You only Lie Twice: A Multi-round Cyber Deception Game of Questionable Veracity

Bilinski, Mark; Ferguson-Walter, Kimberly; Fugate, Sunny; Gabrys, Ryan; Mauger, Justin; Souza, Brian

doi:10.1007/978-3-030-32430-8_5

Cited by 8 publications

(14 citation statements)

References 17 publications

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“…A likely perpetrator will commit a crime if the inequality presented in Figure 1 holds [22,23]. Game theory has been proposed as a mechanism to increase the negative returns and to decrease the positive returns to the perpetrator considering an illegal cybersecurity attack [24,25].…”

Section: Research On the Economics Of Crimementioning

confidence: 99%

The interaction of dark traits with the perceptions of apprehension

Gaia¹,

Murray²,

Sanders³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper integrates dark personality traits with the economics of crime and rational choice theories to identify the role that the Dark Triad and thrill-seeking have on the perceptions of being caught engaging in violating privacy laws.Psychopathy and thrill-seeking had a moderate negative effect on the perceptions of the probability of being apprehended for distributing illegally obtained healthcare information. The implication is that individuals scoring high on the psychopathy and thrillseeking scales will need less money or monetary incentives to violate HIPAA laws. We also found additional support that white hat hackers score high on the Machiavellian, psychopathy and thrill-seeking scales. We also validated a previous finding that a white hat hacker might drift towards grey hat and black hat hacking.

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Section: Research On the Economics Of Crimementioning

confidence: 99%

The interaction of dark traits with the perceptions of apprehension

Gaia¹,

Murray²,

Sanders³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…First, among studies on game theory with MDP and POMDP, Bilinski et al [49] have described solutions based on masking games and Stackelberg games that can hide the essence of the defender node through processes such as inquiries between attackers and defenders, payment of costs, and selection of the final compromise target. These solutions make it possible to probabilistically analyze the attacker's potential behaviors before and after the exploit.…”

Section: Game-theoretic Defensive Deception With Non-mtdmentioning

confidence: 99%

SOD2G: A Study on a Social-Engineering Organizational Defensive Deception Game Framework through Optimization of Spatiotemporal MTD and Decoy Conflict

Seo

Kim

2021

Electronics

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Existing moving target defense (MTD) and decoy systems are conceptually limited in avoiding and preventing attackers’ social-engineering real-time attacks by organization through either structural mutations or induction and isolation only using static traps. To overcome the practical limitations of existing MTD and decoy and to conduct a multi-stage deception decision-making in a real-time attack-defense competition, the current work presents a social-engineering organizational defensive deception game (SOD2G) as a framework, consi dering hierarchical topologies and fingerprint characteristics by organization. The present work proposed and applied deception concepts and zero-sum-based two-player game models as well as attacker and defender decision-making process based on deceivable organizational environments and vulnerability information. They were designed in consideration of limited organizational resources so that they could converge in the positive direction to secure organizational defender dominant share and optimal values of the defender deception formulated by both scenario and attribute. This framework could handle incomplete private information better than existing models and non-sequentially stratified, and also contributed to the configuration of the optimal defender deception strategy. As the experimental results, they could increase the deception efficiency within an organization by about 40% compared to existing models. Also, in the sensitivity analysis, the proposed MTD and decoy yielded improvements of at least 60% and 30% in deception efficiency, respectively, compared to the existing works.

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“…Bilinski et al [30] introduced game-theoretic deception procedures based on a masking game in which a defender masks the true nature of a device. In this game, an attacker can ask the defender whether a given device is real or fake at each round of the game.…”

Section: Pros and Consmentioning

confidence: 99%

“…• Dataset Collection: The success of ML techniques hugely depends on whether reliable datasets are available or not. Node compromise [30,73]; APT [75]; Reconnaissance [45] Enterprise networks; SDNs…”

Section: A Key Steps Of Implementing Ml-based Defensive Deceptionmentioning

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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You only Lie Twice: A Multi-round Cyber Deception Game of Questionable Veracity

Cited by 8 publications

References 17 publications

The interaction of dark traits with the perceptions of apprehension

The interaction of dark traits with the perceptions of apprehension

SOD2G: A Study on a Social-Engineering Organizational Defensive Deception Game Framework through Optimization of Spatiotemporal MTD and Decoy Conflict

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

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