sql_injection/sql_injection.ipynb

{
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   "source": [
    "## SQL Injection Exercise\n",
    "\n",
    "<div style=\"float: right;\"><img src=\"files/images/sql_injection.gif\" width=\"300px\"></div>\n",
    "<font size=4>\"SQL injection is a code injection technique, used to attack data driven applications, in which malicious SQL statements areA SQL injection attack consists of insertion or \"injection\" of a SQL query via the input data from the client to the application. A successful SQL injection exploit can read sensitive data from the database, modify database data (Insert/Update/Delete), execute administration operations on the database (such as shutdown the DBMS), recover the content of a given file present on the DBMS file system and in some cases issue commands to the operating system. SQL injection attacks are a type of injection attack, in which SQL commands are injected into data-plane input in order to effect the execution of predefined SQL commands.\" -OWASP\n",
    "<br>\n",
    "<br><br>\n",
    "** All Code and IPython Notebooks for this talk http://clicksecurity.github.io/data_hacking **\n",
    "<br><br>\n",
    "Tools:\n",
    "<ul>\n",
    "  <li>sqlmap: Automatic SQL injection and database takeover tool (http://sqlmap.org)\n",
    "  <li>JBroFuzz (https://www.owasp.org/index.php/JBroFuzz)\n",
    "  <li>sqlparse (https://github.com/andialbrecht/sqlparse)\n",
    "  <br>\n",
    "  <br><br>\n",
    "  <li> IPython: A mad scientist notebook! (http://ipython.org)\n",
    "  <ul><li> What did you do?\n",
    "  <li> How did you do it?\n",
    "  <li> Can I repoduce it?\n",
    "  <li> Easy to share:\n",
    "  <ul>\n",
    "  <li>[NB Viewer](http://nbviewer.ipython.org)\n",
    "  <li>[Reddit IPython](http://www.reddit.com/r/ipython)\n",
    "  </ul>\n",
    "  </ul>\n",
    "  <br>\n",
    "  <li> Pandas: Python Data Analysis Library (http://pandas.pydata.org)\n",
    "  <ul>\n",
    "  <li> A fast and efficient DataFrame object\n",
    "  <li> Great set of IO Tools\n",
    "  <li> Fantastic handling of missing data\n",
    "  <li> Flexible reshaping and pivoting\n",
    "  <li> Slicing, indexing, and subsetting\n",
    "  </ul>\n",
    "</ul>\n",
    "\n",
    "Approach:\n",
    "<ul>\n",
    "  <li> Gather data (Thanks to Ray VanHoose for his assistance!)\n",
    "  <li> Exploration and Understanding\n",
    "  <li> Some Simple Statistics\n",
    "  <li> Feature Vectors\n",
    "  <li> Random Forest Machine Learning\n",
    "</ul>\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.19.0'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.feature_extraction\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.20.3'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.2'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.2.0'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import data_hacking\n",
    "data_hacking.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plotting defaults\n",
    "import matplotlib.pyplot as plt\n",
    "# 使用%matplotlib命令可以将matplotlib的图表直接嵌入到Notebook之中\n",
    "%matplotlib inline\n",
    "# ref: https://matplotlib.org/users/customizing.html#matplotlib-rcparams\n",
    "# matplotlib的全局设置参数\n",
    "plt.rcParams['font.size'] = 18.0 # The font.size property is the default font size for text, given in pts.\n",
    "plt.rcParams['figure.figsize'] = 12.0, 5.0 # figure size in inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A plotting helper method\n",
    "def plot_it(df,label_x,label_y):\n",
    "    fig, ax = plt.subplots(subplot_kw={'facecolor':'#EEEEE5'})\n",
    "    ax.grid(color='grey', linestyle='solid')\n",
    "    df.T.plot(kind='bar', logx=False, rot=0, ax=ax, colormap='PiYG')\n",
    "    ax.legend(loc=0, prop={'size':14})\n",
    "    plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)\n",
    "    plt.xlabel(label_x)\n",
    "    plt.ylabel(label_y)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "malicious    12892\n",
      "legit         1003\n",
      "Name: type, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>create user name identified by 'pass123'</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>create user name identified by pass123 tempora...</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>exec sp_addlogin 'name' , 'password'</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             raw_sql       type\n",
       "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious\n",
       "1           create user name identified by 'pass123'  malicious\n",
       "2  create user name identified by pass123 tempora...  malicious\n",
       "3               exec sp_addlogin 'name' , 'password'  malicious\n",
       "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in a set of SQL statements from various sources\n",
    "import os\n",
    "basedir = 'data'\n",
    "filelist = os.listdir(basedir) \n",
    "df_list = [] # DataFrame对象list\n",
    "for file in filelist:\n",
    "    # ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html\n",
    "    # 将csv文件数据读取到pandas框架的DataFrame对象\n",
    "    df = pd.read_csv(os.path.join(basedir,file), sep='\\r\\n', names=['raw_sql'], header=None, engine='python')\n",
    "    # 这里把合法('legit')SQL数据全部放在了一个独立文件中，文件命名为legit.txt\n",
    "    # 完成了数据标记过程\n",
    "    df['type'] = 'legit' if file.split('.')[0] == 'legit' else 'malicious'\n",
    "    df_list.append(df)\n",
    "\n",
    "# ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html\n",
    "# ignore_index设置为True表示合并后的pandas对象list采用0, ..., n - 1下标，忽略原始输入pandas对象的下标\n",
    "dataframe = pd.concat(df_list, ignore_index=True)\n",
    "# ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html\n",
    "# 缺失值处理，inplace=True表示就地删除包含None值的行，默认axis=0表示按行检查和处理，axis=1表示按列处理\n",
    "dataframe.dropna(inplace=True)\n",
    "# ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html\n",
    "# dataframe['type']的类型是<class 'pandas.core.series.Series'>\n",
    "print(dataframe['type'].value_counts())\n",
    "# 默认返回前5行数据 ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/huangwei/.pyenv/versions/3.6.2/bin/python\r\n"
     ]
    }
   ],
   "source": [
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the SQLParse module: sqlparse is a non-validating SQL parser module for Python\n",
    "# https://github.com/andialbrecht/sqlparse\n",
    "# pip install sqlparse\n",
    "import sqlparse\n",
    "def parse_it(raw_sql):\n",
    "    parsed = sqlparse.parse(raw_sql)\n",
    "    # ref: https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries\n",
    "    # 以下是非常pythonic的写法，学名是comprehension\n",
    "    return [token._get_repr_name() for parse in parsed for token in parse.tokens if token._get_repr_name() != 'Whitespace']\n",
    "\n",
    "# ref: https://www.python-course.eu/python3_lambda.php\n",
    "dataframe['parsed_sql'] = dataframe['raw_sql'].map(lambda x: parse_it(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比 [长亭科技的SQL注入检测方案](https://blog.chaitin.cn/sqlchop-the-sqli-detection-engine/) ，基于SQLParse的这里的SQL注入检测算法存在以下缺陷：\n",
    "\n",
    "* SQLParse不是一个基于SQL语法检查和解析引擎的分析器，因此对于一些SQL注入的代码混淆手段存在天然缺陷\n",
    "* （WAF的通病和挑战）缺少对后端被保护应用、数据库和业务的上下文理解。例如：\n",
    "    * 博客程序允许用户发送富文本数据，用户编写了一篇研究SQL注入技巧的文章\n",
    "    * 攻击者使用了一些针对特定数据库的SQL语法特性来构造攻击向量，但针对当前被保护数据库来说，这些攻击向量并不会达到攻击效果。因此，检测引擎基于当前应用环境会产生大量误报\n",
    "\n",
    "根据上述长亭科技的博客推荐，可以试试用[client9/libinjection](https://github.com/client9/libinjection/)来替换掉或补充到这里的SQLParse的角色和作用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "      <th>parsed_sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Float, Float, Float, Erro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>create user name identified by 'pass123'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>create user name identified by pass123 tempora...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>exec sp_addlogin 'name' , 'password'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             raw_sql       type  \\\n",
       "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
       "1           create user name identified by 'pass123'  malicious   \n",
       "2  create user name identified by pass123 tempora...  malicious   \n",
       "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
       "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
       "\n",
       "                                          parsed_sql  \n",
       "0  [Single, Identifier, Float, Float, Float, Erro...  \n",
       "1  [DDL, Keyword, Identifier, Keyword, Keyword, S...  \n",
       "2  [DDL, Keyword, Identifier, Keyword, Keyword, I...  \n",
       "3              [Keyword, Identifier, IdentifierList]  \n",
       "4              [Keyword, Identifier, IdentifierList]  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Looking at the SQL tokens is 'kinda' interesting but sequences of tokens and transitions\n",
    "# between tokens seems more meaningful so we're also going to compute sequences by\n",
    "# computing NGrams for every SQL statement...\n",
    "def ngrams(lst, N):\n",
    "    ngrams = []\n",
    "    for n in range(N):\n",
    "        # ref: https://docs.python.org/3/library/functions.html#zip\n",
    "        # python内置函数，zip将多个迭代器“对位”合并为一个新迭代器，合并后的新迭代器长度取决于最短的输入迭代器，超长部分会被截断舍弃\n",
    "        # 官方建议zip仅用于处理长度相等的迭代器合并，避免数据截断丢失\n",
    "        ngrams += zip(*(lst[i:] for i in range(n+1)))\n",
    "    return [str(tuple) for tuple in ngrams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataframe['sequences'] = dataframe['parsed_sql'].map(lambda x: ngrams(x, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We'd like to run some simple statistics to see what correlations the data might contain. Here we want to see if certain tokens or sets of transitions are indicative of malicious sql statements.\n",
    "\n",
    "> In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended.\n",
    "\n",
    "> G-tests是 似然率 或者 最大似然显著性统计检验。\n",
    "\n",
    "#### G-test is for goodness of fit to a distribution and for independence in contingency tables. It's related to chi-squared, multinomial and Fisher's exact test, please see http://en.wikipedia.org/wiki/G_test.\n",
