Yunpeng Luo (UC Irvine), Ningfei Wang (UC Irvine), Bo Yu (PerceptIn), Shaoshan Liu (PerceptIn) and Qi Alfred Chen (UC Irvine)

Autonomous Driving (AD) is a rapidly developing technology and its security issues have been studied by various recent research works. With the growing interest and investment in leveraging intelligent infrastructure support for practical AD, AD system may have new opportunities to defend against existing AD attacks. In this paper, we are the first t o systematically explore such a new AD security design space leveraging emerging infrastructure-side support, which we call Infrastructure-Aided Autonomous Driving Defense (I-A2D2). We first taxonomize existing AD attacks based on infrastructure-side capabilities, and then analyze potential I-A2D2 design opportunities and requirements. We further discuss the potential design challenges for these I-A2D2 design directions to be effective in practice.

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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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Demo #7: A Simulator for Cooperative and Automated Driving...

Mohammed Lamine Bouchouia (Telecom Paris - Institut Polytechnique de Paris), Jean-Philippe Monteuuis (Qualcomm Technologies Inc), Houda Labiod (Telecom Paris - Institut Polytechnique de Paris), Ons Jelassi (Telecom Paris - Institut Polytechnique de Paris), Wafa Ben Jaballah (Thales) and Jonathan Petit (Qualcomm Technologies Inc)

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Demo #9: Dynamic Time Warping as a Tool for...

Mars Rayno (Colorado State University) and Jeremy Daily (Colorado State University)

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Fine-Grained Coverage-Based Fuzzing

Bernard Nongpoh (Université Paris Saclay), Marwan Nour (Université Paris Saclay), Michaël Marcozzi (Université Paris Saclay), Sébastien Bardin (Université Paris Saclay)

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