmQmOkJPXC+JUgBjWZIyh4oXVU2OkqsgS5jwC1btozXXnuNxsZGYmJifC7btGkTGRkZxMXFUVxcHKAKxVglIUaIQHG7AJWnVcLt9oSUytNQVQg1xZ6wYh99H4i4XdBS5zldKjQS4tM9oSY+AxLHQUyyp4XH7QYUTwgSQ2rZsmW88sorbN26lXvuucfnso0bN3Lbbbdx+PBhn/P37dvHK6+8QkFBAR0dHURGRjJv3jyeeOIJkpKSeryfyspKlixZwqOPPsoTTzzhPf+TTz7hL3/5C2fOnCEkJIS5c+fywx/+kNRUT0vTmjVrKC4uZufOnd7rFBUV8Zvf/Ia9e/diNpsZN24cDz30EHfffbf3mN/+9re8/PLLbNiwgaysLO/5b731Fv/5n//J66+/zty5cwE4ePAgL774ImfOnMFms5GVlcUDDzzA6tWrr/JZFQNBQowQQ8XlvDAupKUOKk5DdaEntNQWg8Me2PqGg642KD7qOXXTaCEhE9ImQeokyJjs6a4C3+dUDJopU6aQnJzM5s2bfULMiRMnqKqqYsWKFT4hZvfu3TzyyCNMmTKFxx9/HL1ez6FDh1i3bh3nzp3jo48+6vN9v/rqqzz33HNMnTqV73//+1gsFl577TUOHz7Me++912M31okTJ3jooYfQ6/U88MADGI1GNm7cyFNPPUVxcTH/+q//2q/HX1payqOPPkpycjLf/va3MRgMfPzxx/z7v/87gASZAJK/fiEGy8VdQ631UHgYSo5B6XHPh7XoG5cTqs55TrvXec4zJXgCTdpEyMiF6KQLM6lGWBfUSLFs2TLefPNNOjs7CQsLAzytMCkpKUyZMsXn2FdffRWj0cjrr79OcHAwAPfddx9Op5OPP/6Yuro64uPjr3ifbW1t/PrXv2bOnDm88sor6HQ6APLz8/n617/O2rVr+da3vuV3vZ///Oe4XC7effdd0tLSAHjooYd4/PHHeeWVV1i1ahUTJ07s82PfvHkzXV1dPPPMM0ydOhWAO++8k/vuu4+ioqI+344YeBJihBgoiuI5qdWeMStFh6HkOJQe8wzCFQOnudZzOrLN83OYEbKnQ/YMzykoxNNtpVKP+AHDw0X3uJgvvviCW265BfCEmOXLl/sd+9///d+0t7d7AwxAZ2cnBoMBALPZ3Kf73LVrFzabjQceeMAbYADmzZvHu+++S2Zmpt91GhsbOXz4MHfeeac3wACo1Woee+wxtm3bxqZNm/oVYhISEgB44YUX+M53vkN+fj56vZ61a9f2+TbE4JAQI8S16P6gBE/X0Kk9cHqfZzyLGDqdLVCwzXNSqSF5PGTnw4RZnnE14JkyLuNprtqMGTOIiYlhy5Yt3HLLLRQWFlJcXMxzzz3nd6xGo6GmpoaXX36Zc+fOUVlZSXV1Ncr5dYXcbnef7rOqqgqgx7CSl5fX7+t0j3vpPqavVq5cybZt21i/fj27d+8mKiqKhQsXcuutt7JkyZJ+3ZYYWBJihOiv7nEYLicUHYHTe+HsfuhsDXRlAjxhpfKM5/T53yEk3BNmpiyCrGmekCOBpt/UajVLlizh448/xm63s3HjRhITE3sME6+99hrPPvssaWlpzJ49m8WLF5Obm8uXX37J73//+z7fZ1/DzsWUyyzA2H17F7fq9OV+tVotL7zwAo8//jibNm1ix44dbNy4kY8//pg77rijxyAnhoaEGCH6wuXyjLVw2DytLaf2QFHB6Jw9NNqYOy600gSFwsQ5kHsdjMuTQNNPy5Yt4+2332bv3r1s3LiRZcuWobqku85ms/Hiiy+Sn5/P66+/jl6v9162bt26ft1f9yym8vJyv+6fp556ikmTJvHggw/6nJ+SkgLQ43Tv7vO6u4fU59crcjgcPsc1NDT4/FxXV0dxcTHz588nKyuLxx57jJaWFr797W/z/vvv8+Mf/5jo6Oh+PTYxMGTbASF6o3SvYuvyBJZ/vADPfx3WvugJMRJgRh5rlyfM/PVn8IuHYd3vPIOtvasWyzYKlzNv3jwiIiJ44403OHXqFCtWrPA7xmq1YrFYSE9P9wkw1dXVbNy4EQCXy9Wn+1uwYAF6vZ63337b5zoFBQW8++67Pa7SGxMTw7Rp09iwYQPl5eXe891ut7cVaPHixQDExsYCcPLkSe9xdrvdbwXiN998k4cffphTp055zzMajaSnp6NSqbxhSAw9aYkR4lLdrS515XB4Mxz7Esztga5KDDRLJxze4jmFRcG0G2HWSs8CfDJ1u0c6nY4bb7yRdevWERsby4wZM/yOiYyMJD8/n48++oiIiAgmTJhAeXk577zzDhaLBYCurr6tJm0ymfjBD37A888/z5o1a7j55ptpa2vjjTfeICMjw68VpttTTz3F1772Ne6++24eeOABTCYTGzduZN++faxZs4bJkycDnpalp59+mmeffZb6+nrCw8N57733/ELWvffey9///nceffRR7r//fuLi4jh+/DgffPABt912G0ajsT9PoxhA8lcqBHi+hatUYLfAoc2eD7b68itfT4wOna2w8wPPKX0y5C+F3EWeINM946wPQpMNg1llvwxWLcuXL2fdunUsW7as1xaI3/zmNzz33HOsX78eq9VKQkICq1evZvny5dxzzz3s2rWL/Pz8Pt3fI488QmxsLK+99hrPP/88RqORJUuW8MMf/tA71ftSeXl5vP322/zmN7/hr3/9K3a7nezsbJ599lnuvPNO73FGo5E//elPvPDCC/zud78jMjKSVatWsWjRIh5++GHvcUlJSbz++uv89re/5a233qK1tdW7Zsw//dM/9f3JEwNOpVxuFJQQo133N+66Mti73tPq4hgee9+IAAsK8YydmbUCEjKxdnVRUl5OZmYmQUH+G12O9g0gxdWzWq2UlJT0+toRV09aYsTY072ei6LAiV2wfwNUnAl0VWK4sZrhwGeeU/J4mHM76KJ7HTczHMPCcKxJiIEkIUaMHd1dRuZ22LMeDm2SlXNF31Sdg01/geseBrMJDPoL6wPJYnpCBIyEGDH6ud2eMQ3tjfDFPzyrvLqcga5KjERulyf42rs8+zeFRoFW52mdkTAjxJCTECNGL7fLs/5HUzVsfxtO7jq/E7IQ10hRPGGmqw2CwiDcCDqDhBkhhpiEGDH6dE+RrimG7e/AuYOy/ocYPNZOzykoDMJNoNNLmBFiiEiIEaNHd7dRTRFsfsOzW7QQQ0XCjBBDTkKMGPm6w0tzjWfw5Zn9ga5IjGUXh5kIE2glzAgxWCTEiJGru4uoqxW2/BWObPdsEyDEYDj/eutzz2R3mAkOh4hoz/gsCTJjkizHNngkxIiRye32rK67/R3Y/wk4HVe+jhDXQGfrBJcTs8NFsK4fG0ZaOjxhJjQKwoyeICNhZkzp6upCpVJdcfds0X8SYsTI4nKBCti9Dr78h2dBMiGGgMZpJ6qsgHp9EGAkRKfpXxZpbYS2VgiLhOAw6WIa5RRFwel00t7eTnt7O1FRUWg0slv6QJNtB8TI0D3upeQYfPwHaKwMdEViDFKA2vGLaE2ffm0bRGp0niAja8yMehqNhri4OCIjI1HJ73nASYgRw5/bDeY22PAnz1ovQgSYS6PHERR27eEjazpcf7dn3EwfN5kUI4dWq0Wj0Uh4GUQSYsTw1d11tOtD+OJdsFsDXZEQA88QDDc9AHNu9QR26XIQos8kxIjh53zzulJ6HNX630vXkRgbksfDqu9CbIp0LwnRRxJixPDicqG4nag+/TMc3BToaoQYWmoNLFgFN97n2WBSWmWEuCwJMWJ46G59KT6K6sOXoa0h0BUJETjGBLj9f8G4PBn4K8RlSIgRgedyobgcqD59BQ5J64sQXjOXw82PgEojrTJC9EBCjAgcb+vLkfOtL42BrkiI4Sc6Ce76ESSOkxYZIS4hIUYEhsuForg8rS8HPgt0NUIMb2qNZyr2DXd7wr9aWmWEAAkxIhDcbpTmalRvPw8NFYGuRoiRIyUHVv9vz8aSEmSEkBAjhpDiBpUa5eAmVJ/+CRz2QFckxMijD4KVj8CMpd6/KSHGKgkxYmh0D95d9zs4viPQ1Qgx8uXdAF95XAb9ijFNQowYdIrbjVJbgvofv4Tm2kCXI8ToEZcG9z8JkbHSvSTGJAkxYvB0r2+x92PY+Bq4nIGuSIjRxxACX/0eTJob6EqEGHISYsSgUNwuUBRU6/8HDm8JdDlCjH4LVsHSrwEye0mMHRJixIBTXE4USxfqvz8LlWcCXY4QY0f6FLj3X8AQKuNkxJggIUYMKMXl8ox/efs5aG8KdDlCjD0R0fDgf3g2kpQWGTHKSYgRA0JRFFQqFcrR7ajW/Rc4Zfq0EAGjD4LVT8D4GbLKrxjVJMSIa+Z2uVCpVag2vQG7Pgh0OUII8Kwfs+JhmHd7oCsRYtBIiBHXxOVwoEZB9d6v4PTeQJcjhLjU7Jvhlm+BAqhlYTwxukiIEVfNZbehctpRv/U0VMgAXiGGrfEz4J5/AY1WxsmIUUVCjLgqTrsNdVcb6r/+FJqqA12OEOJK4tNhzX9CcLjMXBKjhoQY0S+KouB22FE116L+20+hoyXQJQkh+soYDw//HMJMEmTEqCAhRvSZy+VCpbih8izqvz8DVnOgSxJC9Fe4Cb7+MzAlSNeSGPEkxIg+cdhtaDQaKCxA/e4vZAq1ECNZSLinayk+XYKMGNEkxIgrspi7MBgMcPagZxNH2QNJiJHPEOxZFC8lR2YtiRFLQoy4rM72dkJCQ+DMAdTvvSABRojRRKeHe38C46ZJkBEjkoQY0au25ibCIyNRnd7nWQfG7Qp0SUKIgabRelb3zZktQUaMOPKKFT1qrq8jPDIKTu2VACPEaOZywj9+CecOgtsd6GqE6BcJMcJPc30tUTExcHIX6rUSYIQY9VxOeOd5KCqQICNGFAkxwkdDTTVRphg4uRv1+y/KG5oQY4XLCW8/B6XH5IuLGDEkxAivusoKjCYTlBxF/f5vJMAIMdY4HfDWs1B1ToKMGBEkxAgAastLiTIaUdeXo37neZmFJMRY5bDBX38OdWUSZMSwJyFGUF1aTHhEJNquFtRv/n+eNzEhxNhlM8Mb/wktdeCSICOGLwkxY1x1SREhoaEE4UTz15+CpSPQJQkhhgNzhyfI2LqkRUYMWxJixrC6ygp0Oh0RIcFo3vhPaG8KdElCiOGktQHe+KmnNUbGyIlhSELMGNVUV4u1q5Po2DjUf/sZNFUHuiQhxHBUU+yZfq0CZG1UMcxIiBmD2luaqSkrJm18jmcdmOqiQJckhBjOzh2E9f8DKlWgKxHCh4SYMcbc2cHJg3vJnbMA1ZY34Mz+QJckhBgJDm6CL/8R6CqE8CEhZgyxWS3s37aJ2TcsQSnYBjs/CHRJQoiRZMvf4Oh2GR8jhg0JMWOE0+Fg56cfMfuGm1DVFKH66L8CXZIQYiRa9zuoLZGp12JYkBAzBiiKwp7Nn5I7ay5Bbjvqt/+fLGYnhLg6Tgf8/Vmwm6VFRgSchJgx4NjeXcQmJhETF4/6rWegqy3QJQkhRrL2Jnj7/wW6CiEkxIx2ZWdP09JYT870mZ79kOrKAl2SEGI0KD0BG18NdBVijJMQM4o11dVQsHM71628HXavg1N7Al2SEGI02bNeBvqKgJIQM0qZOzv4fN1aFn/lLqgths1vBLokIcRo9NF/QUOFDPQVASEhZhRy2O1sff9t8uYuICwkCPW7v5SBvEKIweGww1vPgNMmLTJiyEmIGWUURWHvlk8JDotgfF6+ZxxMW0OgyxJCjGat9Z6p12r5SBFDS15xo0zhsSNUFheyeNVdsGMtnD0Q6JKEEGPBiV1weIvseC2GlISYUaSprpYdn33EbQ88DBVnYeubgS5JCDGWfPInz87XMj5GDBEJMaOE3WZl6wfvMvO6xYQEB3k2dpRvREKIoWS3wru/CHQVYgyREDMKKIrC7k2fAAp58xah/vRP0NYY6LKEEGNRTTFskdmQYmhIiBkFzhw5xIl9u7n9wW+gnNkPBdsCXZIQYizbvQ6Kj0q3khh0EmJGuMbaarave49lq+9Hr0Y2dhRCBJ6iwNoXwWEDRaZdi8EjIWYEc9htbPvgXRLTMjzTqdf/N3S2BrosIYSAzhb49E+gko8ZMXjk1TWCHfryc2ory1l53xqUo9vh5O5AlySEEBcUbIOiI9KtJAaNhJgRqrq0mL1bPuP2NY+gtVtQbfhjoEsSQgh/H/0O3E5PF5MQA0xCzAhks1rYvv59UsZlk5EzGfWG34O1K9BlCSGEv9YG2PQ6qFSBrkSMQhJiRqD92zZRVVLEynse9MxGOr0v0CUJIUTv9n8KlWelW0kMOAkxI0z5uTMc2L6FpXfdR3BwEKpPpBtJCDHMKW744LeAdCmJgSUhZgSxdHWxff37RBhNTJu3ENX2dzxNtUIIMdw1VsL2d2TKtRhQEmJGkH3bNlJRdI7b13wTpbHas6CUEEKMFLs+gPYmcEuQEQNDQswIUVVSRMHO7cxffguxSamoP/49uJyBLksIIfrO6YBP/gxq+egRA0NeSSOA0+Fg12frURSFhctvRinYBmUnAl2WEEL03+m9UHJMBvmKASEhZgQ4cWAPxaeOs2z1A2hVKlSb/hLokoQQ4upt+KNMuRYDQkLMMNfW3MjezZ8Sm5TKpPyZqHesha62QJclhBBXr6EC9m0At7TGiGsjIWYYUxSFPZs/o6G2miV33OMJL3tkMK8QYhT4/O9gs8hKvuKaSIgZxkrPnOTE/t1MnjmX9AkTUW99Exz2QJclhBDXztoFm9+QbiVxTSTEDFN2m5Vdn32M0+Fg0YpbcdeVwZHPA12WEEIMnMOboblWplyLqyYhZpg6eXAfFUVnmbV4KbHJqag3viaLRAkhRhe3G7b8VaZci6smr5xhqKujnQOfbyY4NIzZ192Eu6gAigoCXZYQQgy8k7ugrkwG+YqrIiFmGDqyewf1VZXMuWk54aZo1DKlWggxWinK+dYYTaArESOQhJhhpqWhnsM7PycyOoa8OQtwn9gJtaWBLksIIQbP2QNQdU5aY0S/SYgZRhRF4cD2LbQ2NjLj+sWERkah3v5OoMsSQojBt/kNaY0R/SYhZhipKS/lxIE9xCYmkjd7vqcVpr480GUJIcTgKzkGJcdlOwLRLxJihgm3282BbZuxdHUxde5CaYURQow9W/8GGmmNEX0nIWaYKD93hnPHDhOXnELuzDkop/dKK4wQYmypOA0VZ2RsjOgzCTHDgNvt5tCOz3E4HEyYNoNwUwyqL98LdFlCCDH0drwnY2NEn0mIGQbKz52h+OQxYhOTmTpzDu6iI56R+kIIMdacPQBN1bKKr+gTCTEB1t0K43Q4SM+ZRFRcAuqdawNdlhBCBIaiwI61soqv6BN5lQRYZdE5Sk4dJyYxiYnT8nHXl0Px0UCXJYQQgXN0O3S1yQ7X4ookxASQoigU7PoSh91GbFIKyZnZqPeuD3RZQggRWC4n7PpAQoy4IgkxAVRdWkzRiSOY4hOZMHU6is3s+QYihBBj3YGN4LQHugoxzEmICaCje3ZgtZiJNEWTPTkX9cGN4JA/WiGEwGaGgxtl8TtxWRJiAqSxtpqzRwswxsaTOXEKWr0B9n8a6LKEEGL4OPCZLH4nLktCTICcLjhIZ3sr4VFGJuZNh9N7oa0h0GUJIcTw0VTt2Y5AFr8TvZAQEwDmzk5O7N9NeJSRhLR0IqJjUcmAXiGE8Ldvgyx+J3olISYACo8fobm+HmNMHFmTcnE3VELZyUCXJYQQw8+Z/Z7p1kL0QELMEHM5nRzbuxOdXk9QSCip48ajPrwl0GUJIcTw5HbBoU3SpSR6JCFmiJUXnqG6tJjo+ATSxueg1mhkWrUQQlzOoS3SpSR6JCFmCCmKwon9e3C5XBiCQ8iaOBml8DB0tgS6NCGEGL5aamWAr+iRhJgh1FhbTdHJYxhj4wiPMhKTlIK6YGugyxJCiOGvYKu0xgg/EmKGUPHJ43R1tBMWGcW4Sbm4LZ1wdn+gyxJCiOHv9F5wOgJdhRhmJMQMEafDwalD+wgODUetVpM1cRLqY1/KH6UQQvSFzQJnD8gKvsKHhJghUlF0jvrqKowxscSnpBEUFuFpHhVCCNE3x76QFXyFDwkxQ6Tw+BHcLif6oCDSsifgbq6F6sJAlyWEECPHuYNgtwa6CjGMSIgZAl0d7Zw7XkB4lAmVSkXquGzUJ3cFuiwhhBhZnA44sQtczkBXIoYJCTFDoOT0Cdqam4gwRRObmIwhJBRO7Ql0WUIIMfIc+wI02kBXIYYJCTGDTFEUTh8+gFajRaPRkJo1Hnd7k3QlCSHE1Sg9Bub2QFchhgkJMYOssbaayuJCImNiAUjLykZ9ajcoSoArE0KIEcjt9rRkS5eSQELMoKsoPIu5o4PQ8Aii4xMIDo+UriQhhLgWZw9Il5IAJMQMKkVRKDx+BH1QECqVipSs8bi72qH8VKBLE0KIkav4qLTECEBCzKBqrq+jpryUCKMJgPRx2ahP7/U0hwohhLg6DpvspSQACTGDqrL4HOaODkLCIwiNiCTMGO1pBhVCCHFtzuwHlSrQVYgAkxAziIpOHEOr06FWq0lMTUdxu6D0eKDLEkKIke/cAVDJR9hYJ6+AQdLW3EhVSRHh57uSEtLSUSrPgc0c4MqEEGIUaG2AxqpAVyECTELMIKkoOkdnexthkVGoVCoSU1JRFxcEuiwhhBg9Tu+VDSHHOAkxg6Tk1AnUGjVqtRpTXDy6oBAoOhLosoQQYvQoKpANIcc4CTGDwNLVRUXROcIjjQAkpKbjtlmg6lyAKxNCiFGk8oy0xIxxEmIGQV1lOZ1trYRGRAKQmJqOSqYDCiHEwHLYoaZIVkAfwyTEDILailJcLic6vR6NVkdMYhIqGQ8jhBADT74gjmkSYgaYoiiUnD6J3hAMQExCAmqN1vOHJoQQYmCVnZAtCMYwCTEDrL2liYaaKsIiPV1J0QlJuK1dMhVQCCEGQ/kpWQV9DJMQM8BqK8oxt7cTGh4BQEx8IlSekz5bIYQYDHYr1JXKe+wYJSFmgFWXFqMA6vPT/mITElBXnQ1sUUIIMZqVHJVxMWOUhJgB5HK5KDl9kuCwMABCIyIxhIR5pgEKIYQYHOWnZVzMGCUhZgC1NjbQ3tJE2Pmp1TEJiZ4LKmV9GCGEGDQ1RYGuQASIhJgB1FRXg6Wri6CQUABiEpJwN9WApSPAlQkhxCjW1gjWrkBXIQJAQswAaqypBkVBrfY8rbHxCagrTwe4KiGEGAOqC2Vw7xgkIWYAVZUUog8KAkCtVhMVGydbDQghxFCoLpLBvWOQhJgBYjWbqa+uIjgsHIDwKKNnkbu6sgBXJoQQY0BNsQzuHYMkxAyQproaLJ0dhJyfmRRpivFcUF8RwKqEEGKMqCkOdAUiACTEDJDG2hocdjs6vQGAyOho3J2tMqhXCCGGQkutZ+E7MaZIiBkg9VUVqNRqVCoVcL4lpr48wFUJIcQYoShQWxroKsQQkxAzABRFobq0iKDQUO95RpMJtYQYIYQYOg0V4HIGugoxhCTEDABLVycdra0EBYcAni0HwqJM0CAhRgghhkxzNZxvDRdjg4SYAdDa1IjVYvaGmAijCZVaLd1JQggxlJpqQK0JdBViCEmIGQBtTY2eQb2G84N6TdGeCxpkZpIQQgyZ5ppAVyCGmISYAdDW3ATgHdQbFhGJu6sdrOZAliWEEGNLc22gKxBDTELMAGiqrUajvdCEGRoe4dnLQwghxNBx2qGjJdBViCHUrxDzk5/8hJycHJ9Tbm4uN9xwA//2b/9GXV3dYNVJZWUlOTk5/PKXvxy0+7gaiqJQV1WJ4fx4GPCEGFVbfQCrEkKIMaqxUvZQGkOuao3mJ598EqPRCIDdbqekpIR33nmH/fv38/777xN2ftXagWQymXj++eeZMGHCgN/2tejqaKero807qBcgLDwcVfWxAFYlhBBjVGMVpE2SLQjGiKv6LS9dupSUlBSf8/Lz8/nOd77DBx98wEMPPTQgxV0sJCSEVatWDfjtXqv25iZsFgvhUSbveSHhEdDWEMCqhBBijGprAGSa9VgxYGNi5s6dC0BhYeFA3eSI0Nnedn67AT0AhqBgNDq9jIkRQohA6GwBjUyzHisGLMRUV1cDkJ6e7j2vrq6OJ598kgULFpCbm8ttt93G3/72N5/rrV27lpycHI4dO8aTTz7J3LlzmTZtGt/4xjc4ffq097jexsT87W9/4+abbyYvL4/bb7+djRs38vDDD7NmzRrvMWvWrGHNmjXs2bOHe++9l7y8PBYuXMjTTz+N1Xpte210dbSjUl2YmRQaEeG5oFXGxAghxJCTgb1jylV1J7W3t9Pc3AyA0+mktLSU5557juTkZO666y4AGhoauOeee7Db7dx///1ER0ezc+dOfvazn1FSUsJTTz3lc5vf//73SU1N5Xvf+x719fW88sorPProo2zbtg2ttucyf/WrX/H73/+eRYsW8dBDD3H69Gl+8IMfEBYWRk5Ojs+xJSUlPP7449x5553cddddbN68mddffx2dTse//Mu/XM3TAEBXezvKRYPIQsPPhxjpThJCiKHX2RroCsQQuqoQc8cdd/idp9Fo+K//+i8izrdE/OpXv6Kzs5MPP/zQO37mwQcf5JlnnuEvf/kLq1evZuLEid7rZ2Vl8cc//vFCYVotL7/8Mnv37mXhwoV+91dZWcmf//xnli5dyssvv+xtCRk3bhzPPfec3/ENDQ38+te/5pZbbgFg9erVLF++nI8++uiaQkx7cyOai0JWcGgYitOByiy7VwshxJDrlJaYseSqupN+8Ytf8Oqrr/Lqq6/yhz/8gZ///Ofk5uby2GOP8cEHH+B2u9m0aRP5+fmEhITQ3NzsPS1fvhyAzz//3Oc2b775Zp+fJ02aBHjCR0+2bt2K0+nkm9/8pjfAgCco9TQ7SqfTsWzZsgsPXK0mJyeHxsZrG7vS3NCATh/k/dkQHOJZ6E4IIcTQM7eD2x3oKsQQuaqWmBkzZvjNTlq1ahW33347zz77LLNnz6ajo4Mvv/yS+fPn93gb3WNoukVHR/v8rD8/UNbdy4uxrKwMgMzMTL/rpaam+h0fHh6OTqfzO7a32+8Ll9NJZ1sLeoPee15QcDAqs4QYIYQICEUBSweERga6EjEEBmwivcFgYPHixbz22mvU13sGtd50000+A2wvFhcX5/Ozqp87jzocDuBC2Lm0lkup1QO/OLG5swO73eazRowhKBjMbQN+X0Oh0+HiFwX1bKnqwO5yc31iOE/OiCc6SMtN685R1eXo8Xp/XZLO7LhQv/Nz3jrZ631t+8p4kkJ1lHfYefZwLfvrPVs0XJ8Yxr/mxxMf4gmcLTYnT+yq4lCjhSnGIJ6fn0xS6IUw+tgX5VyXGMaD40093o8QYgzqaJEQM0YM6GpA3a0awcHBBAcHY7fbWbBggc8xzc3N7N+/32cW09Xovn5JSQlTp071nq8oCmVlZYwfP/6abr8vujracdhshEcZvecZgoNQt4zMmUnf31FJUbuNp+ckkhSq48WjDXxtSxnvr8zkH8szcV20CKbd7eab28pJCNGSHxPS4+3t+KrvwoRtdhcPbSnlhsQwkkJ12F1uHt5WxvhIA28uzcClwNMHa3nsiwrWrshEpVLxh5NNqFUqPlw5jv8+0cgvCur49UJPK+D++i5K2u28vMjY090LIcaq9iZIyAh0FWIIDFjzhMViYcuWLZhMJrKzs7nhhhvYtWsXBQUFPse99NJLfO9737vm9WSWLVuGWq3mzTff9Dl//fr1tLQMzcAuS1enZ40Y3YXWIIPeAJbOIbn/gXSqxcqO2i5+NjuJG5LCGR8ZxPPzkqm3OPi4rB1TkJbY4Aunv55tpt3u4tcLU9Cqe25Fu/j42GAtLx2rx2jQ8PM5iQDUmJ1MNQXz7NwkJkQFMckYxMMToznZYqXF5gKgsM3GkpRw0sL13JwWwdlWm/f2ny+o40fT4nq9fyHEGGU3y7iYMeKqWmI2b97s3XZAURSampp47733qKqq4umnn0ar1fLEE0+wd+9eHn74Ye6//34yMjLYs2cPGzZs4MYbb+S66667psLT0tJ4+OGHeeWVV2hubub666+nuLiYd955x2/sy2CxWSwoioL6ooWV9AYDWLuG5P4HUmmHHYBZcRftAaVTkx6uZ1+9mTvGRXnPL2yz8fqZZp6Zm4TJ0LeX0I6aTjZWdPDXJenoNZ7snB6u5zeLLoytqu5y8Na5ZqYYgzAaPM9pSpiOww1m7smK4kBDF8lhnt/thvI21KhYkRpxTY9bCDEK2ayguJE9jke/qwoxzz77rPf/arWaiIgIJk2axI9+9COWLl0KQGpqKu+++y4vvfQSH374IR0dHSQlJfHd736Xb33rWwMyRuXHP/4xRqORd955h507d5KZmclLL73Ef/zHf/Q4Vmag2awWv8WtdYagERli4oI9L4WaLgdZkZ4xRS63Qq3FSXSQ0+fYl47VMyEqiFWZUX2+/V8drWdJSjizehg7A/DNbWXsrO0iUq/mLzdleMdIPTophm9uKyP37VPEBWv5n+vTcLgVXjzSwDPzkq7ikQohRj27BZBNIMcClaKMzO0+zWYziqIQGur7oagoCtOnT2fFihU8//zzg1rDvq0b2fr+O6RNuLDezf3f+RGq9f8DBzcN6n0PNLtLYdWnRUQbtLywIJlIvYaXjjXwlzNNzI0P5ZXFnjFIFZ12lq8v5DcLU1jex1aQ/fVdPLSljLUrMpliCu7xmFMtVuwuN7870ciRRgsfrBxH4vkBvIqi0Gh1ER2kQa1S8fqZJnbXdfG761L5f4fr+KyinXERBp6Zm0RCyNC0wgkhhrHF98OiO2UTyDFgxLa1nTx5khkzZvDee+/5nL9161asVit5eXmDXoPNakG5KO2rNRpUKjXYbZe51vCk16h4+bpULC431394jlnvnaHF5mJxcjhhugsvk3WlbUQHaVmaEt7n236/pI286KBeAwzAJGMQ02JCePH8oN33Slq9l6lUKmKDtahVKjodLv5wsoknpsWzubKDffVmNtyazZy4EH5+sLb/D1wIMfrYrcgmkGPDiI2p06ZNIyMjg2eeeYaysjJSU1MpKyvjrbfeIisry7v9wWCydHWhVl8YD+P9v8vZyzWGt6wIA++tGEerzYVWDWE6Das/K2Ze/IXWrs2VHdyaFoG6j1Pi3YrC1qoOHp8S43dZTZeDI00WVqZdaNEJ0apJCdNRb+55OvcfTjZyY3IYWZEG3ipsYX58KCFaNctSInj1dHM/H7EQYlSyW0AG/I8JI7YlRqfT8frrr3Prrbfy0Ucf8bOf/Yz169dzxx138NZbbxEc3Pu3/oFiNXf5bDmg0YzcENPpcPHQ5lJOt1iJMmgI02mo7LRzssXKwoQw7zGnWqwsSOh5XEtPCttstNhcLEjwX0X5dKuV7++spLj9QstVu91FSbvdOy7nYnVmB+8UtfLd3FjA8x7lPt8b6nAr3v8LIcY4uxVUI/bjTfTDiG2JAYiPj+dnP/tZwO7f3NXpMzNJfX7WzUgMMWE6DQrw9KFa/s+sBKxOhX/bW828+FDmnw8tp1usKMDEqKAeb6PD7sLhVjAFXXhZnWyxolOrGBfhP9B6YUIYE6MM/OueKv7vrERUwC8KPNOw77poNlS3F482cG+20bsQ3vSYYF4oqOeOcVG8VdjCjNie16sRQowxdmugKxBDRKLqNbBZzD4tMRe6k3ruChnufnV+QO/9m0r55+3lzIwN4beLLmzhUG/1hLMog6bH6z99qJbVG0t8zmuwOInUq3vsftJrVPzxxjTSwvR86/Ny1mwpI0Kv5q9LMgjT+d7H2VYrX9R08OikC9tTrEyNYFFiGA9sLuVMq5WnZiZc9WMXQowizpH5Hiz6b8TOThoO/vzsf2Lu7CA6wbN4W3iUkdvXPAKv/BuUnwpwdUIIMUZlz4CH/iPQVYghIC0x18Dlcvrs+TTSB/YKIcSooMhqvWPFiB4TE0hutxu3y4XqohHwI3lMjBBeWh3ccG+gqxDi6hnjrnyMGBUkxFwlxe1GURTPujDnef8vPXRiJDOEoMy5GZXhygOlrZ3tuJy9hHaVymfguxCDwq2gKApBISG+K8G7XCCvv1FPQsxVcrtdKIrbJ8S43Z5NC+UPR4xoXW2onn0QQiMhOglMiWBKRIlORDElQXQiar1nhpohNAxrRwftbS20t7bS0dZKR2sLHa2txCUlM+em5QF+MEJcG7fTjVorIy+GKwkxV8ntcp3f/FHlcx4AGln6XowCXW2e0/lB6iouWgM1LApMiaiikwg2JRIUnUhMbBKqnEmo9Z41fhTFjbWrE5vVitvtBpUKvdZAUFgoGu2FoO92urHU2THX2jHXdP9rw9rgwO2SVs2edDo6eL/wDY427sfiNJMSlsFd47/O+KjJPR5f2HqKtYVvUNFRjEEbRG70TFaPf5gwXTinm4/xy0P/3uP1YoLjeW7hH1EUhbfO/pHdNduI0Efx0MTHmGSa5j1uY9kHFLed4bG8fx2UxxsoWffEkbAwMtBliMuQEHOVXC43itu3O8nlDTHytIpRrrPVc7oo4HhjSbgRTEmoTAkERSdhMCWiRHt+VunOBxy3G5u5C5vdhsvlQmVQE5IdhGma0Rtw3C43lgZPsLHUOjDX2Lwhx9pgH9NjN/+n6HnanS18PeUHhOui2N7wMb86+H/4yYRfEh+U7HNsna2aX539v8w33cQ92Y/S6Wzn7ao/8l8Hn+N7Wf9JijuLZyb/yec6Jeaz/Kn0F6yIWY21wcHx9oMcrt3DD8b9jOKuM/zx6As8PflPqFQqzK4uPilZy4+y/z+sDaNrarPL6ka2Lxje5NP2Knm6kxSf2UluCTFCQEeL51R2ArioBUelgjAjRCeiMiURFJ2IwZiIOzoRlSkRtc6zIKLidmPubMdqNuN0uVDHq4lKDyU5Iv5CwHG6MTfYsNQ4vMHGcr4Fx9LgGNUbGDfYajjdeYQfZj9NVqhn89m7k7/FyY4C9rd+wW0J9/scv6/5c6J0JlYnfdP7fnVv8qO8WPQfNNpqiTEkEKE2eo+3uay8V/Uqc4w3Mt90EwA11grGheaQHJxBnCGJt6v+QKernXBtJBvr3iM/cj6xhsSheQKGkGfihoIEmeFLPm2vkuJ2ezZ/vDjEuM9/NdRKd5IQfhQFOpo9p9ILAUcDnr+jcBOcb7EJNSUREp2I25SE2hSH6vzflNvtwtbVhdVmx612oU3VEJMVRHCYyTs70O10Y64/H3C6u6hqbJ4WnMaRH3BCtRH8r8x/Iy04y3ueSqVCBZidXX7HzzZeT27ELJ8vXN0tyGaX//Gf1b+HXbFxZ9LXvedF6+PY27wNq8tCifkMQepgQjXhNNsb2NOyjX/PeXHgHuAw4nmaJMAMZxJirpJao0GtUqO4L7Rpu7unVktLjBD9oyjQ3uQ5lRwDLgk4EdFgSkTdPQbH5Ak4KqMJdXfAcbno6ujEYrbhdLrQJGgwZgaTGnkh4LgcFwKOpcaOudbmDTrWppERcEI0oUyJmOlz3uHW3TTYa7krIt/v+ISgFL/zNtW/T4TWSHJwus/5Hc42tjWs5yuJDxKqvbBT/fTIuexr+Zx/Of41NCotD6T+L9QqNetr3+LGmFsJ147ScSNq1YBmmJ/85Ce8//77AGzcuJH09PQej/vpT3/Km2++SUxMDDt37uzz7a9du5Ynn3ySP/7xj1x//fVUVlayZMkSHn30UZ544ok+386aNWsoLi7u130HinzaXiWNRotKpeLiBY/drvOBRgb2CjFwFAXaGj0nv4CjhsjzAceUSHh0EmGmRNzRyaiNJlTnv1C4XS4sXWZsNgdurRttuoaY8SEEh5u803JdDjfmOtv5cOM7BsfWPHwDTnHXaf5W8TumRc4l95Jw05O11X/hRPtBHs34FzQq34+AHY2fEaQJYWH0Mp/z1SoNj2X+G53OdgzqYHRqHZWWEs52HuO+lH9mb/PnfFr3Lnq1gbuTHyE7bMqAPsZAGcw9JDdv3swjjzzid76iKGzatGlA7sNkMvH8888zYcKEfl3vscceo7Ozc0BqGGwSYq6SWqNGpb4kxHRPsZbuJCGGhuKG1gbPqfgocGnAifFMCTclEWpKICQ6CbcpGbXRiErTPYDYhbXLjM3mxKVzo8tQE5sTQkhYtHcxS5fdhbnu/CDj7hlU58fg2FqcAQs4R9v28Vr5i4wLmcjDaT+47LEuxcmbFf/Dvpbt3J/yGHmRc/yO2dvyOXONN6JX++8iDxCmjfD+/4PqN1gZfzcWl5l/VP2Zf53wC9qcrfy59Jf8dNJ/oVP7b/o60miC1IMSZFJTU3sNMYcOHaKhoQGTyXTN9xMSEsKqVav6fb2FCxde830PFQkxV0mt1qC6pDtJURScdhtaQ3AAKxNCAOcDTr3nVHQEuCjgqNUQGQemBNTRiYSYkgjuHoMTFXch4DidWLos2OwO3HoF/Tg14RNDCQ6L8QYcp811vgXHMwbHctEYHFvL4K3evb1xA/+oepX8qPl8LfW7aNW9f3myuiz8sfT/UdR1im+k/5AZUQv8jqmylNJor2O28bor3vepjgJaHI0sMC3hWPsB4gxJxBgSiDEkoOCm3lbj11U1EunDtT5jiQbKsmXLeO2112hsbCQmJsbnsk2bNpGRkUFcXBzFxcUDft+jjYSYq6TWaFCpVBdaX85zWK1og8N7uZYQYlhwu6Gl1nMqKgAuDjgaiIoFUxLq6ERPC44pydNFFRWD6vweaS6HA4vZgt3uxB2koM/SED45jOCwIO8Hn9Pmwlx78SyqC2Nw7K1XH3C+bPyUd6v+zI0xt3DXRbOOeuJ0O/jvkqepspTy+Lj/YEJYbo/HFXadJFwbSXJwxmXv2624+bD6r6xKfAi1SoMKFW4ufJlzKS7PpIdRQBs6OAuXLlu2jFdeeYWtW7dyzz33+Fy2ceNGbrvtNg4fPuxz/r59+3jllVcoKCigo6ODyMhI5s2bxxNPPEFSUlKP99PbmJhPPvmEv/zlL5w5c4aQkBDmzp3LD3/4Q1JTU4Gex8QUFRXxm9/8hr1792I2mxk3bhwPPfQQd999t/eY3/72t7z88sts2LCBrKwLA8/feust/vM//5PXX3+duXPnAnDw4EFefPFFzpw5g81mIysriwceeIDVq1f367mUEHOV1Go1KrUaxeH7x2qzWggOkRAjxIjldkFzredU6DnLN+DEQXQiGlMSYdGJuE2elYzVUUZU3vE1FwWcYAV9toaIKWEEhcVcCDhWT8Ax1zi808O7g469zdVjaeBZ9+Uf1a8yLXIuy+PupMPZ6r1Mp9ajU+kxuzoJ0YShVev4rH4txV2n+XraD0gwJNPuaPEe330MQKWlhKSgK7ee7G/5Ar3a4O2OSg3OotZayamOAtocLahREzdKpltrQwdnUMyUKVNITk5m8+bNPiHmxIkTVFVVsWLFCp8Qs3v3bh555BGmTJnC448/jl6v59ChQ6xbt45z587x0Ucf9fm+X331VZ577jmmTp3K97//fSwWC6+99hqHDx/mvffe67Eb68SJEzz00EPo9XoeeOABjEYjGzdu5KmnnqK4uJh//df+LXJYWlrKo48+SnJyMt/+9rcxGAx8/PHH/Pu/exZd7E+QkRBzDTRaLXar1ec8m80KwWEBqkgIMajcLmiu8Zw4BID3Y06jPR9wktCYEggzJeGOTkQxJaOOvBBwnA4H1i4LNocTghUM4zVETg0nOCzWezcOi/N815TD2zXVPQan4NxuXIqTI217OdK216e8ucYbmWtazEtF/5fvZf2UCWG5HGj5EgWF18p/7fdwuo8BaHO0+MxI6onDbWd97Vt8I/2H3vOM+mjuTn6E18tfQq828LW07/U6pmak0QYP3hYyy5Yt480336Szs5OwMM9nxsaNG0lJSWHKFN+B0a+++ipGo5HXX3+d4GDPcIX77rsPp9PJxx9/TF1dHfHx8Ve8z7a2Nn79618zZ84cXnnlFXQ6T4DNz8/n61//OmvXruVb3/qW3/V+/vOf43K5ePfdd0lLSwPgoYce4vHHH+eVV15h1apVTJw4sc+PffPmzXR1dfHMM88wdepUAO68807uu+8+ioqK+nw7ICHmmmi1OtyXLBtqtdpwB0cgO20IMca4nNBU7Tmd5xNwjPFgSkR7fgZVyPmVjNWRRu+6LRcHHCVEIWiChsi8CILDgry3udD87/xH7RM9roPj6PC04Lw87T3v8f930st9Kv/xcU9d8RidWs/PJ//e7/wF0UtZEL20T/czkmiDBu+dvHtczBdffMEtt9wCeELM8uX++43993//N+3t7d4AA9DZ2YnB4AmLZrO5T/e5a9cubDYbDzzwgDfAAMybN493332XzMxMv+s0NjZy+PBh7rzzTm+AAU9vxGOPPca2bdvYtGlTv0JMQkICAC+88ALf+c53yM/PR6/Xs3bt2j7fRjcJMdfAEByMu9G32ddutUB0RC/XEEKMSS4nNFZ5Tud5Px61ugsBx5RIWHQSIaYklOgkNJFR3uOddgcWsxm7wwVhCkE5WqKmRRB0UcBxmJ3nu6Uc57drOD8Op8aOo7P3LirhTxOkRqUZvIXuZsyYQUxMDFu2bOGWW26hsLCQ4uJinnvuOf9aNBpqamp4+eWXOXfuHJWVlVRXV3tnx7rdfduDo6rK8/rrKazk5eX1+zrd4166j+mrlStXsm3bNtavX8/u3buJiopi4cKF3HrrrSxZsqRftyUh5hqEhIXjcvoOzrNZrSghyb1cQwghLuF0QEOl53TehYCj9wSc6ES0piTCTQm4o5NQTEloIi8sMOew27F2WTwBJ1whOFKLMT+SoNAL3Tr2TueFQcaXjMFxdo3hjah6oY8YvK4k8LRkLFmyhI8//hi73c7GjRtJTEzsMUy89tprPPvss6SlpTF79mwWL15Mbm4uX375Jb//vX/LWG/6GnYudvEyIr3d3sWtOn25X61WywsvvMDjjz/Opk2b2LFjBxs3buTjjz/mjjvu6DHI9UZCzDUICQvH6fTd8Mxus6KSMTFCiIHgtENDhed0njfg6PRgTABTIrroRHSmpPMBJxFNxEUBx2bDarZ6Ak6EQnCUFuOMSwPO+dlTNQ4sl4zBcZrHZsDRRw7+x+OyZct4++232bt3Lxs3bmTZsmV+M81sNhsvvvgi+fn5vP766+j1F9bfWbduXb/ur3sWU3l5uV/3z1NPPcWkSZN48MEHfc5PSfGs+NzTdO/u87q7h7oXjnQ4fD8XGxoafH6uq6ujuLiY+fPnk5WVxWOPPUZLSwvf/va3ef/99/nxj39MdHR0nx6ThJhrEBwa5rNODIDVbEZtCPa8wTjsAapMCDHqOexQX+45nXch4BjA1B1wktCZEnCbklGiE9GE+wYci9mKw+GESAg1aYmeFYUh5ELAsXU4fBf5Oz8Gx1Jrx2kZvQFHFzH4H4/z5s0jIiKCN954g1OnTvHUU/7jkqxWKxaLhfT0dJ8AU11dzcaNGwFwufrWVbhgwQL0ej1vv/02S5YsQXN+PaSCggLeffddfvSjH/ldJyYmhmnTprFhwwYef/xx77gYt9vtbQVavHgxALGxnsHpJ0+e9IYku93utwLxm2++yf/8z//wwQcfMGnSJACMRiPp6ekcOnTIG4b6QkLMNTAEBfmlZnNHu+c/kbE+/d9CCDFkHDaoK/OczvN+LOiDPC040Ynozocct+l8C87FAcdq9QQcpxuiIDRaQ/QcI4bgCx+ktnbP7Kme1sFxWUd2wNEPQYjR6XTceOONrFu3jtjYWGbMmOF3TGRkJPn5+Xz00UdEREQwYcIEysvLeeedd7BYLAB0dflv5NkTk8nED37wA55//nnWrFnDzTffTFtbG2+88QYZGRl+rTDdnnrqKb72ta9x991388ADD2Aymdi4cSP79u1jzZo1TJ48GfC0LD399NM8++yz1NfXEx4eznvvvecXsu69917+/ve/8+ijj3L//fcTFxfH8ePH+eCDD7jtttswGo09ldEjCTHXQB8UzKXdhV0SYoQQw5ndCnWlntN5PgHHlHgh4JzfhwpTIuqwyItuworVYvO04BghNEZH9FzfgGNt61692HcMjqXWjss2/AOOPkKD26WgHsTBvQDLly9n3bp1LFu2rNcWiN/85jc899xzrF+/HqvVSkJCAqtXr2b58uXcc8897Nq1i/x8/80/e/LII48QGxvLa6+9xvPPP4/RaGTJkiX88Ic/9E71vlReXh5vv/02v/nNb/jrX/+K3W4nOzubZ599ljvvvNN7nNFo5E9/+hMvvPACv/vd74iMjGTVqlUsWrSIhx9+2HtcUlISr7/+Or/97W956623aG1t9a4Z80//9E99f/IAlXK5UTvisk4fPsD7r/wP6RMu9C2q1Gru+18/QPXx/8DBgdnESwghAs4Q7Ak450MOpiTcpkSITkIdemFGpt1ixWKx4nR6gopWoyU4NBh90IXBn9bW7n2oLlkHp9aG2zY8PpImfD2B1BUm1FpZMGM4k5aYa6APCkKl8vQNdidoxe3G2tlBcGRcgKsTQogBZLNATbHndJ734z0oxBtw9KZE9NGJuE3Jns03L1rB3GaxYLXYcDpdEK0iLF5L3MJodIaLAk6LvccxOOY6O2770AWckHi9d38sMXxJiLkGQcEhaHV6nA4HesOFgXCdHe0ER8Ve5ppCCDGKWM1QXeQ5nXch4IR6W28MpkQM58fgYEpEHXKh+8JmtmC12nA4XKhiIDxBT1xouE/AsTR3d1HZfcbgWGrtuB0DG3BCEg0SYkYACTHXICQ8Ap1ej8NuuyTEdBAdGSer9gohhLULqgs9p/O8743BYecDThIGUwKGi8fgBId6j7d1mbFY7bicLohVEZGoIz40Ap3hwkeYpcl2oYvq/AJ/5ho7lvqrCzhBsZdf+0QMDxJirkFoeAR6QxAOmw0u2nKkq6MdErMDV5gQQowElk6oOuc5nXch4ISfH3vjab0xmBJxmZJQRSeiDgrxHm8zmz2DjJ1uVHEqIpK0PgFHcSs+LTiW7i6qWjvmOjuK0z/g6CI0aPTyNXQkkBBzDXR6PSHh4bQ0+i7k09XRjirc5NkvxeXs5dpCCCF6ZemAyg6oPOs9y7uGbkjEhYBjSsQQnYjLlOwJOIZgPvjrR3S1dxFhjCDSGOH5NyqCiGkRJN4Yi1Z/UcBp8h+DoxnEPZPEwJIQc42MMXHUV1X6nNfe0uzZsdaU6LPSphBCiAFgbvecKs54z9IApE+GbzyN0+HAYrZgMVuoq6rzu3pIaDARxggiojwhR6vTkjIrhRSjyTsORnErMiZmBJAQc40io2NwXrIyb1tzo+c/cWkSYoQQYqhEJ6O43bS3dlz2MHOXBXOXhdpKT8ApOl3CijuXcuMt13uPkQAzMkib2TUKi4hEhe+L3W61Yu3q8IQYIYQQQyMmic62dtyufi6mp4LwyPArHyeGHWmJuUah4REoeHb6vHgLgtamJuLj0pAsL4QQQ0OJTqG6oo6i0yWo1Sr0Bj2GYAOGIM9Jp9P6bRXjdrlRAeGRsnHvSCQh5hqFRkSg0WpxOhzoLtqcq7W5ibgUCTFCCDFkkrIwdjq59d6VtDa1Ul/TSHNjM10dZpobWnA6nKhUoFKpMQTp0QfpUavU6A0GCTEjlISYaxRhNBEUHIzNavEJMW1Njajy8kGrB6fsZi2EEIMqNBJVuJG4cIhLvLDYqNPppKOtk7bmNtpbO2hvbaelsZX6mgZaGluwWqyYYqOIMEZe5sbFcCUh5hqFRRoJDg3DajETFnHhj6CtudEzQykmGWpLAlihEEKMAQmZPZ6t1WoxRkdhjI7yu8zpcNLe1oFWqyU0LMT/ymLYk4G910ij0RCblIzVbPY5v7WpyfMfGdwrhBCDL3EcuF39uopWp8UUYyQiSgb1jlQSYgZAXFKK3zRrp8NOV1uL5w9LCCHE4JL32jFJQswAiDBFAyoUxXf56obaGtwpEwNTlBBCjCXJ40GtufJxYlSREDMAokwxaM/PULpYY20NqsRMz/YDQgghBochGKLiAl2FCAAJMQMgMjoGQ0gIVovvuJjG2hpUWl2vA86EEEIMAHmPHbMkxAyAsMgoQsMjsF06uLexHpfTAak5AapMCCHGgNSJ/R7UK0YHCTEDQKVSkZiWgcXc5XO+2+2mpb4OJWVCgCoTQogxIH0KyNKiY5KEmAESn5KG2+X0O7+hrhZFBvcKIcTgUKk9u1er5eNsLJLf+gCJSUhCo+lhcG9NNeqoWAgzBqgyIYQYxRIyQB8U6CpEgEiIGSDRCYmEhIVj7vTdAr6xttrzn7RJAahKCCFGufQp4O7nrtVi1JAQM0BCwyMwxsb5hRhLVycdzY0wLi9AlQkhxCiWkQsoVzxMjE4SYgaISqUiZdx4bJdMswaorijHnZUfgKqEEGIUU6kgY4oscjeGSYgZQLGJSfS0cm9tRRlqYxyYEgJTmBBCjEaxqRAUGugqRABJiBlAMYlJGIKD/Vpj6iorcLtcMG5agCoTQohRaFyejIcZ4yTEDCBjbDxhEZF0dbT7nO902GmqrUaRLiUhhBg4OXMCXYEIMAkxA0in15M+Poeu9na/y2oqylEyp8paBkIIMRAMwZAm68OMdfLbH2DJ47JRFDfuS5o4aytKUQeFQNL4AFUmhBCjyLhpoJEBvWOdhJgBlpCaTnBoGJauTp/zm+pqcVgtkD09MIUJIcRoMmEW9LBKuhhbJMQMMFNcAsbYODrb2nzOVxSFqrJS3DlzA1SZEEKMEiqVZzyMRhvoSkSASYgZYGq1msyJU7CaO/0uqywuRJ2YCVFxAahMCCFGicQsCAkPdBViGJAQMwgS0zJQqzW4nL5NndVlJbidTpgoI+qFEOKqTZgFblegqxDDgISYQZCYlkFoeARdHb5dSk6HndqKMtyT5geoMiGEGAUmz/fsXi3GPHkVDIKwyCgS0tLpaG31u6y8+ByqtImyq7UQQlyNmBSIS/OMixFjnoSYQZI5cQpOu91vC4LK4kJQFJgkA3yFEKLfpl4nXUnCS0LMIEkZN56gkFC/qdZ2q5XainLckxcGqDIhhBjB8m6QriThJa+EQRKblExMYhLtLc1+l5UVnkGVPlm6lIQQoj+SssAYL11JwktCzCBRq9WMnzodq7nLr0upougcbrcL8q4PUHVCCDEC5V4HLulKEhdIiBlEqVnjMQT572rtsNmoLC7EPX1JgCoTQogRRqXyfPGTrQbERSTEDKKEtAxiEpJoa27yu6z41AnUcamQlB2AyoQQYoRJmyRd8MKPhJhBpNFoGJ+Xj6Wr069LqbaiDEtHO+TfFKDqhBBiBJm2WPZKEn4kxAyy9PE5PXYpKYpC8ZmTuHOvB60uQNUJIcQIYAiBqdfLXknCj4SYQZaQlkF8ciotDQ1+lxWfOoE6ONSzkZkQQoie5cmXPdEzCTGDTKPRMGnmHGxWM2632+eyjtYWGqsrcU+XLiUhhOjV7JsB5YqHibFHQswQGDcpl7DIKDrbWv0uKzp9AlXWdIiIHvK6hBBi2EuZcH6bAfm4Ev7kVTEEjLFxZOZMpq2p0e+ysrOncTrs579pCCGE8DFzhQzoFb2SEDNEJkybgaIoOB0On/OdDgdFJ4/jnrkcdPoAVSeEEMNQUIhnryQZ0Ct6ISFmiGRMmIgpNp7WJv8BvmeOHkYVHApTbwhAZUIIMUzl3SiL24nLkhAzRAzBIUzMn0lnW5vfZV3tbVSVFOGee1sAKhNCiGFIpYJ5twe6CjHMSYgZQllT8ggKDsbS2el32Zkjh1DHp0Hm1ABUJoQQw0zObDAlyIBecVny6hhCiemZpIzLpqm+1u+yusoK2hrrUaQ1RgghYNFd4JbNHsXlSYgZQmq1mtw5C3C7XTjsdr/LTx89DDmzwJgQgOqEEGKYSJ3omVqtlvEw4vIkxAyxrClTiU1MpqWhzu+y0jOncFitME9aY4QQY9iiO8AlrTDiyiTEDDFDUDB5cxdi7ujwW8HX5XRy+sghlJnLZbdWIcTYFJPs2YpFZiWJPpAQEwATpuUTYTLR3tzkd9mZI4dxOp2wYFUAKhNCiACbv0paYUSfSYgJgEhTDBOnz6KtqQFF8d0PxGG3cfpoAcrslRAaGaAKhRAiAMKMMH2xtMKIPpMQEyCTZszGEBKKubPD77IzBQdxKcD8rwx9YUIIESiL7vSsDyNEH0mICZCkjHFkTJhIc53/dGu7zcqZo4dxz7kFQsIDUJ0QQgyxyBiYvVJmJIl+kRATICqVimkLrketVmM1d/ldfvrwQRSVWlasFEKMDdffA0grjOgfCTEBlJEzmYycSTTWVPtdZrNaOHPsiGcrguCwAFQnhBBDxJQI+UtkLIzoNwkxAaTRaJi+8AZUKhVWi9nv8tOH9uNWqT39xEIIMVrdeB9cMslBiL6QEBNg4yblkpo9gaZq/9YYq8XMqcMHPVsRRMYGoDohhBhkcWkw9TpphRFXRUJMgGm0WmZctxgFNzaLxe/yU4f3Y7NZUZY8GIDqhBBikN30IFyy8KcQfSUhZhjImjyV1OwcGmuq/C5zOhwc2bsLVd4NkDguANUJIcQgSZkAE2V1XnH1JMQMAxqtlvxFN+J2K9itVr/Li08ep72pAffybwSgOiGEGCQ3Pyqr84prIiFmmMiekkfa+AnUV1X4XaYoCod2fYk6MxcmzApAdUIIMcCmXgfJ2dIKI66JhJhhQqvTMWfxMtRqNZbOTr/Lq0uLqasow73s66CWX5sQYgTT6WH5N2QsjLhm8mk4jGROyiV76nTqqyv89lQCOLRzO+rYFJi5IgDVCSHEAFl4p2dvOPlCJq6RvIKGEbVazZzFywgODaOjtcXv8paGegqPH8G95CEIixr6AoUQ4loZ4+G6uyTAiAEhr6JhJjE9kymz5tFUV9tja0zBri9xuBUUGeQrhBiBlJXfRLYXEANFQswwo1KpmHn9TURFx9BSX+d3ud1m5dDOL1DlXQ/j8gJQoRBCXKXsfFQ5MqVaDBwJMcOQKS6e/EU30t7SjMvl9Lu85PQJ6ivLcd/6z6DVBaBCIYToJ30QfOXb4JYp1WLgSIgZpqbNX0RcSioN1f4L4AHs+3wzRMXBwjuGuDIhhLgKNz0AYUZQSyuMGDgSYoap0PAI5i1ZgcNmw2r23xyyvaWZk4cOoFy32rMDrBBCDFcpOTD3NhnMKwacvKKGsUkz5jB+6jTqKst7HOR74sAezF1duG97LADVCSFEH2i0KHd8DxRZE0YMPAkxw5hGq2XBitsJjYigtbHB73KX08nebZtQj8uD2SsDUKEQQlzBdatRmRKkG0kMCgkxw1xCahozr7uJ9uZGnA6H3+W1FWWcPXoY97KHpVtJCDG8xKejXL8aVPJRIwaHvLJGgBnXLSY5M5u6yvIeLz+88wvM5i7cd3xf3iyEEMODWo2y6ruo/HvChRgw8ok3AgSHhjJ/+S2ogK6Odr/LXU4HuzZ/iiplPCz86pDXJ4QQfq5bjSpxnKwJIwaVhJgRIjt3GpNmzqGhuhJ3D5umNdZUc/LQfpTF90N8egAqFEKI81Inotx4L6hkZV4xuCTEjBAqlYoFK24jJj6J+qqKHo85tmcX7S3NuO/4AWi0Q1ugEEIABIWgrP7fqHqYUSnEQJMQM4IYY2JZdPPtuByOHruV3G4XOzd9ArEpsPj+AFQohBjrlNv+F6pwWdRODA0JMSPMxBmzyZ0zn4aqClwu/+W7WxsbOLJnJyy6E7JnBKBCIcSYNW0xqtxFEmDEkJEQM8Ko1WoWrryd+NQM6irKejzm1KH9VJUU4b7zBxARPbQFCiHGJlMCym3/DNKNJIaQhJgRKMJo4vpbVqECOtpaezxm9+ZPsDpduO/+sYyPEUIMLo0W5a7/jUqtlcG8YkhJiBmhsqdOY9qC62mqqcbp9F8Ez2618uWn6yEpC5Y8FIAKhRBjhbLyEZDp1CIAJMSMUJ7ZSreSPC6L2rLSHvdWaqqr5fDOL2DBKpg4NwBVCiFGvfwlqGavRCWbO4oAkFfdCBYSFs6Nt9+FITiYlvq6Ho85c+QQFYVncX/1u2CMH+IKhRCjWlI2ym2PyTgYETASYka49AkTWbD8Vjrb27B0dfZ4zJ4tn2Gx2nHf86+g0w9xhUKIUSk0Evd9P0GlUsk4GBEwEmJGgRnXLSZ39jxqK8pwOZ1+lzvsNrZ/sg4lJhll1fcCUKEQYlRRq3GvfgJVaKRMpxYBJSFmFNBotdz4lbtIGZdNdWlxj+NjWhsb2LXpE1S5C+G61QGoUggxaiz9Gqr0yahk5qMIMAkxo0RYZBQ3ffVugkPDaKqr6fGYiqJzHN27E5Y8KAN9hRBXZ9qNsGCVDOQVw4K8CkeR1KwJLFx5G5bOTsydHT0ec3zfbsrPncZ95w89UyKFEKKvMqeifOU7KIr/JrRCBIKEmFFm+sIbmDp3AXWV5Tjs9h6P2b3pU1qam3Df/++yoq8Qom/i0lDu/QmgoFLJR4cYHuSVOMpoNBpu/MpdZE6c4tl6wO3/jcnlcrL94w+wovYEGX1QACoVQowY4SbcD/4f3BqtjIMRw4qEmFEoJCyc5Xc/QGxCEjVlJT0O9LWazWxb/wFuUyLue38iWxMIIXqmD8L94FMoIeFoZIkGMcxIiBmlYhKSWLr6PvQGQ68DfduaGvn84w9R0iej3PlDkCZiIcTF1BrP/msxKRJgxLAkn1qjWObEKVx/2x3YzGbaW5p7PKa+qoIdn66HSfNQbv2nIa5QCDGcKbf+M6qsadKFJIYtCTGj3PQF1zP7puW0NNRh6erq8ZiqkiL2bt2IatYKuOmBIa5QCDEsrfgGqpnLUFB5VuUVYhiSeD3KqVQqFq64jc7WFo7u2UnyuGx0ev9m4eJTx9EHBTHj+rvB3A571gegWiHEsHDTAzD/K7icDjRaXaCrEaJXEmLGAJ1ez0133ENXRztFJ46Rkj0ebQ9vTKcPH8AQHMyUlY+AuQOObg9AtUKIgLpuNVx/N3abFb1BZi6K4U26k8aIkLBwVt63hrTsCVQWFeJyuXo87siuLyk8fgTlq9+FKQuHuEohREDNux2WPIilq0sCjBgRJMSMIZGmGG5+4OskpKZTVXyuxzVkAPZ/vpnSM6dQ7voRTL1uiKsUQgTErBWw8pt0trUSHBoa6GqE6BMJMWNMdHwitzzwdUxxCVSXFPW4hoyiKOzZ8hnFp06g3PEDyLth6AsVQgyd6Yvhtsdoa24kLDIq0NUI0WcSYsaghNR0Vt63hrDIKGrLS3sNMnu3fkbRyWMod3zP8yYnhBh9Zq1A+cp3aG1qINIUE+hqhOgXCTFjVFp2DsvvfgCdXk99VWWvx+3btonC40dRVn0H8pcMYYVCiEG38Ktw22O0NDYQFR0b6GqE6DcJMWNYdu40ltx5LygKDdW9B5n9n2/m3LEjsOo7MHP5EFYohBg0Nz0Ay75OfVUFprj4QFcjxFVRKT31JYgx5djeXWxe+3dUKjVxySm9HjfzusXkTJ8Jm16Hne8PYYVCiAGjUsHKb8Lc26gqLSYpPVMWsxMjloQYAcDxfbvYvPZtFAXiU1J7PW7q3AVMnbMAZe96VJ++CkrPM5yEEMOQWo1y++MwbTHlhWdIGz9RAowY0STECK8TB/aw6R9voSgKccmpvb65ZU/JY/aNS+HUHlTvvwhOx9AWKoToP60O5Y4foEycS/Gp42RNyZMAI0Y8CTHCx8mD+9j0jzdxuVzEp6T1+iaXMi6bhctvRVV1BvXfnwWreYgrFUL0WUgE7vueREkcR8mZUxJgxKghIUb4OX34ABvf/RtOh4P41PRe3+xiEpO48bavom1rRP3Xn0JHzztlCyECKCYZ1wNPYdcFU1VWRtaUqRJgxKghIUb06HTBQTa9+yY2q4XEywz8izCauOkrdxGkOFD/7edQXz7ElQohepWRi/vef6W1vYOmhnrG504LdEVCDCgJMaJXxaeOs+ndN2ltaiA5Mxu1RtPjccGhYSy+/Q4iIiNRr30RTu8d2kKFEP6mL0a5/XEqS4pw2O2Mmzw10BUJMeAkxIjLqi4t5pO/v0FdZTkp47LR