fl-ids

Federated Learning for Intrusion Detection System

Overview

This project utilizes the power of Federated Learning to create an Intrusion Detection System. It employs the Flower framework (GitHub - adap/flower) for federated learning and the UNSW_NB15 dataset for intrusion detection. The core of this system is a Neural Network (NN) model.

Setup & Installation

To get the project up and running, follow these steps:

1. Download the UNSW_NB15 dataset.
2. Place the UNSW_NB15_training-set.csv and UNSW_NB15_testing-set.csv files in the project’s data folder.

Simulation

There are 3 options to execute simulation:

1. Manually

   • To initiate the server, use the command:

     python server.py
     

   • To launch the clients, execute the following command for each client:

     python client.py
     

   Note: To accurately simulate the project, at least three clients are needed to satisfy the min_fit_clients, min_evaluate_clients, and min_available_clients configuration.

   Note: The server uses port 8080 as default. If you want to run it on a different port, you may need to change it in both client.py and server.py.

2. Using the simulation.py script

   • To run the simulation, use the command:

     python simulation.py
     

3. Containerized simulation by running the Docker compose command

   • To run the simulation, use the command:

     docker-compose up --build
     

Also you can visualize the model’s architecture by generating a plot

• To visualize the model’s structure, use:

  python utils/plot.py
  

Future Enhancements

• Personalize datasets for each client instead of using a common sampled dataset.