Stackelberg Security Games: Looking Beyond a Decade of Success

Sinha, Arunesh; Fang, Fei; An, Bo; Kiekintveld, Christopher; Tambe, Milind

doi:10.24963/ijcai.2018/775

Cited by 118 publications

(89 citation statements)

References 16 publications

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“…Research on Stackelberg Security Games (SSGs) led to the development of algorithms that have greatly improved physical security systems (e.g., protecting ports, scheduling air marshals, and mitigating poachers) through the optimal allocation of limited defense resources (Pita et al, 2008; Shieh et al, 2012; Sinha, Fang, An, Kiekintveld, & Tambe, 2018; Tambe, 2011). Xu et al (2015) extended these models by incorporating elements of signaling , in which a defender (sender) strategically reveals information about their strategy to the attacker (receiver) to influence the attacker's decision‐making (Battigalli, 2006; Cho & Kreps, 1987).…”

Section: Deceptive Signaling For Cybersecuritymentioning

confidence: 99%

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models

Cranford

González

Aggarwal

et al. 2020

Topics in Cognitive Science

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Recent research in cybersecurity has begun to develop active defense strategies using game‐theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance‐based learning cognitive model, built in ACT‐R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.

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Section: Deceptive Signaling For Cybersecuritymentioning

confidence: 99%

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models

Cranford

González

Aggarwal

et al. 2020

Topics in Cognitive Science

Self Cite

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show abstract

“…Then (2d) states that a set of arcs known to the interdictor at round t is updated according to assumption A5. From the game theoretic perspective, the evader's problem (2) can be viewed as a finitely repeated Stackelberg game with incomplete information; see, e.g., [34] and the references therein. Definition 1.…”

Section: The Evader Traverses Along Pathmentioning

confidence: 99%

On greedy and strategic evaders in sequential interdiction settings with incomplete information

Ketkov

Prokopyev

2020

Omega

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We consider a class of sequential network interdiction problem settings where the interdictor has incomplete initial information about the network while the evader has complete knowledge of the network including its structure and arc costs. In each decision epoch, the interdictor can block (for the duration of the epoch) at most k arcs known to him/her. By observing the evader's actions, the interdictor learns about the network structure and costs and thus, can adjust his/her actions in subsequent decision epochs. It is known from the literature that if the evader is greedy (i.e., the shortest available path is used in each decision epoch), then under some assumptions the greedy interdiction policies that block k-most vital arcs in each epoch are efficient and have a finite regret. In this paper, we consider the evader's perspective and explore deterministic "strategic" evasion policies under the assumption that the interdictor is greedy. We first study the theoretical computational complexity of the evader's problem. Then we derive basic constructive properties of optimal evasion policies for two decision epochs when the interdictor has no initial information about the network structure. These properties are then exploited for the design of a heuristic algorithm for a strategic evader in a general setting with an arbitrary time horizon and any initial information available to the interdictor. Our computational experiments demonstrate that the proposed heuristic outperforms the greedy evasion policy on several classes of synthetic network instances under either perfect or noisy information feedback. Finally, some interesting insights from our theoretical and computational results conclude the paper.

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“…Bayesian Stackelberg games 27 then generalise this model by introducing a probability distributions over the set of types for each player, while payoffs are again defined for all the possible combinations of player types over the set of pure strategy profiles. Security games [3][4][5] are a special type of Bayesian Stackelberg game with only one type of leader (defender) but still many possible types of the follower (attacker). Moreover, it assumes that for each pure strategy profile, either the defender or the attacker wins and the other loses.…”

Section: Security Gamesmentioning

confidence: 99%

“…Since today the public security issue is so crucial, 1,2 it is very significant to decide how to allocate limited security resources for the success of security operations globally. [3][4][5][6] Most existing studies address this issue using a special type of (Bayesian) Stackelberg game, known as a security game with two players (a defender and an attacker). In a security game, the defender selects a strategy that can maximise their payoff.…”

Section: Introductionmentioning

confidence: 99%

A decision support framework for security resource allocation under ambiguity

Liu

McAreavey

et al. 2020

Int J Intell Syst

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There has been increasing interest in using Stackelberg game (known as a security game) to allocate limited security resources against different attacker types with a specific probability distribution. However, real problems of this kind often face ambiguous information, such as imprecise, unreliable and absent payoffs, and ambiguous assignments of these payoffs. To this end, based on decision theory and the Dempster-Shafer theory of evidence, this paper proposes a novel framework that can handle these common types of ambiguity. More specifically, this paper deploys the underlying principles of existing rules from decision theory, as a way to characterise different attitudes to ambiguity, during the transformation of ambiguous payoffs into point-valued payoffs. Hence, our framework holds some good properties: (i) it subsumes traditional security games without ambiguous payoffs, (ii) a uniform margin of error will not affect the results and (iii) the influence

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Stackelberg Security Games: Looking Beyond a Decade of Success

Cited by 118 publications

References 16 publications

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models

On greedy and strategic evaders in sequential interdiction settings with incomplete information

A decision support framework for security resource allocation under ambiguity

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