Using Machine Learning to Detect Anomalies in Embedded Networks in Heavy Vehicles

Shirazi, Hossein; Ray, Indrakshi; Anderson, Charles W.

doi:10.1007/978-3-030-45371-8_3

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Securing CAN is a significant step toward ensuring that the critical systems which communicate on the bus are protected from cyber and physical attacks. Approaches toward CAN security include deploying an intrusion detection system (IDS) [7], [8] on the network to detect indications of attacks over the CAN bus, or using cryptographic techniques [9]- [11] and authentications mechanisms [12]- [14]. Although an IDS can be adopted without perturbing bus performance [15], the latter approaches cannot be easily applied to in-vehicle networks due to computational constraints and real-time requirements [16].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Consistent and Repeatable Controller Area Network IDS Evaluation

Agbaje¹,

Anjum²,

Mitra³

et al. 2022

Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security

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The landscape of automotive vehicle attack surfaces continues to grow, and vulnerabilities in the controller area network (CAN) expose vehicles to cyber-physical risks and attacks that can endanger the safety of passengers and pedestrians. Intrusion detection systems (IDS) for CAN have emerged as a key mitigation approach for these risks, but uniform methods to compare proposed IDS techniques are lacking. In this paper, we present a framework for comparative performance analysis of state-of-the-art IDSs for CAN bus to provide a consistent methodology to evaluate and assess proposed approaches. This framework relies on previously published datasets comprising message logs recorded from a real vehicle CAN bus coupled with traditional classifier performance metrics to reduce the discrepancies that arise when comparing IDS approaches from disparate sources.

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