On the Semantic AI Security in CPS: The Case of Autonomous Driving – Alfred Chen

Institute for Assured Autonomy & Computer Science Seminar Series
November 15, 2022

“On the Semantic AI Security in CPS: The Case of Autonomous Driving”
Alfred Chen, University of California, Irvine

Recent years have witnessed a global phenomenon in the real-world development, testing, deployment, and commercialization of AI-enabled cyber-physical systems (CPSs) such as autonomous driving cars, drones, and industrial and home robots. These systems are rapidly revolutionizing a wide range of industries today—from transportation, retail, and logistics (e.g., robo-taxis, autonomous trucks, delivery drones/robots) to domotics, manufacturing, construction, and health care. In such systems, the AI stacks are in charge of safety- and mission-critical decision-making processes such as obstacle avoidance and lane-keeping, which makes their security more critical than ever. Meanwhile, since these AI algorithms are only components of the entire CPS enclosing them, their security issues are only meaningful when studied with direct integration of the semantic CPS problem context, which forms what we call the “semantic AI security” problem space and introduces various new AI security research challenges. In this talk, Alfred Chen will focus on his recent efforts on semantic AI security in one of the most safety-critical and fastest-growing AI-enabled CPS today, autonomous driving (AD) systems. Specifically, his group performed the first security analysis on a wide range of critical AI components in industry-grade AD systems such as 3D perception, sensor fusion, lane detection, localization, prediction, and planning. In this talk he will describe key findings and also how to address corresponding semantic AI security research challenges. Chen will conclude with a recent systemization of knowledge he performed for this growing research space, with a specific emphasis on the most critical scientific gap observed and a proposed solution.

Alfred Chen is an assistant professor of computer science at the University of California, Irvine (UCI). His research interests span AI security, systems security, and network security. His most recent research focuses on AI security in autonomous driving and intelligent transportation. His work has high impact in both academia and industry, with more than 30 research papers in top-tier venues across security, mobile systems, transportation, software engineering, and machine learning; a nationwide U.S. Department of Homeland Security United States Computer Emergency Readiness Team alert with multiple Common Vulnerabilities and Exposures Identifiers; more than 50 news pieces by major media such as Forbes, Fortune, and the BBC; and vulnerability report acknowledgments from the U.S. Department of Transportation, Apple, Microsoft, and more. Recently, Chen's research triggered more than 30 autonomous driving companies and the V2X standardization workgroup to start security vulnerability investigations, some of which confirmed to work on fixes. Chen co-founded the International Workshop on Automotive and Autonomous Vehicle Security (co-located with the Network and Distributed System Security Symposium) and co-created DEF CON’s first AutoDriving-themed hacking competition. He has received various awards such as an NSF CAREER Award, the ProQuest Distinguished Dissertation Award, and the UCI Chancellor’s Award for Excellence in Undergraduate Research Mentorship. Chen received his PhD from the University of Michigan in 2018.