{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **MITRE ATT&CK PYTHON CLIENT**: Data Sources\n",
    "------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals:\n",
    "* Access ATT&CK data sources in STIX format via a public TAXII server\n",
    "* Learn to interact with ATT&CK data all at once\n",
    "* Explore and idenfity patterns in the data retrieved\n",
    "* Learn more about ATT&CK data sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. ATT&CK Python Client Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can install it via PIP: **pip install attackcti**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import ATT&CK API Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from attackcti import attack_client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Import Extra Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import *\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import altair as alt\n",
    "alt.renderers.enable('default')\n",
    "\n",
    "import itertools\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Initialize ATT&CK Client Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lift = attack_client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Getting Information About Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting ALL ATT&CK Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_techniques = lift.get_all_techniques(stix_format=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the first technique in our list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'external_references': [{'external_id': 'T1500',\n",
       "   'source_name': 'mitre-attack',\n",
       "   'url': 'https://attack.mitre.org/techniques/T1500'},\n",
       "  {'url': 'https://www.clearskysec.com/wp-content/uploads/2018/11/MuddyWater-Operations-in-Lebanon-and-Oman.pdf',\n",
       "   'source_name': 'ClearSky MuddyWater Nov 2018',\n",
       "   'description': 'ClearSky Cyber Security. (2018, November). MuddyWater Operations in Lebanon and Oman: Using an Israeli compromised domain for a two-stage campaign. Retrieved November 29, 2018.'},\n",
       "  {'url': 'https://blog.trendmicro.com/trendlabs-security-intelligence/windows-app-runs-on-mac-downloads-info-stealer-and-adware/',\n",
       "   'source_name': 'TrendMicro WindowsAppMac',\n",
       "   'description': 'Trend Micro. (2019, February 11). Windows App Runs on Mac, Downloads Info Stealer and Adware. Retrieved April 25, 2019.'}],\n",
       " 'kill_chain_phases': [{'phase_name': 'defense-evasion',\n",
       "   'kill_chain_name': 'mitre-attack'}],\n",
       " 'x_mitre_version': '1.0',\n",
       " 'url': 'https://attack.mitre.org/techniques/T1500',\n",
       " 'matrix': 'mitre-attack',\n",
       " 'technique_id': 'T1500',\n",
       " 'object_marking_refs': ['marking-definition--fa42a846-8d90-4e51-bc29-71d5b4802168'],\n",
       " 'type': 'attack-pattern',\n",
       " 'modified': '2019-04-29T21:13:49.686Z',\n",
       " 'created_by_ref': 'identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5',\n",
       " 'tactic': ['defense-evasion'],\n",
       " 'id': 'attack-pattern--cf7b3a06-8b42-4c33-bbe9-012120027925',\n",
       " 'technique': 'Compile After Delivery',\n",
       " 'created': '2019-04-25T20:53:07.719Z',\n",
       " 'technique_description': 'Adversaries may attempt to make payloads difficult to discover and analyze by delivering files to victims as uncompiled code. Similar to [Obfuscated Files or Information](https://attack.mitre.org/techniques/T1027), text-based source code files may subvert analysis and scrutiny from protections targeting executables/binaries. These payloads will need to be compiled before execution; typically via native utilities such as csc.exe or GCC/MinGW.(Citation: ClearSky MuddyWater Nov 2018)\\n\\nSource code payloads may also be encrypted, encoded, and/or embedded within other files, such as those delivered as a [Spearphishing Attachment](https://attack.mitre.org/techniques/T1193). Payloads may also be delivered in formats unrecognizable and inherently benign to the native OS (ex: EXEs on macOS/Linux) before later being (re)compiled into a proper executable binary with a bundled compiler and execution framework.(Citation: TrendMicro WindowsAppMac)\\n',\n",
       " 'contributors': ['Ye Yint Min Thu Htut, Offensive Security Team, DBS Bank',\n",
       "  'Praetorian'],\n",
       " 'permissions_required': ['User'],\n",
       " 'data_sources': ['Process command-line parameters',\n",
       "  'Process monitoring',\n",
       "  'File monitoring'],\n",
       " 'technique_detection': 'Monitor the execution file paths and command-line arguments for common compilers, such as csc.exe and GCC/MinGW, and correlate with other suspicious behavior to reduce false positives from normal user and administrator behavior. The compilation of payloads may also generate file creation and/or file write events. Look for non-native binary formats and cross-platform compiler and execution frameworks like Mono and determine if they have a legitimate purpose on the system.(Citation: TrendMicro WindowsAppMac) Typically these should only be used in specific and limited cases, like for software development.',\n",
       " 'platform': ['Linux', 'macOS', 'Windows'],\n",
       " 'system_requirements': ['Compiler software (either native to the system or delivered by the adversary)'],\n",
       " 'defense_bypassed': ['Static File Analysis',\n",
       "  'Binary Analysis',\n",
       "  'Anti-virus',\n",
       "  'Host intrusion prevention systems',\n",
       "  'Signature-based detection']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_techniques[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing semi-structured JSON data into a flat table via **pandas.io.json.json_normalize**\n",
    "* Reference: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_normalized = json_normalize(all_techniques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>capec_id</th>\n",
       "      <th>capec_url</th>\n",
       "      <th>contributors</th>\n",
       "      <th>created</th>\n",
       "      <th>created_by_ref</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>defense_bypassed</th>\n",
       "      <th>detectable_by_common_defenses</th>\n",
       "      <th>detectable_explanation</th>\n",
       "      <th>difficulty_explanation</th>\n",
       "      <th>...</th>\n",
       "      <th>tactic_type</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_description</th>\n",
       "      <th>technique_detection</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>type</th>\n",
       "      <th>url</th>\n",
       "      <th>x_mitre_deprecated</th>\n",
       "      <th>x_mitre_old_attack_id</th>\n",
       "      <th>x_mitre_version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Ye Yint Min Thu Htut, Offensive Security Team...</td>\n",
       "      <td>2019-04-25T20:53:07.719Z</td>\n",
       "      <td>identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "      <td>[Static File Analysis, Binary Analysis, Anti-v...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>Adversaries may attempt to make payloads diffi...</td>\n",
       "      <td>Monitor the execution file paths and command-l...</td>\n",
       "      <td>T1500</td>\n",
       "      <td>attack-pattern</td>\n",
       "      <td>https://attack.mitre.org/techniques/T1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  capec_id capec_url                                       contributors  \\\n",
       "0      NaN       NaN  [Ye Yint Min Thu Htut, Offensive Security Team...   \n",
       "\n",
       "                    created                                  created_by_ref  \\\n",
       "0  2019-04-25T20:53:07.719Z  identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5   \n",
       "\n",
       "                                        data_sources  \\\n",
       "0  [Process command-line parameters, Process moni...   \n",
       "\n",
       "                                    defense_bypassed  \\\n",
       "0  [Static File Analysis, Binary Analysis, Anti-v...   \n",
       "\n",
       "  detectable_by_common_defenses detectable_explanation difficulty_explanation  \\\n",
       "0                           NaN                    NaN                    NaN   \n",
       "\n",
       "   ... tactic_type               technique  \\\n",
       "0  ...         NaN  Compile After Delivery   \n",
       "\n",
       "                               technique_description  \\\n",
       "0  Adversaries may attempt to make payloads diffi...   \n",
       "\n",
       "                                 technique_detection technique_id  \\\n",
       "0  Monitor the execution file paths and command-l...        T1500   \n",
       "\n",
       "             type                                        url  \\\n",
       "0  attack-pattern  https://attack.mitre.org/techniques/T1500   \n",
       "\n",
       "  x_mitre_deprecated x_mitre_old_attack_id x_mitre_version  \n",
       "0                NaN                   NaN             1.0  \n",
       "\n",
       "[1 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_normalized[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Re-indexing Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques = techniques_normalized.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[impact]</td>\n",
       "      <td>Endpoint Denial of Service</td>\n",
       "      <td>T1499</td>\n",
       "      <td>[SSL/TLS inspection, Web logs, Web application...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>[defense-evasion, discovery]</td>\n",
       "      <td>Virtualization/Sandbox Evasion</td>\n",
       "      <td>T1497</td>\n",
       "      <td>[Process monitoring, Process command-line para...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[impact]</td>\n",
       "      <td>Network Denial of Service</td>\n",
       "      <td>T1498</td>\n",
       "      <td>[Sensor health and status, Network protocol an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix                 platform                        tactic  \\\n",
       "0  mitre-attack  [Linux, macOS, Windows]             [defense-evasion]   \n",
       "1  mitre-attack                  [Linux]                 [persistence]   \n",
       "2  mitre-attack  [Linux, macOS, Windows]                      [impact]   \n",
       "3  mitre-attack                [Windows]  [defense-evasion, discovery]   \n",
       "4  mitre-attack  [Linux, macOS, Windows]                      [impact]   \n",
       "\n",
       "                        technique technique_id  \\\n",
       "0          Compile After Delivery        T1500   \n",
       "1                 Systemd Service        T1501   \n",
       "2      Endpoint Denial of Service        T1499   \n",
       "3  Virtualization/Sandbox Evasion        T1497   \n",
       "4       Network Denial of Service        T1498   \n",
       "\n",
       "                                        data_sources  \n",
       "0  [Process command-line parameters, Process moni...  \n",
       "1  [Process command-line parameters, Process moni...  \n",
       "2  [SSL/TLS inspection, Web logs, Web application...  \n",
       "3  [Process monitoring, Process command-line para...  \n",
       "4  [Sensor health and status, Network protocol an...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of  500  techniques\n"
     ]
    }
   ],
   "source": [
    "print('A total of ',len(techniques),' techniques')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Removing Revoked Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_techniques_no_revoked = lift.remove_revoked(all_techniques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of  485  techniques\n"
     ]
    }
   ],
   "source": [
    "print('A total of ',len(all_techniques_no_revoked),' techniques')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Extractinng Revoked Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_techniques_revoked = lift.extract_revoked(all_techniques)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of  15  techniques that have been revoked\n"
     ]
    }
   ],
   "source": [
    "print('A total of ',len(all_techniques_revoked),' techniques that have been revoked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The revoked techniques are the following ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remotely Install Application\n",
      "Insecure Third-Party Libraries\n",
      "Fake Developer Accounts\n",
      "Detect App Analysis Environment\n",
      "Malicious Software Development Tools\n",
      "Biometric Spoofing\n",
      "Device Unlock Code Guessing or Brute Force\n",
      "Malicious Media Content\n",
      "Abuse of iOS Enterprise App Signing Key\n",
      "App Delivered via Web Download\n",
      "App Delivered via Email Attachment\n",
      "Malicious or Vulnerable Built-in Device Functionality\n",
      "Malicious SMS Message\n",
      "Exploit Baseband Vulnerability\n",
      "Stolen Developer Credentials or Signing Keys\n"
     ]
    }
   ],
   "source": [
    "for t in all_techniques_revoked:\n",
    "    print(t['technique'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Updating our Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_normalized = json_normalize(all_techniques_no_revoked)\n",
    "techniques = techniques_normalized.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Techniques Per Matrix\n",
    "Using **altair** python library we can start showing a few charts stacking the number of techniques with or without data sources.\n",
    "Reference: https://altair-viz.github.io/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-mobile-attack</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-pre-attack</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                matrix  technique\n",
       "0         mitre-attack        244\n",
       "1  mitre-mobile-attack         67\n",
       "2     mitre-pre-attack        174"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = techniques\n",
    "data_2 = data.groupby(['matrix'])['technique'].count()\n",
    "data_3 = data_2.to_frame().reset_index()\n",
    "data_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
