{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "worth-sapphire",
   "metadata": {},
   "source": [
    "# CVE Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "postal-angle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:22.542669Z",
     "iopub.status.busy": "2024-07-27T00:24:22.542238Z",
     "iopub.status.idle": "2024-07-27T00:24:23.328573Z",
     "shell.execute_reply": "2024-07-27T00:24:23.327994Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    if (document.body.dataset.jpThemeLight == \"false\" || // Jupyter Lab\n",
       "        document.body.dataset.vscodeThemeKind == \"vscode-dark\" || // VS Code\n",
       "        document.documentElement.dataset.theme == \"dark\" || // Jupyter Book\n",
       "        window.matchMedia('(prefers-color-scheme: dark)').matches) {\n",
       "        document.documentElement.classList.add('dark');\n",
       "    }\n",
       "    else {\n",
       "        document.documentElement.classList.remove('dark');\n",
       "    }\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.magic import register_cell_magic\n",
    "from IPython.display import Markdown\n",
    "import datetime\n",
    "from datetime import date\n",
    "import glob\n",
    "import json\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "from itables import init_notebook_mode, show\n",
    "import itables.options as opt\n",
    "\n",
    "opt.dom = \"tpir\"\n",
    "opt.style = \"table-layout:auto;width:auto\"\n",
    "init_notebook_mode(all_interactive=True, connected=True)\n",
    "\n",
    "@register_cell_magic\n",
    "def markdown(line, cell):\n",
    "    return Markdown(cell.format(**globals()))\n",
    "\n",
    "\n",
    "logging.getLogger('matplotlib.font_manager').disabled = True\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_rows', 50)\n",
    "pd.set_option('display.max_columns', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sophisticated-interstate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:23.331177Z",
     "iopub.status.busy": "2024-07-27T00:24:23.330661Z",
     "iopub.status.idle": "2024-07-27T00:24:43.232918Z",
     "shell.execute_reply": "2024-07-27T00:24:43.232279Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "row_accumulator = []\n",
    "for filename in glob.glob('nvd.jsonl'):\n",
    "    with open(filename, 'r', encoding='utf-8') as f:\n",
    "        nvd_data = json.load(f)\n",
    "        for entry in nvd_data:\n",
    "            cve = entry['cve']['id']\n",
    "            try:\n",
    "                assigner = entry['cve']['sourceIdentifier']\n",
    "            except KeyError:\n",
    "                assigner = 'Missing_Data'\n",
    "            try:\n",
    "                published_date = entry['cve']['published']\n",
    "            except KeyError:\n",
    "                published_date = 'Missing_Data'\n",
    "            try:\n",
    "                attack_vector = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['attackVector']\n",
    "            except KeyError:\n",
    "                attack_vector = 'Missing_Data'\n",
    "            try:\n",
    "                attack_complexity = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['attackComplexity']\n",
    "            except KeyError:\n",
    "                attack_complexity = 'Missing_Data'\n",
    "            try:\n",
    "                privileges_required = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['privilegesRequired']\n",
    "            except KeyError:\n",
    "                privileges_required = 'Missing_Data'\n",
    "            try:\n",
    "                user_interaction = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['userInteraction']\n",
    "            except KeyError:\n",
    "                user_interaction = 'Missing_Data'\n",
    "            try:\n",
    "                scope = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['scope']\n",
    "            except KeyError:\n",
    "                scope = 'Missing_Data'\n",
    "            try:\n",
