AI-Driven Adaptive Intrusion Detection and Prevention System with a Honeypot-Based Feedback Loop

An AI-driven adaptive intrusion detection and prevention system that learns from a honeypot feedback loop.

An AI-driven adaptive intrusion detection and prevention system that learns from a honeypot feedback loop. 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 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.