Reinforcement Learning for Security Alert Prioritization in Financial Systems

A reinforcement learning approach that prioritises security alerts in financial systems so analysts handle the riskiest first.

A reinforcement learning approach that prioritises security alerts in financial systems so analysts handle the riskiest first. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend 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 alerts when a threshold or risk is reached, a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.