    "\n",
    "#### 当一个属性(如性别)有两个(如男，女)或多个变量，而且对于属性变量有着理论的期望比值(如1：1)。G-test就是用于验证观测变量值是否符合理论期望的比值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Helper method\n",
    "def token_expansion(series, types):\n",
    "    _tokens, _types = zip(*[(token,token_type) for t_list,token_type in zip(series,types) for token in t_list])\n",
    "    return pd.Series(_tokens), pd.Series(_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [('Single',), ('Identifier',), ('Float',), ('F...\n",
       "1        [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "2        [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "3        [('Keyword',), ('Identifier',), ('IdentifierLi...\n",
       "4        [('Keyword',), ('Identifier',), ('IdentifierLi...\n",
       "5        [('DML',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "6        [('Keyword',), ('Keyword',), ('Keyword',), ('I...\n",
       "7        [('DML',), ('Keyword',), ('Function',), ('Keyw...\n",
       "8        [('DDL',), ('Keyword',), ('Keyword',), ('Ident...\n",
       "9        [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "10       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "11       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "12       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "13       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "14       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "15       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "16       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "17       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "18       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "19       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "20       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "21       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "22       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "23       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "24       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "25       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "26       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "27       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "28       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "29       [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
       "                               ...                        \n",
       "13865    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13866    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13867    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13868    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13869    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13870    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13871    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13872    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13873    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13874    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13875    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13876    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13877    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13878    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13879    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13880    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13881    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13882    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13883    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13884    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13885    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13886    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13887    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13888    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13889    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13890    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13891    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13892    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13893    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "13894    [('Identifier',), ('Identifier',), ('Identifie...\n",
       "Name: sequences, Length: 13895, dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['sequences']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>legit</th>\n",
       "      <th>malicious</th>\n",
       "      <th>legit_cd</th>\n",
       "      <th>malicious_cd</th>\n",
       "      <th>total_cd</th>\n",
       "      <th>legit_exp</th>\n",
       "      <th>legit_g</th>\n",
       "      <th>malicious_exp</th>\n",
       "      <th>malicious_g</th>\n",
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       "      <td>0.999454</td>\n",
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       "    <tr>\n",
       "      <th>('Identifier',)</th>\n",
       "      <td>1258.0</td>\n",
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       "      <td>0.069353</td>\n",
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       "      <td>-1196.183313</td>\n",
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       "      <td>152.0</td>\n",
       "      <td>7811.0</td>\n",
       "      <td>0.019088</td>\n",
       "      <td>0.980912</td>\n",
       "      <td>7963.0</td>\n",
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       "      <td>-536.733275</td>\n",
       "      <td>7074.574759</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>('Identifier', 'Single', 'Identifier')</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4162.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4162.0</td>\n",
       "      <td>464.350854</td>\n",
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       "      <th>('Identifier', 'Identifier')</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3956.0</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.999747</td>\n",
       "      <td>3957.0</td>\n",
       "      <td>441.479176</td>\n",
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       "      <td>2859.0</td>\n",
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       "      <td>2540.023764</td>\n",
       "      <td>674.194440</td>\n",
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      ],
      "text/plain": [
       "                                         legit  malicious  legit_cd  \\\n",
       "('Single',)                                6.0    10993.0  0.000546   \n",
       "('Single', 'Identifier')                   0.0     8310.0  0.000000   \n",
       "('Identifier',)                         1258.0    16881.0  0.069353   \n",
       "('Punctuation',)                         152.0     7811.0  0.019088   \n",
       "('Identifier', 'Single')                   1.0     4221.0  0.000237   \n",
       "('Single', 'Identifier', 'Single')         0.0     4170.0  0.000000   \n",
       "('Identifier', 'Single', 'Identifier')     0.0     4162.0  0.000000   \n",
       "('Identifier', 'Identifier')               1.0     3956.0  0.000253   \n",
       "('Keyword', 'DML', 'IdentifierList')      28.0     3157.0  0.008791   \n",
       "('Punctuation', 'Punctuation')             1.0     2858.0  0.000350   \n",
       "\n",
       "                                        malicious_cd  total_cd    legit_exp  \\\n",
       "('Single',)                                 0.999454   10999.0  1227.149219   \n",
       "('Single', 'Identifier')                    1.000000    8310.0   927.139741   \n",
       "('Identifier',)                             0.930647   18139.0  2023.753039   \n",
       "('Punctuation',)                            0.980912    7963.0   888.425241   \n",
       "('Identifier', 'Single')                    0.999763    4222.0   471.045004   \n",
       "('Single', 'Identifier', 'Single')          1.000000    4170.0   465.243408   \n",
       "('Identifier', 'Single', 'Identifier')      1.000000    4162.0   464.350854   \n",
       "('Identifier', 'Identifier')                0.999747    3957.0   441.479176   \n",
       "('Keyword', 'DML', 'IdentifierList')        0.991209    3185.0   355.347783   \n",
       "('Punctuation', 'Punctuation')              0.999650    2859.0   318.976236   \n",
       "\n",
       "                                            legit_g  malicious_exp  \\\n",
       "('Single',)                              -63.848275    9771.850781   \n",
       "('Single', 'Identifier')                   0.000000    7382.860259   \n",
       "('Identifier',)                        -1196.183313   16115.246961   \n",
       "('Punctuation',)                        -536.733275    7074.574759   \n",
       "('Identifier', 'Single')                 -12.309907    3750.954996   \n",
       "('Single', 'Identifier', 'Single')         0.000000    3704.756592   \n",
       "('Identifier', 'Single', 'Identifier')     0.000000    3697.649146   \n",
       "('Identifier', 'Identifier')             -12.180262    3515.520824   \n",
       "('Keyword', 'DML', 'IdentifierList')    -142.289978    2829.652217   \n",
       "('Punctuation', 'Punctuation')           -11.530233    2540.023764   \n",
       "\n",
       "                                        malicious_g  \n",
       "('Single',)                             2588.913574  \n",
       "('Single', 'Identifier')                1966.120676  \n",
       "('Identifier',)                         1567.329580  \n",
       "('Punctuation',)                        1546.978855  \n",
       "('Identifier', 'Single')                 996.675974  \n",
       "('Single', 'Identifier', 'Single')       986.609292  \n",
       "('Identifier', 'Single', 'Identifier')   984.716517  \n",
       "('Identifier', 'Identifier')             933.977797  \n",
       "('Keyword', 'DML', 'IdentifierList')     691.183458  \n",
       "('Punctuation', 'Punctuation')           674.194440  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data hacking repository has a simple stats module we're going to use\n",