    "                confidentiality_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['confidentialityImpact']\n",
    "            except KeyError:\n",
    "                confidentiality_impact = 'Missing_Data'\n",
    "            try:\n",
    "                integrity_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['integrityImpact']\n",
    "            except KeyError:\n",
    "                integrity_impact = 'Missing_Data'\n",
    "            try:\n",
    "                availability_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['availabilityImpact']\n",
    "            except KeyError:\n",
    "                availability_impact = 'Missing_Data'\n",
    "            try:\n",
    "                base_score = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['baseScore']\n",
    "            except KeyError:\n",
    "                base_score = '0.0'\n",
    "            try:\n",
    "                base_severity = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['baseSeverity']\n",
    "            except KeyError:\n",
    "                base_severity = 'Missing_Data'\n",
    "            try:\n",
    "                exploitability_score = entry['cve']['metrics']['cvssMetricV31'][0]['exploitabilityScore']\n",
    "            except KeyError:\n",
    "                exploitability_score = 'Missing_Data'\n",
    "            try:\n",
    "                impact_score = entry['cve']['metrics']['cvssMetricV31'][0]['impactScore']\n",
    "            except KeyError:\n",
    "                impact_score = 'Missing_Data'\n",
    "            try:\n",
    "                cwe = entry['cve']['weaknesses'][0]['description'][0]['value']\n",
    "            except KeyError:\n",
    "                cwe = 'Missing_Data'\n",
    "            try:\n",
    "                description = entry['cve']['descriptions'][0]['value']\n",
    "            except IndexError:\n",
    "                description = ''\n",
    "            new_row = {\n",
    "                'CVE': cve,\n",
    "                'Published': published_date,\n",
    "                'AttackVector': attack_vector,\n",
    "                'AttackComplexity': attack_complexity,\n",
    "                'PrivilegesRequired': privileges_required,\n",
    "                'UserInteraction': user_interaction,\n",
    "                'Scope': scope,\n",
    "                'ConfidentialityImpact': confidentiality_impact,\n",
    "                'IntegrityImpact': integrity_impact,\n",
    "                'AvailabilityImpact': availability_impact,\n",
    "                'BaseScore': base_score,\n",
    "                'BaseSeverity': base_severity,\n",
    "                'ExploitabilityScore': exploitability_score,\n",
    "                'ImpactScore': impact_score,\n",
    "                'CWE': cwe,\n",
    "                'Description': description,\n",
    "                'Assigner' : assigner\n",
    "            }\n",
    "            if not description.startswith('rejected reason'): \n",
    "                row_accumulator.append(new_row)\n",
    "        nvd = pd.DataFrame(row_accumulator)\n",
    "\n",
    "nvd['Published'] = pd.to_datetime(nvd['Published'])\n",
    "nvd = nvd.sort_values(by=['Published'])\n",
    "nvd = nvd.reset_index(drop=True)\n",
    "nvd['BaseScore'] = pd.to_numeric(nvd['BaseScore']);\n",
    "nvd['BaseScore'] = nvd['BaseScore'].replace(0, np.nan);\n",
    "nvdcount = nvd['Published'].count()\n",
    "nvd['Published'] = pd.to_datetime(nvd['Published']).apply(lambda x: x.date())\n",
    "nvdcount = nvd['Published'].count()\n",
    "startdate = date(2000, 1, 1)\n",
    "enddate  = date.today()\n",
    "numberofdays = enddate - startdate \n",
    "per_day = nvdcount/numberofdays.days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ceed616",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:43.235536Z",
     "iopub.status.busy": "2024-07-27T00:24:43.235353Z",
     "iopub.status.idle": "2024-07-27T00:24:43.299403Z",
     "shell.execute_reply": "2024-07-27T00:24:43.298848Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "nvd['Published'] = pd.to_datetime(nvd['Published'])\n",