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S/G4FCgiu6urcHwWiQBQYvwITBEEjakKXUHj9EoEzUmgUzuigUDYPgl3Ax+lbBk3+sozo6eDvAC4uMWpm/24k8aklyOL0r9mpYKb9xLJGwDw4/1BeMtInUL5mZq6/cGqgxgihee4igoKhCz7d/aMbwXPET3YQ5syPI5JO9TK4IyAavbNM5yrlvHw5s9ghcfP6j+XsYoNQaqIWgmyXR8sp9BoJNSpqsEk75m/XKyA4/va/Ug4DgivJlZujQBSIAhuhwMRBsIPCeRD070JZN/Wqm/ZlRNCRNL8zMugqXqNtRuI6KJN/hL9u+pdDsd3nDgSFPEHU6KGjdY4gOkTs4lMXkDpFzB1GhK03zYGgU7i8X2BzTqPTvARU7XSXaTv/0XvPV4aGjQgKfkY8jTo60miE8M8LBDo/0PIJjveYiRAKqE4zc2cR/+58Roe1u7mNQueOV0BwI14By2cyILi8VrkzCkSBKLApCkwQBLvFDsJS91kQdBhWABSonOJ1ZBliFZocxvVvglIHbkbHnFblHLrZvX8PBIIODTuNy/uFPqdteQlsgqWRRecKejkEa9ouFjGfwlcXmXTamc97vassLhUau8sRRucbCndO9xIA3VbuRWWo189vmZmuJkwKoA5le7/w6IIV82QeLaeLTBxZNPLp/ESHw915xHmI/v1UV0BwU94CS+YzILikULktCkSBKLBBCkwMBFf1zOzUq9ln3enjF8vOH0vta7iLYYdbHdKdn65l2s7/O9DlFDCneu12n3n8hZnvu7L4dy+hdna6mvAo8Al1s+V24Yl53BH2DpTBgOCq1W3k9wcER+6gZC8KRIEosIYCAcE1RMsjSykQEFxKps25KSC4Ob5KTqNAFIgCyyqwaSC4bLlyX3sFAoLtfVA1BwHBqnImsSgQBaLAKBQICI7CDVuZiYDglrk1ILhlDk1xokAUiAI/21ts/1EnHXOkixB6u/bt27d/z5494YLeFB5nwnH4OP2ydq4CgmtLlwejQBSIAqNVICA4WtdsfMYCghvvwlMWICC4ZQ5NcaJAFIgCGxgR/OKtD19qle5hx798HQ7xIAbP9XUbl6XsbGAlcnWwZy17LG2v1zoO6DVDSfzgFAgIHpx+eToKRIEoMEYFNi0i2DMIum3KHcuG0J5D7H+e/jGmy6PjPPrtpmWvP/cZdI/BZS/PGz68bGa97DNr3RcQXEu28T4UEByvb5KzKBAFosC6CkwMBIUoTwIxIubG0J6+8aiy4bLHy3lSx53Lhs+eJ+xZulcv5++6Z9/HyskiHt326LLBs2fyfnNGf2FSUPt6OeLt8QW6BDaPbXNj5ruXs4c9peRWJU9uNO3efrPXDYHXlf39fPYlZcNn0/CIOY+f83JT5x+Vo+HcRPqYco6y+/9py+PpPNfYTbEtR7eR9lFFBzWoHgENCK7bKkf6XEBwpI5JtqJAFIgCB6HAxEDQE0Q8cUPo80g3I35+9vi1IwoEvhq4WTkzWNASCAVAo26es+txcp4q8kjgdsB1yv87kHIDZs8CFtp83tM8hDnPKPa0kBuVU0FOLCd0vBF4MfBKwJNHust0BFEhT0B8fzl1xIiep5946sjvlZNHHlxsmCeB7w0FQs9WINB7PRFEyL0XcG3gK+UMYk9Omd/Q+iBq1P8+GhCsIuN4EgkIjscXyUkUiAJRoJYCEwNBj5UT+B4OeN6v4CTY+fm1wNXKWbxGzIQzj5b7OPDWAl4CmceuCYcfBTwZxLN4PW7NKOH3gD8t0HeVcqqHaXv28D3L+b7e58khQqF/82QQQc2TOzzn1/x8uhxFZwRzT4FWI5kCq+cDu8r7WsCtgV8u9x46cz6wYOh3RiuNTr4TcP7jBcq5xNr2Mm1hs5crINiLrO0SDQi20z6Wo0AUiAJ9KTBBEPQ84ccBQuH1S9TOSOErSnTw+DJ0OguC3Rm/pymQ95dl6Fe3eMavi0uMuP0QeFMBL8/o/VaBzA4Ef6vY684rdnj3p8W3nylHuV2qpPkh4AsFMAXEvSVf5yp5vXw5D9lzhJ8K/CPwYeBVpVyWx+Fky9rNcxRcjWYa2XxvsfOAvupWQLAvZRulGxBsJHzMRoEoEAV6VGDiINhB4TwI+vfXAI8BPgKcUCDMiKDz+vzOyODlyvw+o3gd0J0ReB9wV+BtwIOA/ypD0p095+kJZ4Ko8xLvVCJ1Dvl2l6DqHMabA+crQ8NGBAW/4wCHdG9fIoR/XiDQ+YHmUXC8x0yE0KjldYGHlL8LpA51fxHws9BZ/QoIVpe0bYIBwbb6x3oUiAJRoA8FJgiC3UIJQav7LAg+GxDUhDEXVRxZhlwFLoeI/ZuQ1UXzjKx9uwzrfmLGNx0ICmVewpZb0hiN6+z5d4d2/6Hc8y7geiVC1yXlNi/OZzSdNxcAfGaZc+hQr5/fUkDVYWhhUvj8arlfePzdAqxGJ82ri0wcgjZ6+ZQypP2EMg/Rv1e9AoJV5WyfWECwvQ+SgygQBaJAbQUmBoKryucKYFfezl+nLfP8vrHDatsOBP9vGSp2eHi368xlFa/zBHe6tPMLZR6h33f58e9egukZih3/LTwKfELdbN5deGI5qsPegQQNCK5a3UZ+f0Bw5A5K9qJAFIgCaygQEFxDtAM/Igi62OOygKA42SsguGWuDwhumUNTnCgQBaLABp4ssiFOm43SbUiW62czIFhf06YpBgSbyh/jUSAKRIFeFNi0iGAvIiTRXhQICPYia7tEA4LttI/lKBAFokBfCgQE+1I26QYEt6wOBAS3zKEpThSIAlEgQ8OpAz0qEBDsUdwWSQcEW6gem1EgCkSBfhVIRLBffaecekBwy7wfENwyh6Y4USAKRIFEBFMHelQgINijuC2SDgi2UD02o0AUiAL9KpCIYL/6Tjn1gOCWeT8guGUOTXGiQBSIAokIpg70qEBAsEdxWyQtCL7tmxc8vIXtO11nz8uf//f7JmV7imW2bk2x3FMsc3w9/Jv0QPXspGOO8Czd3q59+/bt37NnT7igN4XHmXAcPk6/rJ0rQfDoo49u4teWL5FWtlvZtYLE9trNZK0Ho/dash3UQ600b2V3yu36oCpKHj4oBZoAw0HlOA8fUIGA4LAVJB3GsHq37Cjj6/h6CAWmWs+G0DY2dlYgILhlNSMgOKxDp/rSnmK5p1jmluDd0nZ8Pex7NNbaKhAQbKt/desBweqSHjDBdBjD6h04mI7e8fW0fD18aWOxUyAguGV1ISA4rEMDgsPqHTiYjt7x9bR8PXxpYzEguKV1ICA4rGMDgsPqHTiYjt7x9bR8PXxpYzEguKV1QBC810+esqWl2+xi/eRHpzn0rI/48n/VKkUgtJaSy6UTvZfTqeZdrTRvZbcl/La2XbPeJK3VFMjQ8Gp6jf7ugOB4XRQQrOObVp10K7utO+gplnuKZW5dz+q8HZLKOgoEBNdRbcTPBATH65yAYB3ftOqkW9lt3UFPsdxTLHPrelbn7ZBU1lEgILiOaiN+JiA4XucEBOv4plUn3cpu6w56iuWeYplb17M6b4ekso4CAcF1VBvxMwHB8TonIFjHN6066VZ2W3fQUyz3FMvcup7VeTsklXUUCAiuo9qInwkIjtc5AcE6vmnVSbey27qDnmK5p1jm1vWsztshqayjQEBwHdVG/ExAcLzOCQjW8U2rTrqV3dYd9BTLPcUyt65ndd4OSWUdBQKC66g24mcCguN1TkCwjm9addKt7LbuoKdY7imWuXU9q/N2SCrrKBAQXEe1ET8TEByvcwKCdXzTqpNuZbd1Bz3Fck+xzK3rWZ23Q1JZR4GA4DqqjfiZgOB4nRMQrOObVp10K7utO+gplnuKZW5dz+q8HZLKOgoEBNdRbcTPBATH65yAYB3ftOqkW9lt3UFPsdxTLHPrelbn7ZBU1lEgILiOaiN+JiA4XucEBOv4plUn3cpu6w56iuWeYplb17M6b4ekso4CAcF1VBvxMwHB8TonIFjHN6066VZ2W3fQUyz3FMvcup7VeTsklXUUqAGCpwV+so7xPFNfgYBgfU1rpRgQrKNkq066ld3WHfQUyz3FMreuZ3XeDkllHQVWAUHvfSrwAuADxdiFgNcA1wa+vk4G8kxdBQKCdfWsmVpAsI6arTrpVnZbd9BTLPcUy9y6ntV5OySVdRRYBQRN/2XAEcD9gW8CzwO+Clwc+MY6GcgzdRUICNbVs2ZqAcE6arbqpFvZbd1BT7HcUyxz63pW5+2QVNZRYFUQPCvwUuCGxZgQeA3gM+sYzzP1FQgI1te0VooBwTpKtuqkW9lt3UFPsdxTLHPrelbn7ZBU1lFgVRC8NXDcnKF7AH+ReYLryF//mYBgfU1rpRgQrKNkq066ld3WHfQUyz3FMreuZ3XeDkllHQVWBcHjgSsBvw18C3g6cH3gAhkaXkf++s8EBOtrWivFgGAdJVt10q3stu6gp1juKZa5dT2r83ZIKusosCoIOiT8BuDHM8aMEvo35wzmaqxAQLCxAw5gPiBYxzetOulWdlt30FMs9xTL3Lqe1Xk7JJV1FFgWBC9ZVgWfCTgjsH/GmNvHfAr40ToZyDN1FQgI1tWzZmoBwTpqtuqkW9lt3UFPsdxTLHPrelbn7ZBU1lFgGRD8eeA7wAOBSwB33MHQObN9zDry138mIFhf01opBgTrKNmqk25lt3UHPcVyT7HMretZnbdDUllHgWVA8HTA9YDPAmcHhL7Z6N/pgTcBP1gnA3mmrgIBwbp61kwtIFhHzVaddCu7rTvoKZZ7imVuXc/qvB2SyjoKLAOCXboOCb8b+DPghesYyzP9KxAQ7F/jdS0EBNdV7pTPteqkW9lt3UFPsdxTLHPrelbn7ZBU1lFgFRD03m6V8FWA/54xOLt4ZJ185JlKCgQEKwnZQzIBwTqituqkW9lt3UFPsdxTLHPrelbn7ZBU1lFgFRA0ffcLvPOcoRoni1wE+C7w5XUKcZDPXBA4M/DxuXR+DfggcP6yVY7lbHVdAfgo8MNFGQgILlKo3fcBwTrat+qkW9lt3UFPsdxTLHPrelbn7ZBU1lFgVRC8O/CrBdo6e0LUHwHfXicD5ZnfBL4CfA94GnDTuZXJB5H0wkdd/HIu4Ilzdx4FvBw4EvgX4C0LU9r9BgH6WcDHgPcCV5vT8EBJ66OTyiKdhTAaEDwIL/X8aECwjsCtOulWdlt30FMs9xTL3Lqe1Xk7JJV1FFgVBO9a9gz8XDF2GuCRwOPKyuLd8nB02Xbm4eWc4p8ATynPPgoQBL8GHFuOrLsD8J/A5YDfL5tYHwa8Bji0wJlwNHsZ2XNl87kB730sYNrnAX4d+HRJ+1WA2+DcBngdcEvg+QXSLgzcHHgHIPS+qPzbZ/8J+F3A5/8ZMI+fmMuDi2meWc5jNn93KSut31gW27wE+JNi727AHwJPKOc1d3Yth8+6Zc99iyaCpOc7uzDnb0r6Lt451RUQXKcZDPNMQLCOzq066VZ2W3fQUyz3FMvcup7VeTsklXUUWBYEPU/YjaP9721l30DtGUkzenfxBSeLvKwMbT4X+LtyTN0LgL8GblciZMKWexJ6j8OyrlTu5iT+K/D58vf/At4F3GAmH+blosDbC/RdpkTgBEDzdkSJWhrZc5jVYegPAL8BXLaA7HUKNL62pCV8eXzejQtEulL6xSUPAqP3XaxEMTvtHTY/C/AkQNATOC3D64EnF4A0D5bPsgqNQrB5eg5w5VIGAfadwHuKvqb1DEAdH1IAdkd/BwTXaQbDPBMQrKNzq066ld3WHfQUyz3FMreuZ3XeDkllHQWWBUGHR1+6iwEjUw4Xu9fgbpcAc+8S+fLzQ4F9BWyMAgpxgqBDn0bnrgX8P+A/gL8FLl+gUdAymvgw4AElYmjU0CiZgCXwGXV0zqGRytuWz0LbnxbQc9Wzl0PCboztQhcjeQKbl8foeY+2tNGBoMAowJl/I6EduFmuc5QIp7BqXp1X6NCydjuAEw4/AnyoAJ9H9JmmUCmwCpV/XPIRuG4AACAASURBVCKOguxPgfMC3y9RQ9PT5v8/R3Pv3r2POOSQQyzvKa57/cRga66xKRAQrOORVp10K7utO+gplnuKZW5dz+q8HZLKOgosC4Km7X6CTy3DpQ6N+m+vRfsHakOAEaq+UT4LPH4Wupyb5zCoIOhw8CsKTN2+wF0X/RO4TMfhUa+3Ar9ShoJ91ijfTUp0z42vPQPZKJyQaZoOXwtsHfAJgl8vETz/LpB6OXxrRM48zoKgQGgUzzl+gqCgZqTPyKWRvzcXSLtqifSdtQxjmwfhcRYEveeKxZbD2V8ow8QOoTuX0Oik6RtdNBJpngXnBwGXLvfv6O9EBNdpBsM8ExCso3OrTrqV3dYd9BTLPcUyt65ndd4OSWUdBVYBQdN3YYjgIxB5uYpVMHEYVbDb6ZoFQcHLoV8By8/zIGgax5WhUoeMu7l5FyqbVgt+RgRPAB5domudTeFvFgS7zx0IHl6iipcCzJNA5zw/o4fO1XOI1qFuI3ZG54wKzoKg0blrlsimkOcQuZG8bpGMaRq9FFiNTu4tq6CFuBOBx5SI4CdLJLBbEPNXwHVLlNOznD9T5kR+qQyB3wm4D3DPEvG8WZm3KCie6goIrtMMhnkmIFhH51addCu7rTvoKZZ7imVuXc/qvB2SyjoKrAqCrui915yhRdvHHAgEjZQJXMKZ8OQWLs6Nc+6cQ83+zYig1/3KvX5+fImOzcKQIGjUzu9mPwuCzy6w5TCqizW8HDo28nejMoTbFesWwKtnopimaT6cU/hKQFjz8j4XjsxeRjG7zbYd4jZt5yj6jOU0X/7f01m0L4x6ma7pOUfQhS7a78r54ALaQqnQ6UIWI4dGIE91BQTXaQbDPBMQrKNzq066lV1Vi+06dWfZVKL3skrlvm1QYBUQ9MxhFy84xGoEy/lunypDnsLO7LFzB6NNF2008jd/OVTqvL4DzUdcZNv5gALk7IbYPmO0Uz1OXpCAzzsMvduQuNFCI6XO7TNN7/X6uZlnHFZ3bqLf+9n5gg55d3MWz1D+vSgvAcFF3h7R9wHBOs5o1Um3shsQ3LNKP1WlksXXVWRMIhuiwCoNTBD8MOBQ5S+VOW5Gqxy+desTh1RzNVYgEcHGDjiA+YBgHd+06qRb2Q0IBgTrtJzFqbSs44tzlzv6UmAVEPRe5+a55Yr727moorucZ+dihlyNFQgINnZAQLB3B7TqrFrZDQgGBHtvVMVAyzo+VBlj59QKrAKCPu2wqBs8u3rXzaVdEOHcPVcT77h4IaIPq0BAcFi9V7GWiOAqau1+b6vOqpXdgGBAsE7LWZxKyzq+OHe5oy8FVgVB8+HCDuewdZfz2jw6rdYcwb7KOol0A4LjdXNAsI5vWnVWrewGBAOCdVrO4lRa1vHFucsdfSmwKggeU45xm8+Pq2DdDiZXYwUCgo0dcADzAcE6vmnVWbWyGxAMCNZpOYtTaVnHF+cud/SlwCog2K0adjsX5wcaCfRy1etbykrZvvKZdJdUICC4pFANbgsI1hG9VWfVym5AMCBYp+UsTqVlHV+cu9zRlwKrgKDA98ZySofHpuUaoQIBwRE6pWQpIFjHN606q1Z2A4IBwTotZ3EqLev44tzljr4UWAUEzYPHsr1gLjNfLCdl7HaySF95T7o7KBAQHG+1CAjW8U2rzqqV3YBgQLBOy1mcSss6vjh3uaMvBVYBwTMC7yvnAr9hZpWwGyh7+kZAsC8vrZBuQHAFsQa+NSBYR/BWnVUruwHBgGCdlrM4lZZ1fHHuckdfCqwKgu8Gji3nBfeVp6R7EAoEBA9CvJ4fDQjWEbhVZ9XKbkAwIFin5SxOpWUdX5y73NGXAquAoHl4EXBoObu3y5NzB183c5RaX3lNuksoEBBcQqRGtwQE6wjfqrNqZTcgGBCs03IWp9Kyji/OXe7oS4FVQNB7jwMO3yEz2T6mLw+tmG5AcEXBBrw9IFhH7FadVSu7AcGAYJ2WsziVlnV8ce5yR18KrAKC5uEwwLmCs5fbyPwr8OO+Mpl0l1cgILi8VkPfGRCso3irzqqV3YBgQLBOy1mcSss6vjh3uaMvBVYFwb7ykXQrKRAQrCRkD8kEBOuI2qqzamU3IBgQrNNyFqfSso4vzl3u6EuBgGBfyjZKNyDYSPglzAYElxBpiVtadVat7AYEA4JLNIsqt7Ss41UKkETWUiAguJZs430oIDhe3wQE6/imVWfVym5AMCBYp+UsTqVlHV+cu9zRlwIBwb6UbZRuQLCR8EuYDQguIdISt7TqrFrZDQgGBJdoFlVuaVnHqxQgiaylQEBwLdnG+1BAcLy+CQjW8U2rzqqV3YBgQLBOy1mcSss6vjh3uaMvBQKCfSnbKN2AYCPhlzAbEFxCpCVuadVZtbIbEAwILtEsqtzSso5XKUASWUuBgOBaso33oYDgeH0TEKzjm1adVSu7AcGAYJ2WsziVlnV8ce5yR18KBAT7UrZRugHBRsIvYTYguIRIS9zSqrNqZTcgGBBcollUuaVlHa9SgCSylgIBwbVkG+9DAcHx+iYgWMc3rTqrVnYDggHBOi1ncSot6/ji3OWOvhQICPalbKN0A4KNhF/CbEBwCZGWuKVVZ9XKbkAwILhEs6hyS8s6XqUASWQtBQKCa8k23ocCguP1TUCwjm9adVat7AYEA4J1Ws7iVFrW8cW5yx19KRAQ7EvZRukGBBsJv4TZgOASIi1xS6vOqpXdgGBAcIlmUeWWlnW8SgGSyFoKBATXkm28DwUEx+ubgGAd37TqrFrZDQgGBOu0nMWptKzji3OXO/pSICDYl7KN0hUEjz766CZ+bfkSaWW7ld3AQeBgqFfMFOv4FMvc+p0yVH2OnVMr0AQY4oj+FAgI9qftTimnwxhW75adVXwdXw+hwFTr2RDaxsbOCgQEt6xmBASHdehUX9pTLPcUy9wSvFvajq+HfY/GWlsFAoJt9a9uPSBYXdIDJpgOY1i9AwfT0Tu+npavhy9tLHYKBAS3rC4EBId1aEBwWL0DB9PRO76elq+HL20sBgS3tA4EBId1bEBwWL0DB9PRO76elq+HL20sBgS3tA4Igrf7wAcOb1G8H93myJef/qUvm5TtKZbZujXFcm9jmQ87/vgTFr0rpvhjZ4plbgnei+pgvu9XgQwN96vv4Kn/Dwi+f3C7MRgFosBmKXAI+197vuNPuNmiXE8RiqZY5oDgopawvd8HBLfMtwHBLXNoihMFelIgILi7sAHBnipdkh2lAgHBUbpl/UwFBNfXLk9GgSkpEBAMCM4r0BKAp9T2xlbWgODYPHKQ+QkIHqSAeTwKTESBgGBAMCA4kca+oJgBwS2rBwHBLXNoihMFelIgIBgQDAj21Lg2LNmA4IY5bFF2A4KLFMr3USAKqEBAMCAYEMy74H/eBbm2SoGA4Fa5M4WJAr0pEBAMCAYEe2teG5VwQHCj3LU4swHBxRrljigQBRIRPFAdaLloYqq20ybbKRAQbKd9L5YDgr3ImkSjwNYpkIhgIoKJCG5ds16rQAHBtWQb70MBwfH6JjmLAmNSICAYEAwIjqlFtstLQLCd9r1YDgj2ImsSjQJbp0BAMCAYENy6Zr1WgQKCa8k23ocCguP1TXIWBcakQEAwIBgQHFOLbJeXgGA77XuxHBDsRdYkGgW2ToGAYEAwILh1zXqtAgUE15JtvA8FBMfrm+QsCoxJgYBgQDAgOKYW2S4vAcF22vdiOSDYi6xJNApsnQIBwYBgQHDrmvVaBQoIriXbeB8KCI7XN8lZFBiTAgHBgGBAcEwtsl1eAoLttO/FckCwF1mTaBTYOgUCggHBgODWNeu1ChQQXEu28T4UEByvb5KzKDAmBQKCAcGA4JhaZLu8BATbad+L5YBgL7Im0SiwdQoEBAOCAcGta9ZrFSgguJZs430oIDhe3yRnUWBMCgQEA4IBwTG1yHZ5CQi2074XywHBXmRNolFg6xQICAYEA4Jb16zXKlBAcC3ZxvtQQHC8vknOosCYFAgIBgQDgmNqke3yEhBsp30vlgOCvciaRKPA1ikQEAwIBgS3rlmvVaCA4FqyjfehgOB4fZOcRYExKRAQDAgGBMfUItvlZRtA8CLAd4Evt5Oxd8tXAD4K/HCRpYDgIoXyfRSIAioQEAwIBgTzLvifd8HmX78JfAX4HvA04KbA/pEV6y+AZwEfA94LXK3A6zLZ1EcnAXcEvrrogYDgIoXyfRSIAgHBA9eBffv27d+zZ0+T/nGqttMq2ynQpKKvWNyjgTMCDwfuD/wEeArwSOBRgCD4NeBY4BrAHYD/BC4H/D5wJeAw4DXAocCRBaxms3Et4ObA/ymQdmPgPcADCyxfHbhZgcxXAf9c7HxirixnB54JHFFs3AW4BPBG4LPAS4A/AZ4P3A34Q+AJBfC0/w7gguXZSwL3LeUSJC376YG/Kemb3qmugOCKtSu3R4GJKpCIYCKCiQhOtPHPFXsTQPBlZVj0ucDfAccBLwD+GrhdAbdPA6cFvOfXgOsBTweuD/wr8Pny9/8C3gXcAPjUjBa3BJ5RoPGXS9oOOT+7pPtHwLmBF5d0Lgy8FrhYiUR2Sd0ZOAvwpAJ6Zyr5eD3w5AKQ/1LSML9CoyDr0O9zgCsDby+A+84Co0Y4hUbzpxYPAV63W/UNCKZhR4EosIwCAcGAYEBwmZay/fdsCgjeu0TNBKGHAvsKFBkFvCggCDpsaqTN6N7/A/4D+Fvg8gXshDSjiQ8DHlAihkYNjbAJbALgnxWXH18idUbh7leGnu9aAM48nGYG3MzbOUqUUuDU3vmBowosdgAnHH4E+FABvm8BlwWEyl8vUPnHgBHHywA/Bc4LfL/kxfSERcux6xUQ3P5GmxJGgRoKBAQDggHBGi1p89MYOwiaP+FHcPtG+Sws+VlYeyLgEKog6HDwKwqI3R4w8tZF/4Q103Fo1eutwK+UKJ/PXhxwWNcoopewJ7g5tNvZ21uig87xEwQFNSN9Rh8FyTcXSLtqifSdtQxFGwns0utA0HuuWIaKHX7+QhkmdhjcuYTXKekbXfxRyZfw+yDg0uV+9u7d+4hDDjnEIfNTXLf7wPs3v2amBFEgCvSqQEAwIBgQ7LWJbUzimwSCXy9Dv0Khn+dBUDh02NhhVoeMBbx/Ai4EvKmAnxHBE4BHl8hc5yiHho0iOk/vXOU7h32NEHb2bgFcExAqhby3lUjet0siamkE0u+FUMHRlczC5YnAY0pE8JMlEtgtavkr4LolUnlD4DNliPpLBWTvBNwHuGeZG+hcRfMpiJ7qSkRwY9peMhoFmioQEAwIBgSbNsHRGN9kEDTKJqhdqoDXxwHn1Tnv7jszEUHFdni3G/Z9fImszYKUICggdtcfAM+biUYKnj8HvBIQ1rwEQ4dxZy8jkS8sf3CY+kYl2ugz2nfhiP8/Z1nsYnTRy3RNzzmCLmx5dfm7eX0w4GIRgVTodEGJkUMjkAHB0TSlZCQKbJYCAcGAYEBws9psX7kdOwiuWm4XYHgZ+Zu/HGZ1Wxkhcf66LXC2AlwC38kHMOwQsvsW/mCXe4wWut+fc/vOPLNNjOl2z5wO+HH53s/OF3TY2vz59zOUfx8oHzuaT0Rw1SqT+6PANBUICAYEA4LTbPvzpd42EFzXq7cBzlcWZaybxiieCwiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShSCgiOwg3JRBQYvQIBwYBgQHD0zXSQDAYEB5F5OCMBweG0jqUosMkKBAQDggHBTW7B9fIeEKyn5ShS+h8QfN8JLTLz00tf9lan+eiHJ2V7imW2bk2x3NtY5sOOP+HwRe+Kffv27d+zZ0+TvqKV7VZ29cVUbS+qh/m+PwWaNO7+ipOUBcGjjz66iV+n+AKbYpmn2lnF18O/X1tp3sruVNvW8DUrFmcVaAIMcUF/CgQE+9N2p5TTYQyrd8uOMr6Or4dQYKr1bAhtY2NnBQKCW1YzAoLDOnSqL+0plnuKZW4J3i1tx9fDvkdjra0CAcG2+le3fuyxx+4/+eSTq6ebBKNAFIgCUWC7FTj00EM56qijwgXb7eZTlS4O3zKHt4wITtH2FMtsk5liuadY5vh6+A5iqvVseKVjsVMgILhldWGqL5FW5W5ld6oddMtyx9fDvyxbad7Kbsv63dr28LUrFgOCW1oH8gIb1rHRe1i9W3ZW8XV8PYQCU61nQ2gbGzsrkIjgltWMqb5EWpW7ld2WQDRV2/H18C/LVpq3sjvVtjV8zYrFWQUCgltWH/bu3fuIBz3oQY9oUawp2p5ima1bUyz3FMscXw//Jp1qPRte6VjM0HDqQBSIAlEgCkSBKBAFJq5AIoITrwApfhSIAlEgCkSBKDBdBQKC2+v7Q4GzAv8K/LinYp4FOB/wDeA/ZmycHzhNsd2T6Z8lezrg3MCXihH/fQHgO3P5qZ2Hw4CfBz4L/Kgkrha/DHwZ+HZtgzPluzDwLeDfZ2z06Ws1tR59fQl7tf2uptbd783YtvzWLbX/aY9+V9OvzdjosnBe4JszeerD79r+z5ky/xxwoWK3T7/v5OuufLZvy91dNX2923tkp3pdu43rz18s75DZdrtTPavt691sq/F8PVulje+m57kA/7MOdb6srWdPr74k26cCAcE+1W2X9u8ALwBeCtwEuOLcS7xGzi4DfNgt5QAPrv9T4HmA8xOvWjpR4eE+wE9qGNwhjQcBtwMuC9hZ/h3wIeBawKOB43qweyfgkcCLgKOASwG+eAWTxwMPAH61aFPTvB3BPwJ/D1yn2Ff7Pn1tB3mrAtuPLYXZzV5Nv58WOBvwF8CTgH8CfFc9HdhTtP5t4NeA71b2+xkAIedvgKvNAfB5gK+Uv7+rwFlNv5+51J3nljptuzk78F7gBODGwDOBZ/Tg9518feVS37T5QOA3gX+o3MZ3e4/sVM9+WNnXvreOBZ4PHA1cCXj/LvWsdhvfyfb7Shubr2ertPHd9LxCeX/8WfGfvv14ZT1rvu+S1oAKBAQHFHsgU2cEPlY6q68CfwicHvAFUPN6vWsGgLcAQood402BVwKXLJGUN5UX7D/XNFzS+r/lxXYicDPgCODiwMNmonUXLdGzWuYvApxUymcnLYj461og+/OixeWBvwT8fxexqmH/NgU8BLIODrTxgZ58LVjbSf4BcM8CH7vVrdfO6GKZD9bvFywA+LuAHZYgdAngNeX/2rDu2XEKbjX9ft3yI8KIkOka7fYyCimE+yOjy9PLKvv9fgW43l3qtOWctSEodvX+oxX9vpOvLfPrAOvbO4GrA3csP3a6NlDD1zu9RwQyfS6Iz77DhPBavjai78jBOQvs63fh7Mm71LNbVPT1brbvvEM908+rvM9309P386+XSLPty5EUf1TV0rPGOy5pNFIgINhI+B7NOmz5hpmIwjWA3wIeXtmm8OfwgkOjgphRMiN0RsPspL2MjtlZ24HWvBw2MTpmtMoo0bWBxwBCYRc9EkaMFtp51LoEy0+VzumXCiA9B3g7cMPykhXSzJtR2JpD8uptp6jOwsAbgRcO4Gs7QDsOf0jsVrfsZC7Xg9+FICOC/pAQ+IwSqoFD1W8Dfh+4eQ9+94eTHbCR7Q4E/YFhPm5QorEfBN7Rg9/VWrDufkgYHRQ8jXp/EbhmaXN9tPFZX9tmrGcvLz4QUo8skfeavt7pPWLk8293eIdZB2q2cduw9cl+8KllKos/fubr2V3L9zXb+E62bWPz9cz31yq+3klPgdq6+33AyKA/VP1x99DKetZ6zyadgRUICA4s+ADmjJw4pGbkwl/s/vtuJTJY27zDJb68jAT6a1Po9Bd2F300guB8tldVNOycFkHrDgW0/rp0mv5fSPKFZ712WFgwdeiu1iUICraW03mJDpPdF/ir0mkJDf7af2uBiG7+YA37goHR17sDDv84JO2L/Gk9+/q2gENV+nS3uuVwWh9+Px54YvFpp6HQ/2bgwcDjgD78buTTKQYdCApE6mDEpoNTh9U+2YPfjdBY7lkQdN6pP66c5iEo3LoMEddu47O+Vm+nQQhB6nyXorsR8Nq+nn+P2MZ3eocZpa3dxp17aaT534qP/7tUtNl65o9N/S2M12zj87YF7Pl6ZtRy1ff5vJ6Wzaii03T8AekUFud8Coi19azxrksaAysQEBxY8AHMCSICikM5RqT8Fe8ihtpDw0aHfMHcv0TlnL8joDiEKYB5zUZ0ahVdGPGl7PxEX8xeRi2MOgp9RgLnO/Katp0vZSfh5XC0OlwFuHeJPjp87PB47aFhOwM7LCMlDlMajTIiKmT36etZONitbjk3sw+/z4Og9crOTAD6RPGBQ9efq+z32fpj1NsolNEgo0dGcrwcvnTeXG2/+2PjFTP1x+FZy/2RMsXDSKVz9V7dg99nfa0G/uhxyNSoVDdHVT9b72u18Z3eI7vVM39U1vR1B3s3KlMbuvfEfD2zn7Qu1vT1vG1t7FTP/IHtcPWybXw3Pf3R8itlTm0XdXauad/vzFrv3qTTowIBwR7FbZR09wJ3IYNzjRzyMEJl51LzEkzsKJxo7Rwj61I3V9BolSs9Haa9JfDpioYdHnL41Wibdhw685etL1Zf1P7fyIFzZS49t+L0YLPhkPR7yjCoC2G6uXAOUTpkLGw7TO7kbqNHNS8jQYK9i3IsvyDcTebv09c7wcG8PeuBw8O1/T4LgkZPtNFFZc4EnFyi0bX9Pv9DQr0dLnZeqD86nlLmzzkvtLbf5yOCRgL3lXrunNRnl/pu5Lu232d97Y8N3xvWOyOw3dxXh4/9EVLL1zu9R6zn1qn58u2v2Ma7+a6Crbacf+mPWdv4TvVMaKrl691sW6fn65nvGOdLLuvrnfR0SNh3sSNDltX2YtT18xX1rPmuS1oDKxAQHFjwgcw5D6RbgeZKXqGk5sKFbnjWIdLucs6WEOaQrTa9/Ow8tr4uo2/O0XMVrZ2EUOpwtFcfK3dN13mHrhj2MjroS7WbF+giGX9h97FKu4vIaMOrW8DRt68Febfk6SLKu9lzGLG234V8Ozahx8iUw1qzl5FYO8nafrej9keUkcdujmBn1/w8C3CO4KxPavndOm15umFft/twSkDnd+uWQ/F9+H3e1/7Q8IdPd3Wrhmv5+kDvEdvv/DusZht32yt17CK8XZv6l13qmdDklJQabXw328LmTvVsWV8vq6d11QijEe7abaevd33S7VGBgGCP4jZO2l+4v1Aa+9BZEYy85jvRIfLhi90tRZxb09eltv5y7+YTacctT85Rylxzkch8Gdzmw8jUbPmG9vVu9rbd7zvVpyH9bgR0tm4N4Xcj8EaqjCr9YEaAIXy9W/mGaOMtfb2T7Rq+7nw5+97SVis9+3o/J90VFQgIrihYbo8CUSAKRIEoEAWiwLYoEBDcFk+mHFEgCkSBKBAFokAUWFGBgOCKguX2KBAFokAUiAJRIApsiwIBwW3xZMoRBaJAFIgCUSAKRIEVFQgIrihYbo8CUSAKRIEoEAWiwLYoEBDcFk+mHFEgCkSBKBAFokAUWFGBgOCKguX2KBAFokAUiAJRIApsiwIBwW3xZMoRBTZfATdT9hgsN472lIdVL89QdfNrj1VcdgN1T9N4EuCJHi32vVy1jLk/CkSBKFBVgYBgVTmTWBSIAgehgMeXeVqLRwS6efKqlyczuKm3x4Z5CsUyl6fj3BR4VM+bkC+Tl9wTBaJAFBhcgYDg4JLHYBSIAjsoIJC9qpxycBzg+c1GB58G+N2xwCOBrwFGDo8BBEfvfSzw0XLvrYGXAPctzzyonMZhtNCIn+dPX7Qch/d04DcKCHqmrqe1eFa0x8h9oJzP6nFg9weuWs57/YNyn+mfFXh0OXt6p3zG0VEgCkSB0SsQEBy9i5LBKDAJBYS7h5Szou8GvBnw3FcjhH8PPAH4UoG/d5XjEwU0z0AW4AS8xwH3KnDm0PDDAcHtxeXcWs+JFTA9t/gIwLN0LwgYSXRo+GKAab+hDE8/sRzR6HeeZ93dJ1B6DrJHc90P+PQO+fRc3j6PGpxEpUgho0AU6F+BgGD/GsdCFIgCyylwE+A5wIVKZM7IniD4VsCI3bmAmwNvB64JvK1EBwWy9wKC2+WAJhWS6gAAAlpJREFUa5ezoN9Xnn9eAUHh7cnlGeFtHgTvDQih3XzBOwF7y7/nQdAIoBB5fAHN+XxeAvj6csXOXVEgCkSBdgoEBNtpH8tRIAqcUoFblqibQ7c3KkO8DwY+B5yh/PexMmR7eeCDwNmBXwS+WBZ9CGcO73r/PAgKa0b1TP9TO4DgYwDz4PffKp+7KGAHgkLq90uU8tszIDifz5cC342Do0AUiAJjVyAgOHYPJX9RYDoKCGHPB25QhlUdpr0ncFIZgnURiUO/nwVOKHMDX1gWiDis69DwwYCg8wVfCwh12jaC+AslIvg7JfJ3+7Ii2Wjly0uE8Z075PNWwPem47qUNApEgU1VICC4qZ5LvqPA9ing3D23jjHSJtDdEBD0vL4K3Bh4DyBkCWHd368HfLgsFjl32T7GiOC7y4IR5wh2n4W7LiJ4pTIM3UX9tOtcxPvMSKtdh4rPCLweuGyZv2j+HI52qxq3oNkpn9vnoZQoCkSBrVMgILh1Lk2BosBGK3Ba4HTAD0opzlwgzKHa2cUXZwHOVObh1V6U4XDzf5fhaecsdnMGzZs2XZwyf+2Wz412RjIfBaLA9isQENx+H6eEUSAKrKeAkb5nlU2qs9n0ehrmqSgQBUauQEBw5A5K9qJAFGimgJHBcwKfz1YwzXwQw1EgCvSsQECwZ4GTfBSIAlEgCkSBKBAFxqpAQHCsnkm+okAUiAJRIApEgSjQswIBwZ4FTvJRIApEgSgQBaJAFBirAgHBsXom+YoCUSAKRIEoEAWiQM8KBAR7FjjJR4EoEAWiQBSIAlFgrAr8f9QsKCFV2OUDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data_3).mark_bar().encode(x='technique', y='matrix', color='matrix').properties(height = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Techniques With and Without Data Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-c7ba43ddfd65b38f1fd0b608650ec191": [
         {
          "Count of Techniques": 245,
          "Techniques": "Without DS"
         },
         {
          "Count of Techniques": 240,
          "Techniques": "With DS"
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-c7ba43ddfd65b38f1fd0b608650ec191"
         },
         "encoding": {
          "color": {
           "field": "Techniques",
           "type": "nominal"
          },
          "x": {
           "field": "Techniques",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 200
        },
        {
         "data": {
          "name": "data-c7ba43ddfd65b38f1fd0b608650ec191"
         },
         "encoding": {
          "color": {
           "field": "Techniques",
           "type": "nominal"
          },
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Techniques",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 0,
          "dy": -5,
          "type": "text"
         },
         "width": 200
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_source_distribution = pandas.DataFrame({\n",
    "    'Techniques': ['Without DS','With DS'],\n",
    "    'Count of Techniques': [techniques['data_sources'].isna().sum(),techniques['data_sources'].notna().sum()]})\n",
    "bars = alt.Chart(data_source_distribution).mark_bar().encode(x='Techniques',y='Count of Techniques',color='Techniques').properties(width=200,height=300)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of techniques based on ATT&CK Matrix?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>Ind_DS</th>\n",
       "      <th>technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>With DS</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Without DS</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-mobile-attack</td>\n",
       "      <td>Without DS</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-pre-attack</td>\n",