    "import data_hacking.simple_stats as ss\n",
    "\n",
    "# Spin up our g_test class\n",
    "g_test = ss.GTest()\n",
    "\n",
    "# Here we'd like to see how various sql tokens and transitions are related.\n",
    "# Is there an association with particular token sets and malicious SQL statements.\n",
    "tokens, types = token_expansion(dataframe['sequences'], dataframe['type'])\n",
    "df_ct, df_cd, df_stats = g_test.highest_gtest_scores(tokens, types, matches=0, N=0)\n",
    "\n",
    "df_stats.sort_values('malicious_g', ascending=0).head(10)\n",
    "\n",
    "# The table below shows raw counts, conditional distributions, expected counts, and g-test score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Q1LSaAICAqlduy4dOnQiI+NT2/n09HRcXNxIS0ujffuO1K1bj1tuieHLL7de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\nGdMDcbVq1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HMH8NYofwMMAF7GW9fbhj9HXbPS737gKuBF4Aq/378D1fBGjtM6dyveqPZ7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\nEqZzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c417f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot the the head() and the tail() of the dataframe to see who's been naughty or nice\n",
    "sorted_df = df_stats.sort_values('malicious_g', ascending=0)\n",
    "naughty = sorted_df.head(7)\n",
    "nice = sorted_df.tail(7).sort_values('malicious_g', ascending=0)\n",
    "naughty_and_nice = pd.concat([naughty, nice])\n",
    "ax = plot_it(naughty_and_nice[['malicious_g']],'SQL Command Types','G-Test Scores')\n",
    "#ax.set_xlim(.2, 1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "      <th>parsed_sql</th>\n",
       "      <th>sequences</th>\n",
       "      <th>length</th>\n",
       "      <th>entropy</th>\n",
       "      <th>malicious_g</th>\n",
       "      <th>legit_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>create user name identified by pass123 tempora...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, I...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
       "      <td>13</td>\n",
       "      <td>4.028603</td>\n",
       "      <td>-534.768307</td>\n",
       "      <td>1770.669756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grant connect to name; grant resource to name;</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Keyword, Keyword, Identifier, Punctu...</td>\n",
       "      <td>[('Keyword',), ('Keyword',), ('Keyword',), ('I...</td>\n",
       "      <td>10</td>\n",
       "      <td>3.498879</td>\n",
       "      <td>-408.294453</td>\n",
       "      <td>1460.987448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>insert into users(login, password, level) valu...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DML, Keyword, Function, Keyword, Punctuation,...</td>\n",
       "      <td>[('DML',), ('Keyword',), ('Function',), ('Keyw...</td>\n",
       "      <td>6</td>\n",
       "      <td>4.387660</td>\n",
       "      <td>-298.329569</td>\n",
       "      <td>761.956282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CREATE FUNCTION sylph.add_article(char, char, ...</td>\n",
       "      <td>legit</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
       "      <td>9</td>\n",
       "      <td>4.948830</td>\n",
       "      <td>-43.285061</td>\n",
       "      <td>826.968141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CREATE FUNCTION sylph.add_comment(integer, cha...</td>\n",
       "      <td>legit</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
       "      <td>9</td>\n",
       "      <td>5.019598</td>\n",
       "      <td>-40.639876</td>\n",
       "      <td>821.782561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              raw_sql       type  \\\n",
       "2   create user name identified by pass123 tempora...  malicious   \n",
       "6      grant connect to name; grant resource to name;  malicious   \n",
       "7   insert into users(login, password, level) valu...  malicious   \n",
       "46  CREATE FUNCTION sylph.add_article(char, char, ...      legit   \n",
       "47  CREATE FUNCTION sylph.add_comment(integer, cha...      legit   \n",
       "\n",
       "                                           parsed_sql  \\\n",
       "2   [DDL, Keyword, Identifier, Keyword, Keyword, I...   \n",
       "6   [Keyword, Keyword, Keyword, Identifier, Punctu...   \n",
       "7   [DML, Keyword, Function, Keyword, Punctuation,...   \n",
       "46  [DDL, Keyword, Identifier, Keyword, Identifier...   \n",
       "47  [DDL, Keyword, Identifier, Keyword, Identifier...   \n",
       "\n",
       "                                            sequences  length   entropy  \\\n",
       "2   [('DDL',), ('Keyword',), ('Identifier',), ('Ke...      13  4.028603   \n",
       "6   [('Keyword',), ('Keyword',), ('Keyword',), ('I...      10  3.498879   \n",
       "7   [('DML',), ('Keyword',), ('Function',), ('Keyw...       6  4.387660   \n",
       "46  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       9  4.948830   \n",
       "47  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       9  5.019598   \n",
       "\n",
       "    malicious_g      legit_g  \n",
       "2   -534.768307  1770.669756  \n",
       "6   -408.294453  1460.987448  \n",
       "7   -298.329569   761.956282  \n",
       "46   -43.285061   826.968141  \n",
       "47   -40.639876   821.782561  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[dataframe['sequences'].map(lambda x: \"('Punctuation',)\" in x)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float: left; padding:10px;\"><img src=\"files/images/hilarious.jpg\" width=\"400px\"></div>\n",
    "<font size=4>\n",
    "The results above are a mixed bag of both 'legit' and 'malicious'. As we'd expect ('Punctuation') can't really be used as a 'signature' to effectively differentiate malicious sql statements. However, it's cool that the data transformation into the parsed tokens helps us generalize and find interesing malicious patterns. \n",
    "<br><br>So even though these individual features can't be used to differentiate.. when we build a 'feature vector' of a set of features, machine learning algorithms can use those vectors to build non-linear functional decision boundaries in multi-dimensional spaces (and we laugh at this point because it's hard to take yourself serious after saying a bunch of fancy stuff...). BTW the image is a total non sequitur.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "      <th>parsed_sql</th>\n",
       "      <th>sequences</th>\n",
       "      <th>length</th>\n",
       "      <th>entropy</th>\n",
       "      <th>malicious_g</th>\n",
       "      <th>legit_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Float, Float, Float, Erro...</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
       "      <td>7</td>\n",
       "      <td>4.368792</td>\n",
       "      <td>509.513630</td>\n",
       "      <td>-76.722266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>create user name identified by 'pass123'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, S...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
       "      <td>6</td>\n",
       "      <td>4.037326</td>\n",
       "      <td>-356.982564</td>\n",
       "      <td>1526.666886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>create user name identified by pass123 tempora...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, I...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
       "      <td>13</td>\n",
       "      <td>4.028603</td>\n",
       "      <td>-534.768307</td>\n",
       "      <td>1770.669756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>exec sp_addlogin 'name' , 'password'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "      <td>[('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.030493</td>\n",
       "      <td>-305.349103</td>\n",
       "      <td>1218.129498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "      <td>[('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.010013</td>\n",
       "      <td>-305.349103</td>\n",
       "      <td>1218.129498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             raw_sql       type  \\\n",
       "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
       "1           create user name identified by 'pass123'  malicious   \n",
       "2  create user name identified by pass123 tempora...  malicious   \n",
       "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
       "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
       "\n",
       "                                          parsed_sql  \\\n",
       "0  [Single, Identifier, Float, Float, Float, Erro...   \n",
       "1  [DDL, Keyword, Identifier, Keyword, Keyword, S...   \n",
       "2  [DDL, Keyword, Identifier, Keyword, Keyword, I...   \n",
       "3              [Keyword, Identifier, IdentifierList]   \n",
       "4              [Keyword, Identifier, IdentifierList]   \n",
       "\n",
       "                                           sequences  length   entropy  \\\n",
       "0  [('Single',), ('Identifier',), ('Float',), ('F...       7  4.368792   \n",
       "1  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       6  4.037326   \n",
       "2  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...      13  4.028603   \n",
       "3  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.030493   \n",
       "4  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.010013   \n",
       "\n",
       "   malicious_g      legit_g  \n",
       "0   509.513630   -76.722266  \n",
       "1  -356.982564  1526.666886  \n",
       "2  -534.768307  1770.669756  \n",
       "3  -305.349103  1218.129498  \n",
       "4  -305.349103  1218.129498  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating additional feature dimensions for the machine learning to expand its mind into...\n",
    "# We're basically building up features to include into our 'feature vector' for ML\n",
    "import math\n",
    "from collections import Counter\n",
    "def entropy(s):\n",
    "    p, lns = Counter(s), float(len(s))\n",
