Adversarial Modeling in Game-theoretic Frameworks for Securing Cyber-Physical Systems

In an era where intricate cyber and physical systems are integrated into daily life, the imperative for controlling and optimizing them emerges as a critical avenue. This avenue addresses global challenges such as climate change, healthcare equity, security, and resilience, shaping the swiftly evolving concepts of resource sharing and the shared economy. This complexity is particularly evident in critical infrastructures, spanning electrical grids, building management systems, solar farms, autonomous vehicles, and other Cyber-Physical Systems (CPS). Furthermore, securing CPS through decision-making inherently involves collaboration or competition, engaging multiple stakeholders with diverse perspectives and interests. Game theory provides a powerful analytical framework to model strategic conflicts among decision makers to assure security and resilience. The realm of security methodologies in CPS is vast and has garnered considerable attention over the past two decades. Different adversarial models impact CPS in various ways by targeting one or multiple security attributes. Therefore, a fundamental aspect of securing a CPS is the characterization of the adversary type and developing corresponding defense strategies.In the first part of the thesis, we introduce a game-theoretic decision-making framework that captures the interaction between a defender and two types of adversaries: a deterministic adversary, and a stochastic adversary capable of both benign and adversarial actions. We analyze this framework under different information structures and focus on characterizing the Nash equilibrium of the game, particularly emphasizing closed-form solutions. We illustrate how this framework can be applied in various domains such as path planning, motion planning, and in the context of resilient estimation.In the second part of the thesis, we design a game-theoretic framework to encompass state-dependent decision-making and develop defensive strategies against an adversary capable of a complete takeover of a dynamical system. We employ tools from optimization, control theory, and backward induction to solve for the takeover strategies and control policies of both players. We demonstrate the application of this framework in linear dynamical systems. Finally, we present a domain-aware data-driven framework to determine defensive strategies by simulating an adversary in a high-fidelity CPS. We illustrate the application of this data-driven framework in a smart building system. In conclusion, we discuss potential future extensions and the integration of the game-theoretic framework with the data-driven approach.