Dynamic games for secure and resilient control system design

Huang, Yunhan; Chen, Juntao; Huang, Linan; Zhu, Quanyan

doi:10.1093/nsr/nwz218

Cited by 37 publications

(17 citation statements)

References 99 publications

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“…When one subsystem is attacked and fails, the remaining subsystems can adapt to it, maintain their local functions, and recover from the failure of one subsystem. This decentralized mechanism aligns with the recent advances in multi-agent control systems [133], distributed control theory [134], and game theory [85,135]. They can provide a theoretical underpinning for the design of distributed cyber-resilient systems.…”

Section: Feedback Architecturesmentioning

confidence: 53%

“…Besides honeypots, there are emerging technologies to detect and respond against deceptive attacks. In [85,86], the authors model the interactions between stealthy attackers and the defenders as a dynamic Bayesian game of discrete or continuous type where multi-stage response strategies are designed based on belief updates. The convergence of continuous-type Bayesian game under a sequence of finer discretization schemes is discussed in [87].…”

Section: Information-related Vulnerabilitymentioning

confidence: 99%

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Reinforcement Learning for Feedback-Enabled Cyber Resilience

Huang¹,

Huang²,

Zhu³

2021

Preprint

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The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. We introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present works that develop several attack models, in which the attacks target the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. We discuss the potential defense methods to secure them and find that there is a lack of works focusing on the defensive mechanisms for RL-enabled systems. Finally, we discuss the future challenges of RL for cyber security and resiliency and emerging applications of RL-based CRMs.

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Section: Feedback Architecturesmentioning

confidence: 53%

Section: Information-related Vulnerabilitymentioning

confidence: 99%

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Huang¹,

Huang²,

Zhu³

2021

Preprint

Self Cite

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“…Many infectious disease models usually do not incorporate human behaviors that change as the epidemic evolves and the information spreads over the network. Dynamic game theory, which has been applied in many dynamic settings such as management science [14], labor economics [83], and cybersecurity [61], delivers a powerful paradigm to capture dynamic human behaviors [16]. We start the introduction of game-theoretic models in epidemics by presenting a taxonomy in the next section.…”

Section: Game-theoretic Decision-making In Epidemicsmentioning

confidence: 99%

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review

Huang

Zhu

2022

Dyn Games Appl

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This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making. We provide a review of a range of epidemic models and explain the pros and cons of different epidemic models. We exhibit the art of coupling between epidemic models and decision models in the existing literature. More specifically, we provide answers to fundamental questions in human decision-making amid epidemics, including what interventions to take to combat the disease, who are decision-makers, and when and how to take interventions, and how to make interventions. Among many decision models, game-theoretic models have become increasingly crucial in modeling human responses or behavior amid epidemics in the last decade. In this review, we motivate the game-theoretic approach to human decision-making amid epidemics. This review provides an overview of the existing literature by developing a multi-dimensional taxonomy, which categorizes existing literature based on multiple dimensions, including (1) types of games, such as differential games, stochastic games, evolutionary games, and static games; (2) types of interventions, such as social distancing, vaccination, quarantine, and taking antidotes; (3) the types of decision-makers, such as individuals, adversaries, and central authorities at different hierarchical levels. A fine-grained dynamic game framework is proposed to capture the essence of game-theoretic decision-making amid epidemics. We showcase three representative frameworks with unique ways of integrating game-theoretic decision-making into the epidemic models from a vast body of literature. Each of the three frameworks has their unique way of modeling and analyzing and develops results from different angles. In the end, we identify several main open problems and research gaps left to be addressed and filled. KeywordsDynamic games • Stochastic games • Infectious diseases • Epidemic spreading • Human-in-the-loop systems • COVID-19 This article is part of the topical collection "Modeling and Control of Epidemics" edited by Quanyan Zhu,

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“…Many infectious disease models usually do not incorporate human behaviors that change as the epidemic evolves and the information spreads over the network. Dynamic game theory, which has been applied in many dynamic settings such as management science [9], labor economics [69], and cybersecurity [52], delivers a powerful paradigm to capture dynamic human behaviors [11]. We start the introduction of game-theoretic models in epidemics by presenting a taxonomy in the next section.…”

Section: Game-theoretic Decision-making In Epidemicsmentioning

confidence: 99%

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision Making: A Review

Huang¹,

Zhu²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making. We provide a review of a range of epidemic models and explain the pros and cons of different epidemic models. We exhibit the art of coupling epidemic models and decision models in the existing literature. More specifically, fundamental questions in human decision-making amid epidemics such as what interventions are taken to combat the disease, who are decisionmakers, when interventions are taken, and how interventions are modeled.Among many decision models, game-theoretic models have become increasingly crucial in modeling human responses/behavior amid epidemics in the last decade. In this review, we motivate the game-theoretic approach to human decision-making amid epidemics. This review provides an overview of the existing literature by developing a multi-dimensional taxonomy, which categorizes existing works based on multiple dimensions, including 1) types of games, such as differential games, stochastic games, evolutionary games, and static games; 2) types of interventions, such as social distancing, vaccination, quarantine, taking antidotes, etc.; 3) the types of decision-makers, such as individuals, adversaries, and central authorities at different hierarchical levels. A fine-grained dynamic game framework is proposed to capture the essence of game-theoretic decision-making amid epidemics. From a vast body of works, we showcase three representative works with unique ways of integrating gametheoretic decision-making into the epidemic models. The uniqueness of each of these three works distinguishes from each other regarding their models, analytical approaches, and results. In the end, we identify several main open problems and research gaps left to be addressed and filled.

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Dynamic games for secure and resilient control system design

Cited by 37 publications

References 99 publications

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision Making: A Review

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