├── Procfile
├── README.md
├── URL Classification.ipynb
├── app.py
├── requirements.txt
└── templates
    └── index.html


/Procfile:
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1 | web: gunicorn app:app


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/README.md:
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 1 | # Phishing URL Classification
 2 | 
 3 | Malicious Web sites are a cornerstone of Internet criminal activities.
 4 | These Web sites contain various unwanted content such as spam-advertised products, phishing sites, dangerous "drive-by"
 5 | harness that infect a visitor's system with malware. The most influential approaches to the malicious
 6 | URL problem are manually constructed lists in which all malicious web page`s URLs are listed, as
 7 | well as users systems that analyze the content or behavior of a Web site as it is visited.
 8 | 
 9 | The disadvantage of _Blacklisting_ approach is that we have to do the tedious task of searching the list for
10 | presence of the entry. And the list can be very large considering the amount of web sites on the Internet.
11 | Also the list cannot be kept upto date because of the evergrowing growth of web link each and every hour.
12 | 
13 | In the given System we are using **Machine-Learning** techniques to classify a URL as either **Safe** or **Unsafe** in _Real Time_ without even the need to download the webpage.
14 | 
15 | Algorithms we are using in this system are :
16 | 
17 | *	[Random Forest] (https://en.wikipedia.org/wiki/Random_forest)
18 | *	[Logistic Regression] (https://en.wikipedia.org/wiki/Logistic_regression)
19 | *	[Decision Trees] (https://en.wikipedia.org/wiki/Decision+Trees)
20 | * [Gradiant Boosting]
21 | 
22 | The system is presently working only on **Lexical** features(Simple text features of a URL) which includes:
23 | 
24 | *	Length of URL
25 | *	Domain Length
26 | *	Presence of Ip Address in Host Name
27 | *	Presence of Security Sensitive Words in URL
28 | 
29 | and many more(around 22 total). The Host Based Features like country code in which site is hosted, creation date, updation date etc. are still yet to be added to the system and increase accuracy of the classifier but increase the _Latency time_ in classifying the URL as we have to query **WHOIS** servers in order to come up with the Host Based Features.
30 | For this query purpose the **PyWhois** module has been used.
31 | 
32 | ##  About Dataset
33 | For this given system we are using two sources to collect our data,namely:
34 | 
35 | ####  Phishtank.com
36 | For the phishing/malicious URLs we are collecting data from [Phishtank] (https://www.phishtank.com/).
37 | 


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/URL Classification.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "With this analysis, we explored some important features that have proved to be sound and effective in predicting phishing/malicious websites based on lexical characteristics of URL"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 1,
  13 |    "metadata": {
  14 |     "collapsed": false,
  15 |     "deletable": true,
  16 |     "editable": true
  17 |    },
  18 |    "outputs": [],
  19 |    "source": [
  20 |     "import os\n",
  21 |     "import sys\n",
  22 |     "import re\n",
  23 |     "import matplotlib\n",
  24 |     "import pandas as pd\n",
  25 |     "import numpy as np\n",
  26 |     "from os.path import splitext\n",
  27 |     "import ipaddress as ip\n",
  28 |     "import tldextract\n",
  29 |     "import whois\n",
  30 |     "import datetime\n",
  31 |     "from urllib.parse import urlparse"
  32 |    ]
  33 |   },
  34 |   {
  35 |    "cell_type": "code",
  36 |    "execution_count": 2,
  37 |    "metadata": {
  38 |     "collapsed": false,
  39 |     "deletable": true,
  40 |     "editable": true
  41 |    },
  42 |    "outputs": [
  43 |     {
  44 |      "data": {
  45 |       "text/html": [
  46 |        "<div>\n",
  47 |        "<table border=\"1\" class=\"dataframe\">\n",
  48 |        "  <thead>\n",
  49 |        "    <tr style=\"text-align: right;\">\n",
  50 |        "      <th></th>\n",
  51 |        "      <th>URL</th>\n",
  52 |        "      <th>Lable</th>\n",
  53 |        "    </tr>\n",
  54 |        "  </thead>\n",
  55 |        "  <tbody>\n",
  56 |        "    <tr>\n",
  57 |        "      <th>0</th>\n",
  58 |        "      <td>http://www.resumescorecard.com/</td>\n",
  59 |        "      <td>0</td>\n",
  60 |        "    </tr>\n",
  61 |        "    <tr>\n",
  62 |        "      <th>1</th>\n",
  63 |        "      <td>http://www.colorado.edu/MCEN/flowvis/</td>\n",
  64 |        "      <td>0</td>\n",
  65 |        "    </tr>\n",
  66 |        "    <tr>\n",
  67 |        "      <th>2</th>\n",
  68 |        "      <td>http://www.winestars.com/</td>\n",
  69 |        "      <td>0</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>3</th>\n",
  73 |        "      <td>http://eqworld.ipmnet.ru/</td>\n",
  74 |        "      <td>0</td>\n",
  75 |        "    </tr>\n",
  76 |        "    <tr>\n",
  77 |        "      <th>4</th>\n",
  78 |        "      <td>http://advods.com/verty/myaccount/webapps/973f6</td>\n",
  79 |        "      <td>1</td>\n",
  80 |        "    </tr>\n",
  81 |        "  </tbody>\n",
  82 |        "</table>\n",
  83 |        "</div>"
  84 |       ],
  85 |       "text/plain": [
  86 |        "                                               URL  Lable\n",
  87 |        "0                  http://www.resumescorecard.com/      0\n",
  88 |        "1            http://www.colorado.edu/MCEN/flowvis/      0\n",
  89 |        "2                        http://www.winestars.com/      0\n",
  90 |        "3                        http://eqworld.ipmnet.ru/      0\n",
  91 |        "4  http://advods.com/verty/myaccount/webapps/973f6      1"
  92 |       ]
  93 |      },
  94 |      "execution_count": 2,
  95 |      "metadata": {},
  96 |      "output_type": "execute_result"
  97 |     }
  98 |    ],
  99 |    "source": [
 100 |     "df = pd.read_csv(\"/home/surajr/Dropbox/Projects/Xtras/dataset/dataset.csv\")\n",
 101 |     "#df=df.sample(frac=1)\n",
 102 |     "df = df.sample(frac=1).reset_index(drop=True)\n",
 103 |     "df.head()"
 104 |    ]
 105 |   },
 106 |   {
 107 |    "cell_type": "code",
 108 |    "execution_count": 3,
 109 |    "metadata": {
 110 |     "collapsed": false,
 111 |     "deletable": true,
 112 |     "editable": true
 113 |    },
 114 |    "outputs": [
 115 |     {
 116 |      "data": {
 117 |       "text/plain": [
 118 |        "7030"
 119 |       ]
 120 |      },
 121 |      "execution_count": 3,
 122 |      "metadata": {},
 123 |      "output_type": "execute_result"
 124 |     }
 125 |    ],
 126 |    "source": [
 127 |     "len(df) #displaying 5 records"
 128 |    ]
 129 |   },
 130 |   {
 131 |    "cell_type": "code",
 132 |    "execution_count": 4,
 133 |    "metadata": {
 134 |     "collapsed": true,
 135 |     "deletable": true,
 136 |     "editable": true
 137 |    },
 138 |    "outputs": [],
 139 |    "source": [
 140 |     "#2016's top most suspicious TLD and words\n",
 141 |     "Suspicious_TLD=['zip','cricket','link','work','party','gq','kim','country','science','tk']\n",
 142 |     "Suspicious_Domain=['luckytime.co.kr','mattfoll.eu.interia.pl','trafficholder.com','dl.baixaki.com.br','bembed.redtube.comr','tags.expo9.exponential.com','deepspacer.com','funad.co.kr','trafficconverter.biz']\n",
 143 |     "#trend micro's top malicious domains "
 144 |    ]
 145 |   },
 146 |   {
 147 |    "cell_type": "markdown",
 148 |    "metadata": {},
 149 |    "source": [
 150 |     "Hackers can use long URL to hide the doubtful part in the address bar. For example: \n",
 151 |     "http://federmacedoadv.com.br/3f/aze/ab51e2e319e51502f416dbe46b773a5e/?cmd=_home&amp;dispatch=11004d58f5b74f8dc1e7c2e8dd4105e811004d58f5b74f8dc1e7c2e8dd4105e8@phishing.website.html\n",
 152 |     "\n"
 153 |    ]
 154 |   },
 155 |   {
 156 |    "cell_type": "code",
 157 |    "execution_count": 5,
 158 |    "metadata": {
 159 |     "collapsed": false,
 160 |     "deletable": true,
 161 |     "editable": true
 162 |    },
 163 |    "outputs": [],
 164 |    "source": [
 165 |     "# Method to count number of dots\n",
 166 |     "def countdots(url):  \n",
 167 |     "    return url.count('.')"
