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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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving...

Sampath Rajapaksha, Harsha Kalutarage (Robert Gordon University, UK), Garikayi Madzudzo (Horiba Mira Ltd, UK), Andrei Petrovski (Robert Gordon University, UK), M.Omar Al-Kadri (University of Doha for Science and Technology)

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BEAGLE: Forensics of Deep Learning Backdoor Attack for Better...

Siyuan Cheng (Purdue University), Guanhong Tao (Purdue University), Yingqi Liu (Purdue University), Shengwei An (Purdue University), Xiangzhe Xu (Purdue University), Shiwei Feng (Purdue University), Guangyu Shen (Purdue University), Kaiyuan Zhang (Purdue University), Qiuling Xu (Purdue University), Shiqing Ma (Rutgers University), Xiangyu Zhang (Purdue University)

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