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Certitude is a Python package to perform supervised malicious URL classification using a joint set of lexicographic and certificate features.

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All COSSAS projects are hosted on GitLab with a push mirror to GitHub. For issues/contributions check CONTRIBUTING.md

Getting Started

Certitude requires whois, which may not be available on some systems, and is thus distributed as a docker image. If whois is available it can also be installed as a python package, see the development section below.

# pull image from registry
docker pull registry.gitlab.com/cossas/certitude:latest

# print help
docker run -it registry.gitlab.com/cossas/certitude:latest

# example perform training from data in local directory
docker run -it -v $(pwd)/tests/data:/data registry.gitlab.com/cossas/certitude:latest --train /data/newmodel -d /data/testset_labeled.csv

# example performing classification of a url with the trained model
docker run -it -v $(pwd)/tests/data:/data registry.gitlab.com/cossas/certitude:latest --model /data/newmodel --url https://www.tno.nl

Development

To start developing this package, follow these steps:

• Start WSL
• git clone this project, ensuring you do that in the WSL filesystem. Run cd to ensure you're in the WSL home directory
• cd into the just cloned directory
• Run code . to start VS Code
• In a VS Code terminal, run poetry install, poetry shell and finally poetry run pre-commit install

Code flow

Checkout the code flow here

Demo & Test

To see some useful commands and to test the code you can check the makefile:

make demo

Contributing

Contributions to CERTITUDE are highly appreciated and more than welcome. Please read CONTRIBUTING.md for more information about our contributions process.

Maintainance status

This project has been developed until TRL4 and is currently not actively maintained. We envision the following steps to raise the TRL from 4 to 6:

• Technical trials in security pipelines of small to midsized companies.
• Retraining of the default model using company security data.
• Validating the accuracy of the packages' classification method in relevant circumstances.
• Improving the package on shortcomings for the needs of a small to midsized company.