Paolo Cerracchio, Stefano Longari, Michele Carminati, Stefano Zanero (Politecnico di Milano)

The evolution of vehicles has led to the integration of numerous devices that communicate via the controller area network (CAN) protocol. This protocol lacks security measures, leaving interconnected critical components vulnerable. The expansion of local and remote connectivity has increased the attack surface, heightening the risk of unauthorized intrusions. Since recent studies have proven external attacks to constitute a realworld threat to vehicle availability, driving data confidentiality, and passenger safety, researchers and car manufacturers focused on implementing effective defenses. intrusion detection systems (IDSs), frequently employing machine learning models, are a prominent solution. However, IDS are not foolproof, and attackers with knowledge of these systems can orchestrate adversarial attacks to evade detection. In this paper, we evaluate the effectiveness of popular adversarial techniques in the automotive domain to ascertain the resilience, characteristics, and vulnerabilities of several ML-based IDSs. We propose three gradient-based evasion algorithms and evaluate them against six detection systems. We find that the algorithms’ performance heavily depends on the model’s complexity and the intended attack’s quality. Also, we study the transferability between different detection systems and different time instants in the communication.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Human Drivers' Situation Awareness of Autonomous Driving Under Physical-world...

Katherine S. Zhang (Purdue University), Claire Chen (Pennsylvania State University), Aiping Xiong (Pennsylvania State University)

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The Dark Side of E-Commerce: Dropshipping Abuse as a...

Arjun Arunasalam (Purdue University), Andrew Chu (University of Chicago), Muslum Ozgur Ozmen (Purdue University), Habiba Farrukh (University of California, Irvine), Z. Berkay Celik (Purdue University)

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WIP: A First Look At Employing Large Multimodal Models...

Mohammed Aldeen, Pedram MohajerAnsari, Jin Ma, Mashrur Chowdhury, Long Cheng, Mert D. Pesé (Clemson University)

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