Towards Secure and Safe Data-Driven Decision Making in AI-Enabled Autonomous Cyber-Physical Systems | Alfred Chen

Recent years have witnessed a global explosion in the real-world testing, deployment, and commercialization of AI-enabled autonomous Cyber-Physical Systems (CPSs) such as autonomous cars, drones, and robots. These systems are rapidly revolutionizing a wide range of industries today, from transportation, retail, and logistics (e.g., robo-taxi, autonomous truck, delivery drones/robots), to domotics, manufacturing, construction, and healthcare. In such systems, the AI stacks, which popularly adopt data-driven methods such as deep learning, are in charge of highly safety- and mission-critical decision-making processes in the physical world such as obstacle avoidance and lane-keeping, which makes their security more critical than ever to public safety and society.

Over the past few years, my group has been actively studying and developing this research space in the security community, with the main focus on the latest technologies in the transportation domain such as Autonomous Driving (AD) systems and V2X (Vehicle-to-Everything) based intelligent transportation systems. Specifically, we were the first to perform security analysis and/or defense designs on a wide range of critical AI components, especially the data-driven ones, in industry-grade AD systems such as 3D perception, lane detection, sensor fusion, localization, prediction, and planning; first to develop formal verification methods for cooperative AD protocols and traffic-rule conformation; first to characterize AD software bugs; and first to study security of USDOT’s V2X-based intelligent traffic light. In this talk, I will present our representative findings so far on both the attack and defense sides, and also talk about my future research plans.

Bio: Alfred Chen is an Assistant Professor of Computer Science at University of California, Irvine. His research interest spans AI security, systems security, and network security. His most recent research focuses are AI systems security in autonomous vehicles and intelligent transportation. His works have high impacts in both academic and industry with 30+ research papers in top-tier venues across security, mobile systems, transportation, software engineering, and machine learning; a nationwide USDHS US-CERT alert, multiple CVEs; 50+ news coverage by major media such as Forbes, Fortune, and BBC; and vulnerability report acknowledgments from USDOT, Apple, Microsoft, etc. Recently, his research triggered 30+ autonomous driving companies and the V2X standardization workgroup to start security vulnerability investigations; some confirmed to work on fixes. He co-founded the ISOC Symposium on Vehicle Security and Privacy (VehicleSec), and co-created DEF CON’s first autonomous driving-themed hacking competition. He received various awards such as NSF CAREER Award, ProQuest Distinguished Dissertation Award, and UCI Chancellor’s Award for mentoring. Chen received Ph.D. from University of Michigan in 2018.