CANdito: Improving Payload-Based Detection of Attacks on Controller Area Networks

Longari, Stefano; Pozzoli, Carlo Alberto; Nichelini, Alessandro; Carminati, Michele; Zanero, Stefano

doi:10.1007/978-3-031-34671-2_10

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Cited by 5 publications

References 29 publications

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Evaluating the Robustness of Automotive Intrusion Detection Systems Against Evasion Attacks

Longari

Noseda

Carminati

et al. 2023

Lecture Notes in Computer Science

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This paper discusses the robustness of machine learning-based intrusion detection systems (IDSs) used in the Controller Area Networks context against adversarial samples, inputs crafted to deceive the system. We design a novel methodology to deploy evasion attacks and address the domain-specific challenges (i.e., the time-dependent nature of automotive networks) discussing the problem of performing online attacks. We evaluate the robustness of state-of-the-art IDSs on a real-world dataset by performing evasion attacks. We show that, depending on the targeted IDS and the degree of the attacker's knowledge, our approach achieves significantly different evasion rates.

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