 168 |    ]
 169 |   },
 170 |   {
 171 |    "cell_type": "code",
 172 |    "execution_count": 6,
 173 |    "metadata": {
 174 |     "collapsed": false,
 175 |     "deletable": true,
 176 |     "editable": true
 177 |    },
 178 |    "outputs": [],
 179 |    "source": [
 180 |     "# Method to count number of delimeters\n",
 181 |     "def countdelim(url):\n",
 182 |     "    count = 0\n",
 183 |     "    delim=[';','_','?','=','&']\n",
 184 |     "    for each in url:\n",
 185 |     "        if each in delim:\n",
 186 |     "            count = count + 1\n",
 187 |     "    \n",
 188 |     "    return count"
 189 |    ]
 190 |   },
 191 |   {
 192 |    "cell_type": "markdown",
 193 |    "metadata": {},
 194 |    "source": [
 195 |     "If an IP address is used as an alternative of the domain name in the URL, such as “http://125.98.3.123/fake.html”, users can be sure that someone is trying to steal their personal information. Sometimes, the IP address is even transformed into hexadecimal code as shown in the following link “http://0x58.0xCC.0xCA.0x62/2/paypal.ca/index.html”. "
 196 |    ]
 197 |   },
 198 |   {
 199 |    "cell_type": "code",
 200 |    "execution_count": 7,
 201 |    "metadata": {
 202 |     "collapsed": false,
 203 |     "deletable": true,
 204 |     "editable": true
 205 |    },
 206 |    "outputs": [],
 207 |    "source": [
 208 |     "# Is IP addr present as th hostname, let's validate\n",
 209 |     "\n",
 210 |     "import ipaddress as ip #works only in python 3\n",
 211 |     "\n",
 212 |     "def isip(uri):\n",
 213 |     "    try:\n",
 214 |     "        if ip.ip_address(uri):\n",
 215 |     "            return 1\n",
 216 |     "    except:\n",
 217 |     "        return 0"
 218 |    ]
 219 |   },
 220 |   {
 221 |    "cell_type": "code",
 222 |    "execution_count": 8,
 223 |    "metadata": {
 224 |     "collapsed": false,
 225 |     "deletable": true,
 226 |     "editable": true
 227 |    },
 228 |    "outputs": [],
 229 |    "source": [
 230 |     "#method to check the presence of hyphens\n",
 231 |     "\n",
 232 |     "def isPresentHyphen(url):\n",
 233 |     "    return url.count('-')\n",
 234 |     "        "
 235 |    ]
 236 |   },
 237 |   {
 238 |    "cell_type": "markdown",
 239 |    "metadata": {},
 240 |    "source": [
 241 |     "Using “@” symbol in the URL leads the browser to ignore everything preceding the “@” symbol and the real address often follows the “@” symbol. "
 242 |    ]
 243 |   },
 244 |   {
 245 |    "cell_type": "code",
 246 |    "execution_count": 9,
 247 |    "metadata": {
 248 |     "collapsed": true,
 249 |     "deletable": true,
 250 |     "editable": true
 251 |    },
 252 |    "outputs": [],
 253 |    "source": [
 254 |     "#method to check the presence of @\n",
 255 |     "\n",
 256 |     "def isPresentAt(url):\n",
 257 |     "    return url.count('@')"
 258 |    ]
 259 |   },
 260 |   {
 261 |    "cell_type": "markdown",
 262 |    "metadata": {},
 263 |    "source": [
 264 |     "The existence of “//” within the URL path means that the user will be redirected to another website. An example of such URL’s is: “http://www.legitimate.com//http://www.phishing.com”. We examine the presence of “//”"
 265 |    ]
 266 |   },
 267 |   {
 268 |    "cell_type": "code",
 269 |    "execution_count": 10,
 270 |    "metadata": {
 271 |     "collapsed": true,
 272 |     "deletable": true,
 273 |     "editable": true
 274 |    },
 275 |    "outputs": [],
 276 |    "source": [
 277 |     "def isPresentDSlash(url):\n",
 278 |     "    return url.count('//')"
 279 |    ]
 280 |   },
 281 |   {
 282 |    "cell_type": "code",
 283 |    "execution_count": 11,
 284 |    "metadata": {
 285 |     "collapsed": true,
 286 |     "deletable": true,
 287 |     "editable": true
 288 |    },
 289 |    "outputs": [],
 290 |    "source": [
 291 |     "def countSubDir(url):\n",
 292 |     "    return url.count('/')"
 293 |    ]
 294 |   },
 295 |   {
 296 |    "cell_type": "code",
 297 |    "execution_count": 12,
 298 |    "metadata": {
 299 |     "collapsed": false,
 300 |     "deletable": true,
 301 |     "editable": true
 302 |    },
 303 |    "outputs": [],
 304 |    "source": [
 305 |     "def get_ext(url):\n",
 306 |     "    \"\"\"Return the filename extension from url, or ''.\"\"\"\n",
 307 |     "    \n",
 308 |     "    root, ext = splitext(url)\n",
 309 |     "    return ext"
 310 |    ]
 311 |   },
 312 |   {
 313 |    "cell_type": "code",
 314 |    "execution_count": 13,
 315 |    "metadata": {
 316 |     "collapsed": false,
 317 |     "deletable": true,
 318 |     "editable": true
 319 |    },
 320 |    "outputs": [],
 321 |    "source": [
 322 |     "def countSubDomain(subdomain):\n",
 323 |     "    if not subdomain:\n",
 324 |     "        return 0\n",
 325 |     "    else:\n",
 326 |     "        return len(subdomain.split('.'))"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": 14,
 332 |    "metadata": {
 333 |     "collapsed": false,
 334 |     "deletable": true,
 335 |     "editable": true
 336 |    },
 337 |    "outputs": [],
 338 |    "source": [
 339 |     "def countQueries(query):\n",
 340 |     "    if not query:\n",
 341 |     "        return 0\n",
 342 |     "    else:\n",
 343 |     "        return len(query.split('&'))"
 344 |    ]
 345 |   },
 346 |   {
 347 |    "cell_type": "code",
 348 |    "execution_count": 46,
 349 |    "metadata": {
 350 |     "collapsed": true,
 351 |     "deletable": true,
 352 |     "editable": true
 353 |    },
 354 |    "outputs": [],
 355 |    "source": [
 356 |     "'''\n",
 357 |     "featureSet = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\\\n",
 358 |     "'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\\\n",
 359 |     "'presence of suspicious domain','create_age(months)','expiry_age(months)','update_age(days)','country','file extension','label'))'''\n",
 360 |     "\n",
 361 |     "featureSet = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\\\n",
 362 |     "'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\\\n",
 363 |     "'presence of suspicious domain','label'))"
 364 |    ]
 365 |   },
 366 |   {
 367 |    "cell_type": "code",
 368 |    "execution_count": 45,
 369 |    "metadata": {
 370 |     "collapsed": false,
 371 |     "deletable": true,
 372 |     "editable": true
 373 |    },
 374 |    "outputs": [],
 375 |    "source": [
 376 |     "from urllib.parse import urlparse\n",
 377 |     "import tldextract\n",
 378 |     "def getFeatures(url, label): \n",
 379 |     "    result = []\n",
 380 |     "    url = str(url)\n",
 381 |     "    \n",
 382 |     "    #add the url to feature set\n",
