2020
DOI: 10.24251/hicss.2020.232
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Adaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense

Abstract: This paper improves upon recent game-theoretic deceptive signaling schemes for cyber defense using the insights emerging from a cognitive model of human cognition. One particular defense allocation algorithm that uses a deceptive signaling scheme is the peSSE (Xu et al., 2015). However, this static signaling scheme optimizes the rate of deception for perfectly rational adversaries and is not personalized to individuals. Here we advance this research by developing a dynamic and personalized signaling scheme usi… Show more

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Cited by 29 publications
(26 citation statements)
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References 13 publications
(11 reference statements)
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“…However, human decision makers have numerous individual differences in knowledge, strategies, and cognitive capacity that automated systems have to be sensitive to. Cognitive models tuned to the specific experience and decisions of individuals have been developed to control personalized automated processes, such as defensive cyber deception, in which the model generates a deceptive signal that balances the potential benefits of deceiving a potential attacker against the costs of rebuilding trust if the signal is exposed as deceptive ( Cranford E. et al, 2020 ). The cognitive model developed in this study could therefore serve as the basis of a system designed to limit the damages of attacks and disruptions and properly calibrate human trust in automation.…”
Section: Discussionmentioning
confidence: 99%
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“…However, human decision makers have numerous individual differences in knowledge, strategies, and cognitive capacity that automated systems have to be sensitive to. Cognitive models tuned to the specific experience and decisions of individuals have been developed to control personalized automated processes, such as defensive cyber deception, in which the model generates a deceptive signal that balances the potential benefits of deceiving a potential attacker against the costs of rebuilding trust if the signal is exposed as deceptive ( Cranford E. et al, 2020 ). The cognitive model developed in this study could therefore serve as the basis of a system designed to limit the damages of attacks and disruptions and properly calibrate human trust in automation.…”
Section: Discussionmentioning
confidence: 99%
“…The cognitive model developed in this study could therefore serve as the basis of a system designed to limit the damages of attacks and disruptions and properly calibrate human trust in automation. That system could also be used to optimize the complexity of information presented and find the optimal level of transparency by leveraging techniques such as measures and visualization of cognitive salience developed for explainable AI systems ( Somers et al, 2019 ; Cranford E. et al, 2020 ). In conclusion, cognitive models provide promising computational tools to understand, manage, and calibrate trust and reliance in automation.…”
Section: Discussionmentioning
confidence: 99%
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“…Even such a scheme should show improvement in defenses compared to static schemes. Additionally, we also plan to adapt the coverage of targets according to attacker's behavior to further improve defenses (Cranford et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…As Shade et al [15] have pointed out, most research on cyber deception tools tends to focus on honeypots [16], suggesting ways to improve them [17], deliver them as a service [18], or to recognise their deficiencies [18], [19]. Where cyber deception research extends beyond honeypots it still tends to build from a computer science or engineering perspective [18], [19], [20], [21] with a smaller number of examples of research that include the impact of humans on cyber deception through, 'cognitive models and experimental games' [22] and 'computational models of human cognition' [20]. The assumption in such research is one of rational decision-making with a focus on formal rules or models in how decisions are made [23].…”
Section: Background and Related Workmentioning
confidence: 99%