{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CISA KVE EPSS Data Analyis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:06:35.696872Z",
     "iopub.status.busy": "2024-06-13T12:06:35.696682Z",
     "iopub.status.idle": "2024-06-13T12:06:36.061991Z",
     "shell.execute_reply": "2024-06-13T12:06:36.061411Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import requests\n",
    "import os\n",
    "import glob\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:06:36.064813Z",
     "iopub.status.busy": "2024-06-13T12:06:36.064546Z",
     "iopub.status.idle": "2024-06-13T12:06:36.401780Z",
     "shell.execute_reply": "2024-06-13T12:06:36.401250Z"
    }
   },
   "outputs": [],
   "source": [
    "cisa_df = pd.read_csv(\"https://www.cisa.gov/sites/default/files/csv/known_exploited_vulnerabilities.csv\")\n",
    "cisa_df = cisa_df\n",
    "cisa_df.columns = cisa_df.columns.str.strip(\"\\u200b\")\n",
    "cisa_df = cisa_df.rename(columns={\"cveID\": \"CVE\", \"shortDescription\" : \"Description\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:06:36.404476Z",
     "iopub.status.busy": "2024-06-13T12:06:36.404086Z",
     "iopub.status.idle": "2024-06-13T12:06:36.511674Z",
     "shell.execute_reply": "2024-06-13T12:06:36.511045Z"
    }
   },
   "outputs": [],
   "source": [
    "epss = pd.read_csv('epss_scores-current.csv', skiprows=1)\n",
    "epss = epss.rename(columns={\"cve\": \"CVE\", \"epss\" : \"EPSS\", \"percentile\" : \"EPSS Percentile\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:06:36.514515Z",
     "iopub.status.busy": "2024-06-13T12:06:36.514280Z",
     "iopub.status.idle": "2024-06-13T12:07:15.249255Z",
     "shell.execute_reply": "2024-06-13T12:07:15.248534Z"
    }
   },
   "outputs": [],
   "source": [
    "row_accumulator = []\n",
    "for filename in glob.glob('nvdcve-1.1-*.json'):\n",
    "    with open(filename, 'r', encoding='utf-8') as f:\n",
    "        nvd_data = json.load(f)\n",
    "        for entry in nvd_data['CVE_Items']:\n",
    "            cve = entry['cve']['CVE_data_meta']['ID']\n",
    "            try:\n",
    "                base_score = entry['impact']['baseMetricV3']['cvssV3']['baseScore']\n",
    "            except KeyError:\n",
    "                base_score = '0.0'\n",
    "            new_row = { \n",
    "                'CVE': cve, \n",
    "                'CVSS3': base_score,\n",
    "            }\n",
    "            row_accumulator.append(new_row)\n",
    "        nvd = pd.DataFrame(row_accumulator)\n",
    "        \n",
    "nvd['CVSS3'] = pd.to_numeric(nvd['CVSS3']);\n",
    "nvd['CVSS3'] = nvd['CVSS3'].replace(0, np.NaN);       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:07:15.252268Z",
     "iopub.status.busy": "2024-06-13T12:07:15.252057Z",
     "iopub.status.idle": "2024-06-13T12:07:15.355130Z",
     "shell.execute_reply": "2024-06-13T12:07:15.354404Z"
    }
   },
   "outputs": [],
   "source": [
    "epss_kev = pd.merge(cisa_df, epss, left_on='CVE', right_on='CVE')\n",
    "epss_kev_nvd =  pd.merge(epss_kev, nvd, left_on='CVE', right_on='CVE')\n",
    "epss_kev_nvd =  epss_kev_nvd[[\"CVE\", \"CVSS3\", \"EPSS\", \"EPSS Percentile\", \"Description\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CISA KEV Score Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:07:15.358208Z",
     "iopub.status.busy": "2024-06-13T12:07:15.357967Z",
     "iopub.status.idle": "2024-06-13T12:07:16.483860Z",
     "shell.execute_reply": "2024-06-13T12:07:16.483165Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:1345: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  scatter = ax.scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = epss_kev_nvd.plot.scatter(x='CVSS3',\n",
    "                     y='EPSS',\n",
    "                     colormap='jet',\n",
    "                     figsize=(20, 10),\n",
    "                     title='CISA Known Exploited Vulnerabilities');\n",
    "ax.set_xlabel(\"CVSS 3 Base Score\");\n",
    "ax.set_ylabel(\"EPSS Score\");\n",
    "ax.figure.savefig('epss_kev_nvd.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-13T12:07:16.486692Z",
     "iopub.status.busy": "2024-06-13T12:07:16.486084Z",
     "iopub.status.idle": "2024-06-13T12:07:16.503023Z",
