CPE#

Common Platform Enumeration (CPE) is a structured naming scheme for information technology systems, software, and packages. Based upon the generic syntax for Uniform Resource Identifiers (URI), CPE includes a formal name format, a method for checking names against a system, and a description format for binding text and tests to a name. This method of naming is known as a well-formed CPE name (WFN)

You can see this notebook directly via:

Generation time#

from datetime import datetime, timezone, timedelta

timezone_offset = 0.0
tzinfo = timezone(timedelta(hours=timezone_offset))
generation_time = datetime.now(tzinfo).strftime('%Y-%m-%d %H:%M:%S %z')
print(generation_time)

2024-04-16 06:01:44 +0000

Creative Commons#

This notebook and generated diagrams are released with Creative Commons liecense (CC BY 4.0).

CC BY 4.0

import requests
import urllib3

urllib3.disable_warnings()

urls = ['https://mirrors.creativecommons.org/presskit/icons/cc.xlarge.png',
       'https://mirrors.creativecommons.org/presskit/icons/by.xlarge.png']
for url in urls:
    file_name = url.split("/")[-1:][0]
    print(file_name)

    file = requests.get(url, verify=False)
    open(file_name, 'wb').write(file.content)

cc.xlarge.png
by.xlarge.png

CPE data downloading#

All CPE stats are taken from nvd.nist.gov/products/cpe/statistics

from urllib.request import urlopen
import ssl
from bs4 import BeautifulSoup, SoupStrainer

def get_data(url):

    ctx = ssl.create_default_context()
    ctx.check_hostname = False
    ctx.verify_mode = ssl.CERT_NONE

    page = urlopen(url, context=ctx)
    html = page.read().decode("utf-8")
    
    product = SoupStrainer('table')
    soup = BeautifulSoup(html, "html.parser", parse_only=product)
    
    return soup

url = "https://nvd.nist.gov/products/cpe/statistics"
data = get_data(url)

print(len(data))

16

New CPE entries#

CPE data parsing#

import pandas as pd

def pars(data):
    data_table = []
    
    for table in data:
        table_id = table['id']
        table_year = table_id[-4:]
        table_rows = table.find_all("tr")
        number_of_new_cpe_entries_yearly = 0
        number_of_new_cpe_entries_list = []
        data_row = []
        for table_row in table_rows:
            data = table_row.find_all("td")
            
            if data:
                number_of_new_cpe_entries = int(data[1].string.replace(",",""))
                
                number_of_new_cpe_entries_list.append(number_of_new_cpe_entries)
                
                number_of_new_cpe_entries_yearly += number_of_new_cpe_entries
        
        while len(number_of_new_cpe_entries_list) < 12:
            number_of_new_cpe_entries_list.append(0)

        data_row.append(table_year)
        data_row.append(number_of_new_cpe_entries_yearly)
        data_row = data_row + number_of_new_cpe_entries_list

        data_table.append(data_row)
    
    data_columns = ['Year', 'Summary', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
    df = pd.DataFrame (data_table, columns = data_columns)
    df.sort_values(by=['Year'], inplace=True)    
    df.reset_index(drop=True, inplace=True)
    df.index += 1
    return df
    
parsed_data = pars(data)

parsed_data.style.bar(subset=['Summary'], color='#FF6200')

Matplotlib is building the font cache; this may take a moment.
  Year Summary January February March April May June July August September October November December
1 2009 5786 181 422 830 639 612 209 241 110 330 102 1960 150
2 2010 11094 990 234 509 1106 803 886 426 1252 707 1336 1026 1819
3 2011 9263 702 925 583 2252 818 682 430 390 423 470 737 851
4 2012 23235 1148 1003 1622 2501 3228 2259 3124 2887 1583 1830 1110 940
5 2013 15671 1460 1105 1138 1271 932 989 1623 1359 1140 1941 1327 1386
6 2014 16714 1552 1151 1644 2103 2175 1447 1606 886 885 260 1380 1625
7 2015 6503 758 654 849 838 429 736 446 564 81 216 144 788
8 2016 11074 2450 535 871 699 732 1471 321 762 979 452 696 1106
9 2017 18650 746 888 1070 1935 1494 1210 1364 1889 1516 1596 2585 2357
10 2018 56509 2273 2404 1571 2525 10489 5160 3707 3895 3722 10862 6564 3337
11 2019 272322 6810 7197 10466 12979 13181 32837 45459 23763 47195 26717 22731 22987
12 2020 171681 15960 18668 20159 16306 14157 11250 10659 14459 13077 10921 9293 16772
13 2021 189565 11253 14969 18562 15476 13997 18730 20791 16531 14022 16164 17160 11910
14 2022 175793 11299 12125 13566 10410 10262 14432 13652 18251 14397 15560 14888 26951
15 2023 223030 18297 16483 22071 14262 17591 19858 18929 20943 16827 21344 18296 18129
16 2024 39737 20260 16220 2058 1199 0 0 0 0 0 0 0 0

CPE data saving#

CSV file is available in GitHub repository, see:

csv_filename = 'cpe-number-of-new-entries.csv'

parsed_data.to_csv(csv_filename, index=False)

CPE data ploting#

PNG files are available in GitHub repository with two background versions, see:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

import urllib

df = pd.read_csv(csv_filename)

df.plot(x='Year', 
        xlabel='Year',
        y='Summary', 
        ylabel='Number of CPE',
        kind='bar', 
        title='Number of CPE per year')
plt.tight_layout()
plt.legend(['CPE'])
plt.figtext(0.16, 0.02, f"Generated on {generation_time} thanks to limberduck.org based on source: nvd.nist.gov/products/cpe/statistics", ha="left", fontsize=7)
fig = plt.gcf()
fig.set_size_inches(10,6)
fig.patch.set_facecolor('white')
plt.grid(True)

img_cc = plt.imread('cc.xlarge.png')
newax_cc = fig.add_axes([0.88, 0.0, 0.05, 0.05], anchor='NE', zorder=-1)
newax_cc.imshow(img_cc)
newax_cc.axis('off')
img_by = plt.imread('by.xlarge.png')
newax_by = fig.add_axes([0.92, 0.0, 0.05, 0.05], anchor='NE', zorder=-1)
newax_by.imshow(img_by)
newax_by.axis('off')

plt.savefig('cpe-number-of-new-entries-bg-white.png', dpi = 300, facecolor = 'white')
plt.savefig('cpe-number-of-new-entries-bg-transparent.png', dpi = 300, transparent = True)
../../_images/e19e9d6b5133aecf2fcd2fd1a0dbe3cca2d2ca92a866cf15dc82b79175b47638.png