{
 "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('notebook')\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_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>external_references</th>\n",
       "      <th>kill_chain_phases</th>\n",
       "      <th>x_mitre_version</th>\n",
       "      <th>url</th>\n",
       "      <th>matrix</th>\n",
       "      <th>technique_id</th>\n",
       "      <th>object_marking_refs</th>\n",
       "      <th>type</th>\n",
       "      <th>modified</th>\n",
       "      <th>created_by_ref</th>\n",
       "      <th>...</th>\n",
       "      <th>effective_permissions</th>\n",
       "      <th>network_requirements</th>\n",
       "      <th>x_mitre_old_attack_id</th>\n",
       "      <th>detectable_by_common_defenses</th>\n",
       "      <th>difficulty_explanation</th>\n",
       "      <th>difficulty_for_adversary</th>\n",
       "      <th>detectable_explanation</th>\n",
       "      <th>x_mitre_deprecated</th>\n",
       "      <th>tactic_type</th>\n",
       "      <th>revoked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{'external_id': 'T1500', 'source_name': 'mitr...</td>\n",
       "      <td>[{'phase_name': 'defense-evasion', 'kill_chain...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://attack.mitre.org/techniques/T1500</td>\n",
       "      <td>mitre-attack</td>\n",
       "      <td>T1500</td>\n",
       "      <td>[marking-definition--fa42a846-8d90-4e51-bc29-7...</td>\n",
       "      <td>attack-pattern</td>\n",
       "      <td>2019-04-29T21:13:49.686Z</td>\n",
       "      <td>identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 external_references  \\\n",
       "0  [{'external_id': 'T1500', 'source_name': 'mitr...   \n",
       "\n",
       "                                   kill_chain_phases x_mitre_version  \\\n",
       "0  [{'phase_name': 'defense-evasion', 'kill_chain...             1.0   \n",
       "\n",
       "                                         url        matrix technique_id  \\\n",
       "0  https://attack.mitre.org/techniques/T1500  mitre-attack        T1500   \n",
       "\n",
       "                                 object_marking_refs            type  \\\n",
       "0  [marking-definition--fa42a846-8d90-4e51-bc29-7...  attack-pattern   \n",
       "\n",
       "                   modified                                  created_by_ref  \\\n",
       "0  2019-04-29T21:13:49.686Z  identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5   \n",
       "\n",
       "   ... effective_permissions network_requirements x_mitre_old_attack_id  \\\n",
       "0  ...                   NaN                  NaN                   NaN   \n",
       "\n",
       "  detectable_by_common_defenses difficulty_explanation  \\\n",
       "0                           NaN                    NaN   \n",
       "\n",
       "  difficulty_for_adversary detectable_explanation x_mitre_deprecated  \\\n",
       "0                      NaN                    NaN                NaN   \n",
       "\n",
       "  tactic_type revoked  \n",
       "0         NaN     NaN  \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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-4e72abcce3d3f026d281e5dd67d8131a"
       },
       "datasets": {
        "data-4e72abcce3d3f026d281e5dd67d8131a": [
         {
          "matrix": "mitre-attack",
          "technique": 244
         },
         {
          "matrix": "mitre-mobile-attack",
          "technique": 67
         },
         {
          "matrix": "mitre-pre-attack",
          "technique": 174
         }
        ]
       },
       "encoding": {
        "color": {
         "field": "matrix",
         "type": "nominal"
        },
        "x": {
         "field": "technique",
         "type": "quantitative"
        },
        "y": {
         "field": "matrix",
         "type": "nominal"
        }
       },
       "height": 200,
       "mark": "bar"
      },
      "image/png": "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",
      "text/plain": [
       "<VegaLite 3 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(data_3).mark_bar().encode(x='technique', y='matrix', color='matrix').properties(height = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Techniques With and Without Data Sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-c7ba43ddfd65b38f1fd0b608650ec191"
       },
       "datasets": {
        "data-c7ba43ddfd65b38f1fd0b608650ec191": [
         {
          "Count of Techniques": 245,
          "Techniques": "Without DS"
         },
         {
          "Count of Techniques": 240,
          "Techniques": "With DS"
         }
        ]
       },
       "layer": [
        {
         "encoding": {
          "color": {
           "field": "Techniques",
           "type": "nominal"
          },
          "x": {
           "field": "Techniques",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 200
        },
        {
         "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 3 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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "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 3 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>54</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>104</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>107</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",
       "54   mitre-attack  [Linux, macOS]   \n",
       "104  mitre-attack         [macOS]   \n",
       "107  mitre-attack         [macOS]   \n",
       "124  mitre-attack       [Windows]   \n",
       "\n",
       "                                                tactic  \\\n",
       "54   [defense-evasion, persistence, command-and-con...   \n",
       "104                                  [defense-evasion]   \n",
       "107                                      [persistence]   \n",
       "124                                        [discovery]   \n",
       "\n",
       "                       technique technique_id data_sources  Count_DS  \\\n",
       "54                 Port Knocking        T1205          NaN       NaN   \n",
       "104            Gatekeeper Bypass        T1144          NaN       NaN   \n",
       "107       Re-opened Applications        T1164          NaN       NaN   \n",
       "124  Peripheral Device Discovery        T1120          NaN       NaN   \n",
       "\n",
       "         Ind_DS  \n",
       "54   Without DS  \n",
       "104  Without DS  \n",
       "107  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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-a689c98b4c5966a339164dff27be292d"
       },
       "datasets": {
        "data-a689c98b4c5966a339164dff27be292d": [
         {
          "Count of Techniques": 240,
          "Matrix": "mitre-attack"
         }
        ]
       },
       "layer": [
        {
         "encoding": {
          "x": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "y": {
           "field": "Matrix",
           "type": "nominal"