    "Month_Graph = nvd['Published'].groupby(nvd.Published.dt.to_period(\"M\")).agg('count')\n",
    "Year_Graph = nvd['Published'].groupby(nvd.Published.dt.to_period(\"Y\")).agg('count')\n",
    "Week_Graph = nvd['Published'].groupby(nvd.Published.dt.to_period(\"W\")).agg('count')\n",
    "Day_Graph = nvd['Published'].groupby(nvd.Published.dt.to_period(\"D\")).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d46acbd3-cd6e-4079-8a6a-3dacd4b08cfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:43.301894Z",
     "iopub.status.busy": "2024-07-27T00:24:43.301675Z",
     "iopub.status.idle": "2024-07-27T00:24:43.313294Z",
     "shell.execute_reply": "2024-07-27T00:24:43.312845Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      \n",
       "      <th>Published</th>\n",
       "      <th>CVEs</th>\n",
       "      <th>Percentage Of CVEs</th>\n",
       "      <th>Growth YOY</th>\n",
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       "<link href=\"https://www.unpkg.com/dt_for_itables@2.0.11/dt_bundle.css\" rel=\"stylesheet\">\n",
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       "        const data = [[\"1988\", 2, 0.0, NaN], [\"1989\", 3, 0.0, 50.0], [\"1990\", 11, 0.0, 266.67], [\"1991\", 15, 0.01, 36.36], [\"1992\", 14, 0.01, -6.67], [\"1993\", 13, 0.01, -7.14], [\"1994\", 26, 0.01, 100.0], [\"1995\", 25, 0.01, -3.85], [\"1996\", 75, 0.03, 200.0], [\"1997\", 253, 0.1, 237.33], [\"1998\", 247, 0.1, -2.37], [\"1999\", 923, 0.36, 273.68], [\"2000\", 1020, 0.4, 10.51], [\"2001\", 1679, 0.65, 64.61], [\"2002\", 2170, 0.84, 29.24], [\"2003\", 1548, 0.6, -28.66], [\"2004\", 2479, 0.96, 60.14], [\"2005\", 5010, 1.94, 102.1], [\"2006\", 6659, 2.58, 32.91], [\"2007\", 6596, 2.56, -0.95], [\"2008\", 5664, 2.19, -14.13], [\"2009\", 5778, 2.24, 2.01], [\"2010\", 4667, 1.81, -19.23], [\"2011\", 4172, 1.62, -10.61], [\"2012\", 5351, 2.07, 28.26], [\"2013\", 5324, 2.06, -0.5], [\"2014\", 8008, 3.1, 50.41], [\"2015\", 6595, 2.56, -17.64], [\"2016\", 6517, 2.53, -1.18], [\"2017\", 18113, 7.02, 177.93], [\"2018\", 18154, 7.03, 0.23], [\"2019\", 18938, 7.34, 4.32], [\"2020\", 19222, 7.45, 1.5], [\"2021\", 21950, 8.51, 14.19], [\"2022\", 26431, 10.24, 20.41], [\"2023\", 30949, 11.99, 17.09], [\"2024\", 23457, 9.09, -24.21]];\n",
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   ],
   "source": [
    "yg_df = pd.DataFrame(Year_Graph)\n",
    "yg_df.columns = ['Count']\n",
    "yg_df = yg_df.reset_index()\n",
    "yg_df['Percentage Of CVEs'] = ( yg_df['Count'] / \n",
    "                       yg_df['Count'].sum()) * 100\n",
    "yg_df['Growth YOY'] = yg_df['Count'].pct_change()*100\n",
    "yg_df = yg_df.round(2)\n",
    "yg_df = yg_df.rename(columns={\"Count\": \"CVEs\"})\n",
    "show(yg_df, scrollY=\"400px\", scrollCollapse=True, paging=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d1b132c-4d52-40ad-9683-fc5e11caa8c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:43.315572Z",
     "iopub.status.busy": "2024-07-27T00:24:43.315224Z",
     "iopub.status.idle": "2024-07-27T00:24:43.591813Z",
     "shell.execute_reply": "2024-07-27T00:24:43.591207Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cg = yg_df.plot.bar(x='Published', y='Percentage Of CVEs', colormap='jet', figsize=(16, 8), title='Percentage of CVEs Published')\n",
    "cg.set_ylabel(\"Percentage\");\n",
    "cg.set_xlabel(\"Year\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc6c6302-aaac-48ed-9d78-6862b42b8073",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-27T00:24:43.594073Z",
     "iopub.status.busy": "2024-07-27T00:24:43.593888Z",
     "iopub.status.idle": "2024-07-27T00:24:43.858016Z",
     "shell.execute_reply": "2024-07-27T00:24:43.857478Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cg = yg_df.plot.bar(x='Published', y='Growth YOY', colormap='jet', figsize=(16, 8), title='Growth Year Over Year')\n",
    "cg.set_ylabel(\"Percentage\");\n",
    "cg.set_xlabel(\"Year\");"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}