     "shell.execute_reply": "2024-06-13T12:07:16.502362Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CVE</th>\n",
       "      <th>CVSS3</th>\n",
       "      <th>EPSS</th>\n",
       "      <th>EPSS Percentile</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVE-2021-27104</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.01409</td>\n",
       "      <td>0.86505</td>\n",
       "      <td>Accellion FTA contains an OS command injection...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CVE-2021-27102</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00083</td>\n",
       "      <td>0.35561</td>\n",
       "      <td>Accellion FTA contains an OS command injection...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CVE-2021-27101</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.00761</td>\n",
       "      <td>0.81192</td>\n",
       "      <td>Accellion FTA contains a SQL injection vulnera...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CVE-2021-27103</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.01217</td>\n",
       "      <td>0.85338</td>\n",
       "      <td>Accellion FTA contains a server-side request f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CVE-2021-21017</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.64257</td>\n",
       "      <td>0.97891</td>\n",
       "      <td>Acrobat Acrobat and Reader contain a heap-base...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CVE-2021-28550</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.62292</td>\n",
       "      <td>0.97836</td>\n",
       "      <td>Adobe Acrobat and Reader contains a use-after-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CVE-2018-4939</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.96914</td>\n",
       "      <td>0.99720</td>\n",
       "      <td>Adobe ColdFusion contains a deserialization of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVE-2018-15961</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.97436</td>\n",
       "      <td>0.99946</td>\n",
       "      <td>Adobe ColdFusion contains an unrestricted file...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CVE-2018-4878</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.97279</td>\n",
       "      <td>0.99859</td>\n",
       "      <td>Adobe Flash Player contains a use-after-free v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CVE-2020-5735</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.02271</td>\n",
       "      <td>0.89649</td>\n",
       "      <td>Amcrest cameras and NVR contain a stack-based ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CVE  CVSS3     EPSS  EPSS Percentile  \\\n",
       "0  CVE-2021-27104    9.8  0.01409          0.86505   \n",
       "1  CVE-2021-27102    7.8  0.00083          0.35561   \n",
       "2  CVE-2021-27101    9.8  0.00761          0.81192   \n",
       "3  CVE-2021-27103    9.8  0.01217          0.85338   \n",
       "4  CVE-2021-21017    8.8  0.64257          0.97891   \n",
       "5  CVE-2021-28550    8.8  0.62292          0.97836   \n",
       "6   CVE-2018-4939    9.8  0.96914          0.99720   \n",
       "7  CVE-2018-15961    9.8  0.97436          0.99946   \n",
       "8   CVE-2018-4878    9.8  0.97279          0.99859   \n",
       "9   CVE-2020-5735    8.8  0.02271          0.89649   \n",
       "\n",
       "                                         Description  \n",
       "0  Accellion FTA contains an OS command injection...  \n",
       "1  Accellion FTA contains an OS command injection...  \n",
       "2  Accellion FTA contains a SQL injection vulnera...  \n",
       "3  Accellion FTA contains a server-side request f...  \n",
       "4  Acrobat Acrobat and Reader contain a heap-base...  \n",
       "5  Adobe Acrobat and Reader contains a use-after-...  \n",
       "6  Adobe ColdFusion contains a deserialization of...  \n",
       "7  Adobe ColdFusion contains an unrestricted file...  \n",
       "8  Adobe Flash Player contains a use-after-free v...  \n",
       "9  Amcrest cameras and NVR contain a stack-based ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epss_kev_nvd.to_csv(\"epss_kev_nvd.csv\", index=False)\n",
    "epss_kev_nvd.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}