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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WIP: Auditing Artist Style Pirate in Text-to-image Generation Models

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

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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OCPPStorm: A Comprehensive Fuzzing Tool for OCPP Implementations (Long)

Gaetano Coppoletta (University of Illinois Chicago), Rigel Gjomemo (Discovery Partners Institute, University of Illinois), Amanjot Kaur, Nima Valizadeh (Cardiff University), Venkat Venkatakrishnan (Discovery Partners Institute, University of Illinois), Omer Rana (Cardiff University)

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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