6vx3vwbQ6nTMW7qStOwc2PYWfPEuyEtLiKGnUsHi++H6uzl5cC+h4ZGkT5gY6KqEGBQSYsQVNdXV8MnfX6fs7BmSM8dddnfb3NnzyJu3COXkblQfvAR26xBWKsQYpw9C+er3UE2ez96tG0nJzCI5MyvQVQkxaCTEiD5pb2nms3f+xrmjh4lPTb/sLrfJmVksXH4L6pZaz8yllrohrFSIMSo6Cfd9T+IMj2bPls+YMmse0fEJga5KiEElIUb0maWriy1r3+Lo3t3EJCRedrfbSFM0N9y6ihC9DvW7v4CSY0NXqBBjzcQ5uO/4Aa3NzRzevYN5y24mNDwi0FUJMegkxIh+cdhtfLH+Aw5+sZWg0FCi4xN7nbmkNwSxaOWtxKeko/r8LfhyrazwK8RAUmtgyYOw8A7OHjlIdVkpC1fehk5vCHRlQgwJCTGi39xuN0d2fcEXH3+A3WolMWMcanXPe4mqVCpy5ywgd/ZclJLjntlLnS1DW7AQo1G4CffqJyBlAl9s+JCQ0DBm3bi0179FIUYjCTHiqpWcPsGWtW/TUF1FUmYWOr2+12PjklNZtPwW9Co36vd/A4WHh7BSIUaZ7Bm4v/o9LE43n/3jLSbPnEPO9JmyiJ0YcyTEiGvSVFfDpnffovjUceJSUgkJC+/1WENwCPOXriApIwt2rIWtb4LbNYTVCjHC6fQoy76Oas4tFJ84yv7tW1h081dkBpIYsyTEiGtm7uxk+0fvcWT3DsKjjBhj4y57/MT8WUyffx3UFKH+xwvQWj9ElQoxgiWOw33nD1Gi4tj8/jtYurq46at3E2E0BboyIQJGQowYEC6nk/2fb2L3xk9wOh0kpmX0usIvQHR8AotW3EpwUBDqz16FQ5uGsFohRhCVGhZ+FWXx/TTUVPPxm6+RnTuNeUtvvmwXrhBjgYQYMWAURaH41HE+X/cedRXlJKRlEBQS0uvxOr2e/EU3kj0lD3dRAep1/wVtDUNYsRDDXFQs7jt+iCoth92bPuHk/r1cd9tXmZCXL+NfhEBCjBgEbc2NbPvwPU4f2k/Y+e6ly73hJqZlMO+mZRj0etQbX4ODG4euWCGGI5UKZizDvezrmK1W3n/1DxiCgrjpjnuISUgKdHVCDBsSYsSgcDocHPpyG7s3bcBmtpCYkYlGq+31eJ1eT/7CG8jOnYa7+CjqdS9Dq7TKiDEoNgX37Y+jTpvE0b07+WL9++TOmsf85bdgCO69ZVOIsUhCjBhU5efOsO3Dd6ksKSI+Je2ys5cAElLTmXfTcoIMetSbX4cDG2WBPDE2aHWw6C6U6+6io7WFD1//M3abjRtu+ypZU/Kk+0iIHkiIEYOus62V7evf58T+PegMBmKTUi67IJdWp2fGovOtMlWFqDf8AarODWHFQgyx9Cm4b/9fYIxn3+eb2fnJR0zMn8V1t6yS2UdCXIaEGDEkXC4Xpw7uZedn62mqqyU+OY3gsLDLXicmMYk5199EZGwcHNqCassbYO4YooqFGALBYShLv4Zq5jLqK8v58LU/4nA6WLTydqbMnofmMjP8hBASYsQQa2moZ+en6zh5cD9avZ645NTLtsqoVCrGT53OtLkL0Cgu1Fv+Cgc3SReTGNnUGph9M+4b78Wl1rLj0/Xs2fIZ43PzuOH2O4lNTA50hUKMCBJixJDrbpXZ9dnHNNbV9KlVxhAcwvT515E1ZSru6mLUG34PlWeHqGIhBtCEWbiXfwOVKYEzRw7x2Tt/Q6vTMWfxMqYvvF42bxSiHyTEiIBpaWxg5ycfXmiVSUq57AJ5ADEJicy+YQnGuASUo9tRbX1TVvwVI0NcGu4V30SdNY3a8hI2/uPv1JaXMmHaDBauvE1aX4S4ChJiREC5XC5OHdrH7s820FBTiTEugQij6bIzMVQqFeMm5zJtzgIMQUGo9n8CX/wDLDJeRgxDoZEoN94HM5fT2drCzk0bOPjFVuKTU5m37GYmzZgjY1+EuEoSYsSw0N7SzIHtWzi6ZwdWs5n4lDQMwcGXvY5Gq2Pi9JlMmTELteJGvet92LMe7NYhqlqIywgOhwVfwT3nVlwKHN69gy8//gCA3DkLmL/sZpl5JMQ1khAjhg1FUagqLmT3pg0UnzrhnY59pW+phuAQpsyay4TcaWDrQv3FP+DgZ+B0DFHlQlwkKBTmfwX3vNtxq9ScPnKYnZ+uo7m+juTMbBauvI1xk3Jl3RchBoCEGDHsOB0OTh7cx76tn1FfVYkxLv6KXUwAIWHhTJ0zn3GTclE6W1Hv+sCzsaS0zIihYAiBebfhnr8KRaPlzNEC9m7dSHVpETHxicy6YQm5cxdgCLp8C6MQou8kxIhhq6O1hQOfb+bo3p2YOzuJTUq+4oq/AOFRRqbMnEvmxEkoVjPqveth3wawdA5B1WLMCQqBObfgnv9VFK2ec8ePcPCLbZQXniEkLIy8uQuZef1NRJiiA12pEKOOhBgx7FWXFrP/882cO1aAy+kkLjn1iuNlAELCI5iUP4vsyVNRuZ2oD34Gu9dBR8sQVC1Gvag4T8tL/lLQaDl3/ChH9uyg7MwpOL++0dwlK0hMywh0pUKMWhJixIjgdrspPXOSfVs3Unb2DBqthtik5D6tqWEIDmHi9BlMmDodrUYDBVtR7V4HTdVDULkYdVJyUOZ/BSbNw2GzcvbYEU4e2kdF4Vkcdjsp47KZu2QFWVPyLruQoxDi2kmIESOK0+Hg7NHDHPxiK1UlhegMQcQmJqPV6a54XZ1ez/ip05k0bQaG0DDcRUdQ798AZw+AW1YAFpehUsOkubjnr0KdmkNHcxOnjhzi3LECaivKcNisJKZnMvO6xUyYNgO9ISjQFQsxJkiIESOS3Wbl9OGDHPxiC7UV5eiDgohJSEKn11/xumqNhrTsHHKmTiM6MRl3WyPqA595BgF3tQ1B9WLECDfC9Jtwz1iO2hhHfWU5pw4foOzcGRprq7FbLSSkpjPjupuYmD9TwosQQ0xCjBjRrGYzZ44c5PDO7dRWlKHV6ohOSOzzDBBjbBwTpk4nY8Ik1GoVnNyFat8nUHF6kCsXw5ZaDdkzUGYsgwkzcbtclJ07y5mjh2morqSxthqbxUJCahozrlvMxPxZMuNIiACRECNGBbvNyrljBRTs/IKqkkJARUxiEkEhoX26vt4QxLhJU5iQO40wowl3YzXqo9vg6BeyrcFYYUyA/CW485egDjfSUlfDuZPHKTt7io62VprranE6HMQlpzDjusVMyp+FITgk0FULMaZJiBGjitPhoPjUcY7s+oKys6dxOp2Y4hMIDY/o8+JiCanpZOZMJjUrG63egLvsFOqjn8OJnWDtGtwHIIZWcDhMmoc7dxHqcXk4rBZKzp6m6MQxmhvq6Gpvo7m+Fo1GS/K4bKbNX0R27jRpeRFimJAQI0Yll8tF+bnTFOz6ktIzJ7F0dRIeZSIqJgaNRtun29BodaSOyyYzZxIJaRngdsG5g6iOfA6Fh8FpH9THIAZJcBhMnIt7ykJU4/IAFfVV5RSdOklFkWeGUVtzI21NTYSEhjFuUi5T5y0gLTsHjbZvrx0hxNCQECNGNUVRqKss58yRQ5w8uI/Wxnp0egOmuASCQvreFRAUEkL6+ImMy5mEMT4Rt92GqqgA1em9cO4AmGXzyWEtKARy5uLOXeQJLmo1DVUVlJ47S0XROWwWM3arlZbGeixdnUQYTUyeOZfJM+cQn5ImWwQIMUxJiBFjRldHO4XHj3J8/25qSotxOOxERccSbjT1az2P8CgjKePGk5o5jujEJFBAKT+F+sw+OLMPmmsH8VGIPotOggmzcI+fiSp9MqjVNFZXUXruDBVFZ7GazbjdbtpbmmlvbkSt0RKfnMrkWXOZkDedSFNMoB+BEOIKJMSIMcflclFZdI5Th/dz7lgBna2t6AwGomJiCQ4N69e37qCQEJIzskgZl0VCajoarQ53fQXqwkNQcgzKT4LNMoiPRnjpgyBzKmTl487OR21KwOV0UFdZTlVpCZXFhVi6PFtPWM1mWhrqsFktRBhNjM+dxoRpM0jNmtCnNYeEEMODhBgxprU2NVJy+gSnDx+gtrwUS1cXwWHhRMXE9HvwpkarIzEtneTMbJJSUwkOj0Rxu1Cqi1CXHIWS41BxChwylmZAGIIhZQKkTcadPgVVag4qjZau1maqysupLiumrrIcl9MJgMNuo625ia62NnQGA4lpGUyeNZesSbmyr5EQI5SEGCHwbGtQX1XhDTSNNdU4nQ7CIo1EGE19WkTvUmGRUcSnpJGQkkpCciqG0DAUlxOl8izqspNQfQ6qCqGjeRAe0SgUEQ1pkyB1Iu60yaji01Gp1dgtXdRXV1NTUUZNWQmd7RcWLHTY7bQ3N9HZ3oZGo8EYE8f4vOlkTZlKUkYWGo0mgA9ICHGtJMQIcQmnw0FVSRHFJ49x9uhh2pobcblchISFE2E0XfXaIJGmaOJT0ohPTiUuMQlDaBgA7o4WVFXnUFUXQnWhJ9hYxvhA4cgYiM+A+AyUhAyU5Amoo2IB6Gxpoq6mhoaaShqqq+ho9d3Q02G3097SRGebJ7hExcQyfup0MnImkZyZJavqCjGKSIgR4jJsFjNVpcWUF56l6MRRWhvqsdmsBAWHEG40ERIWftUzV4JDw4iOT/Cc4uKJjktAd74Ly93agKqhHFVjFTRWQmOV5zTatkUIDgNTIsSlQUIG7vPBRR3sCXgOq4WWxgaaGuo9q+XWVGO1mH1uQlEUrF1dtLe2YDV3otFoiYqOIft8cEkZly3BRYhRSkKMEH3kdDiorSijqqSIwuNHaKiuwtLViUarJTQigtCIyGv+sAyLjCI6LgFjbBwRRiNRRhOhkVGo1J5uD7elExqrUDdWQksdtDdC2/lTe9PwW7tGq4dwExjjPSdTAooxHsWYAMZ41EGeFZUVxU1nSzPNTY20NjbQ0thAa2MD5s6eW6ScDgedba10trfh+v/bu7PeprEADMOf9zjO1tK9lK2Ii5Hm//+YYRFDC9NC0zaLt9g+c2E3IDQSizrQA+8jWcfuRZOr5NU5J/ZqpV6/r42dXR3/8acOHx/r8PExN6QDfgNEDPAdmqbRxdk7nb56oZMXf+nk5XMtrq+0WpXy/ECD0VjJaPxde2k+57qeBuOJRpubGm1sajTZ1GRjQ8lwtF6SWr+vfCkzv5Qzn8pdXEpFKuVpNy4/Xt+c11X7BG9Td6ORTNOdd0/29kMpiKSgGz+/jgdSMpaSsUwylkkmMv2xnGQkN/oYEqaplc6uNZ/NNO8CZHF9rcXsSrPL6XoD7n9ZlaXSxVzpfKYyz+X6nobjSftT96fPdPDwibb2D9jjAvxmiBjgFhR5pvenJzo7+VtvXjzX29cv17MEfhgoToaKk0RR3P+me9J8iet56g+GSobD9WvEyaAd477CMFQYRfLDSH4UyXVv/0u+KguVWao8S5VlmfI0U95dF2mqLF1qMbtWOp/paz5ujDEq8kzpvI2Wuq7k+YGS4VDb+4c6evpMe0cPtXf0SHHydc/GAvBrImKA/0Gepjo/fdPdo+Slzt68VrqYq8gyGWMU9Hrr4Ih68Q+7I6znBwqjUEEYKQgjuZ4nx3HkuI4cOXJct7122nNJqlcr1VWluq5UrSrV1eqT80pNU3/3+2maRmWeKU9T5elSRZ5LMgqjnpLRWPcfH2v/wSNtH9zX1v6h+oPBF/8ngN8HEQP8AKuy1NWHc12cn2l69k5vX79a76lpv7jbWZWoFyuKY0W9WGGvd6uzNj9T0zSqylJFkatIU+XZUnXVxk8Ux+rFfW3u7mnv6KHu7exp++BQ93b3b2U5DsCvi4gBfpKyyDU9P9P0/Eyzy6mm5//o/btTLa6vVGSZijzXzfyMH0UKw3YGxb8Zg+DORE7TNKqrSlVZqizy7ihUrcp2lslIQRgq6PWUDEfavf9A2/uH2tje0eTeliZb2/yCCMA3I2KAO8QYoyLLNLu8aJ/pcznV1cUHXX0419XFhfJ0oVVZrg85kiPJNEaO58rzfLmeJ8/z5XmeXN9b/+0meNqlK0ft4LSh1C1nmaZR0x3ms7Fp6jZUuuUlyah7dRkj+UGgIAgVRJGS4UibO7va2N7RYDzRYDRej8lofGfiC4DdiBjAEsYYrcpCy/lc2WKubLlQli6VLZfrDbR5mqrIlt3G2kyrolBT16rrSsaYdmOtkYyMZIxM+4/XG25d15XjunJd75Pz9vDDUHF/oMG4DZH+oN3P04v7ivp99eK+ev1Eg/GYnzcD+CGIGOAXVleVVt0Sz82syk3MGNOdN41uPgU835PnB/J9X153+H6w3gAMAHcJEQMAAKzEwjQAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAArETEAAMBKRAwAALASEQMAAKxExAAAACsRMQAAwEpEDAAAsBIRAwAArETEAAAAKxExAADASkQMAACwEhEDAACsRMQAAAAr/QtNMLki5fquYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the parameters\n",
    "plt.rcParams['figure.figsize'] = [13, 5]\n",
    "# sns.set(style = 'white', font_scale=1.3)\n",
    "# fig, ax = plt.subplots(1, 2)\n",
    "ax = plt.subplots()\n",
    "# Bar graph\n",
    "# bar = sns.countplot(df_dataset.label, data=df_dataset, ax=ax[0], palette=['coral', 'mediumorchid'])\n",
    "# bar.set(xlabel='Webpage Type', ylabels='Count')\n",
    "# bar.set_title(\"Distribution of Malicious and Benign Webpage\", bbox={'facecolor': '0.8', 'pad': 5})\n",
    "\n",
    "# Creating the labels for the piechart\n",
    "types = df_dataset['label'].value_counts()\n",
    "labels = list(types.index)\n",
    "aggregate = list(types.values)\n",
    "# percentage = [(x*100)/sum(aggregate) for x in aggregate]\n",
    "# print (\"The percentages of Benign and Malicious Webpages are : \", percentage)\n",
    "\n",
    "# Plotting the Piechart to see the percentage distribution of the Webpages\n",
    "plt.rcParams.update({'font.size': 11})\n",
    "explode = (0, 0.1)\n",
    "ax[1].pie(aggregate, labels=labels, autopct='%1.2f%%', shadow=True, explode=explode, colors=['coral', 'mediumorchid'])\n",
    "plt.title(\"Pie Chart for Malicious and Benign Webpage\", bbox={'facecolor': '0.5', 'pad': 3})\n",
    "plt.legend(labels, loc='best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Malicious and Bening Webpages Distribution by Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>11678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHN</th>\n",
       "      <td>2661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN</th>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEU</th>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKR</th>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LKA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDV</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLF</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country\n",
       "USA    11678\n",
       "CHN     2661\n",
       "JPN     1579\n",
       "DEU      979\n",
       "SKR      823\n",
       "..       ...\n",
       "LKA        1\n",
       "MDV        1\n",
       "MMR        1\n",
       "WLF        1\n",
       "SLE        1\n",
       "\n",
       "[165 rows x 1 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Malicious Webpages Distribution\n",
    "# Iso alpha 3 codes are necessary for choropleth plots [not particulary necessary though :)]\n",
    "df_mal = df_dataset.loc[df_dataset.label == 'Malicious', :]\n",
    "\n",
    "val_mal = df_mal.country.value_counts()\n",
    "val_mal = pd.DataFrame(val_mal)\n",
    "val_mal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>501718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHN</th>\n",
       "      <td>110998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN</th>\n",
