similarity_clusters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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    "execution": {
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     "shell.execute_reply": "2022-06-06T21:52:58.907238Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:58.919119Z",
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     "shell.execute_reply": "2022-06-06T21:52:59.053912Z"
    }
   },
   "outputs": [],
   "source": [
    "f = open(\"cwe_mitigation_ids_temp.json\")\n",
    "w_mitigation = json.load(f)\n",
    "\n",
    "f = open(\"capec_mitigation_temp.json\")\n",
    "ap_mitigation = json.load(f)\n",
    "\n",
    "f = open(\"technique_mitigation_temp.json\")\n",
    "technique_mitigation = json.load(f)\n",
    "\n",
    "f = open(\"cwe_temp.json\")\n",
    "cwe = json.load(f)\n",
    "\n",
    "f = open(\"capec_temp.json\")\n",
    "capec = json.load(f)\n",
    "\n",
    "with open(\"w_dict.json\", \"r\") as f:\n",
    "    w_dict = json.load(f)\n",
    "with open(\"ap_dict.json\", \"r\") as f:\n",
    "    ap_dict = json.load(f)\n",
    "with open(\"technique_dict.json\", \"r\") as f:\n",
    "    technique_dict = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.064446Z",
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     "shell.execute_reply": "2022-06-06T21:52:59.066626Z"
    }
   },
   "outputs": [],
   "source": [
    "w_mitigation_set = set()\n",
    "for mitigation in w_mitigation:\n",
    "    w_mitigation_set.add(mitigation[\"_id\"])\n",
    "ap_mitigation_set = set()\n",
    "for mitigation in ap_mitigation:\n",
    "    ap_mitigation_set.add(mitigation[\"_id\"])\n",
    "technique_mitigation_set = set()\n",
    "for mitigation in technique_mitigation:\n",
    "    technique_mitigation_set.add(mitigation[\"_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.073869Z",
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     "shell.execute_reply": "2022-06-06T21:52:59.079226Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1597\n",
      "1044\n",
      "43\n"
     ]
    }
   ],
   "source": [
    "print(len(w_mitigation_set))\n",
    "print(len(ap_mitigation_set))\n",
    "print(len(technique_mitigation_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.087363Z",
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     "shell.execute_reply": "2022-06-06T21:52:59.097004Z"
    }
   },
   "outputs": [],
   "source": [
    "f = open(\"cwe_cwemitigation_temp.json\")\n",
    "cwe_mitigation = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.118893Z",
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     "shell.execute_reply": "2022-06-06T21:52:59.125515Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of distinct connected CWE mitigations  1597\n",
      "Number of connected CWE mitigations  1597\n",
      "Number of unconnected CWE mitigations  0\n",
      "Number of distinct connected AP mitigations  1044\n",
      "Number of connected AP mitigations  1044\n",
      "Number of unconnected AP mitigations  0\n",
      "Number of distinct connected technique mitigations  43\n",
      "Number of connected technique mitigations  1089\n",
      "Number of unconnected technique mitigations  0\n"
     ]
    }
   ],
   "source": [
    "total = set()\n",
    "count = 0\n",
    "for w in w_dict:\n",
    "    count += len(w_dict[w][\"mitigations\"])\n",
    "    total.update(w_dict[w][\"mitigations\"])\n",
    "print(\"Number of distinct connected CWE mitigations \", len(total))\n",
    "print(\"Number of connected CWE mitigations \", count)\n",
    "print(\"Number of unconnected CWE mitigations \", len(w_mitigation_set - total))\n",
    "\n",
    "total = set()\n",
    "count = 0\n",
    "for ap in ap_dict:\n",
    "    count += len(ap_dict[ap][\"mitigations\"])\n",
    "    total.update(ap_dict[ap][\"mitigations\"])\n",
    "print(\"Number of distinct connected AP mitigations \", len(total))\n",
    "print(\"Number of connected AP mitigations \", count)\n",
    "print(\"Number of unconnected AP mitigations \", len(ap_mitigation_set - total))\n",
    "\n",
    "total = set()\n",
    "count = 0\n",
    "for technique in technique_dict:\n",
    "    count += len(technique_dict[technique][\"mitigations\"])\n",
    "    total.update(technique_dict[technique][\"mitigations\"])\n",
    "print(\"Number of distinct connected technique mitigations \", len(total))\n",
    "print(\"Number of connected technique mitigations \", count)\n",
    "print(\n",
    "    \"Number of unconnected technique mitigations \",\n",
    "    len(technique_mitigation_set - total),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "shell.execute_reply": "2022-06-06T21:52:59.316816Z"
    }
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   "outputs": [
    {
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       "  '_id': 'TechniqueTechnique_mitigation/1538211964',\n",
       "  '_from': 'technique/technique_00281',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00038',\n",
       "  '_rev': '_dVfOFfy--E'},\n",
       " {'_key': '1538211965',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211965',\n",
       "  '_from': 'technique/technique_00282',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00038',\n",
       "  '_rev': '_dVfOFfy--F'},\n",
       " {'_key': '1538211966',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211966',\n",
       "  '_from': 'technique/technique_00293',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00022',\n",
       "  '_rev': '_dVfOFfy--G'},\n",
       " {'_key': '1538211967',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211967',\n",
       "  '_from': 'technique/technique_00294',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00022',\n",
       "  '_rev': '_dVfOFfy--H'},\n",
       " {'_key': '1538211968',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211968',\n",
       "  '_from': 'technique/technique_00295',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00022',\n",
       "  '_rev': '_dVfOFfy--I'},\n",
       " {'_key': '1538211969',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211969',\n",
       "  '_from': 'technique/technique_00296',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00022',\n",
       "  '_rev': '_dVfOFfy--J'},\n",
       " {'_key': '1538211970',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211970',\n",
       "  '_from': 'technique/technique_00175',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00021',\n",
       "  '_rev': '_dVfOFfy--K'},\n",
       " {'_key': '1538211971',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211971',\n",
       "  '_from': 'technique/technique_00175',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00017',\n",
       "  '_rev': '_dVfOFf2---'},\n",
       " {'_key': '1538211972',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211972',\n",
       "  '_from': 'technique/technique_00175',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf2--_'},\n",
       " {'_key': '1538211973',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211973',\n",
       "  '_from': 'technique/technique_00176',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00017',\n",
       "  '_rev': '_dVfOFf2--A'},\n",
       " {'_key': '1538211974',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211974',\n",
       "  '_from': 'technique/technique_00176',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf2--B'},\n",
       " {'_key': '1538211975',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211975',\n",
       "  '_from': 'technique/technique_00177',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00021',\n",
       "  '_rev': '_dVfOFf2--C'},\n",
       " {'_key': '1538211976',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211976',\n",
       "  '_from': 'technique/technique_00177',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00017',\n",
       "  '_rev': '_dVfOFf2--D'},\n",
       " {'_key': '1538211977',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211977',\n",
