Recent years have witnessed a global phenomenon in the real-world development, testing, and commercialization of AI-enabled autonomous Cyber-Physical Systems (CPSs) such as autonomous driving cars, drones, and robots. These systems are rapidly revolutionizing nearly every industry today, ranging from transportation, retail, and logistics (e.g., robo-taxi, autonomous truck, delivery drones/robots), to domotics, manufacturing, construction, and healthcare. Compared to traditional computer systems, these emerging systems have unprecedented capabilities to sense and impact the physical world, which makes their security, safety, and trustworthiness of paramount importance to individuals, technology providers, and also policy-makers.
Over the past few years, my group has been actively studying and developing the security research space of Autonomous Driving (AD) systems and intelligent transportation systems in general, with a focus on their autonomous AI, systems, and networking stacks. Specifically, we performed the first security analysis and/or defense designs 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; 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 (Vehicle-to-Everything) based intelligent traffic light. In this talk, I will talk about our journey so far, with highlights of representative findings, insights, and takeaways.
Alfred Chen is an Assistant Professor of Computer Science at University of California, Irvine. His research interest broadly lies in the security and privacy of computer technologies of high criticality to daily life and society. His recent focus has been mainly on the security/privacy issues in emerging AI/systems/network technologies, especially those empowering the emerging autonomous vehicle and intelligent transportation systems. 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+ auto-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 auto-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.