       "      <td>Without DS</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                matrix      Ind_DS  technique\n",
       "0         mitre-attack     With DS        240\n",
       "1         mitre-attack  Without DS          4\n",
       "2  mitre-mobile-attack  Without DS         67\n",
       "3     mitre-pre-attack  Without DS        174"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = techniques\n",
    "data['Count_DS'] = data['data_sources'].str.len()\n",
    "data['Ind_DS'] = np.where(data['Count_DS']>0,'With DS','Without DS')\n",
    "data_2 = data.groupby(['matrix','Ind_DS'])['technique'].count()\n",
    "data_3 = data_2.to_frame().reset_index()\n",
    "data_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-b39210a0341482429462066eeca2ff18"
       },
       "datasets": {
        "data-b39210a0341482429462066eeca2ff18": [
         {
          "Ind_DS": "With DS",
          "matrix": "mitre-attack",
          "technique": 240
         },
         {
          "Ind_DS": "Without DS",
          "matrix": "mitre-attack",
          "technique": 4
         },
         {
          "Ind_DS": "Without DS",
          "matrix": "mitre-mobile-attack",
          "technique": 67
         },
         {
          "Ind_DS": "Without DS",
          "matrix": "mitre-pre-attack",
          "technique": 174
         }
        ]
       },
       "encoding": {
        "color": {
         "field": "matrix",
         "type": "nominal"
        },
        "x": {
         "field": "technique",
         "type": "quantitative"
        },
        "y": {
         "field": "Ind_DS",
         "type": "nominal"
        }
       },
       "height": 200,
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data_3).mark_bar().encode(x='technique', y='Ind_DS', color='matrix').properties(height = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are those mitre-attack techniques without data sources?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>Count_DS</th>\n",
       "      <th>Ind_DS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS]</td>\n",
       "      <td>[defense-evasion, persistence, command-and-con...</td>\n",
       "      <td>Port Knocking</td>\n",
       "      <td>T1205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[macOS]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Gatekeeper Bypass</td>\n",
       "      <td>T1144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[macOS]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Re-opened Applications</td>\n",
       "      <td>T1164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>[discovery]</td>\n",
       "      <td>Peripheral Device Discovery</td>\n",
       "      <td>T1120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           matrix        platform  \\\n",
       "52   mitre-attack  [Linux, macOS]   \n",
       "103  mitre-attack         [macOS]   \n",
       "106  mitre-attack         [macOS]   \n",
       "124  mitre-attack       [Windows]   \n",
       "\n",
       "                                                tactic  \\\n",
       "52   [defense-evasion, persistence, command-and-con...   \n",
       "103                                  [defense-evasion]   \n",
       "106                                      [persistence]   \n",
       "124                                        [discovery]   \n",
       "\n",
       "                       technique technique_id data_sources  Count_DS  \\\n",
       "52                 Port Knocking        T1205          NaN       NaN   \n",
       "103            Gatekeeper Bypass        T1144          NaN       NaN   \n",
       "106       Re-opened Applications        T1164          NaN       NaN   \n",
       "124  Peripheral Device Discovery        T1120          NaN       NaN   \n",
       "\n",
       "         Ind_DS  \n",
       "52   Without DS  \n",
       "103  Without DS  \n",
       "106  Without DS  \n",
       "124  Without DS  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['matrix']=='mitre-attack') & (data['Ind_DS']=='Without DS')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Techniques without data sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_without_data_sources=techniques[techniques.data_sources.isnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>Count_DS</th>\n",
       "      <th>Ind_DS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS]</td>\n",
       "      <td>[defense-evasion, persistence, command-and-con...</td>\n",
       "      <td>Port Knocking</td>\n",
       "      <td>T1205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[macOS]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Gatekeeper Bypass</td>\n",
       "      <td>T1144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[macOS]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Re-opened Applications</td>\n",
       "      <td>T1164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>[discovery]</td>\n",
       "      <td>Peripheral Device Discovery</td>\n",
       "      <td>T1120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-pre-attack</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[technical-information-gathering]</td>\n",
       "      <td>Spearphishing for Information</td>\n",
       "      <td>T1397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Without DS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             matrix        platform  \\\n",
       "0      mitre-attack  [Linux, macOS]   \n",
       "1      mitre-attack         [macOS]   \n",
       "2      mitre-attack         [macOS]   \n",
       "3      mitre-attack       [Windows]   \n",
       "4  mitre-pre-attack             NaN   \n",
       "\n",
       "                                              tactic  \\\n",
       "0  [defense-evasion, persistence, command-and-con...   \n",
       "1                                  [defense-evasion]   \n",
       "2                                      [persistence]   \n",
       "3                                        [discovery]   \n",
       "4                  [technical-information-gathering]   \n",
       "\n",
       "                       technique technique_id data_sources  Count_DS  \\\n",
       "0                  Port Knocking        T1205          NaN       NaN   \n",
       "1              Gatekeeper Bypass        T1144          NaN       NaN   \n",
       "2         Re-opened Applications        T1164          NaN       NaN   \n",
       "3    Peripheral Device Discovery        T1120          NaN       NaN   \n",
       "4  Spearphishing for Information        T1397          NaN       NaN   \n",
       "\n",
       "       Ind_DS  \n",
       "0  Without DS  \n",
       "1  Without DS  \n",
       "2  Without DS  \n",
       "3  Without DS  \n",
       "4  Without DS  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_without_data_sources.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  245  techniques without data sources ( 51%  of  485  techniques)\n"
     ]
    }
   ],
   "source": [
    "print('There are ',techniques['data_sources'].isna().sum(),' techniques without data sources (',\"{0:.0%}\".format(techniques['data_sources'].isna().sum()/len(techniques)),' of ',len(techniques),' techniques)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Techniques With Data Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_with_data_sources=techniques[techniques.data_sources.notnull()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>Count_DS</th>\n",
       "      <th>Ind_DS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>With DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>With DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[impact]</td>\n",
       "      <td>Endpoint Denial of Service</td>\n",
       "      <td>T1499</td>\n",
       "      <td>[SSL/TLS inspection, Web logs, Web application...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>With DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>[defense-evasion, discovery]</td>\n",
       "      <td>Virtualization/Sandbox Evasion</td>\n",
       "      <td>T1497</td>\n",
       "      <td>[Process monitoring, Process command-line para...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>With DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[impact]</td>\n",
       "      <td>Network Denial of Service</td>\n",
       "      <td>T1498</td>\n",
       "      <td>[Sensor health and status, Network protocol an...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>With DS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix                 platform                        tactic  \\\n",
       "0  mitre-attack  [Linux, macOS, Windows]             [defense-evasion]   \n",
       "1  mitre-attack                  [Linux]                 [persistence]   \n",
       "2  mitre-attack  [Linux, macOS, Windows]                      [impact]   \n",
       "3  mitre-attack                [Windows]  [defense-evasion, discovery]   \n",
       "4  mitre-attack  [Linux, macOS, Windows]                      [impact]   \n",
       "\n",
       "                        technique technique_id  \\\n",
       "0          Compile After Delivery        T1500   \n",
       "1                 Systemd Service        T1501   \n",
       "2      Endpoint Denial of Service        T1499   \n",
       "3  Virtualization/Sandbox Evasion        T1497   \n",
       "4       Network Denial of Service        T1498   \n",
       "\n",
       "                                        data_sources  Count_DS   Ind_DS  \n",
       "0  [Process command-line parameters, Process moni...       3.0  With DS  \n",
       "1  [Process command-line parameters, Process moni...       3.0  With DS  \n",
       "2  [SSL/TLS inspection, Web logs, Web application...       7.0  With DS  \n",
       "3  [Process monitoring, Process command-line para...       2.0  With DS  \n",
       "4  [Sensor health and status, Network protocol an...       5.0  With DS  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_with_data_sources.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  240  techniques with data sources ( 49%  of  485  techniques)\n"
     ]
    }
   ],
   "source": [
    "print('There are ',techniques['data_sources'].notna().sum(),' techniques with data sources (',\"{0:.0%}\".format(techniques['data_sources'].notna().sum()/len(techniques)),' of ',len(techniques),' techniques)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Grouping Techniques With Data Sources By Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a graph to represent the number of techniques per matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-a689c98b4c5966a339164dff27be292d": [
         {
          "Count of Techniques": 240,
          "Matrix": "mitre-attack"
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-a689c98b4c5966a339164dff27be292d"
         },
         "encoding": {
          "x": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "y": {
           "field": "Matrix",
           "type": "nominal"
          }
         },
         "height": 100,
         "mark": "bar",
         "width": 300
        },
        {
         "data": {
          "name": "data-a689c98b4c5966a339164dff27be292d"
         },
         "encoding": {
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "y": {
           "field": "Matrix",
           "type": "nominal"
          }
         },
         "height": 100,
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 10,
          "dy": 0,
          "type": "text"
         },
         "width": 300
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_distribution = pandas.DataFrame({\n",
    "    'Matrix': list(techniques_with_data_sources.groupby(['matrix'])['matrix'].count().keys()),\n",
    "    'Count of Techniques': techniques_with_data_sources.groupby(['matrix'])['matrix'].count().tolist()})\n",
    "bars = alt.Chart(matrix_distribution).mark_bar().encode(y='Matrix',x='Count of Techniques').properties(width=300,height=100)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=10,dy=0).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the techniques belong to **mitre-attack** matrix which is the main **Enterprise** matrix. Reference: https://attack.mitre.org/wiki/Main_Page "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Grouping Techniques With Data Sources by Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to split the **platform** column values because a technique might be mapped to more than one platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_platform=techniques_with_data_sources\n",
    "\n",
    "attributes_1 = ['platform'] # In attributes we are going to indicate the name of the columns that we need to split\n",
    "\n",
    "for a in attributes_1:\n",
    "    s = techniques_platform.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    # \"s\" is going to be a column of a frame with every value of the list inside each cell of the column \"a\"\n",
    "    s.name = a\n",
    "    # We name \"s\" with the same name of \"a\".\n",
    "    techniques_platform=techniques_platform.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    # We drop the column \"a\" from \"techniques_platform\", and then join \"techniques_platform\" with \"s\"\n",
    "\n",
    "# Let's re-arrange the columns from general to specific\n",
    "techniques_platform_2=techniques_platform.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now show techniques with data sources mapped to one platform at the time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Windows</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>[impact]</td>\n",
       "      <td>Endpoint Denial of Service</td>\n",
       "      <td>T1499</td>\n",
       "      <td>[SSL/TLS inspection, Web logs, Web application...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix platform             tactic                   technique  \\\n",
       "0  mitre-attack    Linux  [defense-evasion]      Compile After Delivery   \n",
       "1  mitre-attack    macOS  [defense-evasion]      Compile After Delivery   \n",
       "2  mitre-attack  Windows  [defense-evasion]      Compile After Delivery   \n",
       "3  mitre-attack    Linux      [persistence]             Systemd Service   \n",
       "4  mitre-attack    Linux           [impact]  Endpoint Denial of Service   \n",
       "\n",
       "  technique_id                                       data_sources  \n",
       "0        T1500  [Process command-line parameters, Process moni...  \n",
       "1        T1500  [Process command-line parameters, Process moni...  \n",
       "2        T1500  [Process command-line parameters, Process moni...  \n",
       "3        T1501  [Process command-line parameters, Process moni...  \n",
       "4        T1499  [SSL/TLS inspection, Web logs, Web application...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_platform_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a visualization to show the number of techniques grouped by platform:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-8bdd6c787014aac1ea40c5ce615b2680": [
         {
          "Count of Techniques": 126,
          "Platform": "Linux"
         },
         {
          "Count of Techniques": 211,
          "Platform": "Windows"
         },
         {
          "Count of Techniques": 145,
          "Platform": "macOS"
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-8bdd6c787014aac1ea40c5ce615b2680"
         },
         "encoding": {
          "color": {
           "field": "Platform",
           "type": "nominal"
          },
          "x": {
           "field": "Platform",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 200
        },
        {
         "data": {
          "name": "data-8bdd6c787014aac1ea40c5ce615b2680"
         },
         "encoding": {
          "color": {
           "field": "Platform",
           "type": "nominal"
          },
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Platform",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 0,
          "dy": -5,
          "type": "text"
         },
         "width": 200
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platform_distribution = pandas.DataFrame({\n",
    "    'Platform': list(techniques_platform_2.groupby(['platform'])['platform'].count().keys()),\n",
    "    'Count of Techniques': techniques_platform_2.groupby(['platform'])['platform'].count().tolist()})\n",