       "  '_from': 'technique/technique_00177',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf2--E'},\n",
       " {'_key': '1538211978',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211978',\n",
       "  '_from': 'technique/technique_00178',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00021',\n",
       "  '_rev': '_dVfOFf2--F'},\n",
       " {'_key': '1538211979',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211979',\n",
       "  '_from': 'technique/technique_00178',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00017',\n",
       "  '_rev': '_dVfOFf2--G'},\n",
       " {'_key': '1538211980',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211980',\n",
       "  '_from': 'technique/technique_00178',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf2--H'},\n",
       " {'_key': '1538211981',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211981',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFf2--I'},\n",
       " {'_key': '1538211982',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211982',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00028',\n",
       "  '_rev': '_dVfOFf2--J'},\n",
       " {'_key': '1538211983',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211983',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00010',\n",
       "  '_rev': '_dVfOFf2--K'},\n",
       " {'_key': '1538211984',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211984',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFf6---'},\n",
       " {'_key': '1538211985',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211985',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf6--_'},\n",
       " {'_key': '1538211986',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211986',\n",
       "  '_from': 'technique/technique_00020',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00013',\n",
       "  '_rev': '_dVfOFf6--A'},\n",
       " {'_key': '1538211987',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211987',\n",
       "  '_from': 'technique/technique_00021',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFf6--B'},\n",
       " {'_key': '1538211988',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211988',\n",
       "  '_from': 'technique/technique_00021',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFf6--C'},\n",
       " {'_key': '1538211989',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211989',\n",
       "  '_from': 'technique/technique_00021',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf6--D'},\n",
       " {'_key': '1538211990',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211990',\n",
       "  '_from': 'technique/technique_00021',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00013',\n",
       "  '_rev': '_dVfOFf6--E'},\n",
       " {'_key': '1538211991',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211991',\n",
       "  '_from': 'technique/technique_00026',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFf6--F'},\n",
       " {'_key': '1538211992',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211992',\n",
       "  '_from': 'technique/technique_00026',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf6--G'},\n",
       " {'_key': '1538211993',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211993',\n",
       "  '_from': 'technique/technique_00027',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFf6--H'},\n",
       " {'_key': '1538211994',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211994',\n",
       "  '_from': 'technique/technique_00027',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFf6--I'},\n",
       " {'_key': '1538211995',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211995',\n",
       "  '_from': 'technique/technique_00406',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00032',\n",
       "  '_rev': '_dVfOFf6--J'},\n",
       " {'_key': '1538211996',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211996',\n",
       "  '_from': 'technique/technique_00407',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00032',\n",
       "  '_rev': '_dVfOFf6--K'},\n",
       " {'_key': '1538211997',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211997',\n",
       "  '_from': 'technique/technique_00410',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00038',\n",
       "  '_rev': '_dVfOFg----'},\n",
       " {'_key': '1538211998',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211998',\n",
       "  '_from': 'technique/technique_00411',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00038',\n",
       "  '_rev': '_dVfOFg---_'},\n",
       " {'_key': '1538211999',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538211999',\n",
       "  '_from': 'technique/technique_00412',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00038',\n",
       "  '_rev': '_dVfOFg---A'},\n",
       " {'_key': '1538212000',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212000',\n",
       "  '_from': 'technique/technique_00024',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFg---B'},\n",
       " {'_key': '1538212001',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212001',\n",
       "  '_from': 'technique/technique_00024',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFg---C'},\n",
       " {'_key': '1538212002',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212002',\n",
       "  '_from': 'technique/technique_00024',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFg---D'},\n",
       " {'_key': '1538212003',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212003',\n",
       "  '_from': 'technique/technique_00024',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00013',\n",
       "  '_rev': '_dVfOFg---E'},\n",
       " {'_key': '1538212004',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212004',\n",
       "  '_from': 'technique/technique_00023',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFg---F'},\n",
       " {'_key': '1538212005',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212005',\n",
       "  '_from': 'technique/technique_00023',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFg---G'},\n",
       " {'_key': '1538212006',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212006',\n",
       "  '_from': 'technique/technique_00023',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFg---H'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212007',\n",
       "  '_from': 'technique/technique_00022',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFg---I'},\n",
       " {'_key': '1538212008',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212008',\n",
       "  '_from': 'technique/technique_00022',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFg---J'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212009',\n",
       "  '_from': 'technique/technique_00022',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFg---K'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212010',\n",
       "  '_from': 'technique/technique_00022',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00026',\n",
       "  '_rev': '_dVfOFg---L'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212011',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFg---M'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212012',\n",
       "  '_from': 'technique/technique_00095',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgC---'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212013',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgC--_'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212014',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFgC--A'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212015',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFgC--B'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212016',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgC--C'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212017',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgC--D'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212018',\n",