       "      <td>67207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEU</th>\n",
       "      <td>41142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>37052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NIU</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHN</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country\n",
       "USA   501718\n",
       "CHN   110998\n",
       "JPN    67207\n",
       "DEU    41142\n",
       "GBR    37052\n",
       "..       ...\n",
       "NIU        1\n",
       "KIR        1\n",
       "SHN        1\n",
       "ATA        1\n",
       "GNB        1\n",
       "\n",
       "[234 rows x 1 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
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    "# Benign Webpages Distribution\n",
    "# Iso alpha 3 codes are necessary for choropleth plots [not particulary necessary though :)]\n",
    "df_ben = df_dataset.loc[df_dataset.label == 'Benign', :]\n",
    "\n",
    "val_ben = df_ben.country.value_counts()\n",
    "val_ben = pd.DataFrame(val_ben)\n",
    "val_ben"
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  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Map of Malicious Webpages Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
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           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Malicious Webpages Around the World"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choropleth Map\n",
    "fig = go.Figure(data = go.Choropleth(\n",
    "    locations = val_mal.index,\n",
    "    z = val_mal.country,\n",
    "#     text = val.index,\n",
    "    colorscale = 'reds',\n",
    "    autocolorscale = False,\n",
    "    reversescale = False,\n",
    "    marker_line_color = 'darkgray',\n",
    "    marker_line_width = 0.5,\n",
    "    colorbar_title = 'Number of Webpages',\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Malicious Webpages Around the World',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = False,\n",
    "#         projection_type = 'equirectangular'\n",
    "    ),\n",
    "    annotations = [dict(\n",
    "        x = 0.55,\n",
    "        y = 0.1,\n",
    "        xref = 'paper',\n",
    "        yref = 'paper',\n",
    "        text = 'Source: <a href=\"https://data.mendeley.com/datasets/gdx3pkwp47/2\">\\Dataset [Mendeley Data] </a>',\n",
    "        showarrow = False\n",
    "    )]\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Map of Benign Webpages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "autocolorscale": false,
         "colorbar": {
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         },
         "colorscale": [
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          "CHN",
          "JPN",
          "DEU",
          "GBR",
          "SKR",
          "BRA",
          "FRA",
          "CAN",
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              "#fdca26"
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            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
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             "#bd3786"
            ],
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             "#d8576b"
            ],
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             "#ed7953"
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             "#fb9f3a"
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           ],
           "sequentialminus": [
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             "#0d0887"
            ],
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             "#46039f"
            ],
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             "#7201a8"
            ],
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             "#9c179e"
            ],
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             "#bd3786"
            ],
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             "#d8576b"
            ],
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             "#ed7953"
            ],
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             "#fb9f3a"
            ],
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             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Benign Webpages Around the World"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choropleth Map\n",
    "fig = go.Figure(data = go.Choropleth(\n",
    "    locations = val_ben.index,\n",
    "    z = val_ben.country,\n",
    "#     text = val.index,\n",
    "    colorscale = 'blues',\n",
    "    autocolorscale = False,\n",
    "    reversescale = False,\n",
    "    marker_line_color = 'darkgray',\n",
    "    marker_line_width = 0.5,\n",
    "    colorbar_title = 'Number of Webpages',\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Benign Webpages Around the World',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = False,\n",
    "#         projection_type = 'equirectangular'\n",
    "    ),\n",
    "    annotations = [dict(\n",
    "        x = 0.55,\n",
    "        y = 0.1,\n",
    "        xref = 'paper',\n",
    "        yref = 'paper',\n",
    "        text = 'Source: <a href=\"https://data.mendeley.com/datasets/gdx3pkwp47/2\">\\\n",
    "            Dataset [Mendeley Data] </a>',\n",
    "        showarrow = False\n",
    "    )]\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Network Types Over Malicious and Benign Webpages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [13, 5]\n",
    "sns.set(style = 'whitegrid', font_scale = 1.2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "bar_1 = sns.countplot(x = 'net_type', data = df_mal, order = ['A', 'B', 'C'], palette = 'YlOrBr', ax = ax[0])\n",
    "bar_1.set(title = 'Network Types in Malicious Webpages', xlabel = 'Network Type')\n",
    "\n",
    "bar_2 = sns.countplot(x = 'net_type', data = df_ben, order = ['A', 'B', 'C'], palette = 'YlOrBr', ax = ax[1])\n",
    "bar_2.set(title = 'Network Types in Benign Webpages', xlabel = 'Network Type');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- HTTPS and HTTP Protocols Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IDAxUYGCgRo8ere+++04ODv+rCSYkJGjlypXGCgm9evVSs2bNtGrVKg0fPtwq9k49evRQgQIFtGXLFtWvX98Yo3z+/HlNmDBBJUqUUFhYmPF8xMfHq1+/foqIiFDDhg3Vrl0741x//vmnZs+ebVWZvp87X/u7Xb9+/YHPY0+RkZE6evSoihUrZvyoeXp66qOPPtK7776rbdu2adu2bXJyclKVKlVUt25dvfjii6pUqdJ9z+3r66uyZcsqMjJSH374obESR1pamjZu3KhSpUrJ399fN2/e1LJly/R///d/Cg8PN47v1KmTOnbsqEWLFj3wvCF33m252969ex/oHPaUlpZmrIjQrFmzDOOy+71tad++ffv0008/WSUvmzZtkre3d7qrLmTmO+5erl27po8++kiFChXSokWLjONef/11YxWANm3apLvqy6RJk3T27Fn169dPQ4YMMbb/8MMP6tu3r95++21FREQYd8seVFhYmJKTk7V48WLj+U9OTlanTp20Zs0avfPOO/ecKNLy3v7ll1+M59VyZ+r555+XdDsJ2rFjh6Kiooz4AwcO6Nlnn1XRokUlSYcOHdLs2bNVq1YtffHFF8Z39sCBA9WlSxd98MEHql+/vlXX/djYWH322WdGojxw4EAFBwdr/vz56tChg9UqNSdPnlRQUJDef/99SbdfwyFDhmjz5s1as2aNOnTooFu3bmnq1KkqW7asVq9eLXd3d+P40aNHa8mSJdq2bZuxGsP777+v5ORkLVq0yPjDvH///urcubPNXdEpU6bo5MmTev/99xUUFGRs37Jli/r376+PP/5Y06dPlyTNnz9fZcqU0bJly+ToeDvF6dWrl1588UWFh4dbfR7wP+Qv5C/pIX8hf0lPbslfVq9ebTx/aWlpunHjhrZv366YmBhNmjTJquddZn5Hjx49qmnTpumll16SJA0ZMkTt27dXVFSUTp8+bfU9c6fQ0FD99ttvevvtt43hXmlpaXrjjTe0YcMGq0mrLX7//XetWbPGGLYZHBysFi1aaMWKFfedi4dcI2fnGhRjstCWLVu0ZcsWm+0FCxbU9evXlZqaaiT7xYsX14QJE2y6vDs6OqpGjRo6efKk/vrrL6tk5umnn75vIiPdrrimpaXpiSeeSHf/ggULdPPmTZvt3bt3t5pFvUGDBjYTdvbo0UOvvPKKzReM5YObXjeuV1991Wo+iWrVqqlly5b69ttvFRUVpRo1ahj7XnrpJasPppeXlypXrqz9+/fr2rVrDz3m9Ntvv1VKSooGDBhgldi5u7tr1KhRatWqlVauXGn1oXF1dU13ucR7yei1zwnu/DGSbt8VOHHihHbs2CFHR0eNHj3aqqtm27Zt9dxzz+nLL79UZGSkbty4oaioKEVFRRlfqGPHjrUZk3231q1ba+bMmdq5c6caNWok6XZPgpiYGL3++uuSbv/4mM1mXb58WbGxscYXftWqVRUZGflASyZaPOhSj/Z2/fp1q0TLbDbr+vXr2rVrl06fPq2yZcuqX79+D31ee723pdvJ1rhx4/Tdd98ZP+SJiYn68ccfbVZasMjMd9y9/PDDD7px44aGDh1qdYynp6exdGp8fLwKFixoddytW7e0fv16lSxZUoMHD7ba17BhQzVr1kybNm3Szz///NATBlt+yA8fPmy8Bk5OTpo7d65cXV3vu2KHr6+v3N3drcZy79q1S97e3sZ735Io7dq1S7Vr19aFCxcUHR1tlQCvWLFCZrNZb7/9ttV3duHChdW7d2+NGjVKGzZssEouatWqZdXzoHDhwurXr5/eeOMNrV+/3mrCQXd3d/373/82/u3o6Ki3335bmzdv1tq1a9WhQwelpqbqo48+UrFixaySI8u1lixZYvw+HD16VCdPnlTbtm2tekgULFhQAwcO1LBhw4xtKSkpWrNmjZ599lmr9ku3e074+/tr8+bNunnzpjw8PGQ2m3XlyhWdP3/eeJ8UL15cGzZsML5fYIv8hfwlJyJ/yV65PX9Jb3lr6fZn888//7Talpnf0dKlSxuFGOn273/dunX166+/6uLFi+kWY1JTU7V27VqVLFnSanJrBwcHvf322+kOCZdu52F3zp9VqlQplS9fXsePH1dSUpJcXFwyfB7INXJ2rkExJguNHz/ealby+Ph4nTlzRjNmzNCUKVN07tw5jR8/XtLtF6x9+/ZKSUnR0aNHdfbsWf3+++86duyYMYHS3RW79GZIT0+hQoUkZXxHY8GCBYqOjrbZ3r59e6tkJr3r1a9fX9LtSufx48f1+++/6+zZs0ZXtvTGLt69VKF0+4vh22+/1fHjx62SmbJly2b4eOLj4x86mTl+/Lgk6f/+7/9s9j377LMqUKCAEWNRvHjxh75Dfvdrf6cLFy48UHfrR+XuHyNnZ2cVKVJELVq0UI8ePeTr62tzTIUKFTRhwgSlpKTo8OHD2r17t3788UdFRUVpy5Yt+uuvv7R06VKZTKYMr9u2bVvNnDlTERERRjJzdxffAgUKqGXLllq/fr0CAgLk5+enBg0aKCAgwOpO7IM4ceJEhvtmzpyZbcnOjRs3rK5tMpnk7u6uMmXK6PXXX9drr71m9bl7UPZ6b1uO8/X11datWzV69Gg5ODjoxx9/VHx8fIZDlDLzHXcvlseS3vCGli1bZnjc2bNnlZiYKH9//3TvTNeoUUObNm3S8ePHH7oY06lTJ0VGRmro0KGaPn266tevrwYNGqhOnTrpjkW/m5OTk/z9/XXo0CGZzWb9/fffOnbsmDGZniQ999xzKlSokPEHSXpjuI8ePSpJ+u677/T9999bXeOPP/6QJJtx1P7+/jbtsXwX3P2+8fHxsSlylSlTRoUKFTJi3dzcjNfh7NmzOn36tH7//Xf9+uuvxvwJdxav7rzevdp19uxZxcfHKzU1Nd27x0lJSUpNTdWJEydUo0YNdenSRV988YVatmypqlWrqkGDBmrYsKGqVq1qcyz+h/yF/CU95C/kL7k5fwkLCzN+1y09/o4fP65PPvlEH3zwgf78809j2e/M/I6m95m3FJIzmjfut99+07Vr11SnTh2bx1OiRIkMi3j3