Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

Autonomous vehicles must operate in a complex environment with various social norms and expectations. While most of the work on securing autonomous vehicles has focused on safety, we argue that we also need to monitor for deviations from various societal “common sense” rules to identify attacks against autonomous systems. In this paper, we provide a first approach to encoding and understanding these common-sense driving behaviors by semi-automatically extracting rules from driving manuals. We encode our driving rules in a formal specification and make our rules available online for other researchers.

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PISE: Protocol Inference using Symbolic Execution and Automata Learning

Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

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VulHawk: Cross-architecture Vulnerability Detection with Entropy-based Binary Code Search

Zhenhao Luo (College of Computer, National University of Defense Technology), Pengfei Wang (College of Computer, National University of Defense Technology), Baosheng Wang (College of Computer, National University of Defense Technology), Yong Tang (College of Computer, National University of Defense Technology), Wei Xie (College of Computer, National University of Defense Technology), Xu Zhou (College of Computer,…

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WIP: The Feasibility of High-performance Message Authentication in Automotive...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), Randy Marchany (Virginia Tech), J. Scot Ransbottom (Virginia Tech)

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Understanding the Internet-Wide Vulnerability Landscape for ROS-based Robotic Vehicles...

Wentao Chen, Sam Der, Yunpeng Luo, Fayzah Alshammari, Qi Alfred Chen (University of California, Irvine)

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