Natasha Alkhatib (Télécom Paris), Lina Achaji (INRIA), Maria Mushtaq (Télécom Paris), Hadi Ghauch (Télécom Paris), Jean-Luc Danger (Télécom Paris)

The adoption of external connectivity on modern vehicles and the increasing integration of complex automotive software paved the way for novel attack scenarios exploiting the vulnerabilities of in-vehicle protocols. The Controller Area Network (CAN) bus, a widely used communication network in vehicles between electronic control units (ECUs), therefore requires urgent monitoring. Predicting sophisticated intrusions that affect interdependencies between several CAN signals transmitted by distinct IDs requires modeling two key dimensions: 1) time dimension, where we model the temporal relationships between signals carried by each ID separately 2) interaction dimension where we model the interaction between IDs, i.e., how the state of each CAN ID affects the others. In this work, we propose a novel deep learning-based multi-agent intrusion detection system, AMICA, that uses an attention-based self-supervised learning technique to detect stealthy in-vehicle intrusions, i.e., those that that not only disturb normal timing or ID distributions but also carried data values by multiple IDs, along with others. The proposed model is evaluated on the benchmark dataset SynCAN. Our source code is available at: https://github.com/linaashaji/AMICA

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Cryptographic Oracle-based Conditional Payments

Varun Madathil (North Carolina State University), Sri Aravinda Krishnan Thyagarajan (NTT Research), Dimitrios Vasilopoulos (IMDEA Software Institute), Lloyd Fournier (None), Giulio Malavolta (Max Planck Institute for Security and Privacy), Pedro Moreno-Sanchez (IMDEA Software Institute)

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Takami Sato (University of California, Irvine), Yuki Hayakawa (Keio University), Ryo Suzuki (Keio University), Yohsuke Shiiki (Keio University), Kentaro Yoshioka (Keio University), Qi Alfred Chen (University of California, Irvine)

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OBSan: An Out-Of-Bound Sanitizer to Harden DNN Executables

Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Shuai Wang (The Hong Kong University of Science and Technology)

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Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards...

Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

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