u9a9ijHkGjk716AY8wi5u7urSpUqCg0NVdOmTbVq1Sr17t3buHOydOlSzZo1yxiDVqBAAVWrVk3ly5fXwYMHbcay3euDdidnZ2cVLVpUsbGxio6OthnP+eOPP1r9u3///uneFUnvepcuXdLYsWO1detWmc1mOTg4qGzZsqpRo4b++9//ptue9MaTFilSRJJs7nDd6w+Yu5+PB2E5f0Z32YoVK2aMe7VwdXV96OvkZHf+GD0sR0dH+fn5yc/PT/3791dUVJT69++vX375RT/99NM9Z1gvXbq0/Pz8tGXLFt26dcuYBLZy5cpWdwsmTpyoKlWqaNWqVdq7d6/27t2ryZMnq0qVKho7dmyuX7K0ZMmS2rp1a5af197v7WbNmmnSpEnG3eBNmzapbNmy97zb/bDfcfdi+ePsfr1N7vYgz5N0u6fPw2rYsKHCwsI0f/587dq1S+Hh4QoPD1ehQoWMrrj3U7t2be3YsUNnzpzR8ePHZTabrZIfBwcH1a5dW1u3blViYqIOHDigfPnyWf2RaJmU74svvsjwOteuXbP6t+U7+E758+eXJJt5FjKaE6BIkSJW77F9+/Zp/PjxRsLm4uKiihUrqnLlyrp8+bLxev/9998ZtsHyelhYXvczZ87c8w8Sy+N744039PTTT2vp0qU6dOiQDh48qJkzZ8rb21sffPCBXZdnzs3IX24jf8le5C/Z63HJX6TbhSQPDw/VrFlTc+bMUUBAgL744gt1795dTzzxRKZ+R9P7zN+ryCfd+/dPuv3Y75yb5EGu9SDfL+Qa1nJSrkExxg4sM1lv3LhRJ06cULly5bRhwwZ98MEH8vHx0QcffKDKlSvrqaeekiR98MEHmZ5Q0qJJkyZaunSpNm/erFdffTUrHobMZrP69u2rU6dOqW/fvmratKmeffZZubq66s8//9Ty5cvTPS69P3IsH+hHvdSd5QMfHR2dbrfUa9euGXeucDuxPXTokLZu3ZruF7+fn5969OihKVOm6LfffrvvcnetW7fWmDFj9OOPP8rV1VVXrlxRnz59rGKcnJz02muv6bXXXtOlS5e0c+dObdy4UTt27FDfvn21ZcuWdOcByevs/d5u3ry5Jk2apM2bN6tq1aratm3bPYsNWf0dZ+mOGhcXZ7PPkixbxu3e6c7nKT2WH2DLc2VJbtLrtZOQkGCzrVatWqpVq5bi4+P1888/6/vvv9fq1as1duxYlSlT5r7dqi2JzpEjR/Tzzz8rX758Nn981K1bV5s2bdKRI0d04MABValSxSqJdXd3V758+XTw4MEH/qyk1/PAkgDefWcqvZUMLOewfIdfvHhRvXr1kqurqz766CPVqFFDZcuWVb58+RQREWG1OpWloJbecJO7t1leP8u8QPdjMpnUsWNHdezYUX/99Zd27dqlzZs367vvvlO/fv20devW+w5RwP+Qv5C/5BbkL7lHdr+3PT099eyzz+rw4cM6e/asMYznYX9HM+Nev39S+jlOViDXsJaTcg1WU7ITy5vR8qZet26dJOnTTz9V06ZNjURGul2VkzJ3J8WiS5cuypcvn+bMmXPfJWQfdKjAiRMndPLkSb344osaOnSoqlatalSqLbN/p9dmSzexO0VFRUlKv+tYVrLctU9vKcrffvtNsbGxevbZZx9pG3ITJycnxcbG2tx9vJPlj9W7q8rpadmypZycnLR161Zt3LhRDg4Oevnll43958+f15QpU4xVLEqUKGGselOnTh1FR0frwoUL//BRPZ7s/d4uXbq0nnvuOUVGRmrnzp2Ki4vLcIiSlPXfcRUqVJB0ewK5u82fP1/VqlVLd6LDcuXKycXFRYcPH0632/C+ffskyehWbkkw7r5jc/36dZslPBcuXKhp06ZJup2kNGjQQO+//74++OADSem/NnerUqWK3N3ddfToUe3fv1+VK1e26fZtGcu9f/9+nTp1yuaOi4+Pj1JTU9Nd0vGXX37R5MmT9fPPP1ttv9f3cuXKla22HzlyxOZ34uLFi4qJiTEmz4yMjFRiYqIGDx6szp07q3z58kYX7Lt/HyznT++1vHubt7e3nJ2ddfTo0XTfL1999ZU+++wz/f333/r77781c+ZMY2jDk08+qdatW2vGjBnq0KGDEhISMuwBgYyRv1gjf8mZyF9yj5zw3r77ey0zv6OZUa5cObm7u6f7+3f9+nWdPXv2H18jPeQa1nJSrkExxg4OHjyovXv3qmDBgsaEd5YutHdPILVmzRrjD4qUlJRMX7NSpUrq37+/YmNjFRISku541OvXr2vy5Mn64YcfJCnDmf4tLHca7k6Orl69alQR02vz/PnzrbrcHThwQGvXrlXlypUfaEK/f6Jt27ZydHTUnDlzdP78eWN7fHy8xowZY8TgNsukVaNHj073y+vcuXMKCwtTsWLFVK9evfuer3DhwnrhhRf0448/atu2bapTp45VEuTq6qq5c+dq+vTpVn8o37p1S7GxsUaXddjKjvd2s2bNdP78ec2ZM0dPP/30PbtgZ/V3XNOmTeXu7q6wsDCrlT+uXr2qr7/+Wvnz5093PhlnZ2e9/PLLiomJsVkC8scff9SGDRv09NNPG+OHLcMw7h4PPWfOHJskYceOHZozZ47VpHiSjPaVKFHivo/L0dFRNWvW1K5du3TmzBkjGbrT008/rRIlSmj16tVKS0uzuaPbvn17SdK4ceOs7vbcvHlTo0eP1ty5c23mw9i+fbuREEm371TNnTtXzs7Oat26tVVsbGys5s2bZ/w7OTlZEyZMkCS98sorkjJ+vY8fP66wsDBJ/3u9/fz8VK5cOX377bdGN2Pp9m+SZaUCCxcXF7Vs2VKnTp3SggULrPb99NNP+uSTT7Ry5UoVLFhQ+fPnV1hYmKZOnWpTOLt06ZKkB3tN8D/kL+QvuQX5S+6R3e/tTZs26bffflO5cuWMyVcz8zuaGU5OTmrdurXOnj2rJUuWGNvT0tI0adIkJScn/+NrpIdcI+fmGgxTykJ3Lw+YmpqqU6dO6fvvv1dqaqreeecd405MmzZttH79eg0cOFAvv/yyPDw8dPjwYe3du1dPPvmk/vrrL5sX+GENGDBALi4umjZtmtq0aSN/f39VqlRJLi4uOnfunHbu3KnExER5eXlp1KhR933jlC1bVr6+vtq3b58CAwPl7++vv//+W5GRkbp165bc3NyM8Xl3unr1qtq3b68XX3xRN2/e1KZNm4zuZY9a6dKlNXz4cH388cdq37698Qfdjz/+qPPnz+vll19mqdM71KpVSyNGjNAnn3yiLl26qGbNmqpUqZKcnJx05swZ/fjjj3J2dta8efMeaIJS6fZ73XLn6M0337TaV7RoUXXv3l0LFixQq1at1LBhQzk4OGj79u06ffq0+vfv/9BzhOQV2fHebt68uaZPn65ffvlFffv2vWdsVn/HFSpUSO+//75Gjhyp9u3bq0mTJsqfP782btyo2NhYhYaGZviefOutt3TgwAHNnTtX+/btk5+fn86fP6+tW7cqf/78mjRpknHHtGHDhipWrJg2bNigGzduqGLFioqKitKvv/6qChUq6PLly8Z5Bw0apJ9++kkhISFq0aKFvLy8dOrUKW3btk3ly5c3Jnq8n9q1a2vSpEmSlOE447p162rlypVycXGxmXiuTp06Cg4OVnh4uF5++WU1bNhQzs7OioyM1OXLl9W1a1eb7siWlRxatWolJycnbd68WX/++adGjx5tM4Ggu7u7pk6dqp9++knly5fX7t27jRUKLEsBBwQE6NNPP9Xnn3+uM2fOqEyZMvrtt9+0bds2486j5fU2mUwaM2aMevbsqcDAQDVr1kxPPPGEtm3bZgwFu/OP6+HDhysqKkoTJ07Uli1b5Ovrq+joaH333XdydHTUuHHj5ODgIGdnZw0ePFhjx45Vq1at9OKLL8rV1VX79u3T4cOH1bZtW6vVbvA/5C/kL7kd+UvuYa/39t2rcaWkpOjIkSPauXOnnJycjCWUpcz9jmbWkCFDtH37do0ePVpbtmzRM888o3379unMmTNydXW9b3E5s8g1cmauQTEmC929PKCTk5M8PT0VEBCg4OBgq0mQGjVqpKlTp2ru3Llau3atXF1dVbp0ab3//vvy8/NT+/bt9cMPP6hVq1aZbo/JZFLv3r3VrFkzrVy5Utu3b9e6desUHx+vJ598UvXr11fTpk3VsmXLB/phcnBw0GeffaYpU6Zo586dOnr0qIoXL64GDRqoX79++vTTTxUZGanff//dainId999VwcOHND69evl4OCggIAA/fvf/053ybdHISQkRGXLltX8+fP13XffyWw2q3z58urbt686duxolzbkJj179lTt2rW1ePFi7du3T0eOHFFqaqqeeuopde3aVb169XqoJRubNGkiDw8PJScnWy2RZ/HWW2/p6aef1vLly7V69WqlpqbqmWee0YQJE4wqPNJn7/d2+fLlVb58eZ0+fVrNmze/Z+yj+I5r3769vLy89Pnnn2vTpk1KSUlRpUqV9PHHH99zbhZPT08tW7ZMc+bM0aZNm/Sf//xHnp6eateunfr162f1feXs7Kzw8HBNnjxZu3fvVlRUlP7v//5PS5Ys0ZQpU6yKMb6+vvrPf/6j2bNna8+ePbpy5YqKFSumkJAQ9evXz2bZxYxYkhdXV9d0Vx6Q/pcg+fn5pTs56ahRo1S1alUtWbJE3377rfLlyydvb28NGjQo3c9RYGCgUlJS9J///Ed//fWXKlSooA8//FBNmza1iS1TpoyGDBmiqVOnat++fSpVqpTeeecdhYSEGDFeXl5asGCBpkyZoj179mjHjh0qUaKEgoOD1bdvXzVr1kzbt2+X2WyWyWTS//3f/yksLEzTp09XZGSk8dvQqVMnhYSEWC2baXn9Pv/8c23evFnh4eHy9PRU48aN1b9/f6seCsHBwXryyScVFhamiIgIJSQkqGzZsho5cmSWzT/yOCJ/IX95HJC/5B72eG+ntxpX0aJF1bp1a7322ms2vXsf9nc0szw9PbVkyRJNnjxZ27dv1969e1WtWjWFhYWpb9++Vr9/WYlcI2fmGibzPxnYC9yDZTm+WbNmpfuhAwDkbD4+PqpYsaK++eabLDtnUlKSYmNj9dRTT9ks7blnzx51795dw4YNU+/evbPsmsDDIH8B8Kj8/vvvKl68uE0h+datW/L391fdunU1d+7cbGpd9sjLuQZzxgAAALuJi4tTkyZN1LNnT6uJ8lJTU/XVV19JUpZ1BwcAICfp37+/6tWrZ7PS0MKFC5WcnMzvXxbJLbkGw5QAAIDdeHp6qnnz5tq0aZNeeeUV1a5dW6mpqdq1a5d+/fVXdenS5ZGvVAMAQHbo1q2bxowZo9atW6tJkyZyc3PTf//7X+3atUs+Pj4KDg7O7iY+FnJLrkExBgAA2NXkyZPl5+enNWvW6Ouvv5Z0eyWrMWPGqHPnztncOgAAHo2goCAVLVpU4eHhioiIUHx8vJ566in17dtXffv2TXeuFmRObsg1mDMGAAAAAADAjpgzBgAAAAAAwI4oxgAAAAAAANgRc8bkEFFRUTKbzXJycsrupgAAkKMkJyfLZDLJz88vu5uSJ5CTAACQsazKSyjG5BBms1lM3wMAgC1+H+2LnAQAgIxl1W8kxZgcwnL3qWrVqtncEgAAcpbDhw9ndxPyFHISAAAyllV5CXPGAAAAAAAA2BHFGAAAAAAAADvKkcWYQ4cOqXfv3qpZs6aqVq2qdu3aac2aNVYxU6ZMkY+PT7r/u379uhGXmpqquXPnqlmzZvL19VWbNm0UERGR7nVXrFihVq1aqVq1amrevLkWLVqUbtyWLVvUoUMHVa9eXQEBAQoNDVVKSkqWPX4AAAAAAPD4ynFzxpw+fVrBwcEqWLCgevXqpfz58ysiIkLDhw/X33//rZ49e0qSTp48qdKlS2vQoEE253BzczP+e+LEiVq4cKHat2+v6tWra+PGjRo6dKjS0tLUqlUrI27hwoUaN26cGjdurKCgIO3Zs0djxozRzZs31bdvXyNu8+bNGjRokGrWrKm33npLJ06cUGhoqGJiYjRmzJhH+MwAAAAAAIDHgcmcw6bL79Onj/bt26eNGzfKy8tLkpSWlqbAwECdOHFCO3bsUP78+dW4cWNVq1ZNU6dOzfBc586d00svvaSgoCCNGjVK0u2eMkFBQbpw4YK2bt0qZ2dnXb9+XQ0bNlTdunU1a9YsmUwmSdLQoUO1detWbdu2TZ6enkpNTdWLL76owoULa+nSpcYEd5MnT9a8efP0zTffyMfHJ1OP2zIJEJPlAQBgjd9I++L5BgAgY1n1O5mjhimlpqZq3759ql+/vlGIkSQHBwe99NJLio+P17Fjx3Tz5k1dunRJ5cuXv+f51q9fr7S0NAUFBRnb8uXLp6CgIMXGxmrfvn2SpK1btyo+Pl6BgYFGIUaSgoODlZiYqMjISElSVFSULl68qM6dOxuFGEuc2WzOcPgTAAAAAACARY4qxjg4OOjbb7/V22+/bbPvypUrkm4XU06dOiWz2WwUYxISEpSWlmZzzJEjR+Th4SFvb2+r7ZUrVzb23/n/VapUyVScl5eXihYtauwHAAAAAADISI4qxphMJpUuXVqlSpWy2h4fH6+VK1fK3d1dlSpV0smTJyVJ27dvV6NGjVS9enXVqFFDo0ePVkJCgnFcdHS0VQ8bi2LFikmSLl26JEmKiYmRq6urChUqZBXn4uKiQoUKGXHR0dGSpOLFi6d7TkscAAAAAABARnLcBL53M5vNGjVqlGJjYzVgwAC5uLgYxZjDhw9r4MCB8vDw0A8//KAlS5bo9OnTWrhwoRwcHBQXF6f8+fPbnNPV1VWSjMJNXFycse1uLi4uVnF3Hn93nKX3zj95rPHx8f/oHAAAPG7MZrPVMGIAAIDcLkcXY8xms0aPHq3169e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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [13, 5]\n",
    "sns.set(style = 'whitegrid', font_scale = 1.2)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "bar_1 = sns.countplot(x = 'https', data = df_mal, order = ['HTTPS', 'HTTP'], palette = 'Set2', ax = ax[0])\n",
    "bar_1.set(title = 'Bar Graph for HTTPS vs HTTP for Malicious Webpages', xlabel = 'Protocol')\n",
    "\n",
    "bar_2 = sns.countplot(x = 'https', data = df_ben, order = ['HTTPS', 'HTTP'], palette = 'Set2', ax = ax[1])\n",
    "bar_2.set(title = 'Bar Graph for HTTPS vs HTTP for Benign Webpages', xlabel = 'Protocol');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Bar graph showing the most used TLD in URLs over Malicious and Benign Webpages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "indianred"
         },
         "text": [
          19517,
          1761,
          1402,
          715,
          466,
          246,
          235,
          203,
          173,
          159
         ],
         "textposition": "outside",
         "texttemplate": "%{text:.2s}",
         "type": "bar",
         "x": [
          "com",
          "net",
          "de",
          "org",
          "co.uk",
          "jp",
          "fr",
          "nl",
          "dk",
          "co.jp"
         ],
         "xaxis": "x",
         "y": [
          19517,
          1761,
          1402,
          715,
          466,
          246,
          235,
          203,
          173,
          159
         ],
         "yaxis": "y"
        },
        {
         "marker": {
          "color": "lightsalmon"
         },
         "text": [
          707240,
          147286,
          64035,
          53212,
          46616,
          15225,
          10632,
          10380,
          9041,
          8652
         ],
         "textposition": "outside",
         "texttemplate": "%{text:.2s}",
         "type": "bar",
         "x": [
          "com",
          "org",
          "edu",
          "net",
          "co.uk",
          "ca",
          "com.au",
          "de",
          "org.uk",
          "gov"
         ],
         "xaxis": "x2",
         "y": [
          707240,
          147286,
          64035,
          53212,
          46616,
          15225,
          10632,
          10380,
          9041,
          8652
         ],
         "yaxis": "y2"
