Adaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense

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dc.contributor.authorCranford, Edward
dc.contributor.authorGonzalez, Cleotilde
dc.contributor.authorAggarwal, Palvi
dc.contributor.authorCooney, Sarah
dc.contributor.authorTambe, Milind
dc.contributor.authorLebiere , Christian
dc.date.accessioned2020-01-04T07:32:19Z
dc.date.available2020-01-04T07:32:19Z
dc.date.issued2020-01-07
dc.description.abstractThis 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.232
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63971
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Deception for Defense
dc.subjectact-r
dc.subjectcognitive model
dc.subjectcyberdeception
dc.subjectinstance-based learning theory
dc.subjectsignaling
dc.titleAdaptive Cyber Deception: Cognitively Informed Signaling for Cyber Defense
dc.typeConference Paper
dc.type.dcmiText

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