Gedare Bloom (University of Colorado Colorado Springs)

Best Paper Award Winner ($300 cash prize)!

The controller area network (CAN) is a high-value asset to defend and attack in automobiles. The bus-off attack exploits CAN’s fault confinement to force a victim electronic control unit (ECU) into the bus-off state, which prevents it from using the bus. Although pernicious, the bus-off attack has two distinct phases that are observable on the bus and allow the attack to be detected and prevented. In this paper we present WeepingCAN, a refinement of the bus-off attack that is stealthy and can escape detection. We evaluate WeepingCAN experimentally using realistic CAN benchmarks and find it succeeds in over 75% of attempts without exhibiting the detectable features of the original attack. We demonstrate WeepingCAN on a real vehicle.

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Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

Jinghan Wang (University of California, Riverside), Chengyu Song (University of California, Riverside), Heng Yin (University of California, Riverside)

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XDA: Accurate, Robust Disassembly with Transfer Learning

Kexin Pei (Columbia University), Jonas Guan (University of Toronto), David Williams-King (Columbia University), Junfeng Yang (Columbia University), Suman Jana (Columbia University)

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Car Hacking and Defense Competition on In-Vehicle Network

Hyunjae Kang, Byung Il Kwak, Young Hun Lee, Haneol Lee, Hwejae Lee, and Huy Kang Kim (Korea University)

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