Sustained use of critical infrastructure, such as electrical power and water distribution networks, requires efficient management and control. Facilitated by the advancements in computational devices and non-proprietary communication technology, such as the Internet, the efficient operation of critical infrastructure relies on network decomposition into interconnected subsystems, thus forming networked control systems. However, the use of public and pervasive communication channels leaves these systems vulnerable to cyber attacks. Consequently, the critical infrastructure is put at risk of suffering operation disruption and even physical damage that would inflict financial costs as well as pose a hazard to human health. Therefore, security is crucial to the sustained efficient operation of critical infrastructure. This thesis develops a framework for evaluating and improving the security of networked control systems in the face of cyberattacks. The considered security problem involves two strategic agents, namely a malicious adversary and a defender, pursuing their specific and conflicting goals. The defender aims to efficiently allocate defense resources with the purpose of detecting malicious activities. Meanwhile, the malicious adversary simultaneously conducts cyber attacks and remains stealthy to the defender. We tackle the security problem by proposing a game-theoretic framework and characterizing its main components: the payoff function, the action space, and the available information for each agent. Especially, the payoff function is characterized based on the output-to-output gain security metric that fully explores the worst-case attack impact. Then, we investigate the properties of the game and how to efficiently compute its equilibrium. Given the combinatorial nature of the defender’s actions, one important challenge is to alleviate the computational burden. To overcome this challenge, the thesis contributes several system- and graph-theoretic conditions that enable the defender to shrink the action space, efficiently allocating the defense resources. The effectiveness of the proposed framework is validated through numerical examples. 

Modern critical infrastructures heavily rely on large networked control systems whose sensing and communication layers are vulnerable to incidents such as cyber attacks. This thesis focuses on enhancing their security against stealthy false-data injection and spatio-temporal attacks. In the first part of the thesis, stealthy false-data injection attacks are first investigated for how they can cause significant performance degradation while remaining undetected. To alleviate the performance degradation, the thesis considers the security allocation problem: how to place limited monitoring and defense resources in a network so as to minimize the worst-case impact subject to stealthiness constraints. The problem becomes more practical when adversarial resources and information are assumed to be uncertain or incomplete. We develop optimization-based formulations that yield provably optimal allocation strategies and, under suitable control design, admit computationally efficient solutions for large networks. We then show how graph structure can be exploited to obtain interpretable design rules, including cases in which optimal monitoring decisions can be linked to centrality-based criteria in positive networks. Furthermore, we extend security allocation beyond fully rational players by modeling human-type adversary-defender interactions through cognitive security games, thereby capturing bounded rationality and strategic reasoning. In the second part of the thesis, we study the resilient state estimation problem in large networked control systems with non-periodic and asynchronously sampled measurements, in which sensors must transmit not only measurements but also sampling timestamps to the fusion center. This measurement and communication setup is well-suited to the operation of large control systems and, at the same time, leaves the system vulnerable to spatio-temporal attacks. To handle this possible vulnerability, we develop a secure estimation algorithm that (i) exactly recovers the oracle Kalman estimate in the absence of attacks and (ii) produces a secure state estimate that remains within a neighborhood of the oracle Kalman estimate in the presence of spatio-temporal attacks. Overall, the thesis provides mathematical and computational tools for designing secure monitoring and secure state estimation for large networked control systems.