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       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFgC--E'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212019',\n",
       "  '_from': 'technique/technique_00102',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgC--F'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212020',\n",
       "  '_from': 'technique/technique_00417',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFgC--G'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212021',\n",
       "  '_from': 'technique/technique_00417',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgC--H'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212022',\n",
       "  '_from': 'technique/technique_00380',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFgG---'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212023',\n",
       "  '_from': 'technique/technique_00380',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00013',\n",
       "  '_rev': '_dVfOFgG--_'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212024',\n",
       "  '_from': 'technique/technique_00096',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--A'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212025',\n",
       "  '_from': 'technique/technique_00097',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--B'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212026',\n",
       "  '_from': 'technique/technique_00098',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--C'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212027',\n",
       "  '_from': 'technique/technique_00099',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFgG--D'},\n",
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       "  '_id': 'TechniqueTechnique_mitigation/1538212028',\n",
       "  '_from': 'technique/technique_00099',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--E'},\n",
       " {'_key': '1538212029',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212029',\n",
       "  '_from': 'technique/technique_00100',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgG--F'},\n",
       " {'_key': '1538212030',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212030',\n",
       "  '_from': 'technique/technique_00100',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--G'},\n",
       " {'_key': '1538212031',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212031',\n",
       "  '_from': 'technique/technique_00101',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--H'},\n",
       " {'_key': '1538212032',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212032',\n",
       "  '_from': 'technique/technique_00103',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00025',\n",
       "  '_rev': '_dVfOFgG--I'},\n",
       " {'_key': '1538212033',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212033',\n",
       "  '_from': 'technique/technique_00383',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00030',\n",
       "  '_rev': '_dVfOFgG--J'},\n",
       " {'_key': '1538212034',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212034',\n",
       "  '_from': 'technique/technique_00396',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00022',\n",
       "  '_rev': '_dVfOFgG--K'},\n",
       " {'_key': '1538212035',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212035',\n",
       "  '_from': 'technique/technique_00396',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00027',\n",
       "  '_rev': '_dVfOFgG--L'},\n",
       " {'_key': '1538212036',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212036',\n",
       "  '_from': 'technique/technique_00424',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00008',\n",
       "  '_rev': '_dVfOFgG--M'},\n",
       " {'_key': '1538212037',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212037',\n",
       "  '_from': 'technique/technique_00424',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFgG--N'},\n",
       " {'_key': '1538212038',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212038',\n",
       "  '_from': 'technique/technique_00424',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFgK---'},\n",
       " {'_key': '1538212039',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212039',\n",
       "  '_from': 'technique/technique_00424',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00027',\n",
       "  '_rev': '_dVfOFgK--_'},\n",
       " {'_key': '1538212040',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212040',\n",
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       "  '_from': 'technique/technique_00274',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00032',\n",
       "  '_rev': '_dVfOFhS--E'},\n",
       " {'_key': '1538212499',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212499',\n",
       "  '_from': 'technique/technique_00274',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFhS--F'},\n",
       " {'_key': '1538212500',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212500',\n",
       "  '_from': 'technique/technique_00275',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhS--G'},\n",
       " {'_key': '1538212501',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212501',\n",
       "  '_from': 'technique/technique_00273',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhS--H'},\n",
       " {'_key': '1538212502',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212502',\n",
       "  '_from': 'technique/technique_00389',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00012',\n",
       "  '_rev': '_dVfOFhS--I'},\n",
       " {'_key': '1538212503',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212503',\n",
       "  '_from': 'technique/technique_00564',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00001',\n",
       "  '_rev': '_dVfOFhS--J'},\n",
       " {'_key': '1538212504',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212504',\n",
       "  '_from': 'technique/technique_00237',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00027',\n",
       "  '_rev': '_dVfOFhS--K'},\n",
       " {'_key': '1538212505',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212505',\n",
       "  '_from': 'technique/technique_00547',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhS--L'},\n",
       " {'_key': '1538212506',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212506',\n",
       "  '_from': 'technique/technique_00549',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhS--M'},\n",
       " {'_key': '1538212507',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212507',\n",
       "  '_from': 'technique/technique_00550',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhW---'},\n",
       " {'_key': '1538212508',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212508',\n",
       "  '_from': 'technique/technique_00440',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhW--_'},\n",
       " {'_key': '1538212509',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212509',\n",
       "  '_from': 'technique/technique_00441',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhW--A'},\n",
       " {'_key': '1538212510',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212510',\n",
       "  '_from': 'technique/technique_00442',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00039',\n",
       "  '_rev': '_dVfOFhW--B'},\n",
       " {'_key': '1538212511',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212511',\n",
       "  '_from': 'technique/technique_00381',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00023',\n",
       "  '_rev': '_dVfOFhW--C'},\n",
       " {'_key': '1538212512',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212512',\n",
       "  '_from': 'technique/technique_00120',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00023',\n",
       "  '_rev': '_dVfOFhW--D'},\n",
       " {'_key': '1538212513',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212513',\n",
