Evading Voltage-Based Intrusion Detection on Automotive CAN
Rohit Bhatia, Vireshwar Kumar, Khaled Serag, Z. Berkay Celik, Mathias Payer, Dongyan Xu
摘要
—The controller area network (CAN) is widely adopted in modern automobiles to enable communications among in-vehicle electronic control units (ECUs). Lacking mainstream network security capabilities due to resource constraints, the CAN is susceptible to the ECU masquerade attack in which a compromised (attacker) ECU impersonates an uncompromised (victim) ECU and spoofs the latter’s CAN messages. A cost-effective state-of-the-art defense against such attacks is the CAN bus voltage-based intrusion detection system (VIDS), which identifies the source of each message using its voltage fingerprint on the bus. Since the voltage fingerprint emanates from an ECU’s hardware characteristics, an attacker ECU by itself cannot controllably modify it. As such, VIDS has been proved effective in detecting masquerade attacks that each involve a single attacker. In this paper, we discover a novel voltage corruption tactic that leverages the capabilities of two compromised ECUs (i.e., an attacker ECU working in tandem with an accomplice ECU) to corrupt the bus voltages recorded by the VIDS. By exploiting this tactic along with the fundamental deficiencies of the CAN protocol, we propose a novel masquerade attack called D UET , which evades all existing VIDS irrespective of the features and classification algorithms employed in them. D UET follows a two-stage attack strategy to first manipulate a victim ECU’s voltage fingerprint during VIDS retraining mode, and then impersonate the manipulated fingerprint during VIDS operation mode. Our evaluation of D UET on real CAN buses (including three in two real cars) demonstrates an impersonation success rate of at least 90% in evading two state-of-the-art VIDS
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引用它的顶会 Paper9
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- ZBCAN: A Zero-Byte CAN Defense SystemKhaled Serag, Rohit Bhatia, Akram Faqih, Muslum Ozgur Ozmen 等USENIX Security 2023
它引用的顶会 Paper11
- Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression LearningMatthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu 等S&P 2018 · 被引用 867 次
- Fingerprinting Electronic Control Units for Vehicle Intrusion DetectionKyong-Tak Cho, Kang G. ShinUSENIX Security 2016 · 被引用 524 次
- Error Handling of In-vehicle Networks Makes Them VulnerableKyong-Tak Cho, Kang G. ShinCCS 2016 · 被引用 238 次
- Viden: Attacker Identification on In-Vehicle NetworksKyong-Tak Cho, Kang G. ShinCCS 2017 · 被引用 218 次
- Scission: Signal Characteristic-Based Sender Identification and Intrusion Detection in Automotive NetworksMarcel Kneib, Christopher HuthCCS 2018 · 被引用 162 次
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