Modeling and Analysis of Deception Games Based on Hypergame Theory

Cho, Jin-Hee; Zhu, Mu; Singh, Munindar P.

doi:10.1007/978-3-030-02110-8_4

Cited by 17 publications

(13 citation statements)

References 20 publications

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“…This, in contrast to the “principal-agent” models, makes it possible to exclude strategies with explicit deceptive manipulation of information. At the same time, like in hypergame models ( Cho et al., 2019 ), we place the possibility of being deceived outside the game into the probabilistic assessment g and assume that within one period of the game it is impossible to reduce the epistemic uncertainty associated with it.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Bayesian approach to investment in innovation projects with the presence of fake innovators

Gurtuev

Derkach

Makhosheva

et al. 2020

Heliyon

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The paper proposes a game-theoretic model of interaction between investors and innovators, taking into account the existence of so-called "fake" innovators offering knowingly unprofitable projects. The model is a Bayesian non-cooperative, repetitive game with recalculated payments and partly unobservable ex interim player types. It allows quantifying the parameters of the strategy for all player types to find equilibrium solutions. The model describes rational modes for screening "fake" innovators based on adjusting players' probabilistic estimates.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A Bayesian approach to investment in innovation projects with the presence of fake innovators

Gurtuev

Derkach

Makhosheva

et al. 2020

Heliyon

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“…In addition, they found that many techniques are unable to meet the proposed security definition while emphasizing the importance of assessing deceptive patches based on a clear and meaningful security definition. Cho et al [48] modeled a deception game based on hypergame theory in which an attacker and a defender have different perceptions in a given game. This work examined how a player's (mis)perception can affect its decision making to choose strategies to take, which can affect the player's utility in the given game.…”

Section: ) Honey Patchesmentioning

confidence: 99%

“…• Advanced persistent threats (APT) [48,72,75,87,105,106,124]: An APT attack is the most well-known sophisticated attack performing different types of attacks in the stages of the cyber kill chain (CKC) [103]. In particular, gametheoretic defensive deception research has considered the APT attack but mostly focused on the attacks during the reconnaissance stage.…”

Section: Attacks Countermeasured By Defensive Deception Techniquesmentioning

confidence: 99%

Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

Zhu¹,

Anwar²,

Wan³

et al. 2021

Preprint

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Defensive deception is a promising approach for cyberdefense. Although defensive deception is increasingly popular in the research community, there hasn't been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

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“…A large part of this work is based on game theory and attempts to find equilibria in the presence of attackers and defenders of a certain skill or optimize defense and attack strategies. These approaches comprise Stackelberg Games [1,38], Zero-Determinant Theory [40], empirical game-theoretic analysis [36], Markov modeling [18,27,30,41,42], as well as stochastic petri nets [7,14]. However, none of them attempt to determine a given technique's real effects.…”

Section: Related Workmentioning

confidence: 99%

Automated benchmark network diversification for realistic attack simulation with application to moving target defense

Bajic

Becker²

2021

Int. J. Inf. Secur.

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With numbers of exploitable vulnerabilities and attacks on networks constantly increasing, it is important to employ defensive techniques to protect one’s systems. A wide range of defenses are available and new paradigms such as Moving Target Defense (MTD) rise in popularity. But to make informed decisions on which defenses to implement, it is necessary to evaluate their effectiveness first. In many cases, the full impact these techniques have on security is not well understood yet. In this paper we propose network defense evaluation based on detailed attack simulation. Using a flexible modeling language, networks, attacks, and defenses are described in high detail, yielding a fine-grained scenario definition. Based on this, an automated instantiator generates a wide range of realistic benchmark networks. These serve to perform simulations, allowing to evaluate the security impact of different defenses, both quantitatively and qualitatively. A case study based on a mid-sized corporate network scenario and different Moving Target Defenses illustrates the usefulness of this approach. Results show that virtual machine migration, a frequently suggested MTD technique, more often degrades than improves security. Hence, we argue that evaluation based on realistic attack simulation is a qualified approach to examine and verify claims of newly proposed defense techniques.

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Modeling and Analysis of Deception Games Based on Hypergame Theory

Cited by 17 publications

References 20 publications

A Bayesian approach to investment in innovation projects with the presence of fake innovators

A Bayesian approach to investment in innovation projects with the presence of fake innovators

Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

Automated benchmark network diversification for realistic attack simulation with application to moving target defense

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