FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application
Connected autonomous vehicles (CAVs) are trans- forming the transportation business by incorporating advanced technology such as sensors, communication systems, and artificial intelligence. However, the interconnectedness and complexity of CAVs pose security vulnerabilities, making them possible targets for assaults. Intrusion detection is critical in protecting CAVs from harmful actions. This research investigates the use of federated learning, a privacy-preserving machine learning approach, for intrusion detection in CAVs. Federated Learning (FL) can improve the detection capabilities and robustness of intrusion detection systems in the CAV ecosystem by using the collective capacity of various CAVs while protecting data privacy. This paper provides an in-depth analysis of tailoring FL for collaborative intrusion detection in CAVs, as well as prospective future research areas in this domain. The findings of this study contribute to the advancement of secure and dependable CAV systems, opening the path for the widespread use of connected autonomous vehicles in the transportation industry
First, you need to install the project dependencies. To do this, navigate to the project directory and run the following command:
pip install -r requirements.txt
This will install all the necessary packages for the project.
To run the project, use the following command in your terminal:
Example:
python3 main.py --optimizer= sgd \
--data_split=iid --num_rounds=400 \
--clients_per_round=10 --batch_size = 4\
--num_epochs=5 --poison=20
This will execute the main.py file, which contains the main code for the project.
Note: Here Poison parameter refers to the poison level for the simulation. The parameter has to be in **percentage**
format. e.g For 100 clients 20% poison refers to 20 clients of the 100 clients will be poisoned.