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http://dx.doi.org/10.13089/JKIISC.2021.31.6.1279

GAN Based Adversarial CAN Frame Generation Method for Physical Attack Evading Intrusion Detection System  

Kim, Dowan (Soongsil University)
Choi, Daeseon (Soongsil University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.31, no.6, 2021 , pp. 1279-1290 More about this Journal
Abstract
As vehicle technology has grown, autonomous driving that does not require driver intervention has developed. Accordingly, CAN security, an network of in-vehicles, has also become important. CAN shows vulnerabilities in hacking attacks, and machine learning-based IDS is introduced to detect these attacks. However, despite its high accuracy, machine learning showed vulnerability against adversarial examples. In this paper, we propose a adversarial CAN frame generation method to avoid IDS by adding noise to feature and proceeding with feature selection and re-packet for physical attack of the vehicle. We check how well the adversarial CAN frame avoids IDS through experiments for each case that adversarial CAN frame generated by all feature modulation, modulation after feature selection, preprocessing after re-packet.
Keywords
Vehicle Privacy; Evasion Attack; Machine Learning; Adversarial Example;
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