Alireza Mohammadi (University of Michigan-Dearborn), Hafiz Malik (University of Michigan-Dearborn) and Masoud Abbaszadeh (GE Global Research)

Recent automotive hacking incidences have demonstrated that when an adversary manages to gain access to a safety-critical CAN, severe safety implications will ensue. Under such threats, this paper explores the capabilities of an adversary who is interested in engaging the car brakes at full speed and would like to cause wheel lockup conditions leading to catastrophic road injuries. This paper shows that the physical capabilities of a CAN attacker can be studied through the lens of closed-loop attack policy design. In particular, it is demonstrated that the adversary can cause wheel lockups by means of closed-loop attack policies for commanding the frictional brake actuators under a limited knowledge of the tire-road interaction characteristics. The effectiveness of the proposed wheel lockup attack policy is shown via numerical simulations under different road conditions.

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Demo #2: Policy-based Discovery and Patching of Logic Bugs...

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University) and Dongyan Xu (Purdue University)

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VPNInspector: Systematic Investigation of the VPN Ecosystem

Reethika Ramesh (University of Michigan), Leonid Evdokimov (Independent), Diwen Xue (University of Michigan), Roya Ensafi (University of Michigan)

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Demo #7: Automated Tracking System For LiDAR Spoofing Attacks...

Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

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datAFLow: Towards a Data-Flow-Guided Fuzzer

Adrian Herrera (Australian National University), Mathias Payer (EPFL), Antony Hosking (Australian National University)

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