          }
         },
         "height": 100,
         "mark": "bar",
         "width": 300
        },
        {
         "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 3 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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-8bdd6c787014aac1ea40c5ce615b2680"
       },
       "datasets": {
        "data-8bdd6c787014aac1ea40c5ce615b2680": [
         {
          "Count of Techniques": 126,
          "Platform": "Linux"
         },
         {
          "Count of Techniques": 211,
          "Platform": "Windows"
         },
         {
          "Count of Techniques": 145,
          "Platform": "macOS"
         }
        ]
       },
       "layer": [
        {
         "encoding": {
          "color": {
           "field": "Platform",
           "type": "nominal"
          },
          "x": {
           "field": "Platform",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 200
        },
        {
         "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 3 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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-8712fd25b13f11f698d44ffec8d94852"
       },
       "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": [
        {
         "encoding": {
          "color": {
           "field": "Tactic",
           "type": "nominal"
          },
          "x": {
           "field": "Tactic",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 400
        },
        {
         "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 3 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": [
       "<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</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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-3d8b18347e124d71fa30f0d5c58b47b9"
       },
       "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": [
        {
         "encoding": {
          "color": {
           "field": "Data Source",
           "type": "nominal"
          },
          "x": {
           "field": "Data Source",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": "bar",
         "width": 1200
        },
        {
         "encoding": {
          "color": {
           "field": "Data Source",
           "type": "nominal"
          },
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Data Source",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "height": 300,
         "mark": {
          "align": "center",
          "baseline": "middle",
          "dx": 0,
          "dy": -5,
          "type": "text"
         },
         "width": 1200
        }
       ]
      },
      "image/png": "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",
      "text/plain": [
       "<VegaLite 3 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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-b1ca98c89dbb838b61f181abe8b233a7"
       },
       "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": [
        {
         "encoding": {
          "x": {
           "field": "Number of Data Sources",
           "type": "quantitative"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "mark": "bar",
         "width": 500
        },
        {
         "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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",
      "text/plain": [
       "<VegaLite 3 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": [
      "/opt/conda/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/user_guide/indexing.html#returning-a-view-versus-a-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",
       "    }\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]</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": [
       "<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",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    .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",
       "      <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>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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-48dee17f09c6622cd09e1362aa43116f"
       },
       "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": [
        {
         "encoding": {
          "color": {
           "field": "Data Sources",
           "type": "nominal"
          },
          "x": {
           "field": "Data Sources",
           "type": "nominal"
          },
          "y": {
           "field": "Count of Techniques",
           "type": "quantitative"
          }
         },
         "mark": "bar",
         "width": 500
        },
        {
         "encoding": {
          "color": {
           "field": "Data Sources",
           "type": "nominal"
          },
          "text": {
           "field": "Count of Techniques",
           "type": "quantitative"
          },
          "x": {
           "field": "Data Sources",
           "type": "nominal"
          },
          "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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",
      "text/plain": [
       "<VegaLite 3 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>181</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",
       "181  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",
       "181        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.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json",
       "config": {
        "mark": {
         "tooltip": null
        },
        "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 3 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>667</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>244</th>\n",
       "      <td>Exploitation of Remote Services</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Exploitation for Defense Evasion</td>\n",
       "      <td>Windows Error Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</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>181</th>\n",
       "      <td>File Permissions Modification</td>\n",
       "      <td>Windows event logs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</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",
       "667                            Input Prompt          PowerShell logs\n",
       "1990                     Credential Dumping          PowerShell logs\n",
       "244         Exploitation of Remote Services  Windows Error Reporting\n",
       "317        Exploitation for Defense Evasion  Windows Error Reporting\n",
       "378      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",
       "181           File Permissions Modification       Windows event logs\n",
       "654                          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": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "results2 = lift.get_techniques_by_datasources('pRoceSS MoniTorinG','process commAnd-linE parameters')"
   ]
  },
  {
   "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]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}