Modeling multi-target defender-attacker games with quantal response attack strategies

Zhang, Jing; Wang, Yan; Zhuang, Jun

doi:10.1016/j.ress.2020.107165

Cited by 26 publications

(3 citation statements)

References 37 publications

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“…Regarding future work, we believe that it would be most interesting to derive tighter lower bounds for the optimal defending results in the resource sharing model. In addition, studying mixed strategies against contagious attacks [Bai et al, 2021] and imperfect attackers [Zhang et al, 2021] are also interesting directions.…”

Section: Discussionmentioning

confidence: 99%

Mixed Strategies for Security Games with General Defending Requirements

Bai¹,

Lin²,

Yang³

et al. 2022

Preprint

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The Stackelberg security game is played between a defender and an attacker, where the defender needs to allocate a limited amount of resources to multiple targets in order to minimize the loss due to adversarial attack by the attacker. While allowing targets to have different values, classic settings often assume uniform requirements to defend the targets. This enables existing results that study mixed strategies (randomized allocation algorithms) to adopt a compact representation of the mixed strategies.

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Section: Discussionmentioning

confidence: 99%

Mixed Strategies for Security Games with General Defending Requirements

Bai¹,

Lin²,

Yang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This process typically leads to quantal response equilibrium. There are several prior works that have shown such quantal response equilibrium for security problems, including defense of isolated targets [28], and Stackelberg security games with two players (one attacker and one defender) [29]- [33]. However, this class of games does not incorporate security externalities between multiple defenders and network interdependencies.…”

Section: Quantal Response Equilibriummentioning

confidence: 99%

A Quantal Response Analysis of Human Decision-Making in Interdependent Security Games Modeled by Attack Graphs

Azim,

Cason,

Abdallah

2024

IEEE Access

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Interdependent systems, under the management of multiple decision-makers, confront rapidly growing cybersecurity threats. This paper delves into the realm of security decision-making within these complex interdependent systems managed by multiple defenders. Each defender assumes responsibility for safeguarding a specific subnetwork of the system against potential attacks. The relationships between these assets are depicted through an attack graph, where edges connecting assets signify that the compromise of one asset could expose vulnerabilities in another asset. These edges are associated with probabilities that represent the likelihood of a successful attack, which can be reduced through security investments by the defenders. Our approach involves modeling these systems using game-theoretic frameworks, accounting for the impact of bounded rationality and imperfect best-response behavior-as frequently observed in human decision-making within the domains of behavioral economics and psychology. We first establish the existence of quantal response equilibrium in our interdependent security games. We present illustrative examples to highlight the disparities between the solutions derived from the social optimal perspective and those arising from quantal response equilibrium. Subsequently, we analyze the inefficiency introduced by behavioral players with this type of bounded rationality in terms of the overall social cost of the system. We adapt a widely recognized metric to quantify the extent of this inefficiency, providing bounds and illustrating its exponential growth with an increase in the security budget. To assess our models, we employ a representative real-world interdependent system and compare the game-theoretic optimal investment strategies to those derived from a socially optimal standpoint.

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“…Of more interest is when the game is involved, where individual payoff depends on the actions of others. Game‐theoretic model is applied to study strategic interactions among multiple players, and encompasses a wide range of applications such as economics (Rabin, 1993; Samuelson, 2016), homeland security (Golalikhani & Zhuang, 2011; Zhang et al., 2021; Zhuang & Bier, 2007), risk analysis (Chen et al., 2022; Cox, 2009; Hausken, 2002), pricing decisions (Wang et al., 2022), parking management (Guo et al., 2013), crowd funding (Mahbub et al., 2022), rumor management (Agarwal et al., 2022; Li et al., 2021), fire code inspection (Agarwal et al., 2020), and disaster management (Seaberg et al., 2017). Some research focuses on studying strategic behaviors of people in the epidemic/pandemic context using the classic game theory framework, where each player selects a strategy that is the best response to the strategy chosen by the other (Chang et al., 2020; Huang & Zhu, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model

Wei

Zhuang

2023

Risk Analysis

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The adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID‐19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game‐theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two‐group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long‐term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask‐wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.

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Modeling multi-target defender-attacker games with quantal response attack strategies

Cited by 26 publications

References 37 publications

Mixed Strategies for Security Games with General Defending Requirements

Mixed Strategies for Security Games with General Defending Requirements

A Quantal Response Analysis of Human Decision-Making in Interdependent Security Games Modeled by Attack Graphs

On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model

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