Adaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense Cranford, Edward Gonzalez, Cleotilde Aggarwal, Palvi Cooney, Sarah Tambe, Milind Lebiere , Christian 2020-01-04T07:32:19Z 2020-01-04T07:32:19Z 2020-01-07
dc.description.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 using cognitive modeling. A cognitive model based on a theory of experiential-choice (Instance-Based Learning Theory; IBLT), implemented in a cognitive architecture (Adaptive Control of Thought – Rational; ACT-R), and validated using human experimentation with deceptive signals informs the development of a cognitive signaling scheme. The predictions of the cognitive model show that the proposed solution increases the compliance to deceptive signals beyond the peSSE. These predictions were verified in human experiments, and the results shed additional light on human reactions towards adaptive deceptive signals.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.232
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Cyber Deception for Defense
dc.subject act-r
dc.subject cognitive model
dc.subject cyberdeception
dc.subject instance-based learning theory
dc.subject signaling
dc.title Adaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense
dc.type Conference Paper
dc.type.dcmi Text
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