 383 |     "    result.append(url)\n",
 384 |     "    \n",
 385 |     "    #parse the URL and extract the domain information\n",
 386 |     "    path = urlparse(url)\n",
 387 |     "    ext = tldextract.extract(url)\n",
 388 |     "    \n",
 389 |     "    #counting number of dots in subdomain    \n",
 390 |     "    result.append(countdots(ext.subdomain))\n",
 391 |     "    \n",
 392 |     "    #checking hyphen in domain   \n",
 393 |     "    result.append(isPresentHyphen(path.netloc))\n",
 394 |     "    \n",
 395 |     "    #length of URL    \n",
 396 |     "    result.append(len(url))\n",
 397 |     "    \n",
 398 |     "    #checking @ in the url    \n",
 399 |     "    result.append(isPresentAt(path.netloc))\n",
 400 |     "    \n",
 401 |     "    #checking presence of double slash    \n",
 402 |     "    result.append(isPresentDSlash(path.path))\n",
 403 |     "    \n",
 404 |     "    #Count number of subdir    \n",
 405 |     "    result.append(countSubDir(path.path))\n",
 406 |     "    \n",
 407 |     "    #number of sub domain    \n",
 408 |     "    result.append(countSubDomain(ext.subdomain))\n",
 409 |     "    \n",
 410 |     "    #length of domain name    \n",
 411 |     "    result.append(len(path.netloc))\n",
 412 |     "    \n",
 413 |     "    #count number of queries    \n",
 414 |     "    result.append(len(path.query))\n",
 415 |     "    \n",
 416 |     "    #Adding domain information\n",
 417 |     "    \n",
 418 |     "    #if IP address is being used as a URL     \n",
 419 |     "    result.append(isip(ext.domain))\n",
 420 |     "    \n",
 421 |     "    #presence of Suspicious_TLD\n",
 422 |     "    result.append(1 if ext.suffix in Suspicious_TLD else 0)\n",
 423 |     "    \n",
 424 |     "    #presence of suspicious domain\n",
 425 |     "    result.append(1 if '.'.join(ext[1:]) in Suspicious_Domain else 0 )\n",
 426 |     "     \n",
 427 |     "    '''\n",
 428 |     "      \n",
 429 |     "    #Get domain information by asking whois\n",
 430 |     "    domain = '.'.join(ext[1:])\n",
 431 |     "    w = whois.whois(domain)\n",
 432 |     "    \n",
 433 |     "    avg_month_time=365.2425/12.0\n",
 434 |     "    \n",
 435 |     "                  \n",
 436 |     "    #calculate creation age in months\n",
 437 |     "                  \n",
 438 |     "    if w.creation_date == None or type(w.creation_date) is str :\n",
 439 |     "        result.append(-1)\n",
 440 |     "        #training_df['create_age(months)'] = -1\n",
 441 |     "    else:\n",
 442 |     "        if(type(w.creation_date) is list): \n",
 443 |     "            create_date=w.creation_date[-1]\n",
 444 |     "        else:\n",
 445 |     "            create_date=w.creation_date\n",
 446 |     "\n",
 447 |     "        if(type(create_date) is datetime.datetime):\n",
 448 |     "            today_date=datetime.datetime.now()\n",
 449 |     "            create_age_in_mon=((today_date - create_date).days)/avg_month_time\n",
 450 |     "            create_age_in_mon=round(create_age_in_mon)\n",
 451 |     "            result.append(create_age_in_mon)\n",
 452 |     "            #training_df['create_age(months)'] = create_age_in_mon\n",
 453 |     "            \n",
 454 |     "        else:\n",
 455 |     "            result.append(-1)\n",
 456 |     "            #training_df['create_age(months)'] = -1\n",
 457 |     "    \n",
 458 |     "    #calculate expiry age in months\n",
 459 |     "                  \n",
 460 |     "    if(w.expiration_date==None or type(w.expiration_date) is str):\n",
 461 |     "        #training_df['expiry_age(months)'] = -1\n",
 462 |     "        result.append(-1)\n",
 463 |     "    else:\n",
 464 |     "        if(type(w.expiration_date) is list):\n",
 465 |     "            expiry_date=w.expiration_date[-1]\n",
 466 |     "        else:\n",
 467 |     "            expiry_date=w.expiration_date\n",
 468 |     "        if(type(expiry_date) is datetime.datetime):\n",
 469 |     "            today_date=datetime.datetime.now()\n",
 470 |     "            expiry_age_in_mon=((expiry_date - today_date).days)/avg_month_time\n",
 471 |     "            expiry_age_in_mon=round(expiry_age_in_mon)\n",
 472 |     "            #training_df['expiry_age(months)'] = expiry_age_in_mon\n",
 473 |     "            #### appending  in months Appended to the Vector\n",
 474 |     "            result.append(expiry_age_in_mon)\n",
 475 |     "        else:\n",
 476 |     "            #training_df['expiry_age(months)'] = -1\n",
 477 |     "            result.append(-1)#### expiry date error so append -1\n",
 478 |     "\n",
 479 |     "    #find the age of last update\n",
 480 |     "                  \n",
 481 |     "    if(w.updated_date==None or type(w.updated_date) is str):\n",
 482 |     "        #training_df['update_age(days)'] = -1\n",
 483 |     "        result.append(-1)\n",
 484 |     "    else:\n",
 485 |     "        if(type(w.updated_date) is list):\n",
 486 |     "            update_date=w.updated_date[-1]\n",
 487 |     "        else:\n",
 488 |     "            update_date=w.updated_date\n",
 489 |     "        if(type(update_date) is datetime.datetime):\n",
 490 |     "            today_date=datetime.datetime.now()\n",
 491 |     "            update_age_in_days=((today_date - update_date).days)\n",
 492 |     "            result.append(update_age_in_days)\n",
 493 |     "            #training_df['update_age(days)'] = update_age_in_days #### appending updated age in days Appended to the Vector\n",
 494 |     "        else:\n",
 495 |     "            result.append(-1)\n",
 496 |     "            #training_df['update_age(days)'] = -1\n",
 497 |     "    \n",
 498 |     "    #find the country who is hosting this domain\n",
 499 |     "    if(w.country == None):\n",
 500 |     "        #training_df['country'] = \"None\"\n",
 501 |     "        result.append(\"None\")\n",
 502 |     "    else:\n",
 503 |     "        #training_df['country'] = w.country\n",
 504 |     "        result.append(w.country)\n",
 505 |     "     ''' \n",
 506 |     "    \n",
 507 |     "    #result.append(get_ext(path.path))\n",
 508 |     "    result.append(str(label))\n",
 509 |     "    return result\n",
 510 |     "                  \n",
 511 |     "    #Yay! finally done!  \n",
 512 |     "\n",
 513 |     "\n"
 514 |    ]
 515 |   },
 516 |   {
 517 |    "cell_type": "markdown",
 518 |    "metadata": {},
 519 |    "source": [
 520 |     "a phishing website lives for a short period of time, we believe that trustworthy domains are regularly paid for several years in advance. But whois server takes few requests per day. So I commented out for now."