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Most Occuring Top Level Domain in Malicious Webpages",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 1,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 16
          },
          "showarrow": false,
          "text": "Most Occuring Top Level Domain in Benign Webpages",
          "x": 0.5,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "height": 900,
        "template": {
         "data": {
          "bar": [
           {
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   "source": [
    "mal_tld = pd.DataFrame(df_mal.tld.value_counts()[:10])\n",
    "ben_tld = pd.DataFrame(df_ben.tld.value_counts()[:10])\n",
    "\n",
    "mal = go.Bar(y = mal_tld.tld, x = mal_tld.index.tolist(), text = mal_tld.tld, marker_color = 'indianred')\n",
    "ben = go.Bar(y = ben_tld.tld, x = ben_tld.index.tolist(), text = ben_tld.tld, marker_color = 'lightsalmon')\n",
    "\n",
    "fig = make_subplots(\n",
    "    rows = 2, cols = 1, subplot_titles = (\"Most Occuring Top Level Domain in Malicious Webpages\", \"Most Occuring Top Level Domain in Benign Webpages\"),\n",
    "    specs = [[{'type' : 'bar'}], [{'type' : 'bar'}]]\n",
    ")\n",
    "\n",
    "fig.append_trace(mal, row = 1, col = 1)\n",
    "fig.append_trace(ben, row = 2, col = 1)\n",
    "fig.update_traces(texttemplate = '%{text:.2s}', textposition = 'outside')\n",
    "fig.update_layout(uniformtext_minsize = 10, uniformtext_mode = 'hide', width = 1200, height = 900)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Preprocessing**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dropping Useless Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index         0\n",
       "url           0\n",
       "url_len       0\n",
       "ip_add        0\n",
       "geo_loc       0\n",
       "tld           0\n",
       "who_is        0\n",
       "https         0\n",
       "js_len        0\n",
       "js_obf_len    0\n",
       "content       0\n",
       "label         0\n",
       "country       0\n",
       "net_type      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# detection missing values\n",
    "df_dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>http://members.tripod.com/russiastation/</td>\n",
       "      <td>40</td>\n",
       "      <td>42.77.221.155</td>\n",
       "      <td>Taiwan</td>\n",
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       "      <td>http://www.ddj.com/cpp/184403822</td>\n",
       "      <td>32</td>\n",
       "      <td>3.211.202.180</td>\n",
       "      <td>United States</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Took in cognitivism, whose adherents argue for...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>ARG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>http://www.ff-b2b.de/</td>\n",
       "      <td>21</td>\n",
       "      <td>147.22.38.45</td>\n",
       "      <td>United States</td>\n",
       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>Malicious</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>http://us.imdb.com/title/tt0176269/</td>\n",
       "      <td>35</td>\n",
       "      <td>205.30.239.85</td>\n",
       "      <td>United States</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                       url  url_len         ip_add  \\\n",
       "0      0  http://members.tripod.com/russiastation/       40  42.77.221.155   \n",
       "1      1          http://www.ddj.com/cpp/184403822       32  3.211.202.180   \n",
       "2      2                  http://www.naef-usa.com/       24   24.232.54.41   \n",
       "3      3                     http://www.ff-b2b.de/       21   147.22.38.45   \n",
       "4      4       http://us.imdb.com/title/tt0176269/       35  205.30.239.85   \n",
       "\n",
       "         geo_loc  tld      who_is  https  js_len  js_obf_len  \\\n",
       "0         Taiwan  com    complete  HTTPS    58.0         0.0   \n",
       "1  United States  com    complete  HTTPS    52.5         0.0   \n",
       "2      Argentina  com    complete  HTTPS   103.5         0.0   \n",
       "3  United States   de  incomplete   HTTP   720.0       532.8   \n",
       "4  United States  com    complete  HTTPS    46.5         0.0   \n",
       "\n",
       "                                             content      label country  \\\n",
       "0  Named themselves charged particles in a manly ...     Benign     TWN   \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...     Benign     USA   \n",
       "2  Took in cognitivism, whose adherents argue for...     Benign     ARG   \n",
       "3  fire cumshot sodomize footaction tortur failed...  Malicious     USA   \n",
       "4  Levant, also monsignor georges. In 1800, lists...     Benign     USA   \n",
       "\n",
       "  net_type  \n",
       "0        A  \n",
       "1        A  \n",
       "2        A  \n",
       "3        B  \n",
       "4        C  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>de</td>\n",
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       "      <td>HTTP</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>fire cumshot sodomize footaction tortur failed...</td>\n",
       "      <td>Malicious</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Levant, also monsignor georges. In 1800, lists...</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
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      "text/plain": [
       "   url_len  tld      who_is  https  js_len  js_obf_len  \\\n",
       "0       40  com    complete  HTTPS    58.0         0.0   \n",
       "1       32  com    complete  HTTPS    52.5         0.0   \n",
       "2       24  com    complete  HTTPS   103.5         0.0   \n",
       "3       21   de  incomplete   HTTP   720.0       532.8   \n",
       "4       35  com    complete  HTTPS    46.5         0.0   \n",
       "\n",
       "                                             content      label country  \\\n",
       "0  Named themselves charged particles in a manly ...     Benign     TWN   \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...     Benign     USA   \n",
       "2  Took in cognitivism, whose adherents argue for...     Benign     ARG   \n",
       "3  fire cumshot sodomize footaction tortur failed...  Malicious     USA   \n",
       "4  Levant, also monsignor georges. In 1800, lists...     Benign     USA   \n",
       "\n",
       "  net_type  \n",
       "0        A  \n",
       "1        A  \n",
       "2        A  \n",
       "3        B  \n",
       "4        C  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping columns\n",
    "df_dataset.drop(['index', 'geo_loc', 'url', 'ip_add'], axis=1, inplace=True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Special Characters of Webpages Contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>720.0</td>\n",
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       "      <td>58</td>\n",
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      ],
      "text/plain": [
       "   url_len  tld      who_is  https  js_len  js_obf_len  \\\n",
       "0       40  com    complete  HTTPS    58.0         0.0   \n",
       "1       32  com    complete  HTTPS    52.5         0.0   \n",
       "2       24  com    complete  HTTPS   103.5         0.0   \n",
       "3       21   de  incomplete   HTTP   720.0       532.8   \n",
       "4       35  com    complete  HTTPS    46.5         0.0   \n",
       "\n",
       "                                             content      label country  \\\n",
       "0  Named themselves charged particles in a manly ...     Benign     TWN   \n",
       "1  And filipino field \\n \\n \\n \\n \\n \\n \\n \\n the...     Benign     USA   \n",
       "2  Took in cognitivism, whose adherents argue for...     Benign     ARG   \n",
       "3  fire cumshot sodomize footaction tortur failed...  Malicious     USA   \n",
       "4  Levant, also monsignor georges. In 1800, lists...     Benign     USA   \n",
       "\n",
       "  net_type  special_char  \n",
       "0        A            74  \n",
       "1        A            51  \n",
       "2        A           109  \n",
       "3        B           718  \n",
       "4        C            58  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the Special Characters in the content\n",
    "def count_special(string):\n",
    "    count = 0\n",
    "    for char in string:\n",
    "        if not(char.islower()) and not(char.isupper()) and not(char.isdigit()):\n",
    "            if char != ' ':\n",
    "                count += 1\n",
    "    return count\n",
    "\n",
    "# Adding Feature that shows the Number of Special Character in the Content\n",
    "df_dataset['special_char'] = df_dataset['content'].apply(lambda x: count_special(x))\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lengths of The Webpages Contents\n",
    "\n",
    "    We use the lenghts of the contents instead of the contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>de</td>\n",
       "      <td>incomplete</td>\n",
       "      <td>HTTP</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>Malicious</td>\n",
       "      <td>USA</td>\n",
       "      <td>B</td>\n",
       "      <td>718</td>\n",
       "      <td>8946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>com</td>\n",
       "      <td>complete</td>\n",
