FEEL project

The feel project aims at researching, developing and implementing a federated learning ML model for intrusion detection systems. It was implemented by Pavel Janata's and published for his thesis "Decentralized Federated Learning for Network Security".

This project is funded by NlNet NGI Zero Entrust

Goal

Detect malicious SSL/TLS traffic using federated learning.

Docker setup

You can build a docker image for the anomaly detection experiment. To build the image, simply run

make build_anomaly_detection_docker

and to run it you can either set environmental variables:

make run_client CLIENT_ID=1 DAY=1

or run it directly:

docker run --network-host --volume "$(pwd)/data/":/data stratosphere/feel-ad client --client_id 1 --day 1 --ip_address 127.0.0.1

This way you can also specify additional arguments such as --port or --seed

To run the server use:

make run_server DAY=1

or directly

docker run --network=host --volume "$(pwd)/data/":/data stratosphere/feel-ad server --day 1 --ip_address localhost --load 1 --num_fit_clients=10 --num_evaluate_clients=10