       "  '_from': 'technique/technique_00566',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00041',\n",
       "  '_rev': '_dVfOFhW--E'},\n",
       " {'_key': '1538212514',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212514',\n",
       "  '_from': 'technique/technique_00567',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00041',\n",
       "  '_rev': '_dVfOFhW--F'},\n",
       " {'_key': '1538212515',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212515',\n",
       "  '_from': 'technique/technique_00465',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00013',\n",
       "  '_rev': '_dVfOFhW--G'},\n",
       " {'_key': '1538212516',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212516',\n",
       "  '_from': 'technique/technique_00571',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00023',\n",
       "  '_rev': '_dVfOFhW--H'},\n",
       " {'_key': '1538212517',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212517',\n",
       "  '_from': 'technique/technique_00571',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhW--I'},\n",
       " {'_key': '1538212518',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212518',\n",
       "  '_from': 'technique/technique_00571',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00020',\n",
       "  '_rev': '_dVfOFhW--J'},\n",
       " {'_key': '1538212519',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212519',\n",
       "  '_from': 'technique/technique_00572',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00015',\n",
       "  '_rev': '_dVfOFhW--K'},\n",
       " {'_key': '1538212520',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212520',\n",
       "  '_from': 'technique/technique_00572',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFhW--L'},\n",
       " {'_key': '1538212521',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212521',\n",
       "  '_from': 'technique/technique_00572',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00020',\n",
       "  '_rev': '_dVfOFhW--M'},\n",
       " {'_key': '1538212522',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212522',\n",
       "  '_from': 'technique/technique_00092',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00004',\n",
       "  '_rev': '_dVfOFhW--N'},\n",
       " {'_key': '1538212523',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212523',\n",
       "  '_from': 'technique/technique_00092',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhW--O'},\n",
       " {'_key': '1538212524',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212524',\n",
       "  '_from': 'technique/technique_00236',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00003',\n",
       "  '_rev': '_dVfOFhW--P'},\n",
       " {'_key': '1538212525',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212525',\n",
       "  '_from': 'technique/technique_00236',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00016',\n",
       "  '_rev': '_dVfOFhW--Q'},\n",
       " {'_key': '1538212526',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212526',\n",
       "  '_from': 'technique/technique_00573',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhW--R'},\n",
       " {'_key': '1538212527',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212527',\n",
       "  '_from': 'technique/technique_00573',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00023',\n",
       "  '_rev': '_dVfOFhW--S'},\n",
       " {'_key': '1538212528',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212528',\n",
       "  '_from': 'technique/technique_00574',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00020',\n",
       "  '_rev': '_dVfOFhW--T'},\n",
       " {'_key': '1538212529',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212529',\n",
       "  '_from': 'technique/technique_00574',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00015',\n",
       "  '_rev': '_dVfOFhW--U'},\n",
       " {'_key': '1538212530',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212530',\n",
       "  '_from': 'technique/technique_00574',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00011',\n",
       "  '_rev': '_dVfOFhW--V'},\n",
       " {'_key': '1538212531',\n",
       "  '_id': 'TechniqueTechnique_mitigation/1538212531',\n",
       "  '_from': 'technique/technique_00573',\n",
       "  '_to': 'technique_mitigation/technique_mitigation_00033',\n",
       "  '_rev': '_dVfOFhW--W'},\n",
       " ...]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it looks like technique mitigations are shared between techniques\n",
    "f = open(\"technique_techniquemitigation_temp.json\")\n",
    "technique_techniquemitigation = json.load(f)\n",
    "\n",
    "technique_techniquemitigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.328283Z",
     "iopub.status.busy": "2022-06-06T21:52:59.326820Z",
     "iopub.status.idle": "2022-06-06T21:52:59.331374Z",
     "shell.execute_reply": "2022-06-06T21:52:59.330205Z"
    }
   },
   "outputs": [],
   "source": [
    "for technique in technique_dict:\n",
    "    technique_dict[technique][\"ap\"] = []\n",
    "\n",
    "for ap in ap_dict:\n",
    "    for technique in ap_dict[ap][\"techniques\"]:\n",
    "        technique_dict[technique][\"ap\"].append(ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.346178Z",
     "iopub.status.busy": "2022-06-06T21:52:59.344996Z",
     "iopub.status.idle": "2022-06-06T21:52:59.348973Z",
     "shell.execute_reply": "2022-06-06T21:52:59.349960Z"
    }
   },
   "outputs": [],
   "source": [
    "def bidirectional_path_search(cur, visited):\n",
    "    if cur in visited:\n",
    "        return []\n",
    "    visited.add(cur)\n",
    "    mitigations = []\n",
    "    if cur.startswith(\"capec\"):\n",
    "        for mitigation in ap_dict[cur][\"mitigations\"]:\n",
    "            mitigations.append(mitigation)\n",
    "        for cwe in ap_dict[cur][\"cwes\"]:\n",
    "            res = bidirectional_path_search(cwe, visited)\n",
    "            mitigations.extend(res)\n",
    "        for technique in ap_dict[cur][\"techniques\"]:\n",
    "            res = bidirectional_path_search(technique, visited)\n",
    "            mitigations.extend(res)\n",
    "\n",
    "    if cur.startswith(\"cwe\"):\n",
    "        for mitigation in w_dict[cur][\"mitigations\"]:\n",
    "            mitigations.append(mitigation)\n",
    "        for ap in w_dict[cur][\"ap\"]:\n",
    "            res = bidirectional_path_search(ap, visited)\n",
    "            mitigations.extend(res)\n",
    "\n",
    "    if cur.startswith(\"technique\"):\n",
    "        for mitigation in technique_dict[cur][\"mitigations\"]:\n",
    "            mitigations.append(mitigation)\n",
    "        for ap in technique_dict[cur][\"ap\"]:\n",
    "            res = bidirectional_path_search(ap, visited)\n",
    "            mitigations.extend(res)\n",
    "\n",
    "    return mitigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.372357Z",
     "iopub.status.busy": "2022-06-06T21:52:59.370850Z",
     "iopub.status.idle": "2022-06-06T21:52:59.376607Z",
     "shell.execute_reply": "2022-06-06T21:52:59.375418Z"
    }
   },
   "outputs": [],
   "source": [
    "# for each cwe, ap, and technique, bidirectionally traverse the paths in BRON to find all connected CWE, AP, and technique mitigations\n",
    "\n",
    "clusters = []\n",
    "visited = set()\n",
    "for ap in ap_dict:\n",
    "    if ap not in visited:\n",
    "        connected_mitigations = bidirectional_path_search(ap, visited)\n",
    "        if connected_mitigations:\n",
    "            clusters.append(set(connected_mitigations))\n",
    "for cwe in w_dict:\n",
    "    if cwe not in visited:\n",
    "        connected_mitigations = bidirectional_path_search(cwe, visited)\n",
    "        if connected_mitigations:\n",
    "            clusters.append(set(connected_mitigations))\n",
    "for technique in technique_dict:\n",
    "    if technique not in visited:\n",
    "        connected_mitigations = bidirectional_path_search(technique, visited)\n",
