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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Efficient Normalized Reduction and Generation of Equivalent Multivariate Binary...

Arnau Gàmez-Montolio (City, University of London; Activision Research), Enric Florit (Universitat de Barcelona), Martin Brain (City, University of London), Jacob M. Howe (City, University of London)

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Benchmarking transferable adversarial attacks

Zhibo Jin (The University of Sydney), Jiayu Zhang (Suzhou Yierqi), Zhiyu Zhu, Huaming Chen (The University of Sydney)

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Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural...

Gorka Abad (Radboud University & Ikerlan Technology Research Centre), Oguzhan Ersoy (Radboud University), Stjepan Picek (Radboud University & Delft University of Technology), Aitor Urbieta (Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA))

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Separation is Good: A Faster Order-Fairness Byzantine Consensus

Ke Mu (Southern University of Science and Technology, China), Bo Yin (Changsha University of Science and Technology, China), Alia Asheralieva (Loughborough University, UK), Xuetao Wei (Southern University of Science and Technology, China & Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, SUSTech, China)

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