Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks

Muzun Althunayyan (), Amir Javed (), Omer Rana and Theodoros Spyridopoulos
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Muzun Althunayyan: School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Amir Javed: School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Omer Rana: School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Theodoros Spyridopoulos: School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK

Future Internet, 2024, vol. 16, issue 12, 1-17

Abstract: Intrusion detection systems (IDSs) are crucial for identifying cyberattacks on in-vehicle networks. To enhance IDS robustness and preserve user data privacy, researchers are increasingly adopting federated learning (FL). However, traditional FL-based IDSs depend on a single central aggregator, creating performance bottlenecks and introducing a single point of failure, thereby compromising robustness and scalability. To address these limitations, this paper proposes a Hierarchical Federated Learning (H-FL) framework to deploy and evaluate the performance of the IDS. The H-FL framework incorporates multiple edge aggregators alongside the central aggregator, mitigating single-point failure risks, improving scalability, and efficiently distributing computational load. We evaluate the proposed IDS using the H-FL framework on two car hacking datasets under realistic non-independent and identically distributed (non-IID) data scenarios. Experimental results demonstrate that deploying the IDS within an H-FL framework can enhance the F1-score by up to 10.63%, addressing the limitations of edge-FL in dataset diversity and attack coverage. Notably, H-FL improved the F1-score in 16 out of 24 evaluated scenarios. By enabling the IDS to learn from diverse data, driving conditions, and evolving threats, this approach substantially strengthens cybersecurity in modern vehicular systems.

Keywords: CAN bus; cyberattack; IDS; federated learning; in-vehicle network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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