    "        if connected_mitigations:\n",
    "            clusters.append(set(connected_mitigations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.388218Z",
     "iopub.status.busy": "2022-06-06T21:52:59.386307Z",
     "iopub.status.idle": "2022-06-06T21:52:59.392120Z",
     "shell.execute_reply": "2022-06-06T21:52:59.390240Z"
    }
   },
   "outputs": [],
   "source": [
    "clusters = set([tuple(sorted(i)) for i in clusters])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.400276Z",
     "iopub.status.busy": "2022-06-06T21:52:59.399267Z",
     "iopub.status.idle": "2022-06-06T21:52:59.405905Z",
     "shell.execute_reply": "2022-06-06T21:52:59.404591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2684"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mitigations_found = set()\n",
    "for cluster in clusters:\n",
    "    for mitigation in cluster:\n",
    "        mitigations_found.add(mitigation)\n",
    "len(mitigations_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.412624Z",
     "iopub.status.busy": "2022-06-06T21:52:59.411404Z",
     "iopub.status.idle": "2022-06-06T21:52:59.416360Z",
     "shell.execute_reply": "2022-06-06T21:52:59.417195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "658"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.433675Z",
     "iopub.status.busy": "2022-06-06T21:52:59.432585Z",
     "iopub.status.idle": "2022-06-06T21:52:59.439241Z",
     "shell.execute_reply": "2022-06-06T21:52:59.440152Z"
    }
   },
   "outputs": [
    {
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       " 1,\n",
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       " 2,\n",
       " 1,\n",
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       " 3,\n",
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       " 6,\n",
       " 3,\n",
       " 2]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_lengths = []\n",
    "for cluster in clusters:\n",
    "    cluster_lengths.append(len(cluster))\n",
    "cluster_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.474956Z",
     "iopub.status.busy": "2022-06-06T21:52:59.473150Z",
     "iopub.status.idle": "2022-06-06T21:52:59.737740Z",
     "shell.execute_reply": "2022-06-06T21:52:59.737030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "cluster_lengths.remove(1480)\n",
    "plt.hist(cluster_lengths, bins=60)\n",
    "plt.xlabel(\"Cluster size\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.744311Z",
     "iopub.status.busy": "2022-06-06T21:52:59.743325Z",
     "iopub.status.idle": "2022-06-06T21:52:59.749112Z",
     "shell.execute_reply": "2022-06-06T21:52:59.748435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6986301369863015"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cluster_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.755428Z",
     "iopub.status.busy": "2022-06-06T21:52:59.753970Z",
     "iopub.status.idle": "2022-06-06T21:52:59.760008Z",
     "shell.execute_reply": "2022-06-06T21:52:59.759343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(cluster_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.767379Z",
     "iopub.status.busy": "2022-06-06T21:52:59.766511Z",
     "iopub.status.idle": "2022-06-06T21:52:59.770724Z",
     "shell.execute_reply": "2022-06-06T21:52:59.769902Z"
    }
   },
   "outputs": [],
   "source": [
    "mitigation_to_name = {}\n",
    "for mitigation in w_mitigation:\n",
    "    mitigation_to_name[mitigation[\"_id\"]] = mitigation[\"metadata\"][\"Description\"]\n",
    "for mitigation in ap_mitigation:\n",
    "    mitigation_to_name[mitigation[\"_id\"]] = mitigation[\"metadata\"]\n",
    "for mitigation in technique_mitigation:\n",
    "    mitigation_to_name[mitigation[\"_id\"]] = mitigation[\"name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.777458Z",
     "iopub.status.busy": "2022-06-06T21:52:59.776366Z",
     "iopub.status.idle": "2022-06-06T21:52:59.786506Z",
     "shell.execute_reply": "2022-06-06T21:52:59.785914Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(\"ap_mitigation_descriptions.json\", \"r\") as f:\n",
    "    ap_mitigation_descriptions = list(set(json.load(f)))\n",
    "with open(\"cwe_mitigation_descriptions.json\", \"r\") as f:\n",
    "    cwe_mitigation_descriptions = list(set(json.load(f)))\n",
    "with open(\"tech_mitigation_names.json\", \"r\") as f:\n",
    "    tech_mitigation_names = list(set(json.load(f)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.792648Z",
     "iopub.status.busy": "2022-06-06T21:52:59.791802Z",
     "iopub.status.idle": "2022-06-06T21:52:59.795281Z",
     "shell.execute_reply": "2022-06-06T21:52:59.795853Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_unique_pairs(pairwise_similarities):\n",
    "    unique_pairs = []\n",
    "    for i in range(len(pairwise_similarities)):\n",
    "        for j in range(i + 1, len(pairwise_similarities)):\n",
    "            unique_pairs.append(pairwise_similarities[i][j])\n",
    "\n",
    "    return unique_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:52:59.851889Z",
     "iopub.status.busy": "2022-06-06T21:52:59.835943Z",
     "iopub.status.idle": "2022-06-06T21:53:01.556562Z",
     "shell.execute_reply": "2022-06-06T21:53:01.556025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among APs  0.09346447191636709\n",
      "Among CWEs  0.15633004139561232\n",
      "Among Techs  0.019376344089957965\n",
      "Between APs and CWEs  0.11576254620911312\n",
      "Between APs and Techniques  0.014222136078380314\n",
      "Between CWEs and Techniques  0.0135771463313062\n",
      "Among all mitigations 0.12056129476545321\n"
     ]
    }
   ],
   "source": [
    "# between all cwe/ap/technique mitigations\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(\n",
    "    ap_mitigation_descriptions + cwe_mitigation_descriptions + tech_mitigation_names\n",
    ")\n",
    "\n",
    "ap_mitigation_vectors = vectorizer.transform(ap_mitigation_descriptions)\n",
    "cwe_mitigation_vectors = vectorizer.transform(cwe_mitigation_descriptions)\n",
    "tech_mitigation_vectors = vectorizer.transform(tech_mitigation_names)\n",
    "\n",
    "ap_similarities_all = cosine_similarity(ap_mitigation_vectors)\n",
    "among_aps = np.mean(get_unique_pairs(ap_similarities_all))\n",
    "\n",
    "cwe_similarities_all = cosine_similarity(cwe_mitigation_vectors)\n",
    "among_cwes = np.mean(get_unique_pairs(cwe_similarities_all))\n",
    "\n",
    "tech_similarities_all = cosine_similarity(tech_mitigation_vectors)\n",
    "among_techs = np.mean(get_unique_pairs(tech_similarities_all))\n",
    "\n",
    "all_mitigation_vectors = vectorizer.transform(\n",
    "    ap_mitigation_descriptions + cwe_mitigation_descriptions + tech_mitigation_names\n",
    ")\n",
    "among_all_mitigations = np.mean(\n",
    "    get_unique_pairs(cosine_similarity(all_mitigation_vectors))\n",
    ")\n",
    "\n",
    "ap_cwe_similarities = cosine_similarity(ap_mitigation_vectors, cwe_mitigation_vectors)\n",
    "\n",
    "ap_tech_similarities = cosine_similarity(ap_mitigation_vectors, tech_mitigation_vectors)\n",
    "\n",
    "cwe_tech_similarities = cosine_similarity(\n",
    "    cwe_mitigation_vectors, tech_mitigation_vectors\n",
    ")\n",
    "\n",
    "print(\"Among APs \", among_aps)\n",
    "print(\"Among CWEs \", among_cwes)\n",
    "print(\"Among Techs \", among_techs)\n",
    "print(\"Between APs and CWEs \", np.mean(ap_cwe_similarities))\n",
    "print(\"Between APs and Techniques \", np.mean(ap_tech_similarities))\n",
    "print(\"Between CWEs and Techniques \", np.mean(cwe_tech_similarities))\n",
    "print(\"Among all mitigations\", among_all_mitigations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:53:01.563909Z",
     "iopub.status.busy": "2022-06-06T21:53:01.562796Z",
     "iopub.status.idle": "2022-06-06T21:53:01.842160Z",
     "shell.execute_reply": "2022-06-06T21:53:01.842966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "similarities = {\n",
    "    \"CAPEC\": {\n",
    "        \"CAPEC\": among_aps,\n",
