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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CP-IoT: A Cross-Platform Monitoring System for Smart Home

Hai Lin (Tsinghua University), Chenglong Li (Tsinghua University), Jiahai Yang (Tsinghua University), Zhiliang Wang (Tsinghua University), Linna Fan (National University of Defense Technology), Chenxin Duan (Tsinghua University)

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DEMASQ: Unmasking the ChatGPT Wordsmith

Kavita Kumari (Technical University of Darmstadt, Germany), Alessandro Pegoraro (Technical University of Darmstadt), Hossein Fereidooni (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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DeGPT: Optimizing Decompiler Output with LLM

Peiwei Hu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Ruigang Liang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, China)

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Beyond the Bytes: Understanding the Limitations of Intrinsic Binary...

Peter Lafosse (Owner and Co-Founder of Vector 35 Inc.)

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