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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BliMe: Verifiably Secure Outsourced Computation with Hardware-Enforced Taint Tracking

Hossam ElAtali (University of Waterloo), Lachlan J. Gunn (Aalto University), Hans Liljestrand (University of Waterloo), N. Asokan (University of Waterloo, Aalto University)

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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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AVMON: Securing Autonomous Vehicles by Learning Control Invariants and...

Ahmed Abdo, Sakib Md Bin Malek, Xuanpeng Zhao, Nael Abu-Ghazaleh (University of California, Riverside)

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Securing Lidar Communication through Watermark-based Tampering Detection (Long)

Michele Marazzi, Stefano Longari, Michele Carminati, Stefano Zanero (Politecnico di Milano)

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