    "        \"CWE\": np.mean(ap_cwe_similarities),\n",
    "        \"Technique\": np.mean(ap_tech_similarities),\n",
    "    },\n",
    "    \"CWE\": {\n",
    "        \"CAPEC\": np.mean(ap_cwe_similarities),\n",
    "        \"CWE\": among_cwes,\n",
    "        \"Technique\": np.mean(cwe_tech_similarities),\n",
    "    },\n",
    "    \"Technique\": {\n",
    "        \"CAPEC\": np.mean(ap_tech_similarities),\n",
    "        \"CWE\": np.mean(cwe_tech_similarities),\n",
    "        \"Technique\": among_techs,\n",
    "    },\n",
    "}\n",
    "similarities = pd.DataFrame(similarities)\n",
    "sns.heatmap(similarities, vmin=0, vmax=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:53:01.925022Z",
     "iopub.status.busy": "2022-06-06T21:53:01.884199Z",
     "iopub.status.idle": "2022-06-06T21:53:01.951131Z",
     "shell.execute_reply": "2022-06-06T21:53:01.952243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(mitigation_to_name.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:53:01.959590Z",
     "iopub.status.busy": "2022-06-06T21:53:01.958338Z",
     "iopub.status.idle": "2022-06-06T21:53:01.961449Z",
     "shell.execute_reply": "2022-06-06T21:53:01.962669Z"
    }
   },
   "outputs": [],
   "source": [
    "heatmaps = {}\n",
    "clusters = [list(x) for x in clusters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T21:53:02.003482Z",
     "iopub.status.busy": "2022-06-06T21:53:01.981685Z",
     "iopub.status.idle": "2022-06-06T22:01:13.649419Z",
     "shell.execute_reply": "2022-06-06T22:01:13.647876Z"
    }
   },
   "outputs": [],
   "source": [
    "# BoW similarity\n",
    "similarities_list = []\n",
    "comparisons = [(\"capec\", \"capec\"), (\"cwe\", \"cwe\")]\n",
    "\n",
    "for category in comparisons:\n",
    "    first_clusters = []\n",
    "    second_clusters = []\n",
    "    for cluster in clusters:\n",
    "        cluster_1 = list(filter(lambda x: x.startswith(category[0]), cluster))\n",
    "        if cluster_1:\n",
    "            first_clusters.append(cluster_1)\n",
    "        cluster_2 = list(filter(lambda x: x.startswith(category[1]), cluster))\n",
    "        if cluster_2:\n",
    "            second_clusters.append(cluster_2)\n",
    "    cluster_similarities = []\n",
    "    for i in range(len(first_clusters)):\n",
    "        cluster_1 = first_clusters[i]\n",
    "        cluster_1_names = []\n",
    "        for mitigation in cluster_1:\n",
    "            cluster_1_names.append(mitigation_to_name[mitigation])\n",
    "        cluster_1_vectors = vectorizer.transform(cluster_1_names)\n",
    "        mean_similarity = 0\n",
    "        for j in range(len(second_clusters)):\n",
    "            cluster_2 = second_clusters[j]\n",
    "            cluster_2_names = []\n",
    "            for mitigation in cluster_2:\n",
    "                cluster_2_names.append(mitigation_to_name[mitigation])\n",
    "            cluster_2_vectors = vectorizer.transform(cluster_2_names)\n",
    "            similarities = cosine_similarity(cluster_1_vectors, cluster_2_vectors)\n",
    "            mean_similarity += np.mean(similarities)\n",
    "        mean_similarity /= len(second_clusters)\n",
    "        cluster_similarities.append((first_clusters[i], mean_similarity))\n",
    "    cluster_similarities.sort(key=lambda x: x[1], reverse=True)\n",
    "    clusters_similar_ranked = [x[0] for x in cluster_similarities]\n",
    "    heatmap = [\n",
    "        [float(\"nan\") for j in range(len(clusters_similar_ranked))]\n",
    "        for i in range(len(clusters_similar_ranked))\n",
    "    ]\n",
    "    heatmaps[category] = heatmap\n",
    "    for i in range(len(clusters_similar_ranked)):\n",
    "        cluster_1 = clusters_similar_ranked[i]\n",
    "        cluster_1_names = []\n",
    "        for mitigation in cluster_1:\n",
    "            cluster_1_names.append(mitigation_to_name[mitigation])\n",
    "        cluster_1_vectors = vectorizer.transform(cluster_1_names)\n",
    "        for j in range(len(clusters_similar_ranked)):\n",
    "            cluster_2 = clusters_similar_ranked[j]\n",
    "            cluster_2_names = []\n",
    "            for mitigation in cluster_2:\n",
    "                cluster_2_names.append(mitigation_to_name[mitigation])\n",
    "            cluster_2_vectors = vectorizer.transform(cluster_2_names)\n",
    "            similarities = cosine_similarity(cluster_1_vectors, cluster_2_vectors)\n",
    "            similarities_list.append(similarities)\n",
    "            heatmap[i][j] = np.mean(similarities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:13.658049Z",
     "iopub.status.busy": "2022-06-06T22:01:13.656729Z",
     "iopub.status.idle": "2022-06-06T22:01:13.662949Z",
     "shell.execute_reply": "2022-06-06T22:01:13.663880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('capec', 'capec'), ('cwe', 'cwe')])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmaps.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:13.670678Z",
     "iopub.status.busy": "2022-06-06T22:01:13.669099Z",
     "iopub.status.idle": "2022-06-06T22:01:14.016346Z",
     "shell.execute_reply": "2022-06-06T22:01:14.015158Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(\"heatmap_cwe.json\", \"w\") as f:\n",
    "    json.dump(heatmaps[(\"cwe\", \"cwe\")], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:14.025547Z",
     "iopub.status.busy": "2022-06-06T22:01:14.023876Z",
     "iopub.status.idle": "2022-06-06T22:01:14.118083Z",
     "shell.execute_reply": "2022-06-06T22:01:14.116667Z"
    }
   },
   "outputs": [],
   "source": [
    "heatmaps = {}\n",
    "with open(\"heatmap_cwe.json\", \"r\") as f:\n",
    "    heatmaps[(\"cwe\", \"cwe\")] = json.load(f)\n",
    "with open(\"heatmap_capec.json\", \"r\") as f:\n",
    "    heatmaps[(\"capec\", \"capec\")] = json.load(f)\n",
    "with open(\"heatmap_technique.json\", \"r\") as f:\n",
    "    heatmaps[(\"technique\", \"technique\")] = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:14.140067Z",
     "iopub.status.busy": "2022-06-06T22:01:14.135459Z",
     "iopub.status.idle": "2022-06-06T22:01:15.152808Z",
     "shell.execute_reply": "2022-06-06T22:01:15.153849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "category = (\"cwe\", \"cwe\")\n",
    "\n",
    "sns.heatmap(heatmaps[category], vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:15.190616Z",
     "iopub.status.busy": "2022-06-06T22:01:15.164027Z",
     "iopub.status.idle": "2022-06-06T22:01:15.788928Z",
     "shell.execute_reply": "2022-06-06T22:01:15.789776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "category = (\"capec\", \"capec\")\n",
    "\n",
    "sns.heatmap(heatmaps[category], vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:15.800352Z",
     "iopub.status.busy": "2022-06-06T22:01:15.799322Z",
     "iopub.status.idle": "2022-06-06T22:01:16.643028Z",
     "shell.execute_reply": "2022-06-06T22:01:16.643885Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "category = (\"technique\", \"technique\")\n",
    "\n",
    "sns.heatmap(heatmaps[category], vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:16.662316Z",
     "iopub.status.busy": "2022-06-06T22:01:16.660734Z",
     "iopub.status.idle": "2022-06-06T22:01:16.664638Z",
     "shell.execute_reply": "2022-06-06T22:01:16.665395Z"
    }
   },
   "outputs": [],
   "source": [
    "# 406 x 406\n",
    "\n",
    "similarities = heatmaps[(\"capec\", \"capec\")]\n",
    "\n",
    "unique_similarities = []\n",
    "unique_similarities_clusters = []\n",
    "\n",
    "for i in range(len(similarities)):\n",
    "    for j in range(i + 1, len(similarities)):\n",
    "        unique_similarities_clusters.append(\n",
    "            [clusters_similar_ranked[i], clusters_similar_ranked[j]]\n",
    "        )\n",
    "        unique_similarities.append(similarities[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:16.711504Z",
     "iopub.status.busy": "2022-06-06T22:01:16.706802Z",
     "iopub.status.idle": "2022-06-06T22:01:16.952303Z",
