Hierarchical federated learning-based intrusion detection for in-vehicle networks

Althunayyan, Muzun, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Spyridopoulos, Theodoros ORCID: https://orcid.org/0000-0001-7575-9909 2024. Hierarchical federated learning-based intrusion detection for in-vehicle networks. Future Internet 16 (12) , 451. 10.3390/fi16120451

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
Date of First Compliant Deposit:	4 December 2024
Date of Acceptance:	27 November 2024
Last Modified:	04 Dec 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174476

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