    "    return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\n",
    "dataframe['length'] = dataframe['parsed_sql'].map(lambda x: len(x))\n",
    "dataframe['entropy'] = dataframe['raw_sql'].map(lambda x: entropy(x))\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For each SQL statement aggregate the malicious and legit g-test scores as features\n",
    "import numpy as np\n",
    "def g_aggregate(sequence, name):\n",
    "    try:\n",
    "        g_scores = [df_stats.loc[item][name] for item in sequence]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "    return sum(g_scores)/len(g_scores) if g_scores else 0 # Average\n",
    "dataframe['malicious_g'] = dataframe['sequences'].map(lambda x: g_aggregate(x, 'malicious_g'))\n",
    "dataframe['legit_g'] = dataframe['sequences'].map(lambda x: g_aggregate(x, 'legit_g'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Float, Float, Float, Erro...</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>create user name identified by 'pass123'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, S...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
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       "      <td>-356.982564</td>\n",
       "      <td>1526.666886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>create user name identified by pass123 tempora...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[DDL, Keyword, Identifier, Keyword, Keyword, I...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
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       "      <td>4.028603</td>\n",
       "      <td>-534.768307</td>\n",
       "      <td>1770.669756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>exec sp_addlogin 'name' , 'password'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "      <td>[('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.030493</td>\n",
       "      <td>-305.349103</td>\n",
       "      <td>1218.129498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Keyword, Identifier, IdentifierList]</td>\n",
       "      <td>[('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.010013</td>\n",
       "      <td>-305.349103</td>\n",
       "      <td>1218.129498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             raw_sql       type  \\\n",
       "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
       "1           create user name identified by 'pass123'  malicious   \n",
       "2  create user name identified by pass123 tempora...  malicious   \n",
       "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
       "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
       "\n",
       "                                          parsed_sql  \\\n",
       "0  [Single, Identifier, Float, Float, Float, Erro...   \n",
       "1  [DDL, Keyword, Identifier, Keyword, Keyword, S...   \n",
       "2  [DDL, Keyword, Identifier, Keyword, Keyword, I...   \n",
       "3              [Keyword, Identifier, IdentifierList]   \n",
       "4              [Keyword, Identifier, IdentifierList]   \n",
       "\n",
       "                                           sequences  length   entropy  \\\n",
       "0  [('Single',), ('Identifier',), ('Float',), ('F...       7  4.368792   \n",
       "1  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       6  4.037326   \n",
       "2  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...      13  4.028603   \n",
       "3  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.030493   \n",
       "4  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.010013   \n",
       "\n",
       "   malicious_g      legit_g  \n",
       "0   509.513630   -76.722266  \n",
       "1  -356.982564  1526.666886  \n",
       "2  -534.768307  1770.669756  \n",
       "3  -305.349103  1218.129498  \n",
       "4  -305.349103  1218.129498  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1167de358>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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44YY2RyRJkiYLE3xpgjnggAO4+OKLWbBgAd/73vdYsGABF198MQcccEC7Q5Mk\nSZPAiBP8iPhoRMweon+3iPhoc8KSpq5LLrmEI444gtNPP53NN9+c008/nSOOOIJLLrmk3aFJkqRJ\noJEj+McDLxiifzZw3JiikURmcvLJJ2/QdvLJJ5PpNeckSdLwmjlFZwbwaBO3J01JEcExxxyzQdsx\nxxxDRLQpIkmSNJkMWUUnIp4MPKWqaauI2KHO0C2BtwG3NDE2aUrab7/9OOOMMwB47Wtfy3vf+17O\nOOMM9t9//zZHJkmSJoPhymR+CKjMq0/gM+VSTwD/0aS4pCnroosu4oADDmDJkiWcccYZRAT7778/\nF110UbtDkyRJk8BwCX5feRsUif75wO9qxiSwGrgyM1c0NTppiqok8319fXR3d7c3GEmSNKkMmeBn\n5k+AnwBExLOAJZn58/EITJIkSVLjRnwl28x8RysDkSRJkjR2I07wKyJiF2AXYCuKqTsbyMyvNiEu\nSZIkSaMw4gQ/ImYCZwP7VZrqDEvABF+SJElqk0aO4P83RXJ/BvBj4O6WRCRJkiRp1BpJ8PejOMn2\n/a0KRpIkSdLYNHIl2ycAv21VIJIG9Pb2Mnv2bObNm8fs2bPp7e1td0iSJGmSaOQI/uXAC1sViKRC\nb28vixYtYunSpTz22GNMmzaNnp4eAObPn9/m6CRJ0kTXyBH8I4GDI+KfWhWMJFi8eDFLly5l7ty5\nbLTRRsydO5elS5eyePHidocmSZImgUaO4J9BccXab0fEbcAfgcdqxmRmzmtWcNJU1N/fz5w5czZo\nmzNnDv39/W2KSJIkTSaNHMHfCXgicDPwKLADsGPNslMjO4+I50TECRFxZUTcGRF/j4jfRMSiiNis\nzvjnRsQFEfG3iHggIi6PiFc1sk9pouvq6mL58uUbtC1fvpyurq42RSRJkiaTRq5kO6sF+38n8D7g\nu8DXgUeAucCJwD9HxF6Z+RBARDwbWEHx5eITwH3A4cBFEfEPmXlpC+KTxt2iRYvo6elZPwd/2bJl\n9PT0OEVHkiSNSMNXsm2y84CTM/O+qrYlEXEjsAjooai/D3Ay8BTgpZn5G4CI+CpwHfD5iHheZub4\nhS61RuVE2oULF9Lf309XVxeLFy/2BFtJkjQijUzRASAiZkXEu8ppNLPKto0jYoeI2LiRbWXmVTXJ\nfcW3ytvZ5fY3Aw4E+irJfbn+auCLwHOAlzX6WKSJav78+Vx77bVcdtllXHvttSb3kiRpxBpK8CPi\nFOBG4Ewn4b1vAAAgAElEQVTgBAbm3M8Argfe26S4nlne3lHevgCYDvysztgry1sTfEmSJE15I07w\nI+I9wP8DPg/sD0SlLzPvp5hH/49jDSgipgEfoZhr/42yebvy9s91Vqm0PWOs+5YkSZImu0bm4L8X\nOD8zPxgRW9Xp/x3w/ibE9Blgb+DYzPxD2bZpebu2zvg1NWM2EBHvBt4NMHPmTPr6+poQojQ+Vq9e\n7XtWklrEz1h1qkYS/OdQ1MIfzJ3A1mMJJiL+k+JLwpmZeXJV14Pl7fQ6q82oGbOBzDyTYkoRu+++\ne3Z3d48lRGlc9fX14XtWklrDz1h1qkbm4K8BHlebvsqzgHtHG0hEHA98GPgysKCm+7bytt40nEpb\nvek7kiRJ0pTSSIL/C+Dgeh0RMQM4BLhiNEGUyf1xwNnAu+qUu7yGYnrO3nVW36u8vWo0+5YkSZI6\nSSMJ/ieBvSPiHIqqNgDbRsQBQB9F5ZtPNRpARHyUIrk/B3hnZq6rHVOWw/we0B0RL6xad3PgXRSV\nfX7R6L6liaq3t5fZs2czb948Zs+eTW9vb7tDkiRJk0QjV7K9NCKOAD4LvLVsPqe8fRg4PDPrlbEc\nVES8D/gYcDNwKfDWiKgeckdmXlL+fQwwD7g4Iv4LuJ/iSrbPAF7nRa7UKXp7e1m0aNH6K9lOmzaN\nnp4eAOvhS5KkYTV0JdvMPDMivgu8GXgeRanMG4FvZ+Zo5sBXatfvQDE9p9ZPgEvKfa+MiH2BjwNH\nAxsDvwJek5mXjmLf0oS0ePFili5dyty5c9efALZ06VIWLlxogi9JkobVUIIPkJm3A6c1Y+eZeRhw\nWAPj+4E3NGPf0kTV39/PnDlzNmibM2cO/f39bYpIkiRNJg1dyVZS63V1dbF8+fIN2pYvX05XV1eb\nIpIkSZNJQ0fwI2If4H3ALsBWVF3NtpSZ+ewmxSZNSYsWLaKnp2f9HPxly5bR09PD4sWL2x2aJEma\nBEac4EfE4cASihNq/0BxYqykJqvMs1+4cCH9/f10dXWxePFi599LkqQRaeQI/rHAb4ADMvOuFsUj\nCVixYgUrV65k3bp1rFy5khUrVpjgS5KkEWkkwZ8JfNLkXmqthQsXsmTJEk455RR23XVXrr/+eo46\n6igATjutKee3S5KkDtbISbb9wFNbFYikwllnncUpp5zCkUceyYwZMzjyyCM55ZRTOOuss9odmiRJ\nmgQaSfAXA++NiO1aFYwkWLt2LQsWLNigbcGCBaxdu7ZNEUmSpMmkkSvZficiNgWuj4gLgVXAY48f\nlv/ZxPikKWf69OksWbKEI488cn3bkiVLmD59ehujkiRJk0UjVXSeA5wAPBk4ZJBhCZjgS2Nw+OGH\nr59zv+uuu3Lqqady1FFHPe6oviRJUj2NnGR7OvA04APA5cDfWhKRNMVVTqQ99thjWbt2LdOnT2fB\nggWeYCtJkkakkTn4ewOfyszTMvM3mfmnekurApWmkgsvvHD9nPu1a9dy4YUXtjkiSeocCxcuZMaM\nGcydO5cZM2awcOHCdockNVUjCf59wJ2tCkRSYYcdduCWW25hn3324dxzz2WfffbhlltuYYcddmh3\naJI06VVKEZ900kn88Ic/5KSTTmLJkiUm+eoojST43wbe2KpAJBUqyf0VV1zB1ltvzRVXXLE+yZck\njY2liDUVNJLgfwF4UkRcEBGviogdI2KH2qVVgUpTyXnnnTfkfUnS6FiKWFNBIwn+dcBLgQOBS4CV\nwE11Fklj9KY3vWnI+5Kk0amUIq5mKWJ1mkaq6JxAUQZTUgttv/32rFixgn333ZcPfehD7LvvvqxY\nsYLtt9++3aFJ0qRnKWJNBZE5dXL23XffPa+66qp2hyENq3KibcX222/PzTff3MaIJKlzLFy4kLPO\nOmt9KeLDDz/cUsSaFCLi6szcfdhxJvjSxLP55pvzwAMPrL+/2WabsXr16jZGJEmdY8aMGRvMuZ8+\nfTpr1qxpY0TSyIw0wW9kDj4R8aSI+GhELI+IGyNi77J967L9eaMNWFKhktzPmjWLc845h1mzZvHA\nAw+w+eabtzs0SZr0Ksn9zJkz+fKXv8zMmTNZu3YtM2bMaHdoUtOMOMGPiG2Aq4CPAFsBOwGbAGTm\nXcDbgXe3IEZpSqkk9zfddBPPfOYzuemmm9Yn+ZKksakk97fffjuzZs3i9ttvX5/kS52ikSP4JwLb\nAnsCLweipv9CYF6T4pKmtEsvvXTI+5Kk0evr6xvyvjTZNZLgvx44PTN/Rf1qOn8ELPMhNcGrX/3q\nIe9Lkkavu7t7yPvSZNdIgr81Re37wawDnMAmjdFmm23GqlWr2HHHHbn11lvZcccdWbVqFZtttlm7\nQ5OkSW/69OnccccdbLvttqxatYptt92WO+64wzr46iiN1MG/HXj2EP0vBqzjJ43R6tWr2XzzzVm1\nahWHHHIIYBUdSWqWNWvWMGPGDO644w7e8Y53AFbRUedp5Aj+D4CeiHh6bUdE7AkcSjEPX9IYbbnl\nlkPelySNXu0vov5Cqk7TSIL/MeBR4NfAyRTz8N8eEb3AT4HbgFOaHqE0xVQucrXPPvtw7rnnss8+\n+3DLLbewww47tDs0SZr0ttpqK+655x522203ent72W233bjnnnvYaqut2h2a1DQjTvAz83ZgL+Dn\nwDspqugcAvwzcDHw8sy8pxVBSlNJJbm/4oor2HrrrbniiivWJ/mSpLGpJPfXXnst2267Lddee+36\nJF/qFA1d6Cozb8nMNwBbUpTL3AvYJjP/MTNvbUWA0lR03nnnDXlfkjR6P/jBD4a8L012jVzo6tCI\nmAWQmfdn5i8z8xeVo/YRMSsiDm1NmNLU8qY3vWnI+5Kk0Xvta1875H1psmvkCP6XgX2G6N+zHCNp\nDLbffntWrFjBvvvuy1133cW+++7LihUr2H57LzMhSWO15ZZbct111zF79mxuv/12Zs+ezXXXXWcx\nA3WURspk1l65ttYTKWrhSxqDm2++mR122IEVK1awYsUKoEj6b77ZKrSSNFZ33303W221Fddddx3z\n588HiqT/7rvvbnNkUvM0NAef+lewJSKeArwO+MuYI5LEzTffTGaybNkyMtPkXpKaqPaEWk+wVacZ\nMsGPiOMi4rGIeIwiuf9a5X71AtxNUU3nm+MQsyRJ0qhEDExI2Hnnneu2S5PdcFN0fgN8lWJ6zqHA\n5cAfa8YksBq4EuhtdoCSJEnNlpn09fXR3d1tcq+OM2SCn5kXUl6dNiKeBZyYmZeNR2BSpxnP/0Ay\n686mkyQBL3nJSx53/1e/+lWbopGar5ELXc01uZdGLzMbXp511P+Oaj1J0uBqk3mTe3WaRqrorBcR\nmwNPoc4XhMz0bEBJkjShRQQ777wzK1eubHcoUtM1lOBHxFuADwNdQwybNqaIJEmSWiQz10+ZrE7u\n/fVTnaSRK9keBHyD4kvBFyhOvO0FzgUeAa4GTmhBjJIkSZJGqJE6+P8O9AMvAj5atn0pM98C7A48\nl6LqjiRJ0oRUXfDgoIMOqtsuTXaNJPgvAM7OzDUMXLF2GkBmXgucCRzT3PAkSZKaLzP5wAc+4NQc\ndaRGEvxpFBe0AniovN2iqv8PwOxmBCVJktQq//qv/zrkfWmyayTBvxV4FkBmPgT8FXhpVf9zgQea\nF5okSVLzfe5znxvyvjTZNVJFZwXwagbm338X+GBEPETxReF9wPeaG54kSVLzRQQHHXQQc+fObXco\nUtM1kuCfDhwcEZuUR/AXAXsAx5f911GciCtJkjQhVZfJvOCCCzZolzrFiBP8zPwl8Muq+3cCL4qI\nFwCPAf2ZuW6w9SVJkiS1XiN18F8REdvUtmfm7zLzOmDLiHhFU6OTJElqoupymHvssUfddmmya+Qk\n22XAfkP0zyvHSJIkTWiZySmnnOLUHHWkRhL84b7aTmOgPr4kSdKEdPDBBw95X5rsGknwAYb6mrsP\ncNcYYpEkSWq5888/f8j70mQ35Em2EfEB4ANVTZ+JiMV1hj4VeDLwpSbGJkmS1BIRwR577MEvfvGL\ndociNd1wVXTuBf5U/j2L4kq2d9SMSeBa4Ergv5oZnCRJUjNVl8msTu6di69OMmSCn5lnA2cDRMRN\nwNGZ+d3xCEySJElS4xqpg79jKwORJElqtcHKYUaER/HVMRo9yXa9iNgpIo6NiM9HxPsiYpNmBiZJ\nktQqmcmyZctM6tWRhjvJtgf4V2C/zPxrVft+wHeATSnKZybwnojYJzNXtzBeSZIkSUMY7gj+64G/\n1yT3AXyBIrk/GTgQ+AowG/hQa8KUJEmSNBLDJfgvBJbXtO1DUVHnnMz8cGb+b2b2UFzF9qBGdh4R\nx0TEuRHxx4jIiFg1zPg9I+LSiPh7RNwfET+KiBc1sk9JkqSIYO7cuYPOyZcms+ES/G2AP9a07Usx\nJefbNe0/AHZucP8nAa8C/g/421ADI2Iv4CfAjsBHgeOAXYDLI+L5De5XkiRNQYPNuXcuvjrJcFV0\nHgU2rml7WXn7s5r2u4HpDe7/2Zn5R4CIuBbYfIixnwMeBl6RmX8u1/k20A98Gti/wX1LkiRJHWe4\nI/irKKbkABAR04CXAzdmZu0R962AuxrZeSW5H05E7EzxxeLcSnJfrv9n4Fzg1RGxbSP7liRJU89Q\nZTKlTjFcgv8/wJsi4v0RsSvwcYppO9+pM3YP4KYmx1cx2K8GUFxBN4CXtmjfkiSpw1gmU51suCk6\nnwMOBT5b3g/