     "shell.execute_reply": "2022-06-06T22:01:16.953482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(unique_similarities)\n",
    "plt.xlabel(\"Inter-cluster similarities\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:16.963038Z",
     "iopub.status.busy": "2022-06-06T22:01:16.961265Z",
     "iopub.status.idle": "2022-06-06T22:01:16.967581Z",
     "shell.execute_reply": "2022-06-06T22:01:16.968773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000002"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(unique_similarities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:16.976770Z",
     "iopub.status.busy": "2022-06-06T22:01:16.975593Z",
     "iopub.status.idle": "2022-06-06T22:01:16.981492Z",
     "shell.execute_reply": "2022-06-06T22:01:16.980453Z"
    }
   },
   "outputs": [],
   "source": [
    "highest_similarity_clusters = []\n",
    "for i in range(len(unique_similarities)):\n",
    "    if unique_similarities[i] > 0.99:\n",
    "        highest_similarity_clusters.append(unique_similarities_clusters[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:16.990133Z",
     "iopub.status.busy": "2022-06-06T22:01:16.988777Z",
     "iopub.status.idle": "2022-06-06T22:01:16.993904Z",
     "shell.execute_reply": "2022-06-06T22:01:16.994740Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(highest_similarity_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.005645Z",
     "iopub.status.busy": "2022-06-06T22:01:17.003988Z",
     "iopub.status.idle": "2022-06-06T22:01:17.008044Z",
     "shell.execute_reply": "2022-06-06T22:01:17.007030Z"
    }
   },
   "outputs": [],
   "source": [
    "highest_similarity_clusters_descriptions = []\n",
    "for pair in highest_similarity_clusters:\n",
    "    cluster1 = []\n",
    "    cluster2 = []\n",
    "    for mitigation in pair[0]:\n",
    "        cluster1.append(mitigation_to_name[mitigation])\n",
    "    for mitigation in pair[1]:\n",
    "        cluster2.append(mitigation_to_name[mitigation])\n",
    "    highest_similarity_clusters_descriptions.append([cluster1, cluster2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.013997Z",
     "iopub.status.busy": "2022-06-06T22:01:17.012935Z",
     "iopub.status.idle": "2022-06-06T22:01:17.017087Z",
     "shell.execute_reply": "2022-06-06T22:01:17.016027Z"
    }
   },
   "outputs": [],
   "source": [
    "# highest_similarity_clusters_descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.024253Z",
     "iopub.status.busy": "2022-06-06T22:01:17.023263Z",
     "iopub.status.idle": "2022-06-06T22:01:17.027091Z",
     "shell.execute_reply": "2022-06-06T22:01:17.026165Z"
    }
   },
   "outputs": [],
   "source": [
    "df_dict = {\n",
    "    \"Mitigation description\": [],\n",
    "    \"CAPEC1 id\": [],\n",
    "    \"CAPEC2 id\": [],\n",
    "    \"CAPEC1 name\": [],\n",
    "    \"CAPEC2 name\": [],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.066325Z",
     "iopub.status.busy": "2022-06-06T22:01:17.047551Z",
     "iopub.status.idle": "2022-06-06T22:01:17.098442Z",
     "shell.execute_reply": "2022-06-06T22:01:17.097250Z"
    }
   },
   "outputs": [],
   "source": [
    "for pair in highest_similarity_clusters_descriptions:\n",
    "    df_dict[\"Mitigation description\"].append(pair[0][0])\n",
    "\n",
    "for pair in highest_similarity_clusters:\n",
    "    cluster1 = []\n",
    "    cluster2 = []\n",
    "    for mitigation in pair[0]:\n",
    "        for cwe in ap_dict:\n",
    "            if mitigation in ap_dict[cwe][\"mitigations\"]:\n",
    "                df_dict[\"CAPEC1 name\"].append(ap_dict[cwe][\"name\"])\n",
    "                break\n",
    "    for mitigation in pair[1]:\n",
    "        for cwe in ap_dict:\n",
    "            if mitigation in ap_dict[cwe][\"mitigations\"]:\n",
    "                df_dict[\"CAPEC2 name\"].append(ap_dict[cwe][\"name\"])\n",
    "                break\n",
    "\n",
    "f = open(\"cwe_temp.json\")\n",
    "cwe = json.load(f)\n",
    "f = open(\"capec_temp.json\")\n",
    "capec = json.load(f)\n",
    "\n",
    "for pair in highest_similarity_clusters:\n",
    "    cluster1 = []\n",
    "    cluster2 = []\n",
    "    for mitigation in pair[0]:\n",
    "        for cwe_id in ap_dict:\n",
    "            if mitigation in ap_dict[cwe_id][\"mitigations\"]:\n",
    "                for w in capec:\n",
    "                    if cwe_id == w[\"_id\"]:\n",
    "                        df_dict[\"CAPEC1 id\"].append(w[\"original_id\"])\n",
    "                        break\n",
    "                break\n",
    "    for mitigation in pair[1]:\n",
    "        for cwe_id in ap_dict:\n",
    "            if mitigation in ap_dict[cwe_id][\"mitigations\"]:\n",
    "                for w in capec:\n",
    "                    if cwe_id == w[\"_id\"]:\n",
    "                        df_dict[\"CAPEC2 id\"].append(w[\"original_id\"])\n",
    "                        break\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.107550Z",
     "iopub.status.busy": "2022-06-06T22:01:17.105657Z",
     "iopub.status.idle": "2022-06-06T22:01:17.114379Z",
     "shell.execute_reply": "2022-06-06T22:01:17.115641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mitigation description': ['\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  '\\n                  Assume all input is malicious. Use an \"accept known good\" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.\\n                  When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, \"boat\" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as \"red\" or \"blue.\"\\n                  Do not rely exclusively on looking for malicious or malformed inputs.  This is likely to miss at least one undesirable input, especially if the code\\'s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.\\n                  When validating filenames, use stringent allowlists that limit the character set to be used. If feasible, only allow a single \".\" character in the filename to avoid weaknesses such as CWE-23, and exclude directory separators such as \"/\" to avoid CWE-36. Use a list of allowable file extensions, which will help to avoid CWE-434.\\n                  Do not rely exclusively on a filtering mechanism that removes potentially dangerous characters. This is equivalent to a denylist, which may be incomplete (CWE-184). For example, filtering \"/\" is insufficient protection if the filesystem also supports the use of \"\\\\\" as a directory separator. Another possible error could occur when the filtering is applied in a way that still produces dangerous data (CWE-182). For example, if \"../\" sequences are removed from the \".../...//\" string in a sequential fashion, two instances of \"../\" would be removed from the original string, but the remaining characters would still form the \"../\" string.\\n               ',\n",
       "  'The DTD or schema validation will not catch the duplicate occurrence of the same form name. To find the issue in the implementation, manual checks or automated static analysis could be applied to the xml configuration files.'],\n",
       " 'CAPEC1 id': [],\n",
       " 'CAPEC2 id': [],\n",
       " 'CAPEC1 name': [],\n",
       " 'CAPEC2 name': []}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.124819Z",
     "iopub.status.busy": "2022-06-06T22:01:17.123541Z",
     "iopub.status.idle": "2022-06-06T22:01:17.134897Z",
     "shell.execute_reply": "2022-06-06T22:01:17.133626Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(df_dict[\"Mitigation description\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.142923Z",
     "iopub.status.busy": "2022-06-06T22:01:17.141616Z",
     "iopub.status.idle": "2022-06-06T22:01:17.156772Z",
     "shell.execute_reply": "2022-06-06T22:01:17.155795Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"identical capec mitigations.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.188595Z",
     "iopub.status.busy": "2022-06-06T22:01:17.187864Z",
     "iopub.status.idle": "2022-06-06T22:01:17.192747Z",