 521 |    ]
 522 |   },
 523 |   {
 524 |    "cell_type": "code",
 525 |    "execution_count": 47,
 526 |    "metadata": {
 527 |     "collapsed": false,
 528 |     "deletable": true,
 529 |     "editable": true
 530 |    },
 531 |    "outputs": [],
 532 |    "source": [
 533 |     "for i in range(len(df)):\n",
 534 |     "    features = getFeatures(df[\"URL\"].loc[i], df[\"Lable\"].loc[i])    \n",
 535 |     "    featureSet.loc[i] = features      "
 536 |    ]
 537 |   },
 538 |   {
 539 |    "cell_type": "code",
 540 |    "execution_count": 48,
 541 |    "metadata": {
 542 |     "collapsed": false,
 543 |     "deletable": true,
 544 |     "editable": true
 545 |    },
 546 |    "outputs": [
 547 |     {
 548 |      "data": {
 549 |       "text/html": [
 550 |        "<div>\n",
 551 |        "<table border=\"1\" class=\"dataframe\">\n",
 552 |        "  <thead>\n",
 553 |        "    <tr style=\"text-align: right;\">\n",
 554 |        "      <th></th>\n",
 555 |        "      <th>url</th>\n",
 556 |        "      <th>no of dots</th>\n",
 557 |        "      <th>presence of hyphen</th>\n",
 558 |        "      <th>len of url</th>\n",
 559 |        "      <th>presence of at</th>\n",
 560 |        "      <th>presence of double slash</th>\n",
 561 |        "      <th>no of subdir</th>\n",
 562 |        "      <th>no of subdomain</th>\n",
 563 |        "      <th>len of domain</th>\n",
 564 |        "      <th>no of queries</th>\n",
 565 |        "      <th>is IP</th>\n",
 566 |        "      <th>presence of Suspicious_TLD</th>\n",
 567 |        "      <th>presence of suspicious domain</th>\n",
 568 |        "      <th>label</th>\n",
 569 |        "    </tr>\n",
 570 |        "  </thead>\n",
 571 |        "  <tbody>\n",
 572 |        "    <tr>\n",
 573 |        "      <th>0</th>\n",
 574 |        "      <td>http://www.resumescorecard.com/</td>\n",
 575 |        "      <td>0.0</td>\n",
 576 |        "      <td>0.0</td>\n",
 577 |        "      <td>31.0</td>\n",
 578 |        "      <td>0.0</td>\n",
 579 |        "      <td>0.0</td>\n",
 580 |        "      <td>1.0</td>\n",
 581 |        "      <td>1.0</td>\n",
 582 |        "      <td>23.0</td>\n",
 583 |        "      <td>0.0</td>\n",
 584 |        "      <td>0.0</td>\n",
 585 |        "      <td>0.0</td>\n",
 586 |        "      <td>0.0</td>\n",
 587 |        "      <td>0</td>\n",
 588 |        "    </tr>\n",
 589 |        "    <tr>\n",
 590 |        "      <th>1</th>\n",
 591 |        "      <td>http://www.colorado.edu/MCEN/flowvis/</td>\n",
 592 |        "      <td>0.0</td>\n",
 593 |        "      <td>0.0</td>\n",
 594 |        "      <td>37.0</td>\n",
 595 |        "      <td>0.0</td>\n",
 596 |        "      <td>0.0</td>\n",
 597 |        "      <td>3.0</td>\n",
 598 |        "      <td>1.0</td>\n",
 599 |        "      <td>16.0</td>\n",
 600 |        "      <td>0.0</td>\n",
 601 |        "      <td>0.0</td>\n",
 602 |        "      <td>0.0</td>\n",
 603 |        "      <td>0.0</td>\n",
 604 |        "      <td>0</td>\n",
 605 |        "    </tr>\n",
 606 |        "    <tr>\n",
 607 |        "      <th>2</th>\n",
 608 |        "      <td>http://www.winestars.com/</td>\n",
 609 |        "      <td>0.0</td>\n",
 610 |        "      <td>0.0</td>\n",
 611 |        "      <td>25.0</td>\n",
 612 |        "      <td>0.0</td>\n",
 613 |        "      <td>0.0</td>\n",
 614 |        "      <td>1.0</td>\n",
 615 |        "      <td>1.0</td>\n",
 616 |        "      <td>17.0</td>\n",
 617 |        "      <td>0.0</td>\n",
 618 |        "      <td>0.0</td>\n",
 619 |        "      <td>0.0</td>\n",
 620 |        "      <td>0.0</td>\n",
 621 |        "      <td>0</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>3</th>\n",
 625 |        "      <td>http://eqworld.ipmnet.ru/</td>\n",
 626 |        "      <td>0.0</td>\n",
 627 |        "      <td>0.0</td>\n",
 628 |        "      <td>25.0</td>\n",
 629 |        "      <td>0.0</td>\n",
 630 |        "      <td>0.0</td>\n",
 631 |        "      <td>1.0</td>\n",
 632 |        "      <td>1.0</td>\n",
 633 |        "      <td>17.0</td>\n",
 634 |        "      <td>0.0</td>\n",
 635 |        "      <td>0.0</td>\n",
 636 |        "      <td>0.0</td>\n",
 637 |        "      <td>0.0</td>\n",
 638 |        "      <td>0</td>\n",
 639 |        "    </tr>\n",
 640 |        "    <tr>\n",
 641 |        "      <th>4</th>\n",
 642 |        "      <td>http://advods.com/verty/myaccount/webapps/973f6</td>\n",
 643 |        "      <td>0.0</td>\n",
 644 |        "      <td>0.0</td>\n",
 645 |        "      <td>47.0</td>\n",
 646 |        "      <td>0.0</td>\n",
 647 |        "      <td>0.0</td>\n",
 648 |        "      <td>4.0</td>\n",
 649 |        "      <td>0.0</td>\n",
 650 |        "      <td>10.0</td>\n",
 651 |        "      <td>0.0</td>\n",
 652 |        "      <td>0.0</td>\n",
 653 |        "      <td>0.0</td>\n",
 654 |        "      <td>0.0</td>\n",
 655 |        "      <td>1</td>\n",
 656 |        "    </tr>\n",
 657 |        "  </tbody>\n",
 658 |        "</table>\n",
 659 |        "</div>"
 660 |       ],
 661 |       "text/plain": [
 662 |        "                                               url  no of dots  \\\n",
 663 |        "0                  http://www.resumescorecard.com/         0.0   \n",
 664 |        "1            http://www.colorado.edu/MCEN/flowvis/         0.0   \n",
 665 |        "2                        http://www.winestars.com/         0.0   \n",
 666 |        "3                        http://eqworld.ipmnet.ru/         0.0   \n",
 667 |        "4  http://advods.com/verty/myaccount/webapps/973f6         0.0   \n",
 668 |        "\n",
 669 |        "   presence of hyphen  len of url  presence of at  presence of double slash  \\\n",