    "bars = alt.Chart(platform_distribution,height=300).mark_bar().encode(x ='Platform',y='Count of Techniques',color='Platform').properties(width=200)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the bar chart above we can see that there are more techniques with data sources mapped to the Windows platform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Grouping Techniques With Data Sources by Tactic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, first we need to split the tactic column values because a technique might be mapped to more than one tactic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_tactic=techniques_with_data_sources\n",
    "\n",
    "attributes_2 = ['tactic'] # In attributes we are going to indicate the name of the columns that we need to split\n",
    "\n",
    "for a in attributes_2:\n",
    "    s = techniques_tactic.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    # \"s\" is going to be a column of a frame with every value of the list inside each cell of the column \"a\"\n",
    "    s.name = a\n",
    "    # We name \"s\" with the same name of \"a\".\n",
    "    techniques_tactic = techniques_tactic.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    # We drop the column \"a\" from \"techniques_tactic\", and then join \"techniques_tactic\" with \"s\"\n",
    "\n",
    "# Let's re-arrange the columns from general to specific\n",
    "techniques_tactic_2=techniques_tactic.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now show techniques with data sources mapped to one tactic at the time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux]</td>\n",
       "      <td>persistence</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>[Process command-line parameters, Process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>impact</td>\n",
       "      <td>Endpoint Denial of Service</td>\n",
       "      <td>T1499</td>\n",
       "      <td>[SSL/TLS inspection, Web logs, Web application...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Virtualization/Sandbox Evasion</td>\n",
       "      <td>T1497</td>\n",
       "      <td>[Process monitoring, Process command-line para...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Windows]</td>\n",
       "      <td>discovery</td>\n",
       "      <td>Virtualization/Sandbox Evasion</td>\n",
       "      <td>T1497</td>\n",
       "      <td>[Process monitoring, Process command-line para...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix                 platform           tactic  \\\n",
       "0  mitre-attack  [Linux, macOS, Windows]  defense-evasion   \n",
       "1  mitre-attack                  [Linux]      persistence   \n",
       "2  mitre-attack  [Linux, macOS, Windows]           impact   \n",
       "3  mitre-attack                [Windows]  defense-evasion   \n",
       "4  mitre-attack                [Windows]        discovery   \n",
       "\n",
       "                        technique technique_id  \\\n",
       "0          Compile After Delivery        T1500   \n",
       "1                 Systemd Service        T1501   \n",
       "2      Endpoint Denial of Service        T1499   \n",
       "3  Virtualization/Sandbox Evasion        T1497   \n",
       "4  Virtualization/Sandbox Evasion        T1497   \n",
       "\n",
       "                                        data_sources  \n",
       "0  [Process command-line parameters, Process moni...  \n",
       "1  [Process command-line parameters, Process moni...  \n",
       "2  [SSL/TLS inspection, Web logs, Web application...  \n",
       "3  [Process monitoring, Process command-line para...  \n",
       "4  [Process monitoring, Process command-line para...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_tactic_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a visualization to show the number of techniques grouped by tactic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-8712fd25b13f11f698d44ffec8d94852": [
         {
          "Count of Techniques": 9,
          "Tactic": "exfiltration"
         },
         {
          "Count of Techniques": 11,
          "Tactic": "initial-access"
         },
         {
          "Count of Techniques": 13,
          "Tactic": "collection"
         },
         {
          "Count of Techniques": 14,
          "Tactic": "impact"
         },
         {
          "Count of Techniques": 17,
          "Tactic": "lateral-movement"
         },
         {
          "Count of Techniques": 19,
          "Tactic": "credential-access"
         },
         {
          "Count of Techniques": 21,
          "Tactic": "command-and-control"
         },
         {
          "Count of Techniques": 21,
          "Tactic": "discovery"
         },
         {
          "Count of Techniques": 28,
          "Tactic": "privilege-escalation"
         },
         {
          "Count of Techniques": 33,
          "Tactic": "execution"
         },
         {
          "Count of Techniques": 57,
          "Tactic": "persistence"
         },
         {
          "Count of Techniques": 65,
          "Tactic": "defense-evasion"
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-8712fd25b13f11f698d44ffec8d94852"
         },
         "encoding": {
          "color": {
           "field": "Tactic",
           "type": "nominal"
          },
          "x": {
           "field": "Tactic",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 400
        },
        {
         "data": {
          "name": "data-8712fd25b13f11f698d44ffec8d94852"
         },
         "encoding": {
          "color": {
           "field": "Tactic",
           "type": "nominal"
          },
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Tactic",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 0,
          "dy": -5,
          "type": "text"
         },
         "width": 400
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tactic_distribution = pandas.DataFrame({\n",
    "    'Tactic': list(techniques_tactic_2.groupby(['tactic'])['tactic'].count().keys()),\n",
    "    'Count of Techniques': techniques_tactic_2.groupby(['tactic'])['tactic'].count().tolist()}).sort_values(by='Count of Techniques',ascending=True)\n",
    "bars = alt.Chart(tactic_distribution,width=800,height=300).mark_bar().encode(x ='Tactic',y='Count of Techniques',color='Tactic').properties(width=400)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defende-evasion and Persistence are tactics with the highest nummber of techniques with data sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Grouping Techniques With Data Sources by Data Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the data source column values because a technique might be mapped to more than one data source:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_data_source=techniques_with_data_sources\n",
    "\n",
    "attributes_3 = ['data_sources'] # In attributes we are going to indicate the name of the columns that we need to split\n",
    "\n",
    "for a in attributes_3:\n",
    "    s = techniques_data_source.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    # \"s\" is going to be a column of a frame with every value of the list inside each cell of the column \"a\"\n",
    "    s.name = a\n",
    "    # We name \"s\" with the same name of \"a\".\n",
    "    techniques_data_source = techniques_data_source.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    # We drop the column \"a\" from \"techniques_data_source\", and then join \"techniques_data_source\" with \"s\"\n",
    "\n",
    "# Let's re-arrange the columns from general to specific\n",
    "techniques_data_source_2 = techniques_data_source.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)\n",
    "\n",
    "# We are going to edit some names inside the dataframe to improve the consistency:\n",
    "techniques_data_source_3 = techniques_data_source_2.replace(['Process monitoring','Application logs'],['Process Monitoring','Application Logs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now show techniques with data sources mapped to one data source at the time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process Monitoring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux, macOS, Windows]</td>\n",
       "      <td>[defense-evasion]</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>File monitoring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>[Linux]</td>\n",
       "      <td>[persistence]</td>\n",
       "      <td>Systemd Service</td>\n",
       "      <td>T1501</td>\n",
       "      <td>Process Monitoring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix                 platform             tactic  \\\n",
       "0  mitre-attack  [Linux, macOS, Windows]  [defense-evasion]   \n",
       "1  mitre-attack  [Linux, macOS, Windows]  [defense-evasion]   \n",
       "2  mitre-attack  [Linux, macOS, Windows]  [defense-evasion]   \n",
       "3  mitre-attack                  [Linux]      [persistence]   \n",
       "4  mitre-attack                  [Linux]      [persistence]   \n",
       "\n",
       "                technique technique_id                     data_sources  \n",
       "0  Compile After Delivery        T1500  Process command-line parameters  \n",
       "1  Compile After Delivery        T1500               Process Monitoring  \n",
       "2  Compile After Delivery        T1500                  File monitoring  \n",
       "3         Systemd Service        T1501  Process command-line parameters  \n",
       "4         Systemd Service        T1501               Process Monitoring  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_data_source_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a visualization to show the number of techniques grouped by data sources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-3d8b18347e124d71fa30f0d5c58b47b9": [
         {
          "Count of Techniques": 43,
          "Data Source": "API monitoring"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Access tokens"
         },
         {
          "Count of Techniques": 7,
          "Data Source": "Anti-virus"
         },
         {
          "Count of Techniques": 6,
          "Data Source": "Application Logs"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Asset management"
         },
         {
          "Count of Techniques": 28,
          "Data Source": "Authentication logs"
         },
         {
          "Count of Techniques": 3,
          "Data Source": "BIOS"
         },
         {
          "Count of Techniques": 18,
          "Data Source": "Binary file metadata"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Browser extensions"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "Component firmware"
         },
         {
          "Count of Techniques": 18,
          "Data Source": "DLL monitoring"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "DNS records"
         },
         {
          "Count of Techniques": 6,
          "Data Source": "Data loss prevention"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "Detonation chamber"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Digital certificate logs"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Disk forensics"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "EFI"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Email gateway"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "Environment variable"
         },
         {
          "Count of Techniques": 97,
          "Data Source": "File monitoring"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Host network interface"
         },
         {
          "Count of Techniques": 7,
          "Data Source": "Kernel drivers"
         },
         {
          "Count of Techniques": 13,
          "Data Source": "Loaded DLLs"
         },
         {
          "Count of Techniques": 3,
          "Data Source": "MBR"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "Mail server"
         },
         {
          "Count of Techniques": 9,
          "Data Source": "Malware reverse engineering"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "Named Pipes"
         },
         {
          "Count of Techniques": 27,
          "Data Source": "Netflow/Enclave netflow"
         },
         {
          "Count of Techniques": 8,
          "Data Source": "Network device logs"
         },
         {
          "Count of Techniques": 9,
          "Data Source": "Network intrusion detection system"
         },
         {
          "Count of Techniques": 22,
          "Data Source": "Network protocol analysis"
         },
         {
          "Count of Techniques": 36,
          "Data Source": "Packet capture"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "PowerShell logs"
         },
         {
          "Count of Techniques": 169,
          "Data Source": "Process Monitoring"
         },
         {
          "Count of Techniques": 97,
          "Data Source": "Process command-line parameters"
         },
         {
          "Count of Techniques": 39,
          "Data Source": "Process use of network"
         },
         {
          "Count of Techniques": 9,
          "Data Source": "SSL/TLS inspection"
         },
         {
          "Count of Techniques": 2,
          "Data Source": "Sensor health and status"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Services"
         },
         {
          "Count of Techniques": 9,
          "Data Source": "System calls"
         },
         {
          "Count of Techniques": 3,
          "Data Source": "Third-party application logs"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "User interface"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "VBR"
         },
         {
          "Count of Techniques": 1,
          "Data Source": "WMI Objects"
         },
         {
          "Count of Techniques": 3,
          "Data Source": "Web application firewall logs"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Web logs"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Web proxy"
         },
         {
          "Count of Techniques": 4,
          "Data Source": "Windows Error Reporting"
         },
         {
          "Count of Techniques": 36,
          "Data Source": "Windows Registry"
         },
         {
          "Count of Techniques": 22,
          "Data Source": "Windows event logs"
         }
        ]
       },
       "layer": [
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         "data": {
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         },
         "encoding": {
          "color": {
           "field": "Data Source",
           "type": "nominal"
          },
          "x": {
           "field": "Data Source",
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          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
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         },
         "height": 300,
         "mark": "bar",
         "width": 1200
        },
        {
         "data": {
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         "encoding": {
          "color": {
           "field": "Data Source",
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           "field": "Count of Techniques",
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         "mark": {
          "align": "center",
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          "dx": 0,
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       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_source_distribution = pandas.DataFrame({\n",
    "    'Data Source': list(techniques_data_source_3.groupby(['data_sources'])['data_sources'].count().keys()),\n",
    "    'Count of Techniques': techniques_data_source_3.groupby(['data_sources'])['data_sources'].count().tolist()})\n",
    "bars = alt.Chart(data_source_distribution,width=800,height=300).mark_bar().encode(x ='Data Source',y='Count of Techniques',color='Data Source').properties(width=1200)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few interesting things from the bar chart above:\n",
    "* Process Monitoring, File Monitoring, and Process Command-line parameters are the Data Sources with the highest number of techniques\n",
    "* There are some data source names that include string references to Windows such as PowerShell, Windows and wmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16. Most Relevant Groups Of Data Sources Per Technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number Of Data Sources Per Technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although identifying the data sources with the highest number of techniques is a good start, they usually do not work alone. You might be collecting **Process Monitoring** already but you might be still missing a lot of context from a data perspective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-b1ca98c89dbb838b61f181abe8b233a7": [
