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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HARPO: Learning to Subvert Online Behavioral Advertising

Jiang Zhang (University of Southern California), Konstantinos Psounis (University of Southern California), Muhammad Haroon (University of California, Davis), Zubair Shafiq (University of California, Davis)

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Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

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Demo #3: Detecting Illicit Drone Video Filming Using Cryptanalysis

Ben Nassi, Raz Ben-Netanel (Ben-Gurion University of the Negev), Adi Shamir (Weizmann Institute of Science), and Yuval Elovic (Ben-Gurion University of the Negev)

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Demo #3: I Am Not Afraid of the GPS...

Ali A. Abdallah (UC Irvine), Zaher M. Kassas (UC Irvine) and Chiawei Lee (US Air Force Test Pilot School)

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