 670 |        "0                 0.0        31.0             0.0                       0.0   \n",
 671 |        "1                 0.0        37.0             0.0                       0.0   \n",
 672 |        "2                 0.0        25.0             0.0                       0.0   \n",
 673 |        "3                 0.0        25.0             0.0                       0.0   \n",
 674 |        "4                 0.0        47.0             0.0                       0.0   \n",
 675 |        "\n",
 676 |        "   no of subdir  no of subdomain  len of domain  no of queries  is IP  \\\n",
 677 |        "0           1.0              1.0           23.0            0.0    0.0   \n",
 678 |        "1           3.0              1.0           16.0            0.0    0.0   \n",
 679 |        "2           1.0              1.0           17.0            0.0    0.0   \n",
 680 |        "3           1.0              1.0           17.0            0.0    0.0   \n",
 681 |        "4           4.0              0.0           10.0            0.0    0.0   \n",
 682 |        "\n",
 683 |        "   presence of Suspicious_TLD  presence of suspicious domain label  \n",
 684 |        "0                         0.0                            0.0     0  \n",
 685 |        "1                         0.0                            0.0     0  \n",
 686 |        "2                         0.0                            0.0     0  \n",
 687 |        "3                         0.0                            0.0     0  \n",
 688 |        "4                         0.0                            0.0     1  "
 689 |       ]
 690 |      },
 691 |      "execution_count": 48,
 692 |      "metadata": {},
 693 |      "output_type": "execute_result"
 694 |     }
 695 |    ],
 696 |    "source": [
 697 |     "featureSet.head()"
 698 |    ]
 699 |   },
 700 |   {
 701 |    "cell_type": "code",
 702 |    "execution_count": 28,
 703 |    "metadata": {
 704 |     "collapsed": true,
 705 |     "deletable": true,
 706 |     "editable": true
 707 |    },
 708 |    "outputs": [],
 709 |    "source": [
 710 |     "import matplotlib.pyplot as plt\n",
 711 |     "import pandas as pd\n",
 712 |     "import seaborn as sns\n",
 713 |     "import pickle as pkl\n",
 714 |     "from __future__ import division"
 715 |    ]
 716 |   },
 717 |   {
 718 |    "cell_type": "markdown",
 719 |    "metadata": {},
 720 |    "source": [
 721 |     "Lets visualize the data"
 722 |    ]
 723 |   },
 724 |   {
 725 |    "cell_type": "markdown",
 726 |    "metadata": {},
 727 |    "source": [
 728 |     " URL length Distributions of both Malicious as well as Benign URLs. "
 729 |    ]
 730 |   },
 731 |   {
 732 |    "cell_type": "code",
 733 |    "execution_count": 29,
 734 |    "metadata": {
 735 |     "collapsed": false,
 736 |     "deletable": true,
 737 |     "editable": true
 738 |    },
 739 |    "outputs": [
 740 |     {
 741 |      "data": {
 742 |       "image/png": 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/dro43dpbl9ZUT2uqBVpXParFulpTPY1RS53hHBcXR2Fhof9xfn4+sbGxNR7L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\np10tCxcu5I477qjx2Lp167j44osJDg4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 743 |       "text/plain": [
 744 |        "<matplotlib.figure.Figure at 0x7f36aa382048>"
 745 |       ]
 746 |      },
 747 |      "metadata": {},
 748 |      "output_type": "display_data"
 749 |     }
 750 |    ],
 751 |    "source": [
 752 |     "sns.set(style=\"darkgrid\")\n",
 753 |     "sns.distplot(featureSet[featureSet['label']=='0']['len of url'],color='green',label='Benign URLs')\n",
 754 |     "sns.distplot(featureSet[featureSet['label']=='1']['len of url'],color='red',label='Phishing URLs')\n",
 755 |     "sns.plt.title('Url Length Distribution')\n",
 756 |     "plt.legend(loc='upper right')\n",
 757 |     "plt.xlabel('Length of URL')\n",
 758 |     "\n",
 759 |     "sns.plt.show()"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "markdown",
 764 |    "metadata": {},
 765 |    "source": [
 766 |     "Number of Dots Distributions of both Malicious as well as Benign URLs."
 767 |    ]
 768 |   },
 769 |   {
 770 |    "cell_type": "code",
 771 |    "execution_count": 30,
 772 |    "metadata": {
 773 |     "collapsed": false,
 774 |     "deletable": true,
 775 |     "editable": true
 776 |    },
 777 |    "outputs": [
 778 |     {
 779 |      "data": {
 780 |       "image/png": 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Tpw8HDhzA39+frKwse58TJ07g7++Pv78/mZmZABQUFGCz2co9mhYREZH/qTCo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\n",
 781 |       "text/plain": [
 782 |        "<matplotlib.figure.Figure at 0x7f369dbaa3c8>"
 783 |       ]
 784 |      },
 785 |      "metadata": {},
 786 |      "output_type": "display_data"
 787 |     }
 788 |    ],
 789 |    "source": [
 790 |     "x=featureSet[featureSet['label']=='0']['no of dots']\n",
 791 |     "y=featureSet[featureSet['label']=='1']['no of dots']\n",
 792 |     "plt.hist(x,bins=8, alpha=0.9, label='Benign URLs',color='blue')\n",
 793 |     "#sns.distplot(x,bins=8,color='blue',label='Benign URLs')\n",
 794 |     "plt.hist(y,bins=10, alpha=0.6, label='Malicious URLs',color='red')\n",
 795 |     "#sns.distplot(y,bins=8,color='red',label='Malicious URLs')\n",
 796 |     "plt.legend(loc='upper right')\n",
 797 |     "plt.xlabel('Number of Dots')\n",
 798 |     "plt.title('Distribution of Number of Dots in URL')\n",
 799 |     "plt.show()"
 800 |    ]
 801 |   },
 802 |   {
 803 |    "cell_type": "markdown",
 804 |    "metadata": {},
 805 |    "source": [
 806 |     "Domain length Distributions of both Malicious as well as Benign URLs."
