Intrusion Detection System for Securing IoT Networks Using Federated Learning

An intrusion detection system that secures IoT networks using federated learning.

An intrusion detection system that secures IoT networks using federated learning. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from 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 privacy-preserving training across several clients, with only model updates shared, a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses Flower, Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.