         {
          "Count of Techniques": 21,
          "Number of Data Sources": 1
         },
         {
          "Count of Techniques": 50,
          "Number of Data Sources": 2
         },
         {
          "Count of Techniques": 73,
          "Number of Data Sources": 3
         },
         {
          "Count of Techniques": 56,
          "Number of Data Sources": 4
         },
         {
          "Count of Techniques": 18,
          "Number of Data Sources": 5
         },
         {
          "Count of Techniques": 13,
          "Number of Data Sources": 6
         },
         {
          "Count of Techniques": 8,
          "Number of Data Sources": 7
         },
         {
          "Count of Techniques": 1,
          "Number of Data Sources": 12
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-b1ca98c89dbb838b61f181abe8b233a7"
         },
         "encoding": {
          "x": {
           "field": "Number of Data Sources",
           "type": "quantitative"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "mark": "bar",
         "width": 500
        },
        {
         "data": {
          "name": "data-b1ca98c89dbb838b61f181abe8b233a7"
         },
         "encoding": {
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Number of Data Sources",
           "type": "quantitative"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 0,
          "dy": -5,
          "type": "text"
         },
         "width": 500
        }
       ]
      },
      "image/png": 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Lbzppd2CFR1zKNKCabNsjLu3qtKa69IhLu7r06EEzfrXeA7Xmq4vtrCZx0ZtDmg56M6ApoyMB/e0/0v/eG3gqcCGwPXCvxaXZjV1zh2JxaVenfjjG+ZmZmcUJxCPczsoxq0lcVCqtbZG06NIi3D2AO4DTgMPS3zVncN1MCLw4t1zjiKfUPsLi0o6hH45xfmZmZnEC8Qi3s3LMahMXlWwd4DHALweKuVmaQtJeLjNeFpdyjSOeUvsIi0s7hn44xvmZmZnFCcQj3M7KMatRXOKl64uwuMTx1XRDVSgu6wJvAvTfPbH+0OTk5N3j4+O7pMXk+rdTgMvi9MtG1FSXgyWrNW+15kv8as1brfkys2bPk1rrswtvFTUjPhBlcYljrKnRVigu2jNI05d/AUwBa+tV/csuu+xXe+65p2C/ArgfuCAtLP9+vAbKRdRUlxaX9vVaa33Wmi+LS7M2V2t9Wlya1WexqFobRm03eoXiot2btSniB/sbw6GHHjp1+umna93V6envWjyudVm3Fms0DRJyO4tDMzMzixOIR7idlWPmqaI4y0YRbrR52CoUl9cCZ/Tl/i3A3x544IEPnHPOOVok/juA1l+9IS0i16jM0C63szh6MzOzOIF4hNtZOWYWlzjLRhFutHnYKhQXzQc9GTgH2AL4N+DlCxcuvGr58uXnA68DNkxnbGktzJfySjo/n3I7i3M1MzOLE4hHuJ2VY2ZxibNsFOFGm4etQnF57MC5WB8Dvvryl7/8jPPPP38f4JJUsqXAT9MBoXmFnYdPuZ3FoZqZmcUJxCPczsoxs7jEWTaKcKPNw1aZuPTOxvp0Ojtrc0kLsP+xxx77rSVLlvw98KfpjSOdaq5pJB1NMbTL7SyO3szMLE4gHuF2Vo6ZxSXOslGEG20etsrERZl+FnBNX+518OfHvv3tbz+w/fbba+2LDvzUpfO0lgAP5JV0fj7ldhbnamZmFicQj3A7K8fM4hJn2SjCjTYPW4XiooyvCTwOuA94cAPEvvrUVJLuo8ENE/MKXPhTbmdxoGZmZnEC8Qi3s3LMLC5xlo0i3GjzsFUqLo/IfK31WWu+BmQvrzGspk+ZWRy0mZlZnEA8YqZ2ZnGJs2wU4Rs9D1vN4rLf4rPPnGLqoP6SrGTloguWLTorr3Tz/ym3szhjMzOzOIF4hNtZOWYWlzjLRhFutHnYLC55nGb6lNtZnJ+ZmVmcQDzC7awcM4tLnGWjCDfaPGwWlzxOFpd2nPqjfW/GWZqZmcUJxCM8VRRnVjTCN3oeTotLHieLSztOFpd2/Pw8i/Mzs3LMPOISZ9kowo02D5vFJY+TxaUdJ4tLO35+nsX5mVk5ZhaXOMtGEW60edgsLnmcLC7tOFlc2vHz8yzOz8zKMYuKy/qAtjm/CPhj4DnAJ4BvxbM0PxHLli2bWrx4cbRc85OZvlTdaPMQW1zyOFlc2nGyuLTj5+dZnJ+ZlWMW6eD12ZOBAwDtHqpD53TdAWwDrIhnq3yExSXOtKYbyuISrz93wmbWjkA8uqZnxmDua81brfkSv1rzVmJx7gbAdcDRwNOAI4FXAv8IvAb4Rrz5l4+wuMSZ1tRoLS7x+rO4mFk7AvHomp4ZFpd4/XWdWWTEpScuGm3RwXK/SvJyA7DvsA+X61WExSXeiGt6CFlc4vVncTGzdgTi0TU9M7reCcfpl4+otT5LjLhIcjS68uqE7R3A64DfAbYH/rs8zniKFpc4s5oarcUlXn8WFzNrRyAeXdMzw+ISr7+uM4uMuKisGwOHpBNwP6VTcoFTgK+1R1cmBYtLnGNNDyGLS7z+LC5m1o5APLqmZ0bXO+E4/fIRtdZniRGXHq2tgWcDN6eTcm8E/qc8ymYpWlzi3GpqtBaXeP1ZXMysHYF4dE3PDItLvP66ziw64rKg722iNwF7AI9Pr0Y/0B5f+xQsLrMyXBdQvem/da0DfGhycvLu8fFxLbg+CvhfwPuH9Yq7xaXdPeAOJc7PzMwsTiAe4XZWjllEXNYDrgYuBO4E/hO4FfgCsB3wnXi2ykdYXGZlqtEy1eFfAFPA2sAnr7rqql/ttttuCpTU/Bg4f1h1anFpd0/44RjnZ2ZmFicQj3A7K8csIi69t4oWAk8AngycCdwFPBO4Np6t8hEWl1mZyk6eB3yw/1MnnXTS1FFHHfUR4O3p73rVXaMxGnlZrZfFpR1uPxzj/MzMzOIE4hFuZ+WYRcRlDeBLwC6AXoGWsOgX+7aAfslrFGbol8Vl1ip4LXBG3yfeAvztscce+8CSJUskLZIXXZoSlOS8c3VXqMWlHXE/HOP8zMzM4gTiEW5n5ZhFxEXfukWaRtixLwsvTUcAxHM1DxEWl1mh7plGyrTrsery34CXL1y48Krly5drf56P9onLq4CD05TSPNTU9ElaXNqh9sMxzs/MzCxOIB7hdlaOWVRcet+8WRpt+Rnw63h2ZozQ9MRj+v5Vm9zdD6wFbJneYvrJbN9ncZm1Nh47MDKm19m/etJJJ51x1FFHad3LB1L0ImDzvv9fsIpnT8ri0g61H45xfmZmZnEC8Qi3s3LMouKiX+RHDHx9ybOKDgJOAi5OovJmHaOQRnS0hmYvYAmwfCYEFpcZG4fqWlw/rQW5SUy+Cuz/qU996luHHHLIbWmt0m+Ay4H3AOfGm1q7CItLO35+OMb5mZmZxQnEI9zOyjGListiYOe+6YMD0yGLW6U1L/GcrRpxfNqdt/8NJf361yGOxwFaIKz9Y2ZcU2NxmbUKngVc0/cJTQ99bHJy8oHx8fF3A+9L//ZXwMTqnibSd1tc2t1CfjjG+ZmZmcUJxCPczsoxi4rL4DfrNWi9Hv2MNI0Tz9mqEWcDkiFdOl7gjYBkSb/8rwB6owZae3H7dF9mcZmzCtYEHgfc16uzvhtqkzQtp1G0oVwWl3bY/XCM8zMzM4sTiEe4nZVjFhUXLejUfi66NKWgN08kGNq87AfxbK0SoXUs+qWvV6xvSusrvgdo8e8J6RBH5VfTRMekkZdHfKXFJa8W9l18lkZUHrxetssWx3/+6ttOgLGfnrds4cl5KczPpywu7bj64RjnZ2ZmFicQj3A7K8csIi767OeTSPTnQFM3OmTx3ni2HhGhxbkaCdAlKdIhjtcDt6T1GZImrXXZHVixdOnSibGxMU0vrXItWKC3eX3NRuAzV9zK5TesOrBywB5b8vzttO56eNeKu+9j4qxVtwTaeMN1mDhop+FlKn3z6ZdMcs3kz1fJx6EvHGfncQ1U+TIBEzABEyhNYHx8/BGeEhEX5eclQP9TWpKhBZ4lphZ0gKN24n0q8FNgadrFVVNC2hBt7/RvmpqaUZQ84pLXbPZdvFx7tmiNS//1No+4zMxvv8VnnznFlBaQP3StZOWiC5YtOiuP+vx/yr/q4ozNzMziBOIRbmflmEXFRbvl9s65GcyF1k7oGABtTNf00oZovamKr2uPEY2sAKcBh6VE9dP7upm+wOKSh97iksep/1MWlziz/ohaH9y15kvsas1brfkys2b3aK31OVO+IuLSW1/SWzw7HaHnpkW0zej9Nmr9tI5m1TF50BzGPXMtAra45KG3uORxsrjEOc0U0bWHY7mSN0/JzOLszGz0mUXERTS0B4imizTF8EtAw+YaCdkvodKrthp1GdplcclDb3HJ42RxiXOyuJhZOQLxlCwuo88sIi5aGHtlmsr5eEKj16G/ndaeaAHt0C+LS14VWFzyOFlc4pwsLmZWjkA8JYvL6DOLiIs2f/t34LtpV1VN5RySThDWYlkdvDj0y+KSVwUWlzxOFpc4J4uLmZUjEE/J4jL6zCLiIhqHA38/gEWbw+2fzhSKEyscYXHJA2pxyeNkcYlzsriYWTkC8ZQsLqPPLCouIrJtejVZhyFqgzjt7fJAHNX8RFhc8rhaXPI4WVzinCwuZlaOQDwli8voM8sRF+1oqy32NRWkvVZ0avBUHxptGqfdbvXGz9Avi0teFVhc8jhZXOKcLC5mVo5APCWLy+gzyxEXrW25Gzga0B4qvf1U+uk8Hhh8fTlOr0CExSUPosUlj5PFJc7J4mJm5QjEU7K4jD6zHHERhd9NIyprAxphGbz0CnT/KEycXKEIi0seSItLHieLS5yTxcXMyhGIp2RxGX1mueLSI/HYtI+L1rfokEVdmkr6Z08Vzd5YaruZLC7xm9s758aZ9UfUdg/08lZrvpS/WvNWa77MrNk9Wmt9lto5VydBv3oaNJ4qmqO91NYwLC7xG9ziEmdmcTGzdgTi0bU9ay3I8Tqci1lkxEVrXXRGkF6H1g66a6TElcYPgZXNs1cu0lNFeSwtLnmc+j9lcYkzs7iYWTsC8WiLy+gzi4iLpoS+mA48lLhUeVlc8qrF4pLHyeIS5zRThDuUOEszM7M4gXhE19pZRFxE4++ANwHnpLOK9Detdzmi5anQcdIzRFhc8lBaXPI4WVzinCwuZlaOQDylrnXC8RKWj+gas6i4fDRJSj+5O4BtgBXlccZTtLjkMbO45HGyuMQ5WVzMrByBeEpd64TjJSwf0TVmUXEpT6xwihaXPKAWlzxOFpc4J4uLmZUjEE+pa51wvITlI7rGLCouWwF/Bui16N61EfBm4BflccZTtLjkMbO45HGyuMQ5WVzMrByBeEpd64TjJSwf0TVmEXHR5nNfA3YbwOapoox2VFvDsLhkVNrAR/xWUZxZf0Rt90Avb7XmS/mrNW+15svMmt2jtdZniX1ceq9Dvxs4G1izD9H9zXCVj/KISx5Ti0seJ4+4xDl5xMXMyhGIp9S1TjhewvIRXWOWO+KiV6F1LQe+Cbx/YN8Wi8scbam2hmFxid/8HnGJM/OIi5m1IxCPru1Z65G9eB3OxSxHXNYDrga2neHrPVWUUS+13UwWl4xKG/iIxSXOzOJiZu0IxKNre9bO1QnHS1g+omvMcsRFoy1LAAnMdNe6wDvTCdLliQZT9FRRHjCLSx6n/k9ZXOLMLC5m1o5APLprnXC8hOUjusYsR1z6KenzewPfSxvOPQf4EvBAeZTNUrS45HGzuORxsrjEOc0U0bWHY7mSN0/JzOLszGz0mUXFZQI4HvgD4OfATWkX3UW1yIvFJa/RWlzyOFlc4pwsLmZWjkA8JYvL6DOLiIumiq4ELgMWA/cBrwU+CWwHfCeOq3yExSWPqcUlj5PFJc7J4mJm5QjEU7K4jD6ziLj0Xod+D3BWQqOt/r8L7J6kJk6scITFJQ+oxSWPk8UlzsniYmblCMRTsriMPrOIuOiz/wi8Gvg8cGvaRddvFWW0k9puJotLRqUNfMSLc+PM+iNquwd6eas1X8pfrXmrNV9m1uwerbU+S2xAJyIbpz1cXp/wSFr2TzvqNiNWOMojLnlALS55nDziEufkERczK0cgnlLXOuF4CctHdI1ZZMSlR2tr4LnAD4GfAjcC/1MeZbMULS553CwueZwsLnFOFhczK0cgnlLXOuF4CctHdI1ZVFwWpLeIRO5NwB7A44E/9ltFszem2hqGxSV+83uqKM6sP6K2e6CXt1rzpfzVmrda82Vmze7RWuuzxFRRbwfdC4E7gf9M61y+MA9vFWnTuycCP07VoP+/Zdrk7iezVY1HXPIarsUlj5NHXOKcPOJiZuUIxFPqWiccL2H5iK4xi4y49N4qWgg8AXgycGbaiO6ZwLUFcR4DHAzsCGhn3otS+nulXXx1ZtK0l8UlrxYsLnmcLC5xThYXMytHIJ5S1zrheAnLR3SNWURc1ki75O4C3JCEZe10hpHWvWgUpsS1J3ApcC7wKkCipNeujwMkTzcDM36fxSWvCiwueZwsLnFOFhczK0cgnlLXOuF4CctHdI1ZrrjsBPwS0CnQ56eRkB69l6YRkRI0N0/SorU0J6fjBd6bJOYKQPm9OI3G3D7dF1pc8qrB4pLHyeIS52RxMbNyBOIpda0TjpewfETXmOWIS2+U40TgAwnZZoBGW34G/LoQRq1j0UjLoUmQ/hV4FqD/PlVRC4kAACAASURBVAG4KomLpok0lXTz0qVLJ8bGxnQEwSrXggXyHl+zEfjMFbdy+Q16m/3h64A9tuT526lqh3etuPs+Js5addZx4w3XYeIgufNwr9MvmeSaSZ108fB16AvH2Xl8k+FmzN9uAiZgAiNKYHx8/BGekiMuvUW5H0ujIBKMwUsjMW2vZ6RjA67rG9E5J01PaXpIIy3Ki3o17dS7Yrov9IhLXjV4xCWPU/+n/FZRnFl/RNd+1bUrbZloM4tzNLPRZ5YrLjqjSAtlp7tK7Zy7Ttrg7jfpLaVTAZ0+rdOoj0z//VRAbzVtD9xrcYk30F6ExSXOzuISZ2ZxMbN2BOLRFpfRZxYVl2umQfK/gF1nGgGJI3wwYivg48A+wBRwGnBYSktzBhqVmfbyiEsecYtLHiePuMQ5zRThDiXO0szMLE4gHtG1dpYrLlcDpwAfjSMpFqHFF/ekvVxmTNTiksfb4pLHyeIS52RxMbNyBOIpda0TjpewfETXmOWIy3SLc8uTK5SixSUPpMUlj5PFJc7J4mJm5QjEU+paJxwvYfmIrjHLERdR0tqSXwGz7lpbHmc8RYtLHjOLSx4ni0uck8XFzMoRiKfUtU44XsLyEV1jlisu5UnNU4oWlzywFpc8ThaXOCeLi5mVIxBPqWudcLyE5SO6xsziUr4NTJtibQ3D4hKveL9VFGfWH1HbPdDLW635Uv5qzVut+TKzZvdorfXZ5pBF7dvyCuCytCHc9TVPGXnEJa/hWlzyOHnEJc7JIy5mVo5APKWudcLxEpaP6BqznBGX3uJc7auyL/A5QK9F6+wiXRKb89IbP+WJBlO0uOQBs7jkcbK4xDlZXMysHIF4Sl3rhOMlLB/RNWY54qLP/CPw6llwPR5YdS/08myzUrS4ZGHC4pLHyeIS52RxMbNyBOIpda0TjpewfETXmOWIiyitCfwOcEba9v/f0llFPYI/AlaWxxlP0eKSx8ziksfJ4hLnZHExs3IE4il1rROOl7B8RNeY5YpLj9T6wBbAC4B105SRJKaay+KSVxUWlzxOFpc4J4uLmZUjEE+pa51wvITlI7rGLCouOnZZBx/2Xzox+h1pa/7yRIMpWlzygFlc8jhZXOKcLC5mVo5APKWudcLxEpaP6BqziLisDXwN+A9gIm1I9zpgSTpbaLI8zniKFpc8ZhaXPE4Wlzgni4uZlSMQT6lrnXC8hOUjusYsIi56u0iHG74W+HpCp3Uvt6dTnHt/K081kKLFJQ+WxSWPk8UlzsniYmblCMRT6lonHC9h+YiuMYuIy3qADlu8ATgWuBc4CHg3sD3ww/I44ylaXPKYWVzyOFlc4pwsLmZWjkA8pa51wvESlo/oGrOIuIjWq4B/GcB2MnCk3yqavTHV1jAsLvGb3zvnxpn1R9R2D/TyVmu+lL9a81Zrvsys2T1aa3222Tl3kMTvAy9Mr0hr6qiKKaJeJj3iktdwLS55nDziEufkERczK0cgnlLXOuF4CctHdI1ZdMSlPLHCKVpc8oBaXPI4WVzinCwuZlaOQDylrnXC8RKWj+gaM4tL+TYwbYq1NQyLS7ziPVUUZ9YfUds90MtbrfnytEez9lZrfdaary62M4tLs3sjHFVbo7W4hKsQi0ucmcXFzNoRiEfX9qy1IMfrcC5mEXHRZz8O6LDF3rqWxwLaOfd5wIrm2SsX6amiPJYWlzxOniqKc/JUkZmVIxBPyeIy+sxyxeWAdNDidETuALaxuMzeWGq7mSwu8ZvbIy5xZh5xMbN2BOLRtT1r5xo9iJewfETXmOWKyz7AnwLa8v8SvaWX0GlTuhuBvwbuL48znqJHXPKYWVzyOHnEJc7JIy5mVo5APKWudcLxEpaP6BqzXHHpkZK4XJW2/S9Pr0CKFpc8iBaXPE4Wlzgni4uZlSMQT6lrnXC8hOUjusYsKi7PAI5JJ0P36OmU6MOAX5THGU/R4pLHzOKSx8niEudkcTGzcgTiKXWtE46XsHxE15hFxGUt4FLguQPYvMYlox3V1jAsLhmVNvARr3GJM+uPqO0e6OWt1nwpf7XmrdZ8mVmze7TW+iyxc27vkMXjgTMAiUzvqmJ9izLjEZe8hmtxyePkEZc4J4+4mFk5AvGUutYJx0tYPqJrzCIjLvrs8nRC9InAlMUlvwHV1jAsLvl11/ukR1zizDziYmbtCMSja3vWemQvXodzMYuIi9LSHi6vH8iGp4oy6qW2m8niklFpAx+xuMSZWVzMrB2BeHRtz9q5OuF4CctHdI1ZVFyOAJ4+cBK0Fue+E7i7PM54ip4qymNmccnj1P8pi0ucmcXFzNoRiEd3rROOl7B8RNeYRcVlc2CdabD9aEBm2pDdIr21dGvf3jBaT7NlkqOfzJa4xSUPvcUlj5PFJc5ppoiuPRzLlbx5SmYWZ2dmo88sIi767OeBlw5guQt4CvDzOK5VIpS+XrV+FfA1YBGwS0r3IuBaYC9gSVprM+3XWVzyasHiksfJ4hLnZHExs3IE4ilZXEafWURcROMlwBOB36S3ivYHdgOeBtwbx7VKhN5a+ndgpzTS8j5AwvKkdKTAcYA+czOwNXDndN9nccmrBYtLHieLS5yTxcXMyhGIp2RxGX1mUXEZJKLpG8nGdsBP47imjdgY+GPg74GnAm8CzgWuAJTfi4GDgdstLs2JW1zi7LzGJc6sP8IdSpyfmZlZnEA8omvtLCouewMSi971B8C7kmDcEsc1bYTW0WgRsITldcDbgRPSUQO9V7I1paSRl0dcHnHJqwWLSx4nj7jEOXnExczKEYin1LVOOF7C8hFdYxYRl5nWuJyf1qW03YRu07Qr72dTtfwh8Kw0kiMp0kjLemmty+46jXrp0qUTY2Nj2hBvlWvBAh2p5Gs2Ap+54lYuv0Fvsj98HbDHljx/u82GCm7F3fcxcZaWMz18bbzhOkwcpBnE4V6nXzLJNZOrLuU69IXj7Dy+yXAz5m83ARMwgRElMD4+/ghPiYiLsDx54JwirXX54cBmdE3x6W2iq9N6lhXA0cAa6UDHIwGN9mjq6EJg+5nW1HjEJQ+/R1zyOPV/ylNFcWb9EV37VdeutGWizSzO0cxGn1lUXPT5A4HXpsW5Orvob2daKBvE13urSItydWkqSOci6fXn09JBjvq7fnpfN1PaFpc86haXPE4WlzinmSLcocRZmpmZxQnEI7rWzqLiojd7tN6k/9Kry3pNue1UUS/NxyYpGny9WnMY98y10Z3FJa/RWlzyOFlc4pwsLmZWjkA8pa51wvESlo/oGrOIuGh9iaZy/gH4G+A+YN/0xs+soyDlMc+cosUlj7bFJY+TxSXOyeJiZuUIxFPqWiccL2H5iK4xi4hLb5+VtwGXJHTaeE4LZ7VY9sryOOMpWlzymFlc8jhZXOKcLC5mVo5APKWudcLxEpaP6BqziLisnXa01d4tf56mbLRodtvC+7i0qhWLSx4+i0seJ4tLnJPFxczKEYin1LVOOF7C8hFdYxYRF9HaIb2W3P/OrPZaOaM8ymYpWlzyuFlc8jhZXOKcLC5mVo5APKWudcLxEpaP6BqzqLiImA5Z3ApYE7hN+6mUx9g8RYtLHjuLSx4ni0uck8XFzMoRiKfUtU44XsLyEV1jlisu6wPHpikhbcf/AHBq2ktFO+fqbZ8qLotLXjVYXPI4WVzinCwuZlaOQDylrnXC8RKWj+gas1xx0Rb7f51OZdYeLiuBDwNaqKtzhHoyU55oMEWLSx4wi0seJ4tLnJPFxczKEYin1LVOOF7C8hFdY5YjLr3XoLVj7TvTaEuP3OvTyIt2sr2hPM54ihaXPGYWlzxOFpc4J4uLmZUjEE+pa51wvITlI7rGLCIupwAfHUCmtS4/8OvQczek2hqGxWXuOhv8hLf8jzPrj6jtHujlrdZ8KX+15q3WfJlZs3u01vqcKV8Rcfk2cDjwq4Smf4v+pyWBaUatYJRHXPJgWlzyOHnEJc7JIy5mVo5APKWudcLxEpaP6BqzHHERpTcDGnHR9RHgl8CidOih17hktKPaGobFJaPSBj7iEZc4M4+4mFk7AvHo2p61HtmL1+FczHLFRelov5ZPDWTh/wBL+kZhmuewUKRHXPJAWlzyOHnEJc7JIy5mVo5APCWLy+gzi4iLaGgPlycAirs7jbzEKc1jhMUlD67FJY+TxSXOyeJiZuUIxFOyuIw+s6i4xIms5giLSx5wi0seJ4tLnJPFxczKEYinZHEZfWYWl3gdN4qo7WayuMSr0Wtc4sz6I2q7B3p5qzVfyl+teas1X2bW7B6ttT7bvFXUjMSQojzikgfe4pLHySMucU4ecTGzcgTiKXWtE46XsHxE15h5xKV8G5g2xdoahsUlXvEecYkz84iLmbUjEI+u7Vnrkb14Hc7FzOLSnGkosrabyeISqr4HP2xxiTOzuJhZOwLx6NqetXN1wvESlo/oGjOLS/k24BGXFkz3fdc5v8fYylsHkvjhecsWbtki2SKhFpd2GLv2cGxX2jLRZhbnaGajz8ziEq/jnIiNgRPSUQifAU6enJz81fj4eI/3PoDOgDo/J7H5+IxHXOJULS5xZh5xMbN2BOLRFpfRZ2ZxidfxXBFrAVcAlwP/ALwXWHHTTTct2mqrrTYBdky7EE939tNcaRf7d4tLHKXFJc7M4mJm7QjEoy0uo8/M4hKv47kitgZ0kvY2wG+AJwHXXHfddZvtuOOOLwGOBl4GvHWaQyvnSrvYv1tc4igtLnFmFhcza0cgHm1xGX1mFpd4Hc8VsSbweOCO9MG3AG/6/ve/v+PTnva0Hu+/A3Ropc59GsplcYljt7jEmVlczKwdgXi0xWX0mVlc4nWcG7EtcBawIbDP5OTkLX1rXE4FvukRl0ei9OLc3OY1/edqfWgrt7XmrdZ8mVmze6HW+qw1X11sZxaXZvfGXFFvAD4OvBT4IjA10GgtLjMQtLjM1bRm/3c/HOP8zMzM4gTiEW5n5ZhZXOIs54p4CnALsBNwU3p7SL82f+YRl7nQgcVlbkazfcIPxzg/MzOzOIF4hNtZOWYWlzjLuSL2AL428KE7rr322s122mmnHu8PA9enUZm50puXf/calzhWr3GJM+uPqPXBXWu+ujiE366FlImutT5rzVcX25nFpcy9MmcqtTVai8ucVfaID1hc4swsLmbWjkA8urZnba8EtebL4hJvY8UjfMhiHlKLSx6n/k9ZXOLMLC5m1o5APLpWQag1XxaXeBubLmJz4HHAj4G70ge0qZu2fL8b+MlsX1OTuOw3ce76U7/+1YO74261+UZ73XT7XZfqf5+3bOHeZVA1T8XiEmdncYkzs7iYWTsC8ehaBaHWfFlc4m1sMOLAtLfJacBiYFfgu8BFwLXAXsASYPlMX1WhuPxqIK/3nLds4QbtUbVLweIS52dxiTOzuJhZOwLx6FoFodZ8WVzibaw/Qp25RlS0edvPgRcBEhmNUmgX2uMAfeZmQLvT3jnd11lc8irB4pLHqf9TFYvLkencq5vSw/HJwDuApwNnp6Mn7ouXuGxErQ/uWvPVxQ6lbItpllqt9VlrvrrYzmpbnLtZ2nFW+foQoFOCnwCcm87/0d8vBg4Gbre4NLuxFWVxibOrUFx+F3gucA6wG/CNG2+8cerpT3/6dcCngK8D/w84aZhvsPVI1/rgrjVfXexQ4ndV+Yha67PWfHWxndUmLmL4+8BngR8Br02/FnXS8lWA8qtpomM08rJ06dKJsbGx4web/oIFC8rfDQ1SvO/+lbzjE1evErnOWmvw/j/ZpUFqZUM+c8WtXH5D71SC36Z9wB5b8vzt5I7Du1bcfR8TZ2lW8OFr4w3XYeIgbYsz3Ov0Sya5ZlKDgQ9fh75wnJ3HdXbm6r9OPfVUrrjiCi677DI++9nPssMOO3DHHXew7777cvnll7Puuuty4YUXcuWVV3L88Y+4TVZ/hv2NJmACJhAk0Lf/2UORtYmLFq1eogGBNPStjL4xbeimkZb10lqX3XXi8nTl91RRXqvwiEsep/5PVTjiouytDXwLOFQjLjfddNPUVltt9U/AGunvkv590n0VL3TBiFp/cdaaL6GvNW+15svMmt2wtdbnTPmqSVwkJTcAiwANU6wPaF7+5YDm8CU1T00nL28P3GtxadZIFWVxibOrVFy07ktTQ6+RuFxzzTVTO++883eS+GuqSIvZtdj9A/ESl43o2sOxbOmbpWZmcW5mNvrMahKXJwHXAP1zFW8FTkkP3sNSdWjOQA/qaS+PuOQ1WotLHqcOjLj0hP9BcTnttNOmDj/88MuBPXVGlt7EB74AzCj7cRLNItyhxLmZmZnFCcQjutbOahKXuWhLaO5Jbx7N+FmLy1wYf/vvFpc8Tl0Tl3PPPXdqv/32uy1tJaA9jzRieSKwI/BAvNTlIrr2cCxX8uYpmVmcnZmNPrMuiUtWbVhcsjBZXPIwrfKpSqeKNOKiqVVNsV6b1ri8B3hvyrw2cdQaFy1uH+rlDiWO38zMLE4gHtG1dmZxiddxdkTaOdcb0GUT8+nQAVTTfrTvAbRRWif2M+D+tumWiO/aw7FEmdumYWZxgmY2+swsLvE6zo6wuGSjeuiD+77rnN9jbKX27+m/fnjesoU68mGoV6UjLrxi8VnPWgmvFJyX7bLF8Z+/+ja9ScR5yw6aGCqwgS93hxKvDTMzsziBeETX2pnFJV7H2REWl2xUFpc4qocifisuY1rY3n9987xlC3dukWzx0K49HIsDaJCgmcWhmdnoM7O4xOs4O8Liko3K4hJHZXFpwaw/tNaOTnmsNW+15svMmt0UtdZnF/ZxaUZ8IMqLc/Mw+q2iPE79n6p7qsgjLvEa/W1ErQ/tmvNmZvHWZmblmHnEJc4yO8IjLtmoPOISR+URlxbMPOLSDp474Tg/MyvHzOISZ5kdYXHJRmVxiaOyuLRgZnFpB8+dcJyfmZVjZnGJs8yOsLhko7K4xFFZXFows7i0g+dOOM7PzMoxs7jEWWZHWFyyUVlc4qgsLi2YWVzawXMnHOdnZuWYWVziLLMjLC7ZqCwucVQWlxbMLC7t4LkTjvMzs3LMLC5xltkRFpdsVBaXOKouiIsOTj0onUw9lY4j0GGpOhDyB8CH9VJPi6IXCXWHEsdoZmYWJxCP8OvQcWatIywucYTeOTfOrMIN6NYBtgPeCUhe9tYBj0uWLJk69thjdQCkxOX5gE5/3xbQeUpDu9wJx9GbmZnFCcQjLC5xZq0jLC5xhBaXOLMKxeUpaTTl2cCVwKuAqb333nvll7/8Zf3t34E1ge8AzwF+Hi91uQh3wnGWZmZmcQLxCItLnFnrCItLHKHFJc6sQnHpFWJ/4G3pdOqV3//+96ee9rSnra0jlYDd0siLRmaGegikO+F4mzMzM4sTiEdYXOLMWkdYXOIILS5xZhWLywLgiJ64pIeQxEV/fx5wALAr8KN4qctFuBOOszQzM4sTiEdYXOLMWkdYXOIILS5xZh0RlzU++MEP/uboo49+DPDrVMqzgAuB0+OlLhfhTjjO0szMLE4gHmFxiTNrHWFxiSO0uMSZdURc2GWXXR64+uqrFwESlk2BS4HXA1+Pl7pchDvhOEszM7M4gXiExSXOrHWExSWO0OISZ1axuLwyiYkW56783Oc+N/XKV+pPD11/Beg/D8RLXS7CnXCcpZmZWZxAPMLiEmfWOsLiEkdocYkzq1hcVilMegitCzwOuA/4Zby05SPcCceZmpmZxQnEIywucWatIywucYQWlzizjolLdZteuhOOtzkzM7M4gXiExSXOrHWExSWO0OISZ1azuOx3zNkXTU1N6U0ittp8o71uuv2uS1euXPnXF5y46Ivxks5PhDvhOFczM7M4gXiExSXOrHWExSWO0OISZ9YBcXlxf6lWrlz5EotLXj3XKgi15ktUa81brfnqIrPqhm3zbueZP7Vs2bKpxYsXV1Eui0u8Ni0ucWYWlziz/gh3KHF+ZmZmcQLxCI+4xJm1jrC4xBFaXOLMLC5xZhYXM2tHIB5t2SvHrIqRiXhxPOLSltm+i5d/JB1y15/U285btvDktmm3ibe4xOlZXOLMLC5m1o5APNriUo6ZxSXOMjvCIy7ZqB76oMUlzsziEmdmcTGzdgTi0RaXcswsLnGW2REWl2xUFpc4qociLC4t4FW8mFOlqrWzqzVfZtbsXqi1Pru2xkXbgf9Mu22malgL2BK4G/jJbFXjxbl5DddTRXmc+j+13+Kzz5xi6qD+v61k5aILli3SFvZDuywu7dDX+tB2J9ysXmutz1rz1cV2VtuIyzrAk4ELgOcAPwfWBy4CrgX2ApYAy2dq0haXvJvd4pLHyeIS57QKs9/u4+LXoRtirLWzqzVfXeyEGzaNomG11mdXRlxelMTkqcA2wApAh7Lpfx8HbADcDGwN3DldzVlc8tqzxSWPk8UlzqlD4rIF8I4XvOAFb//KV75yOPAP6SiCdoUuGN21DqVg0RsnZWZxdF1jVtuIi4hrl81vA7sncVkKnAtcASi/FwMHA7dbXOINtBdhcYmz81RRA2b1jrjoOfMN4FPnnHPO3xx44IHfAU4CPh4v5fxFdK1DmT8S+SmbWT6r3ie7xqxGcVkvTQv1xOU84ATgqiQumiY6Jo28PKKGPOKS12gtLnmcPOIS59SREZfNgW9p7dzk5OS94+PjBwB7Am9vV+Ky0V3rUMqWvllqZhbn1jVmXRCXNwK3pJGWVaRm6dKlE2NjY8cPVtOCBQviNTcPEffdv5J3fOLqVVJeZ601eP+f7DIP3xZL8jNX3MrlN9yxStABe2zJ87fbLJZQ4U+vuPs+Js7ScqaHr403XIeJg3Yq/E3x5E6/ZJJrJrXs6uHr0BeOs/P4JvHECkbc9rN7eP8/37BKils8fn3esf92Bb+lWVKnXHAjN/541VndI/7o6Tz9SY9tlmChqKmpKd761reycuVKtt12W0466STOOOMMnvMcLa3zZQImUAuB8fHxR3hKF8Tl1cCRwN6A1r5cCGwP3DsdWI+45DU3j7jkcer/lKeKGjCrd6poY+CrwPmnnHLKO4444ghNFZ0GfCBeyvmL6Nov4fkjkZ+ymeWz6n2ya8xqFZcr0xtEWpyrPOqBcliCrJ/e181UNRaXvEZrccnjZHGJc1qFWb3i8rI05bzn5OTkyvHxcS34/8JsP4rakWgW3bUOpVkpy0aZWZxn15jVKC4zUdccxj1pL5cZa8biktdoLS55nCwucU4dEZdnAf8K7Do5Oflf4+Pj+wInAjsCD7QrdbnornUo5UrePCUzi7PrGrMuiUtWbVhcsjBhccnjZHGJc+qIuOjZ95fAe1N+7wL2SS8BtCt0weiudSgFi944KTOLo+saM4tLvI6zI7zlfzaqhz7os4rizLxzbpxZX8RGV1555Z3Pfvaz9Xr0/a1SmofgrnUo84AgnKSZhZF17mgJi0u8jrMjLC7ZqCwucVQPRVhcWsCr+DwglcqdcLxuzWz0mVlc4nWcHWFxyUZlcYmjsri0YPZH7zzzxWussYaOEnnoGhsb++K5S1/zkhbJFg91JxxHamajz8ziEq/j7AiLSzYqi0sclcWlBTOLSwt4FY8EeZSqWb12TfYsLs3qOSvK4pKFaZUPeY1LnJmniuLMLC5xZv0RtXZ0Fpdm9VprfXblkMVm1Pui/FZRHkK/VZTHqf9T3oCuAbNK93GxuMTr0uJiZu0IxKMtLnFmrSM84hJH6BGXODOPuMSZWVzizCwuZtaOQDza4hJn1jrC4hJHaHGJM7O4xJlZXOLMLC5m1o5APNriEmfWOsLiEkdocYkzs7jEmVlc4swsLmbWjkA82uISZ9Y6wuISR2hxiTOzuMSZWVzizCwuZtaOQDza4hJn1jrC4hJHaHGJM7O4xJlZXOLMLC5m1o5APNriEmfWOsLiEkdocYkzs7jEmVlc4swsLmbWjkA82uISZ9Y6wuISR2hxiTOzuMSZWVzizCwuZtaOQDza4hJn1jrC4hJHaHGJM7O4xJlZXOLMLC5m1o5APNriEmfWOsLiEkdocYkzs7jEmVlc4swsLmbWjkA82uISZ9Y6wuISR2hxiTOzuMSZWVzizCwuZtaOQDza4hJn1jrC4hJHaHGJM7O4xJlZXOLMLC5m1o5APNriEmfWOsLiEkdocYkzs7jEmVlc4swsLmbWjkA82uISZ9Y6wuISR2hxiTOzuMSZWVzizCwuI8tsn1NPPfXiN7zhDWPtSlg+2uJSnumcKVpc5kT0iA9YXOLMLC5xZhaXODOLy8gx2xjYEThlYmJi24mJCYtLuypuHr1s2bKpxYsXV1EBFpd4PVpc4swsLnFmFpc4M4vLyDF7MXA08LKJiQksLu3qt1W0xSUP376Ll38EeOvAp9923rKFJ+elMD+fsrjEuVpc4swsLnFmFpeRZfZ3xx9//JtOOOGEKn7w57Sz6jLarmmAxSWPoMUlj1P/p/ZbfPaZU0wd1P+3laxcdMGyRWfFUysXYXGJs7S4xJnldCjtUi0TPdO6iDKpN0+l1nwBp05MTLzeIy7N67Z1pMUlD6HFJY+TxSXOaRVmx5x90dTUlIakH7pWrlz5kgtOXPTFdim3i7a4tONXcSdMrXmrNV8Wl3b3QpFoi0seRotLHieLS5yTxaUdsy6MbFTcCVtc4s3PIy5xZmUjLC55PC0ueZwsLnFOFpd2zCwu7fjVKlW15gv48Pve9763vfvd765u6Yhfh253LzSK9ltFcWxenBtn5jUucWaeKooz64JQKY+1CkKt+eois+oMq93t5MW5ufw84pJL6uHPeXFuA2Ze4xKH1hdRa2dXa7662Am3aiCFgmutz66PuKwFbAncDfxktrryVFFeS7a45HHyVFGck6eK2jHrwshGrR2dxSW/7e27ePmXe5/eavON9rrp9rsuHVtvg5efO7HfPfmpzO8nuywu6wMXAdcCewFLgOUz4bK45DUki0seJ4tLeSKapgAAD6FJREFUnJPFpR0zi0s7frVKVW352nfx8l8B6l8fusbW22ADi0u79teLXgRsAxwHbADcDGwN3Dld8haXPOgWlzxOFpc4J4tLO2YWl3b8ahOEXmlqy5fFpV07myt6KXAucAWgNTkXAwcDt1tc5kI3879bXOLsvMalATOvcYlD64uorbOrtRO27MWbmcUlziwScR5wAnBVEhdNEx2jkZelS5dOjI2NHd+f2Nprr81vfvObSPr+rAmYgAmYgAmYQGUENt10U17/+tc/4iWiLrxV9EbgljTSsl5a67I7sKL2EZf+/NU0hTXIrda81Zov8as1b7Xmy8ya9Qi11met+XI7e3S0sy6Iy6uBI4G9gacCFwLbA/daXJo1UotLe261PrhrzZc7lGZtrtb6rDVfbmePjnbWBXFRHk8DDktVshNw3UzVU+sNVWu+fKM/Om70ZqUsG1XrPVBrvnxvNmt/tdZnrfnqYjvrgrj0Wu9mgN4v114uM161No5a89XFRtvscVY2qtb6rDVfbmfN2l+t9VlrvtzOHh3trEviklUjWrB7zDHHTGR9eDV+qNZ8CUGteas1X2bW7MaptT5rzZfbmdtZMwLxqFrvgZnyNXLiEq8yR5iACZiACZiACXSFgMWlKzXlfJqACZiACZiACTy4oduoXpsCjwVuBe6vpJDK039XkpdeNjYCnpReL5/1HKjVnO/HpHzdNdf5VKs5X72v0/lZTwR+PKTvn+5r1d579/TUTLtLDym/TwD0n/8CfjGkPAx+rbY7X3vgj9oGvabnxSZpt/CaNqdSO9sc+E9A92cNl56tPwNWpsxkn2+3GjI/mDd9ZQ19wXTMxoFfp35zNaB5kEN/vWU990dVXP4I+ARwJvAKYJchPyz1gNTbUKcCOwIPrI4WkfEdO6Q3tJYBBwJ/Dfx9Rtx8f+T3ga8DnwZelv5beazp0iaI2sG5lvrUHkc3AFcDkhbtffSXfQ/yYbLbGbgU+ACg9We7Ad8YZobSdx8NvLRPVF4OPAv4VgV5Oxx4C/AVQMee7Ar8qIJ86by4fwVOAY4AtKfWd4aYr3WAJwMXAM8Bfp7O38k+324e8z5T3obdF0yXr43TPfrV9INRMvGmeeyrpstD9nN/FMWl9wBXI74DeHv6VaWH5rCu/w28C7gSeFUlnYlYaE8cHalwWfoFIFmYcY+c1QhPN4x+mesBqRtKN1MtgiAMe6abXEdR1FKfWyRRUWcncanl6m0a+bw02viUNFKle6GmS3KgEbQPVpApMftBuhd/CRwLfBf4zJDztkb6QaG9tSRRz0h50g+g3kjH6s7ii9LBu9rjS2faaWPS0Pl285jh6fJWQ18wXb600asEUGcCqp6/CSwAvj9PfKbLQ/ZzfxTFRQ/wL/R1dM8FXgKscjTAPFXGbMnqgf259ItuWDf5YP40TKdhew1DqwPW0Qr6xTns/K2ZTF+dnR5CegX+nUOos+m+UkPkGj3QTX1y2hixhhE0HTza/5DZFzi/AmbqhDUKpOFnjbx8EngroCmZWi49M5RH3aPTbmw5hIzqh44EQUeeSEafOdP5bKsxb+ovLgEOSeIiYfkooFGYYT4zNN337TT6I3EJnW83z/wG86avq6EvGMyXpv8kLOoP9CzR/bDlTDvUF2I2mIfs5/4oiot+Bfxd382k///mNPJSiHejZPRr4OxKxKC/AFrjotGoVwISBf3Sq+X6Y2B/QEOIMnR1fsO8NG8uaTk0TS9oRKgG0RMTCbqmEz6Wfnl+cbZT1FcjxN4I6FGA8nRiWrP0vtWYh7m+6kPAvwE6B62Gq7fppvatuhxQ/hYCX64gc3+WfuBoWvmkdD/sM2RxGTwKZsbz7YbAb7pjamroC6bLl9rd64GPA3+YBgDmE9lMR/jM+dwfRXHZIN3ge6TO5SDgd1PnPJ+VMFfaslgN9dbS0Sm/+qWpId8/T6MH981ViNX071proKFKLf7rDU/rRtL89TAvSbDm87Vzs6audJ0DqI0N8xen8qE541799X4Z6yF08zCBAbofvwc8PW0gWcOvzX4kj0sjVbo/7xwyq97XS9Q1jbtdeoZJ2rUuSCMJw770q1jPME0r/A+wuIIRl8EOMHS+3TwDna5zrqEvGMzXusCX0jNXP/S1xmW+r8E8ZD/3R1FcekPTemhrHl2/VrTAbdjzwzVY9mBD1MiUhgR1pIIartrDrDsTz3dLTumrzrTQ9P+ltxe0WLI3f72asjDt10gOtOZGU2vqVLTYurcgcJj50ndrHYRGpN6fhFT1WkNnrFGqK9Kop/Kkc8f0t2GuOeuvK41UaZGpFlrXsjZIU7iTwLbAbYBGOSSAw2am58Pnk6zontR0ltainT7kxj/YAYbOt5vnvHdlxEXMNJKskT1dmsbR+qr5vAbZZD/3R1FcBFpz6XpI6tJbMlr0M+xfxFslQRj2fHCvIfamPfTg7l0altZhlsNesyFWXwN0zIOuN1TytlP/Taw8akh12MPkvTxp9EyLrLVIUZem2P5lPp86gbT770eNAGk0tJZX7/8E0CiCJLSmS+tIekIgeZEg67+Hfen51Zuy0uJ0tbNhvz6uDlA/UpU3rXEJnW83z0AH86avq6EvGMyXRs76R/T0Yst8/1gczEP2c39UxUWNQ68gb5jeLJrntunk54GAOhMN42s4uqaFnPNQ1KJJakRIC0yHvR5osFAardIeDfP9K64ozCEn1ttnRlNYtYwG9T9btSdVTfkarK6s8+2GXMf++lUJZD33R1lc3CBMwARMwARMwARGjIDFZcQq1MUxARMwARMwgVEmYHEZ5dp12UzABEzABExgxAhYXEasQl0cEzABEzABExhlAhaXUa5dl80ETMAETMAERoyAxWXEKtTFMQETMAETMIFRJmBxGeXaddlMoHsEtBGiXoEf5tXbaFD7k6yOHUSHWVZ/twl0joDFpXNV5gybwIMEeps3/U46o0hHN/T+9pFCG6odkE5L1vEG2thrPi/tP/MPwMvSZms6qby/nL0jFvQ3HaL6N8DFc2RIG1rpqAGdkZR7nIU2YNRBgr3rKuBA4D/ms/BO2wRMIJ+AxSWflT9pAjUR6B1toW3hdUr129Omi/8OnJJO7W2bX4mLTv/V2Tk68G8+L51hdEs6wO/DfedS9cqps3E+kc5k0hb4unR+zzdmyZS2MRcL7Sacs4lh77v02d7RCf83HXRYw47S88nfaZtAZwhYXDpTVc6oCaxCoDe60huJUCd+LaAzZNRZawt7jbzobBlt/S8xUMevc3nuStt735ROgdV0iDrqw9LZUIrV2V4Sl38E/g+grfF1MvY7gVvT57RFuM430Xe8I50vpVOgJQrauff704z8vDAdlaDPaOREn9duumemLduVls4z6p1S3iunDrdclHZq3SEddKk8Kz/9aeqID+VLI1H/nI6N0KnPOnhPh2Hq/CuVXyM2Ot24/2BFne2kPPfSFXCdeaZzqY5LB37+ZTqjR//2lnQSvY7PmIm1djHWd301MdS29I8HlgESK+VNJ2V/examypfqQKfmavRIn9fxDr5M4FFJwOLyqKx2F3oECPRGB9Th75Q66n3T6IA6Z3XgOq+rN/rS65QlOBIFddA6N+hjqaMWkv8NvCId7qdzSnQOk8RFgnFe6pwVt0s6S0prUdSJvhbYE9gemOiTI0lN/3lJ+m6NCP0T8GlAoxk6lmP3JEknAn+VZEAng+vqiYtOmJa46Byt3t8kUEcDEjCNOimPn0rn1vwFcEySMZ12q+3pJWNvBX4BnJH+t0aUele/DOqsFrHTtJEOidRZZ5qakiRJZp6U8ipmYjgXa32HThJXviSAKrdOZdfBiTrYdNf090GmzwQ+l8qtUbU/Bfar5ADNEbiNXIQuErC4dLHWnGcT+G3nrc5Snb1kQCMS6pR1oOh04jKeOvh+cZHw6Jf+NwGJwWvSoXnqiHvioo5dQqKOXwIgMVHHqUPttP5D36/j6DVao4MAJRc6VPFF05yXpE5XYtJLT4ct6jBN5emnaapIHbVGjgZlQvnTiIkEol9cFgAvSSNKjwXemwRBn5XI6SBMTXVp0e+r0qm3KrdGnsSrX1z0nTofSCMbSveVKRMSPI2USDgkSB9Moy9fSqM3+q7+KbrpWGvtzoWp7NcDL0iSqXU4OlNHo0DXTcP0+UnwxEijOpI1jbbMNkXm+8MERpqAxWWkq9eFG2ECPXHpjaho6uFdqbzqkHsjLupkNfWjjlIdXr+49E8vSWB0pL2mL9SZ94tLb42L5ERTFj1xOS1NXegtHP1H0xgaRdC0lCRh8ER2jY7oFFqN/miKRpIiadKIy8/TKNDgupXZpookQRo50SiQRnF0mvKxwH+l79fJxSqLvk9y05v26o36DIqLppckb59NgiCcEhylIfFQ+vpO/X89O5Weyt0Tl9lYa5TqGuBZff+taT0tStZhohskiZyO6dqJuUa1JFOSHE1faUG2LxN41BGwuDzqqtwFHhECg+KiDlC/wrV2RB3yJ9Mv+BvT+gwJjqRgJnGZTJ3ioLioc9a6Dr3lIwHSpZEVjRpc0DcCI2HQ37VmZSZx6Y2wLEnTNpKt56WpKZ0cLQGZTlw0svSEJGZaT6LpKY1SaHpFUqU8Kt83AF9JIyOSCYmLREDSISGReEmSFKNpJa176ZVJ5doiycBtwMHplG3JlkaUJHJ660mLofWW0e+mdTnvBj6UyVr1szmgaTDlWeXQ1NYmwB+k9UmDTCVcF6X1SZpWkvhpempwZGpEmrWLYQJzE7C4zM3InzCBGglM9+qzfo1rtKDXIR+SRgmUf4mMOm798tcv9t5aFQmIpmskLprmmU5ceuVXnKZMNHLwbOBfk0Do31+XRj/0RtAT+6Z1BtlpQaumW3rXS1PH3FuD0xuZ6P374CJk/V3rT/4wjdZsmqRKwqa/a1pHUqPpIEmKRoGUb8mGFv7q0roVTVdpzYnWjUz15ee5fYt69WfFSoC0vkVvNmm6R/KiS/GHpzeWcliLmy5NQym2V5YXJ/GZiekb0pRXL5sSvvekdS81tk3nyQTmlYDFZV7xOnETGDoBrdnQHibaTK3NpfUjeiW6P5010zSHFpTmvG7c+/6NAI2waLpIbx+1vXr50HTT4KV/0yiN8igWmnbR4uS5Lo2C6FIeB9lJjPS3we+LsO4xUBo5THt5l2T1vwk1Vzn87yYwcgQsLiNXpS6QCZiACZiACYwuAYvL6NatS2YCJmACJmACI0fA4jJyVeoCmYAJmIAJmMDoErC4jG7dumQmYAImYAImMHIELC4jV6UukAmYgAmYgAmMLgGLy+jWrUtmAiZgAiZgAiNHwOIyclXqApmACZiACZjA6BL4/x+o5i4wFTeUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_source_distribution_2 = pandas.DataFrame({\n",
    "    'Techniques': list(techniques_data_source_3.groupby(['technique'])['technique'].count().keys()),\n",
    "    'Count of Data Sources': techniques_data_source_3.groupby(['technique'])['technique'].count().tolist()})\n",
    "\n",
    "data_source_distribution_3 = pandas.DataFrame({\n",
    "    'Number of Data Sources': list(data_source_distribution_2.groupby(['Count of Data Sources'])['Count of Data Sources'].count().keys()),\n",
    "    'Count of Techniques': data_source_distribution_2.groupby(['Count of Data Sources'])['Count of Data Sources'].count().tolist()})\n",
    "\n",
    "bars = alt.Chart(data_source_distribution_3).mark_bar().encode(x ='Number of Data Sources',y='Count of Techniques').properties(width=500)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx=0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image above shows you the number data sources needed per techniques according to ATT&CK:\n",
    "* There are 71 techniques that require 3 data sources as enough context to validate the detection of them according to ATT&CK\n",
    "* Only one technique has 12 data sources\n",
    "* One data source only applies to 19 techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create subsets of data sources with the data source column defining and using a python function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/26332412/python-recursive-function-to-display-all-subsets-of-given-set\n",
    "def subs(l):\n",
    "    res = []\n",
    "    for i in range(1, len(l) + 1):\n",
    "        for combo in itertools.combinations(l, i):\n",
    "            res.append(list(combo))\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before applying the function, we need to use lowercase data sources names and sort data sources names to improve consistency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = techniques_with_data_sources[['data_sources']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df.iterrows():\n",
    "    row[\"data_sources\"]=[x.lower() for x in row[\"data_sources\"]]\n",
    "    row[\"data_sources\"].sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[netflow/enclave netflow, network device logs,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[process command-line parameters, process moni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[netflow/enclave netflow, network device logs,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        data_sources\n",
       "0  [file monitoring, process command-line paramet...\n",
       "1  [file monitoring, process command-line paramet...\n",
       "2  [netflow/enclave netflow, network device logs,...\n",
       "3  [process command-line parameters, process moni...\n",
       "4  [netflow/enclave netflow, network device logs,..."
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply the function and split the subsets column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/ipython/7.3.0/libexec/vendor/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['subsets']=df['data_sources'].apply(subs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_sources</th>\n",
       "      <th>subsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[[file monitoring], [process command-line para...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[[file monitoring], [process command-line para...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[netflow/enclave netflow, network device logs,...</td>\n",
       "      <td>[[netflow/enclave netflow], [network device lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[process command-line parameters, process moni...</td>\n",
       "      <td>[[process command-line parameters], [process m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[netflow/enclave netflow, network device logs,...</td>\n",
       "      <td>[[netflow/enclave netflow], [network device lo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        data_sources  \\\n",
       "0  [file monitoring, process command-line paramet...   \n",
       "1  [file monitoring, process command-line paramet...   \n",
       "2  [netflow/enclave netflow, network device logs,...   \n",
       "3  [process command-line parameters, process moni...   \n",
       "4  [netflow/enclave netflow, network device logs,...   \n",
       "\n",
       "                                             subsets  \n",
       "0  [[file monitoring], [process command-line para...  \n",
       "1  [[file monitoring], [process command-line para...  \n",
       "2  [[netflow/enclave netflow], [network device lo...  \n",
       "3  [[process command-line parameters], [process m...  \n",
       "4  [[netflow/enclave netflow], [network device lo...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the subsets column values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_with_data_sources_preview = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes_4 = ['subsets']\n",
    "\n",
    "for a in attributes_4:\n",
    "    s = techniques_with_data_sources_preview.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    s.name = a\n",
    "    techniques_with_data_sources_preview = techniques_with_data_sources_preview.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    \n",
    "techniques_with_data_sources_subsets = techniques_with_data_sources_preview.reindex(['data_sources','subsets'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_sources</th>\n",
       "      <th>subsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[process command-line parameters]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[process monitoring]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process monitoring]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        data_sources  \\\n",
       "0  [file monitoring, process command-line paramet...   \n",
       "1  [file monitoring, process command-line paramet...   \n",
       "2  [file monitoring, process command-line paramet...   \n",
       "3  [file monitoring, process command-line paramet...   \n",
       "4  [file monitoring, process command-line paramet...   \n",
       "\n",
       "                                             subsets  \n",
       "0                                  [file monitoring]  \n",
       "1                  [process command-line parameters]  \n",
       "2                               [process monitoring]  \n",
       "3  [file monitoring, process command-line paramet...  \n",
       "4              [file monitoring, process monitoring]  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_with_data_sources_subsets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add three columns to analyse the dataframe: subsets_name (Changing Lists to Strings), subsets_number_elements ( Number of data sources per subset) and number_data_sources_per_technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "techniques_with_data_sources_subsets['subsets_name']=techniques_with_data_sources_subsets['subsets'].apply(lambda x: ','.join(map(str, x)))\n",
    "techniques_with_data_sources_subsets['subsets_number_elements']=techniques_with_data_sources_subsets['subsets'].str.len()\n",
    "techniques_with_data_sources_subsets['number_data_sources_per_technique']=techniques_with_data_sources_subsets['data_sources'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_sources</th>\n",
       "      <th>subsets</th>\n",
       "      <th>subsets_name</th>\n",
       "      <th>subsets_number_elements</th>\n",
       "      <th>number_data_sources_per_technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring]</td>\n",
       "      <td>file monitoring</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[process command-line parameters]</td>\n",
       "      <td>process command-line parameters</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[process monitoring]</td>\n",
       "      <td>process monitoring</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>file monitoring,process command-line parameters</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process monitoring]</td>\n",
       "      <td>file monitoring,process monitoring</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        data_sources  \\\n",
       "0  [file monitoring, process command-line paramet...   \n",
       "1  [file monitoring, process command-line paramet...   \n",
       "2  [file monitoring, process command-line paramet...   \n",
       "3  [file monitoring, process command-line paramet...   \n",
       "4  [file monitoring, process command-line paramet...   \n",
       "\n",
       "                                             subsets  \\\n",
       "0                                  [file monitoring]   \n",
       "1                  [process command-line parameters]   \n",
       "2                               [process monitoring]   \n",
       "3  [file monitoring, process command-line paramet...   \n",
       "4              [file monitoring, process monitoring]   \n",
       "\n",
       "                                      subsets_name  subsets_number_elements  \\\n",
       "0                                  file monitoring                        1   \n",
       "1                  process command-line parameters                        1   \n",
       "2                               process monitoring                        1   \n",