 807 |    ]
 808 |   },
 809 |   {
 810 |    "cell_type": "code",
 811 |    "execution_count": 31,
 812 |    "metadata": {
 813 |     "collapsed": false,
 814 |     "deletable": true,
 815 |     "editable": true
 816 |    },
 817 |    "outputs": [
 818 |     {
 819 |      "data": {
 820 |       "image/png": 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q2hYRkeYUzlFSW9t2t3ZGhjV1p8JZREQOp3COklDLubVu7YwMtZxFRKRlCuco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\nYII=\n",
 821 |       "text/plain": [
 822 |        "<matplotlib.figure.Figure at 0x7f369dbaae80>"
 823 |       ]
 824 |      },
 825 |      "metadata": {},
 826 |      "output_type": "display_data"
 827 |     }
 828 |    ],
 829 |    "source": [
 830 |     "sns.set(style=\"darkgrid\")\n",
 831 |     "sns.distplot(featureSet[featureSet['label']=='0']['len of domain'],color='blue',label='Benign URLs')\n",
 832 |     "sns.distplot(featureSet[featureSet['label']=='1']['len of domain'],color='red',label='Malicious URLs')\n",
 833 |     "sns.plt.title('Domain Length Distribution')\n",
 834 |     "plt.legend(loc='upper right')\n",
 835 |     "plt.xlabel('Length of Domain/Host')\n",
 836 |     "sns.plt.show()"
 837 |    ]
 838 |   },
 839 |   {
 840 |    "cell_type": "code",
 841 |    "execution_count": 36,
 842 |    "metadata": {
 843 |     "collapsed": false,
 844 |     "deletable": true,
 845 |     "editable": true
 846 |    },
 847 |    "outputs": [
 848 |     {
 849 |      "data": {
 850 |       "text/plain": [
 851 |        "'\\nsns.set(style=\"whitegrid\")\\nsns.distplot(featureSet[featureSet[\\'label\\']==0][\\'create_age(months)\\'],color=\\'green\\',label=\\'Benign URLs\\')\\nsns.distplot(featureSet[featureSet[\\'label\\']==1][\\'create_age(months)\\'],color=\\'red\\',label=\\'Malicious URLs\\')\\nsns.plt.title(\\'Creation Age Distribution\\')\\nplt.legend(loc=\\'upper right\\')\\nplt.xlabel(\\'Age of Domain (Months)\\')\\nsns.plt.show()\\n'"
 852 |       ]
 853 |      },
 854 |      "execution_count": 36,
 855 |      "metadata": {},
 856 |      "output_type": "execute_result"
 857 |     }
 858 |    ],
 859 |    "source": [
 860 |     "'''\n",
 861 |     "sns.set(style=\"whitegrid\")\n",
 862 |     "sns.distplot(featureSet[featureSet['label']==0]['create_age(months)'],color='green',label='Benign URLs')\n",
 863 |     "sns.distplot(featureSet[featureSet['label']==1]['create_age(months)'],color='red',label='Malicious URLs')\n",
 864 |     "sns.plt.title('Creation Age Distribution')\n",
 865 |     "plt.legend(loc='upper right')\n",
 866 |     "plt.xlabel('Age of Domain (Months)')\n",
 867 |     "sns.plt.show()\n",
 868 |     "'''"
 869 |    ]
 870 |   },
 871 |   {
 872 |    "cell_type": "markdown",
 873 |    "metadata": {
 874 |     "deletable": true,
 875 |     "editable": true
 876 |    },
 877 |    "source": [
 878 |     "I can query minimal URL's per day using whois to get domain information. Waiting to get complete feature set, hence commented out"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": 39,
 884 |    "metadata": {
 885 |     "collapsed": false,
 886 |     "deletable": true,
 887 |     "editable": true
 888 |    },
 889 |    "outputs": [
 890 |     {
 891 |      "name": "stderr",
 892 |      "output_type": "stream",
 893 |      "text": [
 894 |       "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
 895 |       "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
 896 |      ]
 897 |     }
 898 |    ],
 899 |    "source": [
 900 |     "import sklearn.ensemble as ek\n",
 901 |     "from sklearn import cross_validation, tree, linear_model\n",
 902 |     "from sklearn.feature_selection import SelectFromModel\n",
 903 |     "from sklearn.externals import joblib\n",
 904 |     "from sklearn.naive_bayes import GaussianNB\n",
 905 |     "from sklearn.metrics import confusion_matrix\n",
 906 |     "from sklearn.pipeline import make_pipeline\n",
 907 |     "from sklearn import preprocessing\n",
 908 |     "from sklearn import svm\n",
 909 |     "from sklearn.linear_model import LogisticRegression\n"
 910 |    ]
 911 |   },
 912 |   {
 913 |    "cell_type": "markdown",
 914 |    "metadata": {
 915 |     "deletable": true,
 916 |     "editable": true
 917 |    },
 918 |    "source": [
 919 |     "Let's just see the number of Benign and Phishin/Malicious emails"
 920 |    ]
 921 |   },
 922 |   {
 923 |    "cell_type": "code",
 924 |    "execution_count": 40,
 925 |    "metadata": {
 926 |     "collapsed": false,
 927 |     "deletable": true,
 928 |     "editable": true
 929 |    },
 930 |    "outputs": [
 931 |     {
 932 |      "data": {
 933 |       "text/plain": [
 934 |        "label\n",
 935 |        "0    3494\n",
 936 |        "1    3536\n",
 937 |        "dtype: int64"
 938 |       ]
 939 |      },
 940 |      "execution_count": 40,
 941 |      "metadata": {},
 942 |      "output_type": "execute_result"
 943 |     }
 944 |    ],
 945 |    "source": [
 946 |     "featureSet.groupby(featureSet['label']).size()"
 947 |    ]
 948 |   },
 949 |   {
 950 |    "cell_type": "markdown",
 951 |    "metadata": {
 952 |     "deletable": true,
 953 |     "editable": true
 954 |    },
 955 |    "source": [
 956 |     "Separate label and feature set. It would have been nice if I had domain features too. "
 957 |    ]
 958 |   },
 959 |   {
 960 |    "cell_type": "code",
 961 |    "execution_count": 49,
 962 |    "metadata": {
 963 |     "collapsed": false,
 964 |     "deletable": true,
 965 |     "editable": true
 966 |    },
 967 |    "outputs": [],
 968 |    "source": [
 969 |     "X = featureSet.drop(['url','label'],axis=1).values\n",
 970 |     "y = featureSet['label'].values"
 971 |    ]
 972 |   },
 973 |   {
 974 |    "cell_type": "markdown",
 975 |    "metadata": {},
 976 |    "source": [
 977 |     "### Time for Machine Learning"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 50,
 983 |    "metadata": {
 984 |     "collapsed": true,
 985 |     "deletable": true,
 986 |     "editable": true
 987 |    },
 988 |    "outputs": [],
 989 |    "source": [
 990 |     "model = { \"DecisionTree\":tree.DecisionTreeClassifier(max_depth=10),\n",
 991 |     "         \"RandomForest\":ek.RandomForestClassifier(n_estimators=50),\n",
 992 |     "         \"Adaboost\":ek.AdaBoostClassifier(n_estimators=50),\n",
 993 |     "         \"GradientBoosting\":ek.GradientBoostingClassifier(n_estimators=50),\n",
 994 |     "         \"GNB\":GaussianNB(),\n",
 995 |     "         \"LogisticRegression\":LogisticRegression()   \n",
 996 |     "}"
 997 |    ]
 998 |   },
 999 |   {
1000 |    "cell_type": "markdown",
1001 |    "metadata": {
1002 |     "deletable": true,
1003 |     "editable": true
1004 |    },
1005 |    "source": [
1006 |     "##### cross validation"
1007 |    ]
1008 |   },