gFoopMetFxBbA64BTmx1gabvy9s91+iptz6i3YkS8G3g3wMyZM+nr62t6cFIr+Z6V\npObr6+tj9erVG3zG+nmrTjFkgp+Z90fESykS5J0pTob9YmbeWzO0C/gy8M2WRFmU5ARYW6dvTc2Y\nDWTmmcCZALvvvnt2d3c3PTipZX70fXzPSlLzdXd309fXt8FnrJ+36hTDHcEnM/9OzRH7OmOupJgq\n0yoPlrf1TuKdUTNGkiRpSM65Vycbbg7+RHFbeVtvGk6lrd70HUmSpPUsk6mpYLIk+L8sb/eu07cX\nRV3+q8cvHEmSNFll5gYn2Zrcq9NMigQ/M1cCVwFvjojKCbeUf78Z+HFm3t6u+CRJkqSJYtg5+K0U\nEYcAzyrvbgNsHBEfLu//KTPPqRr+AWAZxZVrTyvbFlJ8Sfm38YhXkiRJmujamuADPcAra9r+s7z9\nCbA+wc/MFRHRDZxYLgmsAN6cmb9tfaiSJGmiGs+TZp3So4muaQl+RDwPeEFmfnuk62RmdyP7yMyf\nAfMaDE2SJHW40STds47+Pqs+/roWRCO1VzPn4P8T0NvE7UmSJElq0KQ4yVaSJEnSyJjgS5IkSR3E\nBF+SJEnqICb4kiRJUgdpd5lMaVJ64ccu5r6HHhmXfc06+vst38cWmzyR3x63f8v3I0mSWm/IBD8i\n/k5Rb34knjj2cKTJ4b6HHhmX0mp9fX10d3e3fD/j8SVCkiSNj+GO4F/NyBN8SZIkSW02ZILf6IWo\nJEmSJLWXJ9lKkiRJHWTEJ9lGxEbAXsALgCcBdwArM3N5i2KTJEmS1KARJfgRcQhwErBdpYlybn5E\nrAI+mplfrxo/PTPXNjdUSZIkScMZNsGPiI8BHwYeBHopTry9H3gy8FLgQOCrEbFjZp4YEZsBFwKv\nblnUkiRJkuoarkzmHOAjwKXA2zLzzjpjtga+BhwXEVcCxwN7Nj9USZIkScMZ7gj+B4CbgAMzc029\nAZl5V0QcBFwH/AhYB7yzqVFKkiRJGpHhqujsA3x1sOS+ouw/h2Ju/tsy85wmxSdJkiSpAcMl+FsD\nfxrhtlYB6zLz3DFFJEmSJGnUhkvw7wWePsJtPR24Z2zhSJIkSRqL4RL8XwJvjYghx5X988vxkiRJ\nktpkuAT/LGA34IsRsXG9ARHxxKpxZzY3PEmSJEmNGLKKTmZeGBFfAw4DuiPiHODXwH3AFsBLgH8B\nZgHnZOZ3WxqtJEmSpCGN5Eq2h1GcaPtvFDXxs6ovgDXAiRT17yVJkiS10bAJfmauAz4SEZ8DXgfM\npriK7f3AtcD3610AS5IkSdL4G8kRfADKJP4rrQtFkiRJ0liNOMGvFREbAXsAzwCuz8zrmhaVJEmS\npFEZrvxld0R8LiKeVtO+I3A1cDnwTeB3EfGl1oUpSZIkaSQiMwfvjPgKsHdmPrem/SfAy4ErgJ8D\nBwC7Au/MzLNbFu0Y7b777nnVVVe1Owx1gOef/fx2h9B017z9mnaHIEkAvPBjF3PfQ4+0O4ym2WKT\nJ/Lb4/ZvdxjqABFxdWbuPty44abo7AFcXLPh51Ek9z/NzO6y7SMU5TMPBSZsgi81y9/7P86qj7+u\n5fvp6+uju7u75fuZdfT3W74PSRqp+x56xM9YaQyGu9DVtsCNNW3dFKUyv1hpyMyHgG8AL2hmcJIk\nSZIaM1yCPx14qKbtZeXtT2rab6G4+JUkSZKkNhkuwb8Z2K2mbQ7w18y8paZ9U+DeZgUmSZIkqXHD\nJfiXA4dGxGyAiDgY2AX4YZ2xzwf+3NzwJEmSJDViuAT/ZIppOr+NiL8C5wEPA5+uHhQR04ADgeWt\nCFKSJEnSyAxZRSczb4qIVwLHATsDvwBOrHNRq7nA3cCFLYlSmoDGrSrCj1q/ny02eWLL9yFJI/Wk\nrqN5/tlHj8/OxqH235O6AFpfFUiqGPZKtpl5FfCPw4y5lGKKjjQljEf5Nii+RIzXviRporAUsTQ2\nw03RkSRJkjSJmOBLkiRJHcQEX5IkSeogJviSJElSBzHBlyRJkjqICb4kSZLUQUzwJUmSpA5igi9J\nkiR1EBN8SZIkqYOY4EuSJEkdxARfkiRJ6iAm+JIkSVIHMcGXJEmSOshG7Q5AkiSp1qyjvz8+O/pR\n6/ezxSZPbPk+pGom+JIkaUJZ9fHXjct+Zh39/XHblzSenKIjSZIkdRATfEmSJKmDmOBLkiRJHcQE\nX5IkSeogJviSJElSB7GKjjROImJ0653S+DqZOap9SdJk5WesNGBSHcGPiCdExIci4vcRsSYibomI\nT0fEZu2OTRpOZja8LFu2bFTrSdJU42esNGBSJfjAfwGnAtcDC4FzgX8FvhcRk+2xSJIkSU03aabo\nRMRuFEn9dzLzn6rabwI+B7wF+EabwpMkSZImhMl01Hs+EMBnatrPAh4E/mXcI5IkSZImmMmU4L8M\nWAf8oroxM9cAvyn7JUmSpCltMiX42wF3ZebaOn1/BraOiI3HOSZJkiRpQpk0c/CBTYF6yT3Amqox\nD1d3RMS7gXcDzJw5k76+vlbFJzXd6tWrfc9KUov4GatONZkS/AeBpw3SN6NqzAYy80zgTIDdd989\nu7u7WxKc1Ap9fX34npWk1vAzVp1qMk3RuY1iGs70On3PoJi+83CdPkmSJGnKmEwJ/i8p4t2jujEi\nZgAvAq5qR1CSJEnSRDKZEvxvAQl8sKb9cIq5918f94gkSZKkCSYm0yWXI+I04P3A+cAPgC6KK9le\nAbwqM9cNs/6dwJ9aHafURFsDd7U7CEnqUH7GarJ5VmZuM9ygyZbgT6M4gv9uYBbFP8pvAR/NzNVt\nDE1qiYi4KjN3b3ccktSJ/IxVp5pUCb401fifjyS1jp+x6lSTaQ6+JEmSpGGY4EsT25ntDkCSOpif\nsepITtGRJEmSOohH8CVJkqQOYoIvtUBEdEdERsRh47Cvr0SEP8VJmtLqfRZGxPHlZ/GsUWxv1OtK\n7WaCL3WgiDgoIo5vdxySJGn8meBLk9/hwCY1bQcBx7UhFkmaSE6k+HwczUUux7Ku1FYbtTsASWOT\nmY8Aj7Q7DkmaaDLzUeDR8V5XajeP4EvjJApHRMTVEfFgRKyOiGURMbfO2E0j4tSI+EtEPBQRV0bE\nvEHmmG7QFhF9wNvLv7NqOazFD1GS1ouIw8rPnnkR8dGI+FP5efbziNirHPPKiFgeEQ+Un3cfqdnG\n/hHxrYj4Y7nuvRFxcUS8coQx1J1HHxFPjojFEdEfEWsi4u4yjreMYN1ZEXFORNwREWsj4v8i4qSI\n2LRm3KDnR5Xb/UpN26ER8YvyMT5QPuavR8Q2I3msUjWP4Evj5xxgPnAe8GVgOvA24JKIeGNmfrdq\n7LnAa4ELgEuBHYHzgZtGsJ/FFF/eXw4cUtW+YqwPQJJG4ePANOCzwMbAvwEXR8ShwFKKWvRfB/4Z\nOCEibsrMr5XrHgZsCXwVuBV4BvAu4LKImJuZlzcaTEQ8BVgO7EbxeXxGGd+LgdcD3xxi3WcBvwC2\nAE4HbgS6gWOAfSNiXnnkv9GYDgHOBi4HPgo8BGxP8f/A04A7G92mpjYTfGkcRMTBFMn8ezLzzKr2\nzwJXAp+NiO9lZkbEayk+1L+YmYdXjf0x8P3h9pWZl0TE24CXV/0nKUntMg3YKzMfBoiI64ELKQ5k\n7J2ZV5XtSynmu78PqHx2HZ6ZD1RvLCKWANdRJNUNJ/jASRTJ/Qafx+W2h5vZcBKwDfC6zPxB2XZ6\nRHwS+HeKX0+XjiKmg4G/A6+q+YLw0VFsS3KKjjRO/oXiw/uCiNi6sgBPAb4HzAJ2Kcf+Y3l7avUG\nyv9M+scnXElqmjMqyX2pkpT/vJLcA5RjfsHAZyHVyX1EbB4RWwGPAT8H9mw0kDKBfwvQX5vcl/tb\nN8y6BwK/rkruK04G1lEk6qNxH7Ap8LqIiFFuQ1rPI/jS+OgCngTcMcSYmcANFNNx1gEr64z5Q7kt\nSZos/lh9JzP/Vuaw9aYc/g3YqnInIp5NMe3wAIoDIhtsahSxbA08FfjRKNbdBtic4teDDQPJvCci\n/gLsNIrtQvHLwCsopmXeHRE/AX4IfCsz/z7KbWoKM8GXxkdQzKF86xBjrq2578WrJHWCxxpsB4oj\n9sBPgc2AzwDXUPwSuo5ies6rmhhjKwx2gu3jcq/MvDEidgXmlcsrgbOAj0XEKzLz/1oaqTqOCb40\nPm4EngNcmZmrhxm7imL63C48fkrOc0e4P78cSJrs5gHbAe/MzC9Xd0TEiaPc5l0UvxK8cBTr3knx\nBWO32o6IeCrwdOA3Vc33lH1bZuY9Ve11j/Jn5lrgB+VCeT7W94EjKc5LkEbMOfjS+Pgqxb+3k+t1\nRsTMqrvfK28/VDPmtYx8es7qcp0tGwtTkiaMyhH+DeakR8T+jGL+PayfY98L7BoRPbX9Q81/L9f9\nHvDiiHhNTffRFJ/x51e13VDevrpm7L/V2e/WdXb5q/LWz3E1zCP40jjIzPMi4svA+yPiJcD/UhxJ\neiawN7AzA0d1fgBcBBxefuhXymS+G/gd8IIR7PJK4P0U1R2+T3EhrJ9n5kjKbErSRLAcuB34dFmL\n/lbgRRTlf68Bnj/K7X6YYnrPF8svC8spvkS8mCIvOmSIdY8F9qMomHA6xblSrwD+P4rpRGdXje2l\nmFt/ZkQ8j+KI/msozgOodXFE3EtxAvItFOcbHEbxa+w5o3qUmtJM8KVxkpnvjIhlFIn6MRT1oG+n\nOEpzTNW4jIh/ojixbD7wDxSJ/cHAe6mqMDGEXor/rN4CvJniyNI7GFkdfUlqu8y8NyIOAD4BLKTI\nWa6mKCPcwygT/PIk370pkvU3MlCi8nrgtGHW/VNE7AmcQFEd7SkUXzxOBk6sLnGZmfeXv7yeWu5r\nNfCdcr2/1Wz6DIrrALyH4oj93cCvgYWZuWw0j1NTW2Q6VVeaLCLiGuCJmfm8dsciSZImJufgSxNQ\nRGxSp+11wGzgkvGPSJIkTRYewZcmoIg4mWKKzTKKC6C8CHgncD/wosy8tY3hSZKkCcwEX5qAynmb\nRwO7AltQnJz1Y+AjmVnvAliSJEmACb4kSZLUUZyDL0mSJHUQE3xJkiSpg5jgS5IkSR3EBF+SJEnq\nICb4kjTFRcSLIuL4iJjV7lgkSWNngi9JehFwHDCrzXFIkprABF+SJEnqICb4kjSFRcTxwJfLu8si\nIsvlvvL28EHWuy4iVkZElPf7ImJVROwUEReW698fEedHxE511o+IOCIiro6IByNidUQsi4i5LXuw\nkjRFmOBL0tT2HeDM8u+TgEPK5fXA7cA7a1eIiL0orrL8pdzwaombAX3Aw8AxwFLgtcAVEbFtzWbO\nAf4bWAn8B8UUoS2ASyLiwGY8MEmaqrySrSRNcRFxGMVR/LmZ2VfVfhJFor5bZl5f1X4W8A5gh8y8\nrWzrA14JfDYzP1g19mCKLxFfyMwFNW3vycwzq8ZuBFwJbAXslP4HJUmj4hF8SdJgzgIS6Kk0xP/f\nzr2DRhVEARj+BxQRUYIIERFJEZVgZSWiRbp0YqXgE9QmFtba2amFlaAgEkSEFCJGfBaCsRHtLALi\nAyI2xigo6BJQ8VjMDVzWm3Vhgwl3/w9uMY8d5lZ79uzMSWkFsBd4MBvcNzlbbkTELeAVsLvUfQD4\nBoyllNbMPkAPcId82XfjPL6HJHUVA3xJUqWImAQeAQdTSkuL7j3ASuBKxUe+RsRURf9LoLf4cQAw\nUKzxEfjU9Jwu5vTOxztIUjdastAbkCQtapeBG8Au4CY5mz8F3OtgzUQO5ve1mDPRwfqS1NUM8CVJ\nrc663wamgaMppQlgB3AuIn5VzO1JKa2tyOIPANMR0Sjab4BNwLOI+N7h3iVJTTyiI0maDbJXNw9E\nxE/gKjBErnQDuTrOXE6WG8WF2s3AWKn7Gvn750zVAiklj+dIUgesoiNJXS6ltAGYBF4AF4EGMBkR\nz4vxfuA1+WjNk4gYrFhjHNgCzJAr4YyTL8oeB74AWyPiQ2n+CLkSz1PgLvAZWA9sB/oj4q/a+ZKk\n9pjBl6QuFxHvyfXulwOXgFFguDT+FnhcNFtl7xvAILCMXE3nGPAQ2FkO7os1jwCHgN/kUpwXgMPk\nfxNOdfpOktTNzOBLkv4ppXSfnF1fFxEzFePjQF9E9P3nrUmSmpjBlyS1VBzRGQKuVwX3kqTFxSo6\nkqRKKaVt5Ao4J4AfwPmF3ZEkqR1m8CVJcxkGRoBVwP6IeLew25EktcMz+JIkSVKNmMGXJEmSasQA\nXyNDwcAAAAAnSURBVJIkSaoRA3xJkiSpRgzwJUmSpBoxwJckSZJqxABfkiRJqpE/esrzL1gMgoAA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118efb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OtEvj4IEHHmCLLbbgu9/97uo57W984xu5//77Wx2aJEmaAEactEfEccD3MvO6\nQep3A96SmSc1KzhpMpk2bRr77rsvmUlE0NPTw6JFi1odliRJmgAamR5zAvCcIep3B45fp2ikSay/\nv5/tt98egO23357+/v7WBiRJkiaMZs5p7wQebWJ/0qQREWQme+21F+effz577bXX6hF3SZKk4Qw5\nPSYitgSeUFW0TUQ8tU7TrYG3AX9tYmzSpJGZ7Lnnnlx00UV8//vfJyLYc889ueaaa1odmiRJmgCG\nG2n/ELC03BL4bNX96u1q4JVA35hFKk1wBx10EKtWrWLRokWsWrWKgw46qNUhSZKkCWK4E1H7y9sA\njgPOB/5Y0yaBFcCVmXlFU6OTJomtt96ao48+milTptDd3c28efM4+uij2XrrrVsdmiRJmgBipJdh\nj4ivAH2ZedXYhtSYadOm5eLFi1sdhjSkhQsXcuSRR7Jy5UoeeeQRNtpoIzo7O/nSl77kiLuk9cKz\nz3l2q0NoumvfeW2rQ9AkEBFXZ+a04dqNeMnHzHzXuoUkrb8qifnJJ5/M9ddfzzOf+UyOOeYYE3ZJ\n6437rz+NZaftN+bH6e/vp6enZ8yP03XUxWN+DKlawxdXioidgZ2BbSimzawlM7/ahLikSeeKK67g\nxhtvZNWqVdx4441cccUVJu2SJGlEGrm40lTgHOBVlaI6zRIYcdIeEc+imCv/POApwEbAzcAlwKcy\n8+8j7UtqZ729vfT19TF37ly6u7tZsmQJs2fPBmD+/Pktjk6SJLW7RkbaP0eRsH8RuAy4qwnH3wF4\nMsUJrrdQrPP+bOAI4MCI2CMzb2/CcaSWWrBgAXPnzmXWrFn09/cza9YsAObMmWPSLkmShtVI0v4q\nihNR39+sg2fmz4Cf1ZZHxC+A84BDgU8263hSqwwMDDBz5sy1ymbOnMmHP/zhFkUkSZImkkauiLoB\n8IexCqTG8vL2ieN0PGlMdXR00Ne39mUM+vr66OjoaFFEkiRpImlkpP1y4LljEUREdAKbA51ANzC3\nrLpkLI4njbcZM2asnsNeWad99uzZjxt9lyRJqqeRpH0WsCgiLsvM7zY5jncD1RN7lwFvz8zLm3wc\nqSUq89bnzJnDwMAAHR0dzJw50/nskiRpRBq5uNJlFCeOPh24FbgJeKymWWbmvg0HEbEDsAvFaPue\nwP7A2Zl5+iDtj6A4WZWpU6c+/5vf/Gajh5RaZsWKFWy++eatDkOSxtWhP3qAs1+72ZgfZ7w+Y8fr\n8Wjymz5JpFduAAAegUlEQVR9enMvrgTsRLGk483l/aeOJrB6MvMWitVjAC6IiO8Cv42ITTPz1Drt\nzwDOgOKKqONxEQWpWcbrwh+S1FZ+dPG4fPaN22fsOD0eqaKRK6J2jWEctcf6Y0RcA7wXeFzSLkmS\nJK1PGlk9ZrxtAmzd6iAkSZKkVms4aY+Iroh4d0QcExFdZdnGEfHUiNi4wb6eNEj5dGB34MpG45Mk\nSZImm0bmtBMRcylWkZlCMb/91xQrvXQCS4Bjgc820OUXI+LJFFdYXV7283zgQOB+wCvPSJI0SXQd\ndfH4HOhHY3+crTbZaMyPIVUbcdIeEUcCHwX+B/gBcGmlLjPvi4jvA/9GY0n7QuAQ4B3AdhRfBJYD\nXwI+lZk3D7GvJEmaIJadtt+4HKfrqIvH7VjSeGpkpP29wPmZ+cGI2KZO/R+B9zdy8Mw8DzivkX0k\nSZKk9U0jc9qfCfxkiPo7gG3XLRxp8urt7aWzs5Pp06fT2dlJb29vq0OSJEkTRCMj7SuBoa4isCNw\nz7qFI01Ovb299PX1MXfuXLq7u1myZAmzZ88G8KqokiRpWI2MtP8GeFO9iojopJiX/qtmBCVNNgsW\nLOCtb30rZ511Fvvttx9nnXUWb33rW1mwYEGrQ5MkSRNAIyPtnwJ+HBHnAmeVZU+KiNcAJwI7AAc3\nOT5pUhgYGOBXv/oVZ511Fo899hhTpkzhsMMOY2BgoNWhSZKkCWDEI+2Z+VPgPcC/Az8ti88FLgGe\nC8zIzF83PUJpEogIXve61zF9+nQ23HBDpk+fzute9zoiotWhSZKkCaChddoz84xyacf/AHYBArgB\nOC8z/zYG8UmTQmayYMECnvGMZ9Dd3c28efNYsGABmdnq0CRJ0gTQUNIOkJm3AZ45JzVgt912Y+ed\nd2bOnDkMDAzQ0dHBG97wBm644YZWhyZJkiaAhpN2SY075phjOOaYY/jhD3+4ek774Ycfzsknn9zq\n0CRJ0gTQUNIeEXsD7wN2BrahmB5TLTPz6U2KTZo0DjroIKBY+vH6669n11135eSTT15dLkmSNJQR\nJ+0RMQPoAx4G/gzcPFZBSZPRQQcdxEEHHUR/fz89PT2tDkeSJE0gjYy0zwF+D7wmM+8co3gkSZIk\n1Wjk4kpTgTNN2KXRWbhwIbvvvjv77rsvu+++OwsXLmx1SJIkaYJoZKT9euCJYxWINJktXLiQY445\nhjPPPHOtE1EB57VLkqRhNTLSfjLw3oh4ylgFI01WJ598MmeeeeZaF1c688wzXT1GkiSNyIhH2jPz\nexGxKbAkIi4ElgGPPb5ZfryJ8UmTwvXXX89LX/rStcpe+tKXcv3117coIkmSNJE0snrMM4GTgC2B\ndwzSLAGTdqnGrrvuyoknnsgFF1ywesnHAw44gF133bXVoUmSpAmgkTntXwC2Bz4AXA78c0wikiah\n6dOnc+qpp7L99tsDcNddd3Hqqafy3ve+t8WRSZKkiaCROe0vBj6dmfMz8/eZubzeNlaBShPZBRdc\nwJZbbklnZyeZSWdnJ1tuuSUXXHBBq0OTJEkTQCNJ+73AHWMViDSZ3XLLLZx33nksXbqUyy67jKVL\nl3Leeedxyy23tDo0SZI0ATSStJ8HvHmsApEkSZJUXyNJ+5eALSLigoh4RUQ8LSKeWruNVaDSRLbD\nDjtwyCGHsGjRIh599FEWLVrEIYccwg477NDq0CRJ0gTQyImof6JYHWYa8G9DtJuyThFJk9AnP/lJ\nPvCBD3DYYYexfPlydtxxRx577DHmzZvX6tAkSdIE0EjSfhJF0i6pQZWrnp588slEBJttthmnnHKK\nV0OVJEkj0sjFlU4YwzikSe+KK67gxhtvZNWqVdx4441cccUVJu2SJGlEGhlplzRKvb299PX1MXfu\nXLq7u1myZAmzZ88GYP78+S2OTpIktbtGTkQlIraIiOMi4pcRcUNEvLgs37Ys32VswpQmtgULFjB3\n7lxmzZpFZ2cns2bNYu7cuSxYsKDVoUmSpAlgxEl7RGwHLAY+BmwD7ARsApCZdwLvBI4YgxilCW9g\nYICZM2euVTZz5kwGBgZaFJEkSZpIGhlp/wTwJGAv4GVA1NRfCOzbpLikSaWjo4O+vr61yvr6+ujo\n6GhRRJIkaSJpZE77G4AvZObvImKbOvU3AYc2JSppkpkxY8bqOezd3d3MmzeP2bNnP270XZIkqZ5G\nkvZtgRuHqF8FdK5bONLkVDnZdM6cOQwMDNDR0cHMmTM9CVWSJI1II9NjbgOePkT9nsDN6xaONHnN\nnz+flStXsmjRIlauXGnCLkmSRqyRpP0S4PCIeHJtRUTsBRxCMa9dkiRJUhM1krSfCDwKXAOcSnF1\n1HdGxELgF8CtwNxGDh4Rz4yIkyLiyoi4IyLuj4jfR8QxEbFZI31JkiRJk9WIk/bMvA14EXAVcBjF\n6jHvAP4TuBR4WWbe3eDxDwM+BPwFOAn4KPBnipVqroiITRrsT5IkSZp0Groiamb+FXhjRGwJPIsi\ncb9xFMl6xXeAUzPz3qqyvoi4ATgGOBz43Cj7liRJkiaFRi6udEhEdAFk5n2Z+dvM/E0lYY+Irog4\npJGDZ+bimoS94lvl7e6N9CdJkiRNRo3Maf8KsPcQ9XuVbZphh/L2H03qT5IkSZqwGknaa6+AWmsj\nirXa10lETAE+RnHS6zfWtT9JkiRpomtoTjvFijGPExFPAPYD/r7OEcFngRcDczLzz4Mc7wjgCICp\nU6fS39/fhMNK42PFihW+ZyVpDPkZq8koMuvm4UVlxPHAcQ3095nM/K9RBxPxceBY4IzMPHIk+0yb\nNi0XL1482kNK466/v5+enp5WhyFJk1LXURez7LT9Wh2GNGIRcXVmThuu3XAj7b8HvkoxNeYQ4HLg\nppo2CawArgQWNh5qISJOoEjYvwLMHG0/kiRJ0mQzZNKemRdSXuU0InYEPpGZP2t2EGXCfjxwDvDu\nHGr4X5IkSVrPjHhOe2ZOH4sAIuI4ioT9XOCwzFznk1klSZKkyaTRE1EBiIjNgSdQZ/WZzLy5gX7e\nB5wI3Az8FDg4Yq1Fav6RmT8ZTYySJEnSZNFQ0h4RB1LMO991iGZTGujyBeXtUymmxtT6OWDSLkmS\npPVaI1dEPYBi3fQNgS9RnJy6EPg28AhwNXBSIwfPzEMzM4bYehrpT5IkSZqMGrm40keA64E9WLMM\n5FmZeSAwDXgWxWozkiRJkpqokaT9OcA5mbmSNVc+nQKQmdcBZwBHNzc8SZIkSY0k7VOAu8p/P1Te\nblVV/2dg92YEJUmSJGmNRpL2W4AdATLzIeB24PlV9c8CHmheaJIkSZKgsdVjrgBeyZr57N8HPhgR\nD1Ek/+8DLmpueJIkSZIaSdq/ALwpIjYpR9qPAV4InFDW/4niZFVJkiRJTdTIFVF/C/y26v4dwB4R\n8RzgMeB6r2YqSZIkNV8j67S/PCK2qy3PzD9m5p+ArSPi5U2NTppEent76ezsZPr06XR2dtLb29vq\nkCRJ0gTRyPSYRcA7KC6wVM++ZV0jV0SV1gu9vb309fUxd+5curu7WbJkCbNnzwZg/vz5LY5OkiS1\nu0ZWj4lh6qewZv12SVUWLFjA3LlzmTVrFp2dncyaNYu5c+eyYMGCVocmSZImgEaSdoAcom5v4M51\niEWatAYGBpg5c+ZaZTNnzmRgYKBFEUmSpIlkyKQ9Ij4QETdFxE1l0Wcr92u2fwLvAX4w5hFLE1BH\nRwd9fX1rlfX19dHR0dGiiCRJ0kQy3Jz2e4Dl5b+7KK6I+o+aNglcB1wJ/Hczg5MmixkzZqyew97d\n3c28efOYPXv240bfJUmS6hkyac/Mc4BzACJiKXBUZn5/PAKTJpPKyaZz5sxhYGCAjo4OZs6c6Umo\nkiRpRCJzqGnq7W/atGm5ePHiVochjVh/fz89PT2tDkOS2l7EcGtgNM9Ez4c0cUXE1Zk5bbh2jZ6I\nWn2AnSJiTkR8PiLeFxGbjLYvSZKkWpnZ8LZo0aJR7Se1uyGnx0TE4cD/A16VmbdXlb8K+B6wKcVS\nkAkcGRF7Z+aKMYxXkiRJWu8MN9L+BuD+moQ9gC9RJOynAvsDZwO7Ax8amzAlSZKk9ddwSftzgV/W\nlO1NsZLMuZl5bGb+IDMPp7hi6gHND1GSJElavw2XtG8H3FRT9hKK6TDn1ZRfAjyjSXFJkiRJKg2X\ntD8KbFxT9oLy9tc15XcBXilGkiRJarLhkvZlFNNhAIiIKcDLgBsy8581bbcB7mxqdJIkSZKGTdq/\nC/x7RLw/IrqB0yimzHyvTtsXAkubHJ8kSZK03htyyUfgf4BDgNPL+wH8FfhMdaOI2ArYD5jX7AAl\nSZKk9d2QSXtm3hcRzweOoDjJ9C/AlzPznpqmuwJfAb45JlFKkiRJ67HhRtrJzPupGVmv0+ZK4Mpm\nBSVJkiRpjeHmtEuSJElqMZN2SZIkqc2ZtEuSJEltzqRdkiRJanMm7ZIkSVKba1rSHhG7RMR/NrjP\n0RHx7Yi4KSIyIpY1Kx5JkiRpsmjmSPtbgIUN7nMK8AqK9d//2cRYJEmSpElj2HXax9jTM/MmgIi4\nDti8xfFIkiRJbaelc9orCbskSZKkwXkiqiRJktTmTNolSZKkNtfqOe2jEhFHAEcATJ06lf7+/tYG\nJDVgxYoVvmclaYz4GavJKjJz8MqI+4HBG6xtI2DjzJwyqkDKE1Ezs6uR/aZNm5aLFy8ezSGllujv\n76enp6fVYUjSpNLb28uCBQsYGBigo6ODGTNmMH/+/FaHJQ0rIq7OzGnDtRtupP1qRp60S5Ikjbve\n3l76+vqYO3cu3d3dLFmyhNmzZwOYuGvSGDJpz8yecYpDkiRpVBYsWMDcuXOZNWsW/f39zJo1C4A5\nc+aYtGvS8ERUSZI0oQ0MDDBz5sy1ymbOnMnAwECLIpKab8QnokbEhsCLgOcAWwD/AG7MzF+O9uAR\n8Q5gx/LudsDGEXFseX95Zp472r4lSdL6oaOjg76+vtUj7AB9fX10dHS0MCqpuUaUtJfJ9SnAUypF\nlHPdI2IZcFxmfr2qfUdmjuTr7eHAPjVlHy9vfw6YtEuSpCHNmDFj9Rz27u5u5s2bx+zZsx83+i5N\nZMMm7RFxInAs8CCwkOLk1PuALYHnA/sDX42Ip2XmJyJiM+BC4JXD9e2ceUmStK4q89bnzJmzevWY\nmTNnOp9dk8qQSXtEvBT4GPBT4G2ZeUedNtsCXwOOj4grgROAvZofqiRJUn3z589n/vz5LqurSWu4\nkfYPAEuB/TNzZb0GmXlnRBwA/An4EbAKOKypUUqSJEnrseFWj9kb+OpgCXtFWX8uxVz3t3kCqSRJ\nktQ8wyXt2wLLR9jXMmBVZn57nSKSJEmStJbhkvZ7gCePsK8nA3evWziSJEmSag2XtP8WODgihmxX\n1h9UtpckSZLURMMl7QuA3YAvR8TG9RpExEZV7c5obniSJEmShlw9JjMvjIivAYcCPRFxLnANcC+w\nFfA84O1AF3BuZn5/TKOVJEmS1kMjuSLqoRQno36YYs32rKoLYCXwCYr12SVJkiQ12bBJe2auAj4W\nEf8D7AfsTnE11PuA64CL6110SZIkSVJzjGSkHYAyMT977EKRJEmSVM+Ik/ZaEbEh8ELgX4Almfmn\npkUlSZIkabXhlnLsiYj/iYjta8qfBlwNXA58E/hjRJw1dmFKkiRJ66/hlnw8FHhNZt5eU3428Gzg\nCuC/gSXAOyPinc0OUJIkSVrfDZe0vxC4tLogInYBXgb8IjNflpkfKdvdABwyJlFKkiRJ67HhkvYn\nUSTj1Xooln38cqUgMx8CvgE8p5nBSZIkSRo+ae8AHqope0F5+/Oa8r9SXHBJkiRJUhMNl7TfDOxW\nU/ZS4PbM/GtN+abAPc0KTJIkSVJhuKT9cuCQiNgdICLeBOwM/LBO22cDf2tueJIkSZKGS9pPpZgi\n84eIuB34DvAw8JnqRhExBdgf+OVYBClJkiStz4ZM2jNzKbAPcAlwF8UIe0+dCylNL+svHIsgJUmS\npPXZsFdEzczFwL8N0+anFNNjJEmSJDXZcNNjJEmSJLWYSbskSZLU5kzaJUmSpDZn0i5JkiS1OZN2\nSZIkqc2ZtEuSJEltzqRdkiRJanMm7ZIkSVKbM2mXJEmS2pxJuyRJktTmWp60R8QGEfGhiPjfiFgZ\nEX+NiM9ExGatjk2SJElqBy1P2oH/BuYBS4Be4NvA/wMuioh2iE+SJElqqQ1befCI2I0iUf9eZr6l\nqnwp8D/AgcA3WhSeJEmS1BZaPZJ9EBDAZ2vKFwAPAm8f94gkSdKE09vbS2dnJ9OnT6ezs5Pe3t5W\nhyQ1VUtH2oEXAKuA31QXZubKiPh9WS9JkjSo3t5e+vr6mDt3Lt3d3SxZsoTZs2cDMH/+/BZHJzVH\nq0fanwLcmZkDder+BmwbERuPc0ySJGkCWbBgAXPnzmXWrFl0dnYya9Ys5s6dy4IFC1odmtQ0rR5p\n3xSol7ADrKxq83B1RUQcARwBMHXqVPr7+8cqPqnpVqxY4XtWkppoYGCA7u5u+vv7V3/Gdnd3MzAw\n4OetJo1WJ+0PAtsPUtdZ1WYtmXkGcAbAtGnTsqenZ0yCk8ZCf38/vmclqXk6OjpYsmQJs2bNWv0Z\nO2/ePDo6Ovy81aTR6qT9VqA7IjrqTJH5F4qpMw/X2U+SJAmAGTNmrJ7D3t3dzbx585g9ezYzZ85s\ncWRS87Q6af8t8GrghcDllcKI6AT2AH7RorgkSdIEUTnZdM6cOQwMDNDR0cHMmTM9CVWTSqtPRP0W\nkMAHa8pnUMxl//q4RyRJkiac+fPns3LlShYtWsTKlStN2DXptHSkPTOvjYjPA++PiO8BlwC7UlwR\n9ed4YSVJkiSp5dNjoBhlX0axGsx+wJ3AfOC4zFzVwrgkSZKkttDypD0zHwM+U26SJEmSarR6Trsk\nSZKkYZi0S5IkSW3OpF2SJElqcybtkiRJUpuLzGx1DOskIu4Alrc6DqkB21KskiRJaj4/YzXR7JiZ\n2w3XaMIn7dJEExGLM3Naq+OQpMnIz1hNVk6PkSRJktqcSbskSZLU5kzapfF3RqsDkKRJzM9YTUrO\naZckSZLanCPtkiRJUpszaZdGICJ6IiIj4tBxONbZEeFPYJLWe/U+DyPihPLzuGsU/Y16X6nVTNql\nCSAiDoiIE1odhyRJag2Tdqn9zAA2qSk7ADi+BbFIUrv5BMVn5GgurLgu+0ottWGrA5C0tsx8BHik\n1XFIUjvKzEeBR8d7X6nVHGmXRikK74mIqyPiwYhYERGLImJ6nbabRsS8iPh7RDwUEVdGxL6DzNdc\nqywi+oF3lv/Oqu3QMX6IkrSWiDi0/PzZNyKOi4jl5WfaVRHxorLNPhHxy4h4oPzM+1hNH6+OiG9F\nxE3lvvdExKURsc8IY6g7Lz0itoyIkyPi+ohYGRF3lXEcOIJ9uyLi3Ij4R0QMRMRfIuKUiNi0pt2g\n5xyV/Z5dU3ZIRPymfIwPlI/56xEx7CXrpVqOtEujdy5wEPAd4CtAB/A24CcR8ebM/H5V228Drwcu\nAH4KPA04H1g6guOcTPEF+2XAO6rKr1jXByBJo3QaMAU4HdgY+DBwaUQcApxJsVb614H/BE6KiKWZ\n+bVy30OBrYGvArcA/wK8G/hZREzPzMsbDSYingD8EtiN4jP5i2V8ewJvAL45xL47Ar8BtgK+ANwA\n9ABHAy+JiH3LEfpGY3oHcA5wOXAc8BDwrxT/F2wP3NFon1q/mbRLoxARb6JI0I/MzDOqyk8HrgRO\nj4iLMjMj4vUUH9JfzswZVW0vAy4e7liZ+ZOIeBvwsqr/9CSplaYAL8rMhwEiYglwIcUAxYszc3FZ\nfibF/PH3AZXPrxmZ+UB1ZxHRB/yJIlFuOGkHTqFI2Nf6TC77Hm5WwSnAdsB+mXlJWfaFiPgU8BGK\nXzrPHEVMbwLuB15Rk/QfN4q+JKfHSKP0dooP4wsiYtvKBjwBuAjoAnYu2/5beTuvuoPyP4frxydc\nSWqqL1YS9lIl0b6qkrADlG1+w5rPQ6oT9ojYPCK2AR4DrgL2ajSQMik/ELi+NmEvj7dqmH33B66p\nStgrTgVWUSTfo3EvsCmwX0TEKPuQVnOkXRqdXYEtgH8M0WYq8H8UU2FWATfWafPnsi9Jmkhuqr6T\nmf8s89J6U/7+CWxTuRMRT6eY9vcaioGOtboaRSzbAk8EfjSKfbcDNqcY5V87kMy7I+LvwE6j6BeK\nEfyXU0yLvCsifg78EPhWZt4/yj61HjNpl0YnKOYjHjxEm+tq7nvBJEmTxWMNlgPFyDrwC2Az4LPA\ntRS/Wq6imBrziibGOBYGOwn1cflUZt4QEd3AvuW2D7AAODEiXp6ZfxnTSDXpmLRLo3MD8Ezgysxc\nMUzbZRRT0Xbm8dNhnjXC45nwS5oM9gWeAhyWmV+proiIT4yyzzspRvOfO4p976D40rBbbUVEPBF4\nMvD7quK7y7qtM/PuqvK6o/GZOQBcUm6U5zhdDMyimOcvjZhz2qXR+SrF38+p9SojYmrV3YvK2w/V\ntHk9I58as6LcZ+vGwpSktlIZiV9rjndEvJpRzGeH1XPWFwLdEXF4bf1Q88nLfS8C9oyI19ZUH0Xx\nOX9+Vdn/lbevrGn74TrH3bbOIX9X3vpZroY50i6NQmZ+JyK+Arw/Ip4H/IBitGcH4MXAM1gz8nIJ\n8GNgRvkhXlny8Qjgj8BzRnDIK4H3U6xocDHFxZeuysyRLBkpSe3il8BtwGfKtdJvAfagWM72WuDZ\no+z3WIqpNV8uvwD8kuKLwZ4Uuc47hth3DvAqioUFvkBx/tHLgbdSTOU5p6rtQoq56mdExC4UI++v\npZhXX+vSiLiH4iTdv1LM3z+U4pfTc0f1KLVeM2mXRikzD4uIRRTJ99EUaxXfRjGScnRVu4yIt1Cc\neHUQ8DqKZP1NwHupWlVhCAsp/vM5EPgPitGfdzGydd4lqS1k5j0R8Rrgk0AvRR5yNcWyuIczyqS9\nPBH2xRQJ+JtZs9ziEmD+MPsuj4i9gJMoVgZ7AsWXiVOBT1Qv15iZ95W/ks4rj7UC+F653z9ruv4i\nxTr1R1KMrN8FXAP0Zuai0TxOrd8i06myUqtExLXARpm5S6tjkSRJ7cs57dI4iIhN6pTtB+wO/GT8\nI5IkSROJI+3SOIiIUymmtyyiuODGHsBhwH3AHpl5SwvDkyRJbc6kXRoH5RzIo4BuYCuKk5cuAz6W\nmfUuuiRJkrSaSbskSZLU5pzTLkmSJLU5k3ZJkiSpzZm0S5IkSW3OpF2SJElqcybtkjQJRcQeEXFC\neal4SdIEZ9IuSZPTHsDxQFeL45AkNYFJuyRJktTmTNolaZKJiBOAr5R3F0VEltu95e2MQfb7U0Tc\nGBFR3u+PiGURsVNEXFjuf19EnB8RO9XZPyLiPRFxdUQ8GBErImJRREwfswcrSesJk3ZJmny+B5xR\n/vsU4B3l9gbgNuCw2h0i4kUUV+w9K9e+6t5mQD/wMHA0cCbweuBXEfGkmm7OBT4H3Aj8F8X0nK2A\nn0TE/s14YJK0vvKKqJI0CUXEoRSj7dMzs7+q/BSK5Hu3zFxSVb4AeBfw1My8tSzrB/YBTs/MD1a1\nfRPFF4MvZebMmrIjM/OMqrYbAlcC2wA7pf/pSNKoONIuSeuXBUACh1cKImIz4K3ADysJe43Tqu9k\n5vnAn4EDqorfDtwPXBAR21Y24AnARRQnxO7cxMchSesVk3ZJWo9k5lLgp8A7ImKjsvg/gS2AL9fZ\n5Z7MvK1O+fXA1DLhB9i17OMfwB012wllm6nNeAyStD7asNUBSJLG3RnAt4H9ge9SjLrfBly8Dn0G\nRYJ+8BBtrluH/iVpvWbSLkmT01Bzxy8EbgcOj4jrgJcAczPz0TptnxART6oz2r4rcHtmPlDevwF4\nJnBlZq5Yx9glSTWcHiNJk1Mlcd66tiIzHwHO/v/t3CFLBEEYh/Fnklj8ACJyQTCYTCIYrt2HEBTE\nosGsn8BkErQZRLAaRGyeRbCZRTixWASTHBh8DbNh0VUODnS5fX5tdpZhNu3Ly8wf6JATXiCnwvxk\nuzwoLp3OAmelx8fkf8pu1QIpJY/GSNIQTI+RpBGUUpoGesAdcAC8Ab2IuC3mZ4B78rGW64hoV6zR\nBeaAPjkBpku+TLoJvALzEfFcev+InEBzA5wDL8AUsAjMRMS3bHdJ0mDstEvSCIqIJ3Ie+zhwCJwC\nG6X5B+CqGP7WZX8D2sAYOUVmHbgElsoFe7HmGrACfJBjJfeBVXLXf2fYb5KkJrPTLkkNlVK6IHfB\nJyOiXzHfBVoR0frjrUmSvrDTLkkNVByP6QAnVQW7JKleTI+RpAZJKS2Qk1+2gHdg7393JEkahJ12\nSWqWDeAImACWI+Lxf7cjSRqEZ9olSZKkmrPTLkmSJNWcRbskSZJUcxbtkiRJUs1ZtEuSJEk1Z9Eu\nSZIk1ZxFuyRJklRznxIB/0ZZy8+EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118efb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots show you the distribution of the data (spread).\n",
    "# http://en.wikipedia.org/wiki/Box_plot\n",
    "\n",
    "# Plot the length and entropy of SQL statements\n",
    "# Fixme Brian: make these pretty\n",
    "dataframe.boxplot('length','type')\n",
    "plt.ylabel('SQL Statement Length')\n",
    "dataframe.boxplot('entropy','type')\n",
    "plt.ylabel('SQL Statement Entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119486e80>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RlWZ4Ffh3wO9KKXdOrB/q1QOz2ZDw0CVUZq8h+8h9GHYZYeVYvOQaOHQJWSPE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stDbN5cyv77t6dx43z84tLJt27dBlq12iclx27I6tq4OucoKSll+fLvycnJJicneWlxvFlERERE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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167de2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the classes up so we can set colors, size, labels\n",
    "fig, ax = plt.subplots(subplot_kw=dict(facecolor='#EEEEE5'))\n",
    "ax.grid(color='grey', linestyle='solid')\n",
    "cond = dataframe['type'] == 'malicious'\n",
    "evil = dataframe[cond]\n",
    "legit = dataframe[~cond]\n",
    "plt.scatter(legit['length'], legit['entropy'], s=140, c='#aaaaff', label='Legit', alpha=.7)\n",
    "plt.scatter(evil['length'], evil['entropy'], s=40, c='r', label='Injections', alpha=.3)\n",
    "plt.legend()\n",
    "plt.xlabel('SQL Statement Length')\n",
    "plt.ylabel('SQL Statement Entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a7adb70>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KxphPRORA4NfAJUAe8CbwFWNMTXKjxpiQiCwErsFJhFYKrMbJmHxLin5chLO1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11830aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the classes up so we can set colors, size, labels\n",
