Cyber-Physical Security Through the Lens of AI-Enabled Systems

Speaker: Zhiyuan YU
Washington University in St. Louis

Title: Cyber-Physical Security Through the Lens of AI-Enabled Systems

Date: Tuesday, 11 February 2025

Time: 10:00am - 11:00am

Join Zoom Meeting: https://hkust.zoom.us/j/96688516988?pwd=qfj1PQIjEi0I75lwVGfY7PurdPDRBW.1
Meeting ID: 966 8851 6988
Passcode: 202526

Abstract:

Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. Under this paradigm shift, I systematically explore and mitigate the threats inherent in AI-enabled cyber-physical systems. The research objectives are threefold: (1) investigating how the interplay of cyber and physical components opens up novel attack and defense vectors, (2) developing robust defense strategies grounded by physical laws and constraints, and (3) benchmarking and theoretically analyzing security trade-offs from algorithmic, system-level, and human-centric perspectives. By bridging the gap between cyber and physical domains, my research enhances the resilience and trustworthiness of modern CPS while retaining system efficiency and usability.

Biography:

Zhiyuan Yu is a final-year Ph.D. candidate in Computer Science at Washington University in St. Louis, specializing in the security and privacy of AI-enabled cyber-physical systems. His research focuses on bridging cyber and physical components in embodied AI to develop resilient defenses, across diverse domains like autonomous systems, medical imaging, and generative AI (GenAI) applications. His work has received the Distinguished Paper Award at USENIX Security 2024 and the Distinguished Artifact Award at USENIX Security 2023. Zhiyuan’s work has also won the 2024 Federal Trade Commission Voice Cloning Challenge. He has been named a Machine Learning and Systems Rising Star in 2024.