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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 human-in-the-loop 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.
This dissertation aims to 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 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 humancentric perspectives. By bridging the gap between cyber and physical domains, my work seeks to enhance the resilience and trustworthiness of modern CPS while retaining system efficiency and usability.