A survey of learning-based intrusion detection systems for in-vehicle networks

Althunayyan, Muzun, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945 and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2026. A survey of learning-based intrusion detection systems for in-vehicle networks. Computer Networks 10.1016/j.comnet.2026.112031

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Abstract

Connected and Autonomous Vehicles (CAVs) have advanced modern transportation by improving the efficiency, safety, and convenience of mobility through automation and connectivity, yet they remain vulnerable to cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect—known, unknown, and combined known-unknown attacks—while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1389-1286
Date of First Compliant Deposit:	25 January 2026
Date of Acceptance:	16 January 2026
Last Modified:	26 Jan 2026 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/184156

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