Fed-IoTIDS

FedMSE: Semi-supservised federated learning for IoT network intrusion detection

This repository includes the prepared data for all subnetworks in the IoT network and the source code for reproducing the experimental results

Project structure

1. Data/ folder: Store data for experiments
2. Notebook/: Jupyter notebook for prepare data for training and testing
3. src/: Source code for experiments
   • Checkpoint/ --> Store the experimental report in json format and store latent data of SAE in each run
   • Configuration/ --> Store the config file to load the data
   • DataLoader/ --> Module for data preparation and preprocessing
   • Evaluator/ --> Testing phase module
   • Model/ --> Machine learning model definition
   • Trainer/ --> Modules for local training and global aggregation
   • Utils/ --> Utils for calculate the sim score
   • Visualization/ --> Jupyter notebook for visualizing the data and results

How to run the experiments

1. Configure the data for experimental

config_file = f"Configuration/scen2-nba-iot-10clients.json"

2. Configure some hyper-parameters for experiment

num_participants = 0.5
epoch = 100
num_rounds = 20
lr_rate = 1e-5
shrink_lambda = 10

3. Install dependency libraries

pip install -r requirements.txt

4. Run the experiment

python main.py