     "shell.execute_reply": "2022-06-06T22:01:17.193246Z"
    }
   },
   "outputs": [],
   "source": [
    "cwes = []\n",
    "for pair in highest_similarity_clusters:\n",
    "    cluster1 = []\n",
    "    cluster2 = []\n",
    "    for mitigation in pair[0]:\n",
    "        for cwe in ap_dict:\n",
    "            if mitigation in ap_dict[cwe][\"mitigations\"]:\n",
    "                cluster1.append(ap_dict[cwe][\"techniques\"])\n",
    "                break\n",
    "    for mitigation in pair[1]:\n",
    "        for cwe in ap_dict:\n",
    "            if mitigation in ap_dict[cwe][\"mitigations\"]:\n",
    "                cluster2.append(ap_dict[cwe][\"techniques\"])\n",
    "                break\n",
    "    cwes.append([cluster1, cluster2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.200568Z",
     "iopub.status.busy": "2022-06-06T22:01:17.199901Z",
     "iopub.status.idle": "2022-06-06T22:01:17.203758Z",
     "shell.execute_reply": "2022-06-06T22:01:17.204920Z"
    }
   },
   "outputs": [],
   "source": [
    "most_similar_cluster_lengths = []\n",
    "for cluster in clusters_similar_ranked[:80]:\n",
    "    most_similar_cluster_lengths.append(len(cluster))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.210863Z",
     "iopub.status.busy": "2022-06-06T22:01:17.210165Z",
     "iopub.status.idle": "2022-06-06T22:01:17.217171Z",
     "shell.execute_reply": "2022-06-06T22:01:17.216022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(most_similar_cluster_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.262537Z",
     "iopub.status.busy": "2022-06-06T22:01:17.261371Z",
     "iopub.status.idle": "2022-06-06T22:01:17.453838Z",
     "shell.execute_reply": "2022-06-06T22:01:17.455134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.hist(most_similar_cluster_lengths)\n",
    "plt.xlabel(\"Cluster size\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:17.472801Z",
     "iopub.status.busy": "2022-06-06T22:01:17.471446Z",
     "iopub.status.idle": "2022-06-06T22:01:19.187243Z",
     "shell.execute_reply": "2022-06-06T22:01:19.188304Z"
    }
   },
   "outputs": [],
   "source": [
    "# pairwise CWE mitigation similarities within each of the clusters\n",
    "clusters = [list(x) for x in clusters]\n",
    "clusters.sort(key=len)\n",
    "\n",
    "mean_std = {\"mean\": [], \"standard deviation\": []}\n",
    "comparisons = [(\"cwe\", \"cwe\")]\n",
    "\n",
    "pairwise_similarities = []\n",
    "\n",
    "for category in comparisons:\n",
    "    for cluster in clusters:\n",
    "        if len(cluster) > 1:\n",
    "            filtered_cluster = list(filter(lambda x: x.startswith(\"cwe\"), cluster))\n",
    "            cluster_names = []\n",
    "            for mitigation in cluster:\n",
    "                cluster_names.append(mitigation_to_name[mitigation])\n",
    "            cluster_vectors = vectorizer.transform(cluster_names)\n",
    "            similarities = cosine_similarity(cluster_vectors)\n",
    "            unique_similarities = get_unique_pairs(similarities)\n",
    "            pairwise_similarities.extend(unique_similarities)\n",
    "            mean_std[\"mean\"].append(np.mean(similarities))\n",
    "            mean_std[\"standard deviation\"].append(np.std(similarities))\n",
    "\n",
    "pairwise_similarities.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:19.247728Z",
     "iopub.status.busy": "2022-06-06T22:01:19.246628Z",
     "iopub.status.idle": "2022-06-06T22:01:19.687687Z",
     "shell.execute_reply": "2022-06-06T22:01:19.685914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "847\n",
      "1099938\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for similarity in pairwise_similarities:\n",
    "    if similarity >= 1:\n",
    "        count += 1\n",
    "\n",
    "print(count)\n",
    "print(len(pairwise_similarities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:01:19.717717Z",
     "iopub.status.busy": "2022-06-06T22:01:19.699263Z",
     "iopub.status.idle": "2022-06-06T22:03:22.378273Z",
     "shell.execute_reply": "2022-06-06T22:03:22.379273Z"
    }
   },
   "outputs": [],
   "source": [
    "# pairwise CWE mitigation similarities within each of the largest clusters\n",
    "clusters = [list(x) for x in clusters]\n",
    "clusters.sort(key=len)\n",
    "\n",
    "comparisons = [(\"cwe\", \"cwe\")]\n",
    "\n",
    "pairwise_similarities = []\n",
    "\n",
    "filtered_clusters = []\n",
    "for cluster in clusters:\n",
    "    filtered_cluster = list(filter(lambda x: x.startswith(\"cwe\"), cluster))\n",
    "    if filtered_cluster:\n",
    "        filtered_clusters.append(filtered_cluster)\n",
    "\n",
    "for i in range(len(filtered_clusters)):\n",
    "    cluster_1 = filtered_clusters[i]\n",
    "    cluster_1_names = []\n",
    "    for mitigation in cluster_1:\n",
    "        cluster_1_names.append(mitigation_to_name[mitigation])\n",
    "    cluster_1_vectors = vectorizer.transform(cluster_1_names)\n",
    "    for j in range(i + 1, len(filtered_clusters)):\n",
    "        cluster_2 = filtered_clusters[j]\n",
    "        cluster_2_names = []\n",
    "        for mitigation in cluster_2:\n",
    "            cluster_2_names.append(mitigation_to_name[mitigation])\n",
    "        cluster_2_vectors = vectorizer.transform(cluster_2_names)\n",
    "        similarities = cosine_similarity(cluster_1_vectors, cluster_2_vectors)\n",
    "        pairwise_similarities.extend(similarities.flatten())\n",
    "\n",
    "\n",
    "pairwise_similarities.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:03:22.405495Z",
     "iopub.status.busy": "2022-06-06T22:03:22.389501Z",
     "iopub.status.idle": "2022-06-06T22:03:22.885411Z",
     "shell.execute_reply": "2022-06-06T22:03:22.886111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "156\n",
      "1046253\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for similarity in pairwise_similarities:\n",
    "    if similarity == 1:\n",
    "        count += 1\n",
    "\n",
    "print(count)\n",
    "print(len(pairwise_similarities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:03:22.895225Z",
     "iopub.status.busy": "2022-06-06T22:03:22.894283Z",
     "iopub.status.idle": "2022-06-06T22:03:22.901980Z",
     "shell.execute_reply": "2022-06-06T22:03:22.902830Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_std_df = pd.DataFrame(mean_std)\n",
    "mean_std_df.to_csv(\"mean_std_largest_clusters_similarity.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:03:22.912800Z",
     "iopub.status.busy": "2022-06-06T22:03:22.911921Z",
     "iopub.status.idle": "2022-06-06T22:03:22.917946Z",
     "shell.execute_reply": "2022-06-06T22:03:22.916627Z"
    }
   },
   "outputs": [],
   "source": [
    "# mitigation types in the 1480 mitigations cluster\n",
    "max_len = 0\n",
    "max_cluster = None\n",
    "for cluster in clusters:\n",
    "    if len(cluster) > max_len:\n",
    "        max_len = len(cluster)\n",
    "        max_cluster = cluster\n",
    "\n",
    "cwe_count = 0\n",
    "ap_count = 0\n",
    "tech_count = 0\n",
    "for mitigation in max_cluster:\n",
    "    if mitigation.startswith(\"cwe\"):\n",
    "        cwe_count += 1\n",
    "    if mitigation.startswith(\"capec\"):\n",
    "        ap_count += 1\n",
    "    if mitigation.startswith(\"technique\"):\n",
    "        tech_count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-06T22:03:22.925150Z",
     "iopub.status.busy": "2022-06-06T22:03:22.923930Z",
     "iopub.status.idle": "2022-06-06T22:03:22.929128Z",
     "shell.execute_reply": "2022-06-06T22:03:22.930054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cwe mitigations  673\n",
      "Ap mitigations  776\n",
      "Technique mitigations  31\n"
     ]
    }
   ],
   "source": [
    "print(\"Cwe mitigations \", cwe_count)\n",
    "print(\"Ap mitigations \", ap_count)\n",
    "print(\"Technique mitigations \", tech_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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