    "fig, ax = plt.subplots(subplot_kw=dict(facecolor='#EEEEE5'))\n",
    "ax.grid(color='grey', linestyle='solid')\n",
    "plt.scatter(legit['malicious_g'], legit['legit_g'], s=140, c='#aaaaff', label='Legit', alpha=.7)\n",
    "plt.scatter(evil['malicious_g'], evil['legit_g'], s=40, c='r', label='Injections', alpha=.3)\n",
    "plt.legend()\n",
    "plt.ylabel('Legit SQL G-Test Score')\n",
    "plt.xlabel('Malicious SQL G-Test Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In preparation for using scikit learn we're just going to use\n",
    "# some handles that help take us from pandas land to scikit land\n",
    "\n",
    "# List of feature vectors (scikit learn uses 'X' for the matrix of feature vectors)\n",
    "X = dataframe.as_matrix(['length', 'entropy','legit_g','malicious_g'])\n",
    "\n",
    "# Labels (scikit learn uses 'y' for classification labels)\n",
    "y = np.array(dataframe['type'].tolist())  # Yes, this is weird but it needs \n",
    "                                            # to be an np.array of strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Random Forest is a popular ensemble machine learning classifier.\n",
    "# http://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "#\n",
    "import sklearn.ensemble\n",
    "clf = sklearn.ensemble.RandomForestClassifier(n_estimators=20) # Trees in the forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ref: [sklearn.model_selection.cross_val_score v0.19.0](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html)\n",
    "\n",
    "> sklearn.model_selection.cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’)\n",
    "\n",
    "cv : int, cross-validation generator or an iterable, optional\n",
    "\n",
    "Determines the cross-validation splitting strategy. Possible inputs for cv are:\n",
    "\n",
    "* None, to use the default 3-fold cross validation,\n",
    "* integer, to specify the number of folds in a (Stratified)KFold,\n",
    "* An object to be used as a cross-validation generator.\n",
    "* An iterable yielding train, test splits.\n",
    "\n",
    "n_jobs : integer, optional\n",
    "\n",
    "The number of CPUs to use to do the computation. -1 means ‘all CPUs’.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.98418404  0.99856219  0.99928058  0.99784017  0.99640029  0.97552196\n",
      "  0.99856012  0.99640029  0.99784017  0.99424046]\n"
     ]
    }
   ],
   "source": [
    "# Now we can use scikit learn's cross validation to assess predictive performance.\n",
    "import sklearn.model_selection\n",
    "scores = sklearn.model_selection.cross_val_score(clf, X, y, cv=10, n_jobs=-1)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Wow 99% accurate! There is an issue though...\n",
    "# Recall that we have ~13k 'malicious SQL statements and\n",
    "# we only have about 1k 'legit' SQL statements, so we dive \n",
    "# in a bit and look at the predictive performance more deeply.\n",
    "\n",
    "# Train on a 80/20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test, index_train, index_test = train_test_split(X, y, dataframe.index, test_size=0.2)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Stats\n",
      "legit/legit: 97.21% (209/215)\n",
      "legit/malicious: 2.79% (6/215)\n",
      "malicious/legit: 0.16% (4/2564)\n",
      "malicious/malicious: 99.84% (2560/2564)\n"
     ]
    },
    {
     "data": {
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6ZmZmJXFHKjMzsxJIw6IjVf8/AzMzsz7hkq6ZmfUHVy+bmZmVxB2pzMzMSjBM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\nxEHXzMysJA66ZmZmJfn/zFMiDh87BiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118f60358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot the results of the 80/20 split in a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "labels = ['legit', 'malicious']\n",
    "cm = confusion_matrix(y_test, y_pred, labels)\n",
    "\n",
    "def plot_cm(cm, labels):\n",
    "    \n",
    "    # Compute percentanges\n",
    "    percent = (cm*100.0)/np.array(np.matrix(cm.sum(axis=1)).T)  # Derp, I'm sure there's a better way\n",
    "    \n",
    "    print('Confusion Matrix Stats')\n",
    "    for i, label_i in enumerate(labels):\n",
    "        for j, label_j in enumerate(labels):\n",
    "            print(\"%s/%s: %.2f%% (%d/%d)\" % (label_i, label_j, (percent[i][j]), cm[i][j], cm[i].sum()))\n",
    "\n",
    "    # Show confusion matrix\n",
    "    # Thanks kermit666 from stackoverflow :)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.grid(b=False)\n",
    "    cax = ax.matshow(percent, cmap='coolwarm')\n",
    "    plt.title('Confusion matrix of the classifier')\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticklabels([''] + labels)\n",
    "    ax.set_yticklabels([''] + labels)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Truth')\n",
    "    plt.show()\n",
    "\n",
    "plot_cm(cm, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "      <th>parsed_sql</th>\n",
       "      <th>sequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Float, Float, Float, Erro...</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>anything' or 'x'='x</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Identifier, Single, Identifier, Single, Ident...</td>\n",
       "      <td>[('Identifier',), ('Single',), ('Identifier',)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>'; exec master..xp_cmdshell 'ping aaa.bbb.ccc....</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Error, Single]</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Error',), ('S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>'; if not(select system_user) &lt;&gt; 'sa' waitfor ...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Single, Integer, Punctuat...</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Single',), ('...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>'; if is_srvrolemember('sysadmin') &gt; 0 waitfor...</td>\n",
       "      <td>malicious</td>\n",
       "      <td>[Single, Identifier, Single, Integer, Punctuat...</td>\n",
       "      <td>[('Single',), ('Identifier',), ('Single',), ('...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                raw_sql       type  \\\n",
       "0        '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
       "1033                                anything' or 'x'='x  malicious   \n",
       "1038  '; exec master..xp_cmdshell 'ping aaa.bbb.ccc....  malicious   \n",
       "1043  '; if not(select system_user) <> 'sa' waitfor ...  malicious   \n",
       "1044  '; if is_srvrolemember('sysadmin') > 0 waitfor...  malicious   \n",
       "\n",
       "                                             parsed_sql  \\\n",
       "0     [Single, Identifier, Float, Float, Float, Erro...   \n",
       "1033  [Identifier, Single, Identifier, Single, Ident...   \n",
       "1038                [Single, Identifier, Error, Single]   \n",
       "1043  [Single, Identifier, Single, Integer, Punctuat...   \n",
       "1044  [Single, Identifier, Single, Integer, Punctuat...   \n",
       "\n",
       "                                              sequences  \n",
       "0     [('Single',), ('Identifier',), ('Float',), ('F...  \n",
       "1033  [('Identifier',), ('Single',), ('Identifier',)...  \n",
       "1038  [('Single',), ('Identifier',), ('Error',), ('S...  \n",
       "1043  [('Single',), ('Identifier',), ('Single',), ('...  \n",
       "1044  [('Single',), ('Identifier',), ('Single',), ('...  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Documentation in sqlparse for the mapping can be found here: \n",
    "# https://github.com/andialbrecht/sqlparse/blob/master/sqlparse/keywords.py\n",
    "# or here\n",
    "# https://github.com/andialbrecht/sqlparse/blob/master/sqlparse/lexer.py\n",
    "\n",
    "# Here we look at example of the SQL sequence that G-Test has indicated are good\n",
    "# indicators of SQL injections.\n",
    "dataframe[dataframe['sequences'].map(lambda x: \"('Single', 'Identifier')\" in x)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"float: right; padding:10px;\"><img src=\"files/images/sad_party.jpg\" width=\"200px\"></div>\n",
    "<font size=4>Yea! Those scores look totally awesome, lets have a Party! But later we realize that if your system is processing 1 Million sql statements that with the predictive performance above you'll get tens of thousands of false positives, so now it's a Sad Party :(\n",
    "<br><br>\n",
    "But our Mom said we were still cool.. so we're going to exercise another nice feature. Most of the machine learning algorithm in scikit learn have a companion function to the normal 'predict' function... called 'predict_proba' where the model will compute the probability of that class matching based on various metrics. For instance, random forest bases the probability function on how many of the trees in the forest voted one way or the other.. so a probability of .7 means that 70% of the trees voted one way and the other 30% voted the other way.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Stats\n",
      "legit/legit: 98.60% (212/215)\n",
      "legit/malicious: 1.40% (3/215)\n",
      "malicious/legit: 0.62% (16/2564)\n",
      "malicious/malicious: 99.38% (2548/2564)\n"
     ]
    },
    {
     "data": {
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wqmC/FUnta/eRAsxM4HrSh7XhNJB18nFd3j6uZv0E6oyjq0qzH/B35k8bdxZpTtUB+5La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\n8v8BVmZFPyyqB1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6d2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the precition probabilities and use them to mimimize our false positives\n",
    "# Note: This is simply a trade off, it means we'll miss a few of the malicious\n",
    "# ones but typically false alarms are a death blow to any new 'fancy stuff' so\n",
    "# we definitely want to mimimize the false alarms.\n",
    "y_probs = clf.predict_proba(X_test)[:,1]\n",
    "thres = .95 # This can be set to whatever you'd like\n",
    "y_pred[y_probs<thres] = 'legit'\n",