       "      <td>HTTPS</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "      <td>USA</td>\n",
       "      <td>C</td>\n",
       "      <td>58</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   url_len  tld      who_is  https  js_len  js_obf_len      label country  \\\n",
       "0       40  com    complete  HTTPS    58.0         0.0     Benign     TWN   \n",
       "1       32  com    complete  HTTPS    52.5         0.0     Benign     USA   \n",
       "2       24  com    complete  HTTPS   103.5         0.0     Benign     ARG   \n",
       "3       21   de  incomplete   HTTP   720.0       532.8  Malicious     USA   \n",
       "4       35  com    complete  HTTPS    46.5         0.0     Benign     USA   \n",
       "\n",
       "  net_type  special_char  content_len  \n",
       "0        A            74         1155  \n",
       "1        A            51          611  \n",
       "2        A           109         1320  \n",
       "3        B           718         8946  \n",
       "4        C            58          722  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Length of the Content\n",
    "df_dataset['content_len'] = df_dataset['content'].apply(lambda x: len(x))\n",
    "df_dataset.drop(['content'], axis=1, inplace=True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convert Categorical Features Into Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tld', 'who_is', 'https', 'country', 'net_type']"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the categorical features that needs to be converted into numeric features for modelling \n",
    "categorical = df_dataset.select_dtypes('object').columns.tolist()\n",
    "ls = [element for element in categorical if element not in ['label']]\n",
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>url_len</th>\n",
       "      <th>tld</th>\n",
       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
       "      <th>js_len</th>\n",
       "      <th>js_obf_len</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "      <th>net_type</th>\n",
       "      <th>special_char</th>\n",
       "      <th>content_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>1155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>103.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>282</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>532.8</td>\n",
       "      <td>1</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
       "      <td>8946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   url_len  tld  who_is  https  js_len  js_obf_len  label  country  net_type  \\\n",
       "0       40  195       0      1    58.0         0.0      0      213         0   \n",
       "1       32  195       0      1    52.5         0.0      0      220         0   \n",
       "2       24  195       0      1   103.5         0.0      0        7         0   \n",
       "3       21  282       1      0   720.0       532.8      1      220         1   \n",
       "4       35  195       0      1    46.5         0.0      0      220         2   \n",
       "\n",
       "   special_char  content_len  \n",
       "0            74         1155  \n",
       "1            51          611  \n",
       "2           109         1320  \n",
       "3           718         8946  \n",
       "4            58          722  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This le_dict will save the Label Encoder Class so that the same Label Encoder instance can be used for the test dataset\n",
    "le_dict = {}\n",
    "\n",
    "for feature in ls:\n",
    "    le = LabelEncoder()\n",
    "    le_dict[feature] = le\n",
    "    df_dataset[feature] = le.fit_transform(df_dataset[feature])\n",
    "\n",
    "df_dataset.label.replace({'Malicious' : 1, 'Benign' : 0}, inplace = True)\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Nomalization - Standartization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url_len</th>\n",
       "      <th>tld</th>\n",
       "      <th>who_is</th>\n",
       "      <th>https</th>\n",
       "      <th>js_len</th>\n",
       "      <th>js_obf_len</th>\n",
       "      <th>label</th>\n",
       "      <th>country</th>\n",
       "      <th>net_type</th>\n",
       "      <th>special_char</th>\n",
       "      <th>content_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.287743</td>\n",
       "      <td>-0.546414</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.675901</td>\n",
       "      <td>-0.135859</td>\n",
       "      <td>0</td>\n",
       "      <td>0.840422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.770540</td>\n",
       "      <td>-0.452765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.267393</td>\n",
       "      <td>-0.546414</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.736697</td>\n",
       "      <td>-0.135859</td>\n",
       "      <td>0</td>\n",
       "      <td>0.930792</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.020689</td>\n",
       "      <td>-0.958673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.822529</td>\n",
       "      <td>-0.546414</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.172952</td>\n",
       "      <td>-0.135859</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.819036</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.389878</td>\n",
       "      <td>-0.299318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.030705</td>\n",
       "      <td>-0.246055</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.641729</td>\n",
       "      <td>8.742076</td>\n",
       "      <td>1</td>\n",
       "      <td>0.930792</td>\n",
       "      <td>1</td>\n",
       "      <td>6.233631</td>\n",
       "      <td>6.792698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.059217</td>\n",
       "      <td>-0.546414</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.803020</td>\n",
       "      <td>-0.135859</td>\n",
       "      <td>0</td>\n",
       "      <td>0.930792</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.944556</td>\n",
       "      <td>-0.855445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    url_len       tld  who_is  https    js_len  js_obf_len  label   country  \\\n",
       "0  0.287743 -0.546414       0      1 -0.675901   -0.135859      0  0.840422   \n",
       "1 -0.267393 -0.546414       0      1 -0.736697   -0.135859      0  0.930792   \n",
       "2 -0.822529 -0.546414       0      1 -0.172952   -0.135859      0 -1.819036   \n",
       "3 -1.030705 -0.246055       1      0  6.641729    8.742076      1  0.930792   \n",
       "4 -0.059217 -0.546414       0      1 -0.803020   -0.135859      0  0.930792   \n",
       "\n",
       "   net_type  special_char  content_len  \n",
       "0         0     -0.770540    -0.452765  \n",
       "1         0     -1.020689    -0.958673  \n",
       "2         0     -0.389878    -0.299318  \n",
       "3         1      6.233631     6.792698  \n",
       "4         2     -0.944556    -0.855445  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the 'content_len' and 'special_char' in training data\n",
    "ss_dict = {}\n",
    "\n",
    "for feature in ['content_len', 'special_char', 'url_len', 'tld', 'js_len', 'js_obf_len', 'country']:\n",
    "    ss = StandardScaler()\n",
    "    ss_fit = ss.fit(df_dataset[feature].values.reshape(-1, 1))\n",
    "    ss_dict[feature] = ss_fit\n",
    "    d = ss_fit.transform(df_dataset[feature].values.reshape(-1, 1))\n",
    "    df_dataset[feature] = pd.DataFrame(d, index = df_dataset.index, columns = [feature])\n",
    "\n",
    "df_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Train and Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_dataset.drop('label', axis=1)\n",
    "y = df_dataset.label\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy in %</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>99.78</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>99.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>99.88</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Support Vector Machines</th>\n",
       "      <td>99.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Accuracy in %  F1-score  Recall  Precision\n",
       "Logistic Regression              99.78      0.95    0.93       0.97\n",
       "Decision Tree                    99.93      0.99    0.99       0.98\n",
       "Naive Bayes                      99.88      0.97    0.95       1.00\n",
       "Support Vector Machines          99.96      0.99    0.99       0.99"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classificaation\n",
    "classifiers = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "    'Support Vector Machines': SVC()\n",
    "}\n",
    "results=pd.DataFrame(columns=['Accuracy in %', 'F1-score', 'Recall', 'Precision'])\n",
    "for method, func in classifiers.items():\n",
    "    func.fit(x_train,y_train)\n",
    "    pred = func.predict(x_test)\n",
    "    results.loc[method]= [100*np.round(accuracy_score(y_test,pred),decimals=4),\n",
    "                         round(f1_score(y_test,pred),2),\n",
    "                         round(recall_score(y_test,pred),2),\n",
    "                         round(precision_score(y_test,pred),2)]\n",
    "results"
   ]
  }
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