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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Zubair Ahmad (Università Ca’ Foscari Venezia), Samuele Casarin (Università Ca’ Foscari Venezia), and Stefano Calzavara (Università Ca’ Foscari Venezia)

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Hazard Integrated: Understanding Security Risks in App Extensions to...

Mingming Zha (Indiana University Bloomington), Jice Wang (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences), Yuhong Nan (Sun Yat-sen University), Xiaofeng Wang (Indiana Unversity Bloomington), Yuqing Zhang (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences), Zelin Yang (National Computer Network Intrusion Protection Center, University of Chinese Academy…

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Vision-Based Two-Factor Authentication & Localization Scheme for Autonomous Vehicles

Anas Alsoliman, Marco Levorato, and Qi Alfred Chen (UC Irvine)

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DrawnApart: A Deep-Learning Enhanced GPU Fingerprinting Technique

Naif Mehanna (University of Lille, CNRS, Inria), Tomer Laor (Ben-Gurion University of the Negev)

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