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Thesis Title: AI-Powered System-Scientific Defense for High-Confidence Cyber-Physical Systems: Modeling, Analysis, and Design
Ph.D. Thesis Author: Linan Huang | Email
Advisor: Quanyan Zhu
Granting Institution: New York University
Access Full Thesis | Presentation video
Abstract: This dissertation develops Defense through AI-powered SYstem-scientific methods (DAISY) for high-confidence Cyber-Physical Systems (CPSs). We start by designating five generations of Security Paradigms (SPs) that have evolved since the birth of the Internet. Positioned as the foundation of 5G-SP, DAISY enables the following six dimensions of the evolution of security solutions, i.e., from empirical to theoretical, technical to socio-technical, single-agent to multi-agent, secure to resilient, add-on to built-in, and reactive to proactive security. This dissertation have a multitude of impacts in both theory and practice. First, the established models and frameworks enable quantitative design and circumvent laborious trial-and-error design procedures. Second, leveraging a broad range of system science tools and AI, this dissertation lays solid theoretical foundations to characterize fundamental limits and tradeoffs, discover security principles and laws, and design strategic security mechanisms. Finally, we develop efficient and scalable algorithms to create implementable technologies for critical CPS application fields.
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