Integrated Phishing Detection Approach Using Random Forest and XGBoost

An integrated phishing detection approach that combines random forest and XGBoost classifiers.

An integrated phishing detection approach that combines random forest and XGBoost classifiers. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want practical security experience: understanding the threat, building the defence or detector, testing it against realistic attacks and reporting the results.

The main parts are a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses XGBoost, Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.