Open Access

ARTICLE

Robust Federated Learning for Intrusion Detection in Autonomous Vehicles against Poisoning Attacks

Ulysses Lam^1,*, Jin-Hee Cho², Hyuk Lim³, Terrence Moore⁴, Frederica Free-Nelson⁴, Hyunjae Kang¹, Dan Dongseong Kim¹

1 School of Electrical Engineering and Computer Science, the University of Queensland, Brisbane, QLD, Australia
2 Department of Computer Science, Virginia Tech, Falls Church, VA, USA
3 School of Energy Engineering, Korea Institute of Energy Technology, Naju-Si, Republic of Korea
4 US DEVCOM Army Research Laboratory, Adelphi, MD, USA

* Corresponding Author: Ulysses Lam. Email:

(This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)

Computer Modeling in Engineering & Sciences 2026, 147(3), 54 https://doi.org/10.32604/cmes.2026.084062

Received 15 April 2026; Accepted 04 June 2026; Issue published 30 June 2026

Abstract

Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real vehicles. The FL-based IDS models achieve high attack-detection performance, while each local client retains their privacy by sharing only local model weights rather than local datasets. Training of local models takes an extremely short time and is feasible in practice for vehicles with low computational resources. Furthermore, different poisoning scenarios are performed on local clients to demonstrate the high robustness of FL models. The FL-based IDS models are highly robust against poisoning attacks and maintain high detection accuracy as long as the majority of local clients are not compromised.

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Keywords

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