CAREER: Securing the AI Stack in Autonomous CPS under Physical-Layer Attacks: A Systems Perspective

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Recent years have witnessed a massive surge in real-world development and deployment of autonomous cyber-physical systems such as autonomous driving cars and delivery drones/robots. To achieve high-level autonomy in complex environments, the Artificial Intelligence (AI) stack plays a central role, a type of “brain,” which makes them highly security-critical. Prior works have studied adversarial attacks against AI algorithms used in autonomous cyber-physical systems, but mostly focus on the AI algorithm-level security properties in complete or partial isolation of the physical context. As these algorithms are only components of the entire system, however, it is both more practically meaningful and effective to study and address their security problems from a systems perspective, especially when under the more general and fundamental physical-layer attack model. This project aims to create a suite of systematic methodologies, solution frameworks, and platforms that can achieve system-level security analysis and defense designs for the AI component of autonomous cyber-physical systems under physical-layer attacks. With the growing deployment and commercialization of autonomous cyber-physical systems in the real world, success in this should directly benefit the safety of everyday lives.This project consists of two research thrusts to cover both the attack and defense sides of the proposed system-level security research. First, to enable system-level security analysis, this project will develop novel system-to-AI and AI-to-system mapping methodologies, by overcoming various design challenges such as systematically maintaining physical realizability and semantic equivalency in physical-layer attack generation, and effectively accommodating the diversity of real-world system designs and implementations. Second, to develop system-level defense designs, this project will systematically identify and leverage novel design opportunities from both individual system and the operation ecosystem perspectives, including new classes of physical invariants, novel attack-resilient sensor fusion designs leveraging system-level properties, and novel designs that leverage other participants in the operation ecosystem and infrastructure support. This project will also develop a simulation-based evaluation platform with uniform and extensible attack and defense development support, which will be used to facilitate both research and education of autonomous cyber-physical system security.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.