{
 "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": "2024-05-02T12:15:31.453682Z",
     "iopub.status.busy": "2024-05-02T12:15:31.453496Z",
     "iopub.status.idle": "2024-05-02T12:15:32.197134Z",
     "shell.execute_reply": "2024-05-02T12:15:32.196552Z"
    },
    "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": "2024-05-02T12:15:32.199424Z",
     "iopub.status.busy": "2024-05-02T12:15:32.199160Z",
     "iopub.status.idle": "2024-05-02T12:15:52.023199Z",
     "shell.execute_reply": "2024-05-02T12:15:52.022642Z"
    },
    "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": "2024-05-02T12:15:52.025986Z",
     "iopub.status.busy": "2024-05-02T12:15:52.025570Z",
     "iopub.status.idle": "2024-05-02T12:15:52.087688Z",
     "shell.execute_reply": "2024-05-02T12:15:52.086950Z"
    },
    "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": "2024-05-02T12:15:52.090329Z",
     "iopub.status.busy": "2024-05-02T12:15:52.090136Z",
     "iopub.status.idle": "2024-05-02T12:15:52.102722Z",
     "shell.execute_reply": "2024-05-02T12:15:52.102194Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
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       "      \n",
       "      <th>Published</th>\n",
       "      <th>CVEs</th>\n",
       "      <th>Percentage Of CVEs</th>\n",
       "      <th>Growth YOY</th>\n",
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       "Loading ITables v2.0.1 from the internet...\n",
       "(need <a href=https://mwouts.github.io/itables/troubleshooting.html>help</a>?)</td>\n",
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       "\n",
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       "<link href=\"https://www.unpkg.com/dt_for_itables@2.0.5/dt_bundle.css\" rel=\"stylesheet\">\n",
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       "        // Define the table data\n",
       "        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, 0.37, 273.68], [\"2000\", 1020, 0.41, 10.51], [\"2001\", 1679, 0.68, 64.61], [\"2002\", 2170, 0.88, 29.24], [\"2003\", 1548, 0.63, -28.66], [\"2004\", 2479, 1.0, 60.14], [\"2005\", 5010, 2.02, 102.1], [\"2006\", 6659, 2.69, 32.91], [\"2007\", 6596, 2.66, -0.95], [\"2008\", 5664, 2.29, -14.13], [\"2009\", 5778, 2.33, 2.01], [\"2010\", 4667, 1.89, -19.23], [\"2011\", 4172, 1.69, -10.61], [\"2012\", 5351, 2.16, 28.26], [\"2013\", 5324, 2.15, -0.5], [\"2014\", 8008, 3.24, 50.41], [\"2015\", 6595, 2.66, -17.64], [\"2016\", 6517, 2.63, -1.18], [\"2017\", 18113, 7.32, 177.93], [\"2018\", 18154, 7.33, 0.23], [\"2019\", 18938, 7.65, 4.32], [\"2020\", 19222, 7.77, 1.5], [\"2021\", 21950, 8.87, 14.19], [\"2022\", 26431, 10.68, 20.41], [\"2023\", 30949, 12.5, 17.09], [\"2024\", 12927, 5.22, -58.23]];\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": "2024-05-02T12:15:52.105008Z",
     "iopub.status.busy": "2024-05-02T12:15:52.104591Z",
     "iopub.status.idle": "2024-05-02T12:15:52.459685Z",
     "shell.execute_reply": "2024-05-02T12:15:52.459038Z"
    },
    "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": "2024-05-02T12:15:52.462068Z",
     "iopub.status.busy": "2024-05-02T12:15:52.461726Z",
     "iopub.status.idle": "2024-05-02T12:15:52.762721Z",
     "shell.execute_reply": "2024-05-02T12:15:52.762174Z"
    },
    "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
}