Accounting for Uncertainty in Deceptive Signaling for Cybersecurity

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dc.contributor.authorCranford, Edward
dc.contributor.authorOu, Han-Ching
dc.contributor.authorGonzalez, Cleotilde
dc.contributor.authorTambe, Milind
dc.contributor.authorLebiere, Christian
dc.date.accessioned2022-12-27T18:55:39Z
dc.date.available2022-12-27T18:55:39Z
dc.date.issued2023-01-03
dc.description.abstractDeceptive signaling has proven an effective method