    "y_pred[y_probs>=thres] = 'malicious'\n",
    "cm = confusion_matrix(y_test, y_pred, labels)\n",
    "plot_cm(cm, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于测试数据集的关系，这个分类器更倾向于报告分类结果为：恶意，主要原因是数据集中恶意样本占比非常高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x11b682208>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also look at what features the learning algorithm thought were the most important\n",
    "importances = zip(['length', 'entropy', 'legit_g', 'malicious_g'], clf.feature_importances_)\n",
    "importances\n",
    "\n",
    "# From the list below we see our feature importance scores. There's a lot of feature selection,\n",
    "# sensitivity study, etc stuff that you could do if you wanted at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_sql</th>\n",
       "      <th>type</th>\n",
       "      <th>parsed_sql</th>\n",
       "      <th>sequences</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>GoogleGitSign in eclipse / datatools/org.eclip...</td>\n",
       "      <td>legit</td>\n",
       "      <td>[Identifier, Keyword, Operation, Operator, Ope...</td>\n",
       "      <td>[('Identifier',), ('Keyword',), ('Operation',)...</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>CREATE TABLE sylph.users ( id SERIAL PRIMARY K...</td>\n",
       "      <td>legit</td>\n",
       "      <td>[DDL, Keyword, Identifier, Punctuation, Identi...</td>\n",
       "      <td>[('DDL',), ('Keyword',), ('Identifier',), ('Pu...</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>SELECT dept, number, SUBSTR(title, 1, 12) AS s...</td>\n",
       "      <td>legit</td>\n",
       "      <td>[DML, Identifier, Punctuation, Builtin, Punctu...</td>\n",
       "      <td>[('DML',), ('Identifier',), ('Punctuation',), ...</td>\n",
       "      <td>malicious</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               raw_sql   type  \\\n",
       "347  GoogleGitSign in eclipse / datatools/org.eclip...  legit   \n",
       "117  CREATE TABLE sylph.users ( id SERIAL PRIMARY K...  legit   \n",
       "610  SELECT dept, number, SUBSTR(title, 1, 12) AS s...  legit   \n",
       "\n",
       "                                            parsed_sql  \\\n",
       "347  [Identifier, Keyword, Operation, Operator, Ope...   \n",
       "117  [DDL, Keyword, Identifier, Punctuation, Identi...   \n",
       "610  [DML, Identifier, Punctuation, Builtin, Punctu...   \n",
       "\n",
       "                                             sequences       pred  \n",
       "347  [('Identifier',), ('Keyword',), ('Operation',)...  malicious  \n",
       "117  [('DDL',), ('Keyword',), ('Identifier',), ('Pu...  malicious  \n",
       "610  [('DML',), ('Identifier',), ('Punctuation',), ...  malicious  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now were going to just do some post analysis on how the ML algorithm performed.\n",
    "# Lets look at the legit samples that were misclassified as malicious\n",
    "test_set = dataframe.loc[index_test]\n",
    "test_set['pred'] = y_pred\n",
    "misclassified = test_set[(test_set['type']=='legit') & (test_set['pred']=='malicious')]\n",
    "misclassified.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Discussion for how to use the resulting models.\n",
    "# Typically Machine Learning comes in two phases\n",
    "#    - Training of the Model\n",
    "#    - Evaluation of new observations against the Model\n",
    "# This notebook is about exploration of the data and training the model.\n",
    "# After you have a model that you are satisfied with, just 'pickle' it\n",
    "# at the end of the your training script and then in a separate\n",
    "# evaluation script 'unpickle' it and evaluate/score new observations\n",
    "# coming in (through a file, or ZeroMQ, or whatever...)\n",
    "#\n",
    "# In this case we'd have to pickle the RandomForest classifier.\n",
    "# See 'test_it' below for how to use them in evaluation mode.\n",
    "\n",
    "\n",
    "# test_it shows how to do evaluation, also fun for manual testing below :)\n",
    "def test_it(sql):\n",
    "    parsed_sql = parse_it(sql)\n",
    "    ngram_list = ngrams(parsed_sql, 3)\n",
    "    malicious_g = g_aggregate(ngram_list, 'malicious_g')\n",
    "    legit_g = g_aggregate(ngram_list, 'legit_g')\n",
    "    _X = [len(parsed_sql), entropy(sql), legit_g, malicious_g]\n",
    "    #print(np.asarray(_X).reshape(1, -1))\n",
    "    print('%-40s: %s' % (sql, clf.predict(np.asarray(_X).reshape(1, -1))[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "select * from employees                 : legit\n",
      "'; exec master..xp_cmdshell             : malicious\n",
      "'any 'x'='x'                            : malicious\n",
      "from dorseys mom xp_cmdshell biache     : malicious\n",
      "select * from your_mom                  : legit\n"
     ]
    }
   ],
   "source": [
    "test_it('select * from employees')\n",
    "test_it(\"'; exec master..xp_cmdshell\")\n",
    "test_it(\"'any 'x'='x'\")\n",
    "test_it('from dorseys mom xp_cmdshell biache')\n",
    "test_it('select * from your_mom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello                                   : legit\n",
      "233                                     : malicious\n",
      "3'                                      : malicious\n",
      "3 order by 1/*                          : malicious\n",
      "3 order by 2/*                          : malicious\n",
      "3 order by 3/*                          : malicious\n",
      "3 order by 4/*                          : malicious\n",
      "3 order by 5/*                          : malicious\n",
      "3 union all select 1,2,3,4/*            : malicious\n",
      "3 union all select 1,2,3,4--            : malicious\n",
      "3 union all select 1,2,@@version,4/*    : malicious\n",
      "3 union all select 1,2,password,4,5 from admin/*: malicious\n",
      "3 union all select 1,2,concat(username,0x3a,password),4,5 from admin/*: malicious\n",
      "3 and 1=1                               : malicious\n",
      "3 and 1=2                               : malicious\n",
      "3 and substring(@@version,1,1)=4        : malicious\n",
      "3 and substsubstringring(@@versversionion,1,1)=4: malicious\n",
      "3 and substring(@@version,1,1)=5        : malicious\n",
      "3 and (select 1)=1                      : malicious\n",
      "3 and (select 1 from mysql.user limit 0,1)=1: malicious\n",
      "3 and (select 1 from users limit 0,1)=1 : malicious\n",
      "3 and (select substring(concat(1,password),1,1) from users limit 0,1)=1: malicious\n",
      "3 and ascii(substring((SELECT concat(username,0x3a,password) from users limit 0,1),1,1))>80: malicious\n",
      "3 and ascii(substring((SELECT concat(username,0x3a,password) from users limit 0,1),2,1))>99: malicious\n",
      "3 and ascii(substring((SELECT concat(username,0x3a,password) from users limit 0,1),1,1))>104: malicious\n",
      "3 and ascii(substring((SELECT concat(username,0x3a,password) from users limit 0,1),1,1))>107: malicious\n",
      "3 and ascii(substring((SELECT concat(username,0x3a,password) from users limit 0,1),1,1))>105: malicious\n",
      "' and (select substr(group_concat(pass),1,1) from users)='a: malicious\n",
      "-99 OR IF((ASCII(MID((select id from tb_users),1,1)) = 100),SLEEP(14),1) = 0 LIMIT 1--: malicious\n",
      "-99 OR (SELECT LOAD_FILE(concat('\\\\\\\\',(select id from tb_users), 'sec.cuc.edu.cn\\\\'))): legit\n",
      "-99 OR (SELECT id from tb_users INTO OUTFILE '\\\\\\\\tmp\\\\share\\\\output.txt'): legit\n",
      "1 OR 2-1=0                              : malicious\n",
      "areyouok                                : legit\n",
      "mamashuotebiechangdeparamshouldbemaliciousisnotalwaysright: legit\n",
      "mamashuo'tebiechangdeparamshouldbemaliciousisnotalwaysright: malicious\n",
      "1 and (SELECT count(*) FROM sysusers AS sys1, sysusers as sys2, sysusers as sys3, sysusers AS sys4, sysusers AS sys5, sysusers AS sys6, sysusers AS sys7, sysusers AS sys8)>1 and 75>(select top 1 ascii(substring(name,1,1)) from sysusers) : malicious\n"
     ]
    }
   ],
   "source": [
    "# 使用非./data目录中的样本数据测试\n",
    "with open('cheatsheet.txt') as cheatsheets:\n",
    "    for case in cheatsheets:\n",
    "        test_it(case.strip('\\n'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions:\n",
    "The combination of IPython, Pandas and Scikit Learn let us pull in some junky SQL data, clean it up, plot it, understand it and slap it with some machine learning!\n",
    "\n",
    "Clearly a lot more formality could be used, plotting learning curves, adjusting for overfitting, feature selection, on and on... there are some really great machine learning resources that cover this deeper material. In particular we highly recommend the work and presentations of Olivier Grisel at INRIA Saclay. http://ogrisel.com/\n",
    "\n",
    " - W. G.J. Halfond, J. Viegas, and A. Orso, \"A Classiﬁcation of SQL Injection Attacks and Countermeasure\",[http://www-bcf.usc.edu/~halfond/papers/halfond06issse.pdf]\n",
    " - A. K. Baranwa1, \"Approaches to detect SQL injection and XSS in web applications,\" 2012. [http://blogs.ubc.ca/computersecurity/files/2012/04/ABaranwal_ApproachesToDetectSQLinjection_XSSinWebApplication.pdf]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Recommended Readings\n",
    "\n",
    "* [Time-Based Blind SQL Injection using Heavy Queries | A practical approach for MS SQL Server, MS Access, Oracle and MySQL databases and Marathon Tool](https://www.defcon.org/images/defcon-16/dc16-presentations/alonso-parada/defcon-16-alonso-parada-wp.pdf)\n",
    "* [free online reference about SQL injection attacks](http://www.sqlinjection.net/)\n",
    "* [SQL Injection Wiki | grouped by DBs](http://www.sqlinjectionwiki.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 后记\n",
    "\n",
    "* 上述机器学习方法建立的分类器模型，如果需要获得良好的分类性能，数据质量和标记质量至关重要！其次是特征工程的作用，至关重要！\n",
    "* 由于每次训练过程是随机划分训练数据集和测试数据集，所以上述notebook脚本多次重复运行得到的分类器的分类性能会有细微差异。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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