1009 |   {
1010 |    "cell_type": "code",
1011 |    "execution_count": 51,
1012 |    "metadata": {
1013 |     "collapsed": false,
1014 |     "deletable": true,
1015 |     "editable": true
1016 |    },
1017 |    "outputs": [],
1018 |    "source": [
1019 |     "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y ,test_size=0.2)"
1020 |    ]
1021 |   },
1022 |   {
1023 |    "cell_type": "code",
1024 |    "execution_count": 53,
1025 |    "metadata": {
1026 |     "collapsed": false,
1027 |     "deletable": true,
1028 |     "editable": true
1029 |    },
1030 |    "outputs": [
1031 |     {
1032 |      "name": "stdout",
1033 |      "output_type": "stream",
1034 |      "text": [
1035 |       "DecisionTree : 0.896159317212 \n",
1036 |       "RandomForest : 0.899715504979 \n",
1037 |       "LogisticRegression : 0.842816500711 \n",
1038 |       "GradientBoosting : 0.875533428165 \n",
1039 |       "Adaboost : 0.871977240398 \n",
1040 |       "GNB : 0.658605974395 \n"
1041 |      ]
1042 |     }
1043 |    ],
1044 |    "source": [
1045 |     "results = {}\n",
1046 |     "for algo in model:\n",
1047 |     "    clf = model[algo]\n",
1048 |     "    clf.fit(X_train,y_train)\n",
1049 |     "    score = clf.score(X_test,y_test)\n",
1050 |     "    print (\"%s : %s \" %(algo, score))\n",
1051 |     "    results[algo] = score"
1052 |    ]
1053 |   },
1054 |   {
1055 |    "cell_type": "code",
1056 |    "execution_count": 54,
1057 |    "metadata": {
1058 |     "collapsed": false,
1059 |     "deletable": true,
1060 |     "editable": true
1061 |    },
1062 |    "outputs": [
1063 |     {
1064 |      "name": "stdout",
1065 |      "output_type": "stream",
1066 |      "text": [
1067 |       "RandomForest\n"
1068 |      ]
1069 |     }
1070 |    ],
1071 |    "source": [
1072 |     "winner = max(results, key=results.get)\n",
1073 |     "print(winner)"
1074 |    ]
1075 |   },
1076 |   {
1077 |    "cell_type": "code",
1078 |    "execution_count": 55,
1079 |    "metadata": {
1080 |     "collapsed": false,
1081 |     "deletable": true,
1082 |     "editable": true
1083 |    },
1084 |    "outputs": [
1085 |     {
1086 |      "name": "stdout",
1087 |      "output_type": "stream",
1088 |      "text": [
1089 |       "False positive rate : 3.634803 %\n",
1090 |       "False negative rate : 4.779412 %\n"
1091 |      ]
1092 |     }
1093 |    ],
1094 |    "source": [
1095 |     "clf = model[winner]\n",
1096 |     "res = clf.predict(X)\n",
1097 |     "mt = confusion_matrix(y, res)\n",
1098 |     "print(\"False positive rate : %f %%\" % ((mt[0][1] / float(sum(mt[0])))*100))\n",
1099 |     "print('False negative rate : %f %%' % ( (mt[1][0] / float(sum(mt[1]))*100)))"
1100 |    ]
1101 |   },
1102 |   {
1103 |    "cell_type": "markdown",
1104 |    "metadata": {
1105 |     "deletable": true,
1106 |     "editable": true
1107 |    },
1108 |    "source": [
1109 |     "### Testing\n",
1110 |     "\n",
1111 |     "Let's test' some of the malicious URL's listed in Trend Micro's website. http://apac.trendmicro.com/apac/security-intelligence/current-threat-activity/malicious-top-ten/"
1112 |    ]
1113 |   },
1114 |   {
1115 |    "cell_type": "code",
1116 |    "execution_count": 52,
1117 |    "metadata": {
1118 |     "collapsed": false,
1119 |     "deletable": true,
1120 |     "editable": true
1121 |    },
1122 |    "outputs": [
1123 |     {
1124 |      "name": "stdout",
1125 |      "output_type": "stream",
1126 |      "text": [
1127 |       "['1']\n"
1128 |      ]
1129 |     }
1130 |    ],
1131 |    "source": [
1132 |     "result = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\\\n",
1133 |     "'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\\\n",
1134 |     "'presence of suspicious domain','label'))\n",
1135 |     "\n",
1136 |     "results = getFeatures('trafficconverter.biz:80/4vir/antispyware/loadadv.exe', '1')\n",
1137 |     "result.loc[0] = results\n",
1138 |     "result = result.drop(['url','label'],axis=1).values\n",
1139 |     "print(clf.predict(result))"
1140 |    ]
1141 |   },
1142 |   {
1143 |    "cell_type": "markdown",
1144 |    "metadata": {
1145 |     "deletable": true,
1146 |     "editable": true
1147 |    },
1148 |    "source": [
1149 |     "Success! One more time :) "
1150 |    ]
1151 |   },
1152 |   {
1153 |    "cell_type": "code",
1154 |    "execution_count": 56,
1155 |    "metadata": {
1156 |     "collapsed": false,
1157 |     "deletable": true,
1158 |     "editable": true
1159 |    },
1160 |    "outputs": [
1161 |     {
1162 |      "name": "stdout",
1163 |      "output_type": "stream",
1164 |      "text": [
1165 |       "['1']\n"
1166 |      ]
1167 |     }
1168 |    ],
1169 |    "source": [
1170 |     "result = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\\\n",
1171 |     "'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\\\n",
1172 |     "'presence of suspicious domain','label'))\n",
1173 |     "\n",
1174 |     "results = getFeatures('am10.ru:80/code.php', '1')\n",
1175 |     "result.loc[0] = results\n",
1176 |     "result = result.drop(['url','label'],axis=1).values\n",
1177 |     "print(clf.predict(result))"
1178 |    ]
1179 |   }
1180 |  ],
1181 |  "metadata": {
1182 |   "kernelspec": {
1183 |    "display_name": "Python 3",
1184 |    "language": "python",
1185 |    "name": "python3"
1186 |   },
1187 |   "language_info": {
1188 |    "codemirror_mode": {
1189 |     "name": "ipython",
1190 |     "version": 3
1191 |    },
1192 |    "file_extension": ".py",
1193 |    "mimetype": "text/x-python",
1194 |    "name": "python",
1195 |    "nbconvert_exporter": "python",
1196 |    "pygments_lexer": "ipython3",
1197 |    "version": "3.5.2"
1198 |   }
1199 |  },
1200 |  "nbformat": 4,
1201 |  "nbformat_minor": 0
1202 | }
1203 | 


--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | """
  3 | Created on Sun May 30 21:40:37 2021
  4 | 
  5 | @author: debanjan
  6 | """
  7 | 
  8 | 
  9 | from flask import Flask, render_template, request
 10 | import sklearn.ensemble as ek
 11 | import ipaddress as ip
 12 | import pandas as pd
 13 | from os.path import splitext
 14 | import tldextract
 15 | from urllib.parse import urlparse
 16 | from joblib import load
 17 | from sklearn import svm,tree
 18 | import pickle
 19 | 
 20 | app = Flask(__name__)
 21 | app.config['SECRET_KEY']='debanjan';
 22 | 
 23 | 
 24 | @app.route("/")
 25 | 
 26 | def home():
 27 |     return render_template("index.html")
 28 | 
 29 | Suspicious_TLD=['zip','cricket','link','work','party','gq','kim','country','science','tk']
 30 | Suspicious_Domain=['luckytime.co.kr','mattfoll.eu.interia.pl','trafficholder.com','dl.baixaki.com.br','bembed.redtube.comr','tags.expo9.exponential.com','deepspacer.com','funad.co.kr','trafficconverter.biz']
 31 | def countdots(url):  
 32 |     return url.count('.')
