{
 "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": "2024-06-16T12:25:23.605463Z",
     "iopub.status.busy": "2024-06-16T12:25:23.605295Z",
     "iopub.status.idle": "2024-06-16T12:25:24.231325Z",
     "shell.execute_reply": "2024-06-16T12:25:24.230851Z"
    },
    "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": "2024-06-16T12:25:24.233552Z",
     "iopub.status.busy": "2024-06-16T12:25:24.233329Z",
     "iopub.status.idle": "2024-06-16T12:26:10.097654Z",
     "shell.execute_reply": "2024-06-16T12:26:10.097116Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "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",
    "                assigner = entry['cve']['CVE_data_meta']['ASSIGNER']\n",
    "            except KeyError:\n",
    "                assigner = 'Missing_Data'\n",
    "            try:\n",
    "                published_date = entry['publishedDate']\n",
    "            except KeyError:\n",
    "                published_date = 'Missing_Data'\n",
    "            try:\n",
    "                attack_vector = entry['impact']['baseMetricV3']['cvssV3']['attackVector']\n",
    "            except KeyError:\n",
    "                attack_vector = 'Missing_Data'\n",
    "            try:\n",
    "                attack_complexity = entry['impact']['baseMetricV3']['cvssV3']['attackComplexity']\n",
    "            except KeyError:\n",
    "                attack_complexity = 'Missing_Data'\n",
    "            try:\n",
    "                privileges_required = entry['impact']['baseMetricV3']['cvssV3']['privilegesRequired']\n",
    "            except KeyError:\n",
    "                privileges_required = 'Missing_Data'\n",
    "            try:\n",
    "                user_interaction = entry['impact']['baseMetricV3']['cvssV3']['userInteraction']\n",
    "            except KeyError:\n",
    "                user_interaction = 'Missing_Data'\n",
    "            try:\n",
    "                scope = entry['impact']['baseMetricV3']['cvssV3']['scope']\n",
    "            except KeyError:\n",
    "                scope = 'Missing_Data'\n",
    "            try:\n",
    "                confidentiality_impact = entry['impact']['baseMetricV3']['cvssV3']['confidentialityImpact']\n",
    "            except KeyError:\n",
    "                confidentiality_impact = 'Missing_Data'\n",
    "            try:\n",
    "                integrity_impact = entry['impact']['baseMetricV3']['cvssV3']['integrityImpact']\n",
    "            except KeyError:\n",
    "                integrity_impact = 'Missing_Data'\n",
    "            try:\n",
    "                availability_impact = entry['impact']['baseMetricV3']['cvssV3']['availabilityImpact']\n",
    "            except KeyError:\n",
    "                availability_impact = 'Missing_Data'\n",
    "            try:\n",
    "                base_score = entry['impact']['baseMetricV3']['cvssV3']['baseScore']\n",
    "            except KeyError:\n",
    "                base_score = '0.0'\n",
    "            try:\n",
    "                base_severity = entry['impact']['baseMetricV3']['cvssV3']['baseSeverity']\n",
    "            except KeyError:\n",
    "                base_severity = 'Missing_Data'\n",
    "            try:\n",
    "                exploitability_score = entry['impact']['baseMetricV3']['exploitabilityScore']\n",
    "            except KeyError:\n",
    "                exploitability_score = 'Missing_Data'\n",
    "            try:\n",
    "                impact_score = entry['impact']['baseMetricV3']['impactScore']\n",
    "            except KeyError:\n",
    "                impact_score = 'Missing_Data'\n",
    "            try:\n",
    "                cwe = entry['cve']['problemtype']['problemtype_data'][0]['description'][0]['value']\n",
    "            except IndexError:\n",
    "                cwe = 'Missing_Data'\n",
    "            try:\n",
    "                description = entry['cve']['description']['description_data'][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('** REJECT **'): # disputed, rejected and other non issues start with '**'\n",
    "                row_accumulator.append(new_row)\n",
    "        nvd = pd.DataFrame(row_accumulator)\n",
    "\n",
    "\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": "2024-06-16T12:26:10.100063Z",
     "iopub.status.busy": "2024-06-16T12:26:10.099877Z",
     "iopub.status.idle": "2024-06-16T12:26:10.161978Z",
     "shell.execute_reply": "2024-06-16T12:26:10.161398Z"
    },
    "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": "2024-06-16T12:26:10.164525Z",
     "iopub.status.busy": "2024-06-16T12:26:10.164090Z",
     "iopub.status.idle": "2024-06-16T12:26:10.176175Z",
     "shell.execute_reply": "2024-06-16T12:26:10.175736Z"
    },
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
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       "      <th>Published</th>\n",
       "      <th>CVEs</th>\n",
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       "<link href=\"https://www.unpkg.com/dt_for_itables@2.0.10/dt_bundle.css\" rel=\"stylesheet\">\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",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d1b132c-4d52-40ad-9683-fc5e11caa8c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T12:26:10.178280Z",
     "iopub.status.busy": "2024-06-16T12:26:10.177957Z",
     "iopub.status.idle": "2024-06-16T12:26:10.485347Z",
     "shell.execute_reply": "2024-06-16T12:26:10.484787Z"
    },
    "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": "2024-06-16T12:26:10.487548Z",
     "iopub.status.busy": "2024-06-16T12:26:10.487217Z",
     "iopub.status.idle": "2024-06-16T12:26:10.753041Z",
     "shell.execute_reply": "2024-06-16T12:26:10.752569Z"
    },
    "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
}