from IPython.core.magic import register_cell_magic
from IPython.display import Markdown
import datetime
from datetime import date
import glob
import json
import logging
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import warnings
import calplot
from itables import init_notebook_mode, show
import itables.options as opt
opt.dom = "tpir"
opt.style = "table-layout:auto;width:auto"
init_notebook_mode(all_interactive=True, connected=True)
@register_cell_magic
def markdown(line, cell):
return Markdown(cell.format(**globals()))
logging.getLogger('matplotlib.font_manager').disabled = True
warnings.filterwarnings("ignore")
pd.set_option('display.width', 500)
pd.set_option('display.max_rows', 50)
pd.set_option('display.max_columns', 10)
row_accumulator = []
for filename in glob.glob('nvd.jsonl'):
with open(filename, 'r', encoding='utf-8') as f:
nvd_data = json.load(f)
for entry in nvd_data:
cve = entry['cve']['id']
try:
assigner = entry['cve']['sourceIdentifier']
except KeyError:
assigner = 'Missing_Data'
try:
published_date = entry['cve']['published']
except KeyError:
published_date = 'Missing_Data'
try:
attack_vector = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['attackVector']
except KeyError:
attack_vector = 'Missing_Data'
try:
attack_complexity = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['attackComplexity']
except KeyError:
attack_complexity = 'Missing_Data'
try:
privileges_required = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['privilegesRequired']
except KeyError:
privileges_required = 'Missing_Data'
try:
user_interaction = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['userInteraction']
except KeyError:
user_interaction = 'Missing_Data'
try:
scope = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['scope']
except KeyError:
scope = 'Missing_Data'
try:
confidentiality_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['confidentialityImpact']
except KeyError:
confidentiality_impact = 'Missing_Data'
try:
integrity_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['integrityImpact']
except KeyError:
integrity_impact = 'Missing_Data'
try:
availability_impact = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['availabilityImpact']
except KeyError:
availability_impact = 'Missing_Data'
try:
base_score = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['baseScore']
except KeyError:
base_score = '0.0'
try:
base_severity = entry['cve']['metrics']['cvssMetricV31'][0]['cvssData']['baseSeverity']
except KeyError:
base_severity = 'Missing_Data'
try:
exploitability_score = entry['cve']['metrics']['cvssMetricV31'][0]['exploitabilityScore']
except KeyError:
exploitability_score = 'Missing_Data'
try:
impact_score = entry['cve']['metrics']['cvssMetricV31'][0]['impactScore']
except KeyError:
impact_score = 'Missing_Data'
try:
cwe = entry['cve']['weaknesses'][0]['description'][0]['value']
except KeyError:
cwe = 'Missing_Data'
try:
description = entry['cve']['descriptions'][0]['value']
except IndexError:
description = ''
new_row = {
'CVE': cve,
'Published': published_date,
'AttackVector': attack_vector,
'AttackComplexity': attack_complexity,
'PrivilegesRequired': privileges_required,
'UserInteraction': user_interaction,
'Scope': scope,
'ConfidentialityImpact': confidentiality_impact,
'IntegrityImpact': integrity_impact,
'AvailabilityImpact': availability_impact,
'BaseScore': base_score,
'BaseSeverity': base_severity,
'ExploitabilityScore': exploitability_score,
'ImpactScore': impact_score,
'CWE': cwe,
'Description': description,
'Assigner' : assigner
}
if not description.startswith('rejected reason'):
row_accumulator.append(new_row)
nvd = pd.DataFrame(row_accumulator)
nvd['Published'] = pd.to_datetime(nvd['Published'])
nvd = nvd.sort_values(by=['Published'])
nvd = nvd.reset_index(drop=True)
nvd['BaseScore'] = pd.to_numeric(nvd['BaseScore']);
nvd['BaseScore'] = pd.to_numeric(nvd['BaseScore']);
nvd['BaseScore'] = nvd['BaseScore'].replace(0, np.NaN);
nvdcount = nvd['Published'].count()
nvdunique = nvd['Published'].nunique()
startdate = date(2000, 1, 1)
enddate = date.today()
numberofdays = enddate - startdate
per_day = nvdcount/numberofdays.days
nvd['Assigner'].replace('416baaa9-dc9f-4396-8d5f-8c081fb06d67','cve@kernel.org',inplace=True)
nvd_frequency = nvd['Assigner'].value_counts()
nvd_frequency = nvd_frequency.reset_index()
nvd_frequency.columns = ['Assigner', 'CVEs']
nvd_frequency['Percentage'] = round((nvd_frequency['CVEs'] /
nvd_frequency['CVEs'].sum()) * 100)
nvd_frequency_no_mitre = nvd_frequency[~nvd_frequency.Assigner.str.contains('cve@mitre.org')]
nvd_frequency_no_mitre = nvd_frequency_no_mitre[nvd_frequency_no_mitre.CVEs > 1].head(20)
plt.figure(figsize=(10,10))
plt.barh("Assigner", "CVEs", data = nvd_frequency_no_mitre, color="#001d82")
plt.xlabel("CVEs");
plt.ylabel("") ;
plt.title("Top 20 CNAs");
nvd_frequency_graph = nvd_frequency[nvd_frequency.CVEs > 100].head(20)
plt.figure(figsize=(10,10))
plt.barh("Assigner", "CVEs", data = nvd_frequency_graph, color="#001d82")
plt.xlabel("CVEs");
plt.ylabel("")
plt.title("Top 20 CNAs Including Mitre");
show(nvd_frequency, scrollY="400px", scrollCollapse=True, paging=False)
Assigner | CVEs | Percentage |
---|---|---|
Loading... (need help?) |
Markdown(f"This report is updated automatically every day, last generated on: **{datetime.datetime.now()}**")
This report is updated automatically every day, last generated on: 2024-04-19 08:15:53.954451