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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Demo #6: Attacks on CAN Error Handling Mechanism

Khaled Serag (Purdue University), Vireshwar Kumar (IIT Delhi), Z. Berkay Celik (Purdue University), Rohit Bhatia (Purdue University), Mathias Payer (EPFL) and Dongyan Xu (Purdue University)

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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On Building the Data-Oblivious Virtual Environment

Tushar Jois (Johns Hopkins University), Hyun Bin Lee, Christopher Fletcher, Carl A. Gunter (University of Illinois at Urbana-Champaign)

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