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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M. Patrick Collins (USC Information Sciences Institute), Alefiya Hussain (USC Information Sciences Institute), J.P. Walters (USC Information Sciences Institute), Calvin Ardi (USC Information Sciences Institute), Chris Tran (USC Information Sciences Institute), Stephen Schwab (USC Information Sciences Institute)

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From Hardware Fingerprint to Access Token: Enhancing the Authentication...

Yue Xiao (Wuhan University), Yi He (Tsinghua University), Xiaoli Zhang (Zhejiang University of Technology), Qian Wang (Wuhan University), Renjie Xie (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Qi Li (Tsinghua University)

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Timing Channels in Adaptive Neural Networks

Ayomide Akinsanya (Stevens Institute of Technology), Tegan Brennan (Stevens Institute of Technology)

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EyeSeeIdentity: Exploring Natural Gaze Behaviour for Implicit User Identification...

L Yasmeen Abdrabou (Lancaster University), Mariam Hassib (Fortiss Research Institute of the Free State of Bavaria), Shuqin Hu (LMU Munich), Ken Pfeuffer (Aarhus University), Mohamed Khamis (University of Glasgow), Andreas Bulling (University of Stuttgart), Florian Alt (University of the Bundeswehr Munich)

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