Alessio Buscemi, Thomas Engel (SnT, University of Luxembourg), Kang G. Shin (The University of Michigan)

The Controller Area Network (CAN) is widely deployed as the de facto global standard for the communication between Electronic Control Units (ECUs) in the automotive sector. Despite being unencrypted, the data transmitted over CAN is encoded according to the Original Equipment Manufacturers (OEMs) specifications, and their formats are kept secret from the general public. Thus, the only way to obtain accurate vehicle information from the CAN bus is through reverse engineering. Aftermarket companies and academic researchers have focused on automating the CAN reverse-engineering process to improve its speed and scalability. However, the manufacturers have recently started multiplexing the CAN frames primarily for platform upgrades, rendering state-of-the-art (SOTA) reverse engineering ineffective. To overcome this new barrier, we present CAN Multiplexed Frames Translator (CAN-MXT), the first tool for the identification of new-generation multiplexed CAN frames. We also introduce CAN Multiplexed Frames Generator (CANMXG), a tool for the parsing of standard CAN traffic into multiplexed traffic, facilitating research and app development on CAN multiplexing.

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Peizhuo Lv (Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Pan Li (Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Shenchen Zhu (Institute of Information Engineering, Chinese Academy of Sciences, China;…

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ActiveDaemon: Unconscious DNN Dormancy and Waking Up via User-specific...

Ge Ren (Shanghai Jiao Tong University), Gaolei Li (Shanghai Jiao Tong University), Shenghong Li (Shanghai Jiao Tong University), Libo Chen (Shanghai Jiao Tong University), Kui Ren (Zhejiang University)

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Cyclops: Binding a Vehicle’s Digital Identity to its Physical...

Lewis William Koplon, Ameer Ghasem Nessaee, Alex Choi (University of Arizona, Tucson), Andres Mentoza (New Mexico State University, Las Cruces), Michael Villasana, Loukas Lazos, Ming Li (University of Arizona, Tucson)

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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