A tool for data scraping and analysis of security certificates from Common Criteria and FIPS 140-2/3 frameworks. This project is developed by the Centre for Research On Cryptography and Security at Masaryk University, Czech Republic.

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Use Docker with docker pull seccerts/sec-certs or just pip install -U sec-certs. For more elaborate description, see docs.


There are two main steps in exploring the world of security certificates:

  1. Data scraping and data processing all the certificates

  2. Exploring and analysing the processed data

For the first step, we currently provide CLI and our already processed fresh snapshot. For the second step, we provide simple API that can be used directly inside our Jupyter notebook or locally, at your machine.

More elaborate usage is described in docs/quickstart. Also, see example notebooks either at GitHub or at docs. From docs, you can also run our notebooks in Binder.

Data scraping#

Run sec-certs cc all for Common Criteria processing, sec-certs fips all for FIPS 140 processing.

Data analysis#

Without needing to run the whole processing pipeline, you can fetch fresh snapshot of the dataset and explore it yourself.

dset = CCDataset.from_web_latest()

# Get certificates with some CVE
vulnerable_certs = [x for x in dset if x.heuristics.related_cves]
df_vulnerable = df.loc[~df.related_cves.isna()]

# Show CVE ids of some vulnerable certificate

# Get certificates from 2015 and newer
df_2015_and_newer = df.loc[df.year_from > 2014]

# Plot distribution of years of certification

Explore data with MyBinder Jupyter notebook#

Most probably, you don’t want to process fresh snapshot of Common Criteria certificates by yourself. Instead, you can use our results and explore them using online Jupyter notebook.

Explore the latest snapshot locally#

In Python, run

from sec_certs.dataset.common_criteria import CCDataset
import pandas as pd

dset = CCDataset.from_web_latest()  # now you can inspect the object, certificates are held in dset.certs
df = dset.to_pandas()  # Or you can transform the object into Pandas dataframe
    './latest_cc_snapshot.json')  # You may want to store the snapshot as json, so that you don't have to download it again
dset = CCDataset.from_json('./latest_cc_snapshot.json')  # you can now load your stored dataset again