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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Predictive Context-sensitive Fuzzing

Pietro Borrello (Sapienza University of Rome), Andrea Fioraldi (EURECOM), Daniele Cono D'Elia (Sapienza University of Rome), Davide Balzarotti (Eurecom), Leonardo Querzoni (Sapienza University of Rome), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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Semi-Automated Synthesis of Driving Rules

Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

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Flow Correlation Attacks on Tor Onion Service Sessions with...

Daniela Lopes (INESC-ID / IST, Universidade de Lisboa), Jin-Dong Dong (Carnegie Mellon University), Pedro Medeiros (INESC-ID / IST, Universidade de Lisboa), Daniel Castro (INESC-ID / IST, Universidade de Lisboa), Diogo Barradas (University of Waterloo), Bernardo Portela (INESC TEC / Universidade do Porto), João Vinagre (INESC TEC / Universidade do Porto), Bernardo Ferreira (LASIGE, Faculdade de…

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Secret-Shared Shuffle with Malicious Security

Xiangfu Song (National University of Singapore), Dong Yin (Ant Group), Jianli Bai (The University of Auckland), Changyu Dong (Guangzhou University), Ee-Chien Chang (National University of Singapore)

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