       "3  file monitoring,process command-line parameters                        2   \n",
       "4               file monitoring,process monitoring                        2   \n",
       "\n",
       "   number_data_sources_per_technique  \n",
       "0                                  3  \n",
       "1                                  3  \n",
       "2                                  3  \n",
       "3                                  3  \n",
       "4                                  3  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_with_data_sources_subsets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it was described above, we need to find grups pf data sources, so we are going to filter out all the subsets with only one data source:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "subsets = techniques_with_data_sources_subsets\n",
    "\n",
    "subsets_ok=subsets[subsets.subsets_number_elements != 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_sources</th>\n",
       "      <th>subsets</th>\n",
       "      <th>subsets_name</th>\n",
       "      <th>subsets_number_elements</th>\n",
       "      <th>number_data_sources_per_technique</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>file monitoring,process command-line parameters</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process monitoring]</td>\n",
       "      <td>file monitoring,process monitoring</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[process command-line parameters, process moni...</td>\n",
       "      <td>process command-line parameters,process monito...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>file monitoring,process command-line parameter...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>[file monitoring, process command-line paramet...</td>\n",
       "      <td>file monitoring,process command-line parameters</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         data_sources  \\\n",
       "3   [file monitoring, process command-line paramet...   \n",
       "4   [file monitoring, process command-line paramet...   \n",
       "5   [file monitoring, process command-line paramet...   \n",
       "6   [file monitoring, process command-line paramet...   \n",
       "10  [file monitoring, process command-line paramet...   \n",
       "\n",
       "                                              subsets  \\\n",
       "3   [file monitoring, process command-line paramet...   \n",
       "4               [file monitoring, process monitoring]   \n",
       "5   [process command-line parameters, process moni...   \n",
       "6   [file monitoring, process command-line paramet...   \n",
       "10  [file monitoring, process command-line paramet...   \n",
       "\n",
       "                                         subsets_name  \\\n",
       "3     file monitoring,process command-line parameters   \n",
       "4                  file monitoring,process monitoring   \n",
       "5   process command-line parameters,process monito...   \n",
       "6   file monitoring,process command-line parameter...   \n",
       "10    file monitoring,process command-line parameters   \n",
       "\n",
       "    subsets_number_elements  number_data_sources_per_technique  \n",
       "3                         2                                  3  \n",
       "4                         2                                  3  \n",
       "5                         2                                  3  \n",
       "6                         3                                  3  \n",
       "10                        2                                  3  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subsets_ok.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we calculate the most relevant groups of data sources (Top 15):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "subsets_graph = subsets_ok.groupby(['subsets_name'])['subsets_name'].count().to_frame(name='subsets_count').sort_values(by='subsets_count',ascending=False)[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subsets_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsets_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>process command-line parameters,process monitoring</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file monitoring,process monitoring</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file monitoring,process command-line parameters</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file monitoring,process command-line parameters,process monitoring</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>process monitoring,process use of network</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>api monitoring,process monitoring</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>process monitoring,windows registry</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packet capture,process use of network</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packet capture,process monitoring</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netflow/enclave netflow,process monitoring</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netflow/enclave netflow,process use of network</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>process command-line parameters,windows registry</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netflow/enclave netflow,packet capture</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>process monitoring,windows event logs</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packet capture,process monitoring,process use of network</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    subsets_count\n",
       "subsets_name                                                     \n",
       "process command-line parameters,process monitoring             88\n",
       "file monitoring,process monitoring                             74\n",
       "file monitoring,process command-line parameters                49\n",
       "file monitoring,process command-line parameters...             42\n",
       "process monitoring,process use of network                      33\n",
       "api monitoring,process monitoring                              32\n",
       "process monitoring,windows registry                            29\n",
       "packet capture,process use of network                          21\n",
       "packet capture,process monitoring                              19\n",
       "netflow/enclave netflow,process monitoring                     18\n",
       "netflow/enclave netflow,process use of network                 17\n",
       "process command-line parameters,windows registry               17\n",
       "netflow/enclave netflow,packet capture                         17\n",
       "process monitoring,windows event logs                          16\n",
       "packet capture,process monitoring,process use o...             16"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subsets_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
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       "datasets": {
        "data-48dee17f09c6622cd09e1362aa43116f": [
         {
          "Count of Techniques": 88,
          "Data Sources": "process command-line parameters,process monitoring"
         },
         {
          "Count of Techniques": 74,
          "Data Sources": "file monitoring,process monitoring"
         },
         {
          "Count of Techniques": 49,
          "Data Sources": "file monitoring,process command-line parameters"
         },
         {
          "Count of Techniques": 42,
          "Data Sources": "file monitoring,process command-line parameters,process monitoring"
         },
         {
          "Count of Techniques": 33,
          "Data Sources": "process monitoring,process use of network"
         },
         {
          "Count of Techniques": 32,
          "Data Sources": "api monitoring,process monitoring"
         },
         {
          "Count of Techniques": 29,
          "Data Sources": "process monitoring,windows registry"
         },
         {
          "Count of Techniques": 21,
          "Data Sources": "packet capture,process use of network"
         },
         {
          "Count of Techniques": 19,
          "Data Sources": "packet capture,process monitoring"
         },
         {
          "Count of Techniques": 18,
          "Data Sources": "netflow/enclave netflow,process monitoring"
         },
         {
          "Count of Techniques": 17,
          "Data Sources": "netflow/enclave netflow,process use of network"
         },
         {
          "Count of Techniques": 17,
          "Data Sources": "process command-line parameters,windows registry"
         },
         {
          "Count of Techniques": 17,
          "Data Sources": "netflow/enclave netflow,packet capture"
         },
         {
          "Count of Techniques": 16,
          "Data Sources": "process monitoring,windows event logs"
         },
         {
          "Count of Techniques": 16,
          "Data Sources": "packet capture,process monitoring,process use of network"
         }
        ]
       },
       "layer": [
        {
         "data": {
          "name": "data-48dee17f09c6622cd09e1362aa43116f"
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         "encoding": {
          "color": {
           "field": "Data Sources",
           "type": "nominal"
          },
          "x": {
           "field": "Data Sources",
           "type": "nominal"
          },
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subsets_graph_2 = pandas.DataFrame({\n",
    "    'Data Sources': list(subsets_graph.index),\n",
    "    'Count of Techniques': subsets_graph['subsets_count'].tolist()})\n",
    "\n",
    "bars = alt.Chart(subsets_graph_2).mark_bar().encode(x ='Data Sources', y ='Count of Techniques', color='Data Sources').properties(width=500)\n",
    "text = bars.mark_text(align='center',baseline='middle',dx= 0,dy=-5).encode(text='Count of Techniques')\n",
    "bars + text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group (Process Monitoring - Process Command-line parameters) is the is the group of data sources with the highest number of techniques. This group of data sources are suggested to hunt 78 techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 17. Let's Split all the Information About Techniques With Data Sources Defined: Matrix, Platform, Tactic and Data Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split all the relevant columns of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process Monitoring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>File monitoring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process Monitoring</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix platform           tactic               technique  \\\n",
       "0  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "1  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "2  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "3  mitre-attack    macOS  defense-evasion  Compile After Delivery   \n",
       "4  mitre-attack    macOS  defense-evasion  Compile After Delivery   \n",
       "\n",
       "  technique_id                     data_sources  \n",
       "0        T1500  Process command-line parameters  \n",
       "1        T1500               Process Monitoring  \n",
       "2        T1500                  File monitoring  \n",
       "3        T1500  Process command-line parameters  \n",
       "4        T1500               Process Monitoring  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_data = techniques_with_data_sources\n",
    "\n",
    "attributes = ['platform','tactic','data_sources'] # In attributes we are going to indicate the name of the columns that we need to split\n",
    "\n",
    "for a in attributes:\n",
    "    s = techniques_data.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    # \"s\" is going to be a column of a frame with every value of the list inside each cell of the column \"a\"\n",
    "    s.name = a\n",
    "    # We name \"s\" with the same name of \"a\".\n",
    "    techniques_data=techniques_data.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    # We drop the column \"a\" from \"techniques_data\", and then join \"techniques_data\" with \"s\"\n",
    "\n",
    "# Let's re-arrange the columns from general to specific\n",
    "techniques_data_2=techniques_data.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)\n",
    "\n",
    "# We are going to edit some names inside the dataframe to improve the consistency:\n",
    "techniques_data_3 = techniques_data_2.replace(['Process monitoring','Application logs'],['Process Monitoring','Application Logs'])\n",
    "\n",
    "techniques_data_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you remember data sources names with a reference to Windows? After splitting the dataframe by platforms, tactics and data sources, are there any macOC or linux techniques that consider windows data sources? Let's identify those rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After splitting the rows of the dataframe, there are some values that relate windows data sources with platforms like linux and masOS.\n",
    "# We need to identify those rows\n",
    "conditions = [(techniques_data_3['platform']=='Linux')&(techniques_data_3['data_sources'].str.contains('windows',case=False)== True),\n",
    "             (techniques_data_3['platform']=='macOS')&(techniques_data_3['data_sources'].str.contains('windows',case=False)== True),\n",
    "             (techniques_data_3['platform']=='Linux')&(techniques_data_3['data_sources'].str.contains('powershell',case=False)== True),\n",
    "             (techniques_data_3['platform']=='macOS')&(techniques_data_3['data_sources'].str.contains('powershell',case=False)== True),\n",
    "             (techniques_data_3['platform']=='Linux')&(techniques_data_3['data_sources'].str.contains('wmi',case=False)== True),\n",
    "             (techniques_data_3['platform']=='macOS')&(techniques_data_3['data_sources'].str.contains('wmi',case=False)== True)]\n",
    "# In conditions we indicate a logical test\n",
    "\n",
    "choices = ['NO OK','NO OK','NO OK','NO OK','NO OK','NO OK']\n",
    "# In choices, we indicate the result when the logical test is true\n",
    "\n",
    "techniques_data_3['Validation'] = np.select(conditions,choices,default='OK')\n",
    "# We add a column \"Validation\" to \"techniques_data_3\" with the result of the logical test. The default value is going to be \"OK\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the inconsistent data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>impact</td>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>T1490</td>\n",
       "      <td>Windows Registry</td>\n",
       "      <td>NO OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>impact</td>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>T1490</td>\n",
       "      <td>Windows event logs</td>\n",
       "      <td>NO OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>impact</td>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>T1490</td>\n",
       "      <td>Windows Registry</td>\n",
       "      <td>NO OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>impact</td>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>T1490</td>\n",
       "      <td>Windows event logs</td>\n",
       "      <td>NO OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>File Permissions Modification</td>\n",
       "      <td>T1222</td>\n",
       "      <td>Windows event logs</td>\n",
       "      <td>NO OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           matrix platform           tactic                      technique  \\\n",
       "105  mitre-attack    macOS           impact        Inhibit System Recovery   \n",
       "107  mitre-attack    macOS           impact        Inhibit System Recovery   \n",
       "110  mitre-attack    Linux           impact        Inhibit System Recovery   \n",
       "112  mitre-attack    Linux           impact        Inhibit System Recovery   \n",
       "187  mitre-attack    Linux  defense-evasion  File Permissions Modification   \n",
       "\n",
       "    technique_id        data_sources Validation  \n",
       "105        T1490    Windows Registry      NO OK  \n",
       "107        T1490  Windows event logs      NO OK  \n",
       "110        T1490    Windows Registry      NO OK  \n",
       "112        T1490  Windows event logs      NO OK  \n",
       "187        T1222  Windows event logs      NO OK  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_analysis_data_no_ok = techniques_data_3[techniques_data_3.Validation == 'NO OK']\n",