 33 | 
 34 | def countdelim(url):
 35 |     count = 0
 36 |     delim=[';','_','?','=','&']
 37 |     for each in url:
 38 |         if each in delim:
 39 |             count = count + 1
 40 |     
 41 |     return count
 42 | 
 43 | def isip(uri):
 44 |     try:
 45 |         if ip.ip_address(uri):
 46 |             return 1
 47 |     except:
 48 |         return 0
 49 |     
 50 | def isPresentHyphen(url):
 51 |     return url.count('-')
 52 | 
 53 | def isPresentAt(url):
 54 |     return url.count('@')
 55 | 
 56 | def isPresentDSlash(url):
 57 |     return url.count('//')
 58 | 
 59 | def countSubDir(url):
 60 |     return url.count('/')
 61 | def get_ext(url):
 62 |     """Return the filename extension from url, or ''."""
 63 |     
 64 |     root, ext = splitext(url)
 65 |     return ext
 66 | def countSubDomain(subdomain):
 67 |     if not subdomain:
 68 |         return 0
 69 |     else:
 70 |         return len(subdomain.split('.'))
 71 | def countQueries(query):
 72 |     if not query:
 73 |         return 0
 74 |     else:
 75 |         return len(query.split('&'))
 76 | featureSet = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\
 77 | 'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\
 78 | 'presence of suspicious domain','label'))
 79 |     
 80 | 
 81 | def getFeatures(url, label): 
 82 |     result = []
 83 |     url = str(url)
 84 |     
 85 |     #add the url to feature set
 86 |     result.append(url)
 87 |     
 88 |     #parse the URL and extract the domain information
 89 |     path = urlparse(url)
 90 |     ext = tldextract.extract(url)
 91 |     
 92 |     #counting number of dots in subdomain    
 93 |     result.append(countdots(ext.subdomain))
 94 |     
 95 |     #checking hyphen in domain   
 96 |     result.append(isPresentHyphen(path.netloc))
 97 |     
 98 |     #length of URL    
 99 |     result.append(len(url))
100 |     
101 |     #checking @ in the url    
102 |     result.append(isPresentAt(path.netloc))
103 |     
104 |     #checking presence of double slash    
105 |     result.append(isPresentDSlash(path.path))
106 |     
107 |     #Count number of subdir    
108 |     result.append(countSubDir(path.path))
109 |     
110 |     #number of sub domain    
111 |     result.append(countSubDomain(ext.subdomain))
112 |     
113 |     #length of domain name    
114 |     result.append(len(path.netloc))
115 |     
116 |     #count number of queries    
117 |     result.append(len(path.query))
118 |     
119 |     #Adding domain information
120 |     
121 |     #if IP address is being used as a URL     
122 |     result.append(isip(ext.domain))
123 |     
124 |     #presence of Suspicious_TLD
125 |     result.append(1 if ext.suffix in Suspicious_TLD else 0)
126 |     
127 |     #presence of suspicious domain
128 |     result.append(1 if '.'.join(ext[1:]) in Suspicious_Domain else 0 )
129 |    #result.append(get_ext(path.path))
130 |     result.append(str(label))
131 |     return result    
132 | 
133 | 
134 | 
135 | @app.route('/model', methods=['GET','POST'])
136 | def model():
137 |     url = request.form['search']
138 |     result = pd.DataFrame(columns=('url','no of dots','presence of hyphen','len of url','presence of at',\
139 |     'presence of double slash','no of subdir','no of subdomain','len of domain','no of queries','is IP','presence of Suspicious_TLD',\
140 |     'presence of suspicious domain','label'))
141 |     
142 |     results = getFeatures(url, '0')
143 |     
144 |     model_1=pickle.load(open('model_colab.pkl','rb'))
145 |     #model_1=load('model.joblib')
146 |     result.loc[0] = results
147 |     result = result.drop(['url','label'],axis=1).values
148 |     pos=0
149 |     neg=0
150 |     pred=model_1.predict(result)
151 |     if(pred=='0'):    
152 |         neg=neg+1
153 |     if(pred=='1'):
154 |         pos=pos+1
155 | 
156 |     if(pos>neg):    
157 |         res="URL seems to be safe"
158 |     else:
159 |         res="URL seems to be not safe"    
160 |     
161 |     return render_template("index.html",Result=res)
162 |     
163 | if __name__ == "__main__":
164 |     
165 |     
166 |     app.run()


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | Flask==1.1.1
 2 | gunicorn==19.9.0
 3 | itsdangerous==1.1.0
 4 | jinja2==2.10.1
 5 | MarkupSafe==1.1.1
 6 | Werkzeug==0.15.5
 7 | numpy>=1.9.2
 8 | scipy>=0.15.1
 9 | scikit-learn>=0.18
10 | matplotlib>=1.4.3
11 | pandas>=0.19
12 | tldextract==3.1.0
13 | 
14 | 


--------------------------------------------------------------------------------
/templates/index.html:
--------------------------------------------------------------------------------
 1 | <!DOCTYPE html>
 2 | <html lang="en">
 3 |     <head>    
 4 |     <title>Check URL</title>
 5 | 	<meta charset="UTF-8">
 6 | 	<meta name="viewport" content="width=device-width, initial-scale=1">    
 7 |     <link rel="stylesheet" type="text/css" href="vendor/bootstrap/css/bootstrap.min.css">
 8 |     <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/css/bootstrap.min.css">        
 9 |     </head>
10 |     <style>
11 |         h1{
12 |             text-align: center;
13 |             padding-top: 200px;
14 |             font-family:cursive;
15 |             color: #8B0000;
16 |             font-size: 50px;
17 |         }
18 |         input{
19 |             width: 60%;
20 |             font-size: 25px;
21 |             margin-top: 3%;
22 |             margin-left: 20%;
23 |             
24 |             text-align: center;
25 |         }
26 |         button{
27 |             margin-left: 45%;
28 |             margin-top: 4%;
29 |             width: 10%;
30 |             height: 10%;
31 |             padding-top: 1%;
32 |             padding-bottom: 1%;
33 |             border-radius: 25px;
34 |             background-color: darkred;
35 |             border-color: darkred;
36 |             color: aliceblue;
37 |         }
38 |         body{
39 |           background: linear-gradient(100deg, #000000 100%, #4169E1 90%);
40 |         }
41 |         h3{
42 |             margin-left: 45%;
43 |             color: gold;
44 |         }
45 |     </style>
46 |     <header>
47 |         
48 |     </header>
49 |     <body>
50 |         <h1>Check your URL here</h1>
51 |         <form action="{{ url_for('model') }}" method="post">
52 |             <input type="text" name="search" placeholder="Paste URL here">
53 |             <button  type="submit">Submit</button>
54 |             <h3 name="result">{{Result}}</h3>        
55 |         </form>
56 | 
57 |     </body>
58 |     <footer>
59 |     
60 |     </footer>
61 | </html>


--------------------------------------------------------------------------------