    "# Finally, we are filtering all the values with NO OK\n",
    "\n",
    "techniques_analysis_data_no_ok.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  37  rows with inconsistent data\n"
     ]
    }
   ],
   "source": [
    "print('There are ',len(techniques_analysis_data_no_ok),' rows with inconsistent data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the impact of this inconsistent data from a platform and data sources perspective?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = techniques_with_data_sources\n",
    "\n",
    "attributes = ['platform','data_sources']\n",
    "\n",
    "for a in attributes:\n",
    "    s = df.apply(lambda x: pandas.Series(x[a]),axis=1).stack().reset_index(level=1, drop=True)\n",
    "    s.name = a\n",
    "    df=df.drop(a, axis=1).join(s).reset_index(drop=True)\n",
    "    \n",
    "df_2=df.reindex(['matrix','platform','tactic','technique','technique_id','data_sources'], axis=1)\n",
    "df_3 = df_2.replace(['Process monitoring','Application logs'],['Process Monitoring','Application Logs'])\n",
    "\n",
    "conditions = [(df_3['data_sources'].str.contains('windows',case=False)== True),\n",
    "              (df_3['data_sources'].str.contains('powershell',case=False)== True),\n",
    "              (df_3['data_sources'].str.contains('wmi',case=False)== True)]\n",
    "\n",
    "choices = ['Windows','Windows','Windows']\n",
    "\n",
    "df_3['Validation'] = np.select(conditions,choices,default='Other')\n",
    "df_3['Num_Tech'] = 1\n",
    "df_4 = df_3[df_3.Validation == 'Windows']\n",
    "df_5 = df_4.groupby(['data_sources','platform'])['technique'].nunique()\n",
    "df_6 = df_5.to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-80ff2ad4d82713c24e6d413241ac9bea"
       },
       "datasets": {
        "data-80ff2ad4d82713c24e6d413241ac9bea": [
         {
          "data_sources": "PowerShell logs",
          "platform": "Linux",
          "technique": 1
         },
         {
          "data_sources": "PowerShell logs",
          "platform": "Windows",
          "technique": 4
         },
         {
          "data_sources": "PowerShell logs",
          "platform": "macOS",
          "technique": 2
         },
         {
          "data_sources": "WMI Objects",
          "platform": "Windows",
          "technique": 1
         },
         {
          "data_sources": "Windows Error Reporting",
          "platform": "Linux",
          "technique": 4
         },
         {
          "data_sources": "Windows Error Reporting",
          "platform": "Windows",
          "technique": 4
         },
         {
          "data_sources": "Windows Error Reporting",
          "platform": "macOS",
          "technique": 4
         },
         {
          "data_sources": "Windows Registry",
          "platform": "Linux",
          "technique": 5
         },
         {
          "data_sources": "Windows Registry",
          "platform": "Windows",
          "technique": 36
         },
         {
          "data_sources": "Windows Registry",
          "platform": "macOS",
          "technique": 5
         },
         {
          "data_sources": "Windows event logs",
          "platform": "Linux",
          "technique": 5
         },
         {
          "data_sources": "Windows event logs",
          "platform": "Windows",
          "technique": 22
         },
         {
          "data_sources": "Windows event logs",
          "platform": "macOS",
          "technique": 5
         }
        ]
       },
       "encoding": {
        "color": {
         "field": "platform",
         "type": "nominal"
        },
        "x": {
         "field": "technique",
         "stack": "normalize",
         "type": "quantitative"
        },
        "y": {
         "field": "data_sources",
         "type": "nominal"
        }
       },
       "height": 200,
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(df_6).mark_bar().encode(x=alt.X('technique', stack=\"normalize\"),    y='data_sources',    color='platform').properties(height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are techniques that consider Windows Error Reporting, Windows Registry, and Windows event logs as data sources and they also consider platforms like Linux and masOS. We do not need to consider this rows because those data sources can only be managed at a Windows environment. These are the techniques that we should not consider in our data base:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>technique</th>\n",
       "      <th>data_sources</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>Input Prompt</td>\n",
       "      <td>PowerShell logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>Credential Dumping</td>\n",
       "      <td>PowerShell logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Exploitation for Defense Evasion</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Exploitation of Remote Services</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>Exploitation for Credential Access</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Exploitation for Privilege Escalation</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>Windows Registry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>Disabling Security Tools</td>\n",
       "      <td>Windows Registry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>Third-party Software</td>\n",
       "      <td>Windows Registry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>Input Capture</td>\n",
       "      <td>Windows Registry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>Process Injection</td>\n",
       "      <td>Windows Registry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Inhibit System Recovery</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>File Permissions Modification</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Create Account</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>Indicator Removal on Host</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td>Obfuscated Files or Information</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  technique             data_sources\n",
       "666                            Input Prompt          PowerShell logs\n",
       "1990                     Credential Dumping          PowerShell logs\n",
       "306        Exploitation for Defense Evasion  Windows Error Reporting\n",
       "315         Exploitation of Remote Services  Windows Error Reporting\n",
       "388      Exploitation for Credential Access  Windows Error Reporting\n",
       "1384  Exploitation for Privilege Escalation  Windows Error Reporting\n",
       "105                 Inhibit System Recovery         Windows Registry\n",
       "1182               Disabling Security Tools         Windows Registry\n",
       "1311                   Third-party Software         Windows Registry\n",
       "1480                          Input Capture         Windows Registry\n",
       "1505                      Process Injection         Windows Registry\n",
       "107                 Inhibit System Recovery       Windows event logs\n",
       "187           File Permissions Modification       Windows event logs\n",
       "630                          Create Account       Windows event logs\n",
       "1364              Indicator Removal on Host       Windows event logs\n",
       "1781        Obfuscated Files or Information       Windows event logs"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_analysis_data_no_ok[['technique','data_sources']].drop_duplicates().sort_values(by='data_sources',ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without considering this inconsistent data, the final dataframe is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matrix</th>\n",
       "      <th>platform</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>data_sources</th>\n",
       "      <th>Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process Monitoring</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>Linux</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>File monitoring</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process command-line parameters</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>macOS</td>\n",
       "      <td>defense-evasion</td>\n",
       "      <td>Compile After Delivery</td>\n",
       "      <td>T1500</td>\n",
       "      <td>Process Monitoring</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         matrix platform           tactic               technique  \\\n",
       "0  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "1  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "2  mitre-attack    Linux  defense-evasion  Compile After Delivery   \n",
       "3  mitre-attack    macOS  defense-evasion  Compile After Delivery   \n",
       "4  mitre-attack    macOS  defense-evasion  Compile After Delivery   \n",
       "\n",
       "  technique_id                     data_sources Validation  \n",
       "0        T1500  Process command-line parameters         OK  \n",
       "1        T1500               Process Monitoring         OK  \n",
       "2        T1500                  File monitoring         OK  \n",
       "3        T1500  Process command-line parameters         OK  \n",
       "4        T1500               Process Monitoring         OK  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "techniques_analysis_data_ok = techniques_data_3[techniques_data_3.Validation == 'OK']\n",
    "techniques_analysis_data_ok.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  1983  rows of data that you can play with\n"
     ]
    }
   ],
   "source": [
    "print('There are ',len(techniques_analysis_data_ok),' rows of data that you can play with')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 18. Getting Techniques by Data Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function gets techniques' information that includes specific data sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_source = 'PROCESS MONITORING'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = lift.get_techniques_by_datasources(data_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sources_list = ['pRoceSS MoniTorinG','process commAnd-linE parameters']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "results2 = lift.get_techniques_by_datasources(data_sources_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AttackPattern(type='attack-pattern', id='attack-pattern--0fff2797-19cb-41ea-a5f1-8a9303b8158e', created_by_ref='identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5', created='2019-04-23T15:34:30.008Z', modified='2019-04-29T14:14:08.450Z', name='Systemd Service', description=\"Systemd services can be used to establish persistence on a Linux system. The systemd service manager is commonly used for managing background daemon processes (also known as services) and other system resources.(Citation: Linux man-pages: systemd January 2014)(Citation: Freedesktop.org Linux systemd 29SEP2018) Systemd is the default initialization (init) system on many Linux distributions starting with Debian 8, Ubuntu 15.04, CentOS 7, RHEL 7, Fedora 15, and replaces legacy init systems including SysVinit and Upstart while remaining backwards compatible with the aforementioned init systems.\\n\\nSystemd utilizes configuration files known as service units to control how services boot and under what conditions. By default, these unit files are stored in the <code>/etc/systemd/system</code> and <code>/usr/lib/systemd/system</code> directories and have the file extension <code>.service</code>. Each service unit file may contain numerous directives that can execute system commands. \\n\\n* ExecStart, ExecStartPre, and ExecStartPost directives cover execution of commands when a services is started manually by 'systemctl' or on system start if the service is set to automatically start. \\n* ExecReload directive covers when a service restarts. \\n* ExecStop and ExecStopPost directives cover when a service is stopped or manually by 'systemctl'.\\n\\nAdversaries have used systemd functionality to establish persistent access to victim systems by creating and/or modifying service unit files that cause systemd to execute malicious commands at recurring intervals, such as at system boot.(Citation: Anomali Rocke March 2019)(Citation: gist Arch package compromise 10JUL2018)(Citation: Arch Linux Package Systemd Compromise BleepingComputer 10JUL2018)(Citation: acroread package compromised Arch Linux Mail 8JUL2018)\\n\\nWhile adversaries typically require root privileges to create/modify service unit files in the <code>/etc/systemd/system</code> and <code>/usr/lib/systemd/system</code> directories, low privilege users can create/modify service unit files in directories such as <code>~/.config/systemd/user/</code> to achieve user-level persistence.(Citation: Rapid7 Service Persistence 22JUNE2016)\", kill_chain_phases=[KillChainPhase(kill_chain_name='mitre-attack', phase_name='persistence')], external_references=[ExternalReference(source_name='mitre-attack', url='https://attack.mitre.org/techniques/T1501', external_id='T1501'), ExternalReference(source_name='Linux man-pages: systemd January 2014', description='Linux man-pages. (2014, January). systemd(1) - Linux manual page. Retrieved April 23, 2019.', url='http://man7.org/linux/man-pages/man1/systemd.1.html'), ExternalReference(source_name='Freedesktop.org Linux systemd 29SEP2018', description='Freedesktop.org. (2018, September 29). systemd System and Service Manager. Retrieved April 23, 2019.', url='https://www.freedesktop.org/wiki/Software/systemd/'), ExternalReference(source_name='Anomali Rocke March 2019', description='Anomali Labs. (2019, March 15). Rocke Evolves Its Arsenal With a New Malware Family Written in Golang. Retrieved April 24, 2019.', url='https://www.anomali.com/blog/rocke-evolves-its-arsenal-with-a-new-malware-family-written-in-golang'), ExternalReference(source_name='gist Arch package compromise 10JUL2018', description='Catalin Cimpanu. (2018, July 10). ~x file downloaded in public Arch package compromise. Retrieved April 23, 2019.', url='https://gist.github.com/campuscodi/74d0d2e35d8fd9499c76333ce027345a'), ExternalReference(source_name='Arch Linux Package Systemd Compromise BleepingComputer 10JUL2018', description='Catalin Cimpanu. (2018, July 10). Malware Found in Arch Linux AUR Package Repository. Retrieved April 23, 2019.', url='https://www.bleepingcomputer.com/news/security/malware-found-in-arch-linux-aur-package-repository/'), ExternalReference(source_name='acroread package compromised Arch Linux Mail 8JUL2018', description='Eli Schwartz. (2018, June 8). acroread package compromised. Retrieved April 23, 2019.', url='https://lists.archlinux.org/pipermail/aur-general/2018-July/034153.html'), ExternalReference(source_name='Rapid7 Service Persistence 22JUNE2016', description='Rapid7. (2016, June 22). Service Persistence. Retrieved April 23, 2019.', url='https://www.rapid7.com/db/modules/exploit/linux/local/service_persistence')], object_marking_refs=['marking-definition--fa42a846-8d90-4e51-bc29-71d5b4802168'], x_mitre_contributors=['Tony Lambert, Red Canary'], x_mitre_data_sources=['Process command-line parameters', 'Process monitoring', 'File monitoring'], x_mitre_detection=\"Systemd service unit files may be detected by auditing file creation and modification events within the <code>/etc/systemd/system</code>, <code>/usr/lib/systemd/system/</code>, and <code>/home/<username>/.config/systemd/user/</code> directories, as well as associated symbolic links. Suspicious processes or scripts spawned in this manner will have a parent process of ‘systemd’, a parent process ID of 1, and will usually execute as the ‘root’ user.\\n\\nSuspicious systemd services can also be identified by comparing results against a trusted system baseline. Malicious systemd services may be detected by using the systemctl utility to examine system wide services: <code>systemctl list-units -–type=service –all</code>. Analyze the contents of <code>.service</code> files present on the file system and ensure that they refer to legitimate, expected executables.\\n\\nAuditing the execution and command-line arguments of the 'systemctl' utility, as well related utilities such as <code>/usr/sbin/service</code> may reveal malicious systemd service execution.\", x_mitre_permissions_required=['root', 'User'], x_mitre_platforms=['Linux'], x_mitre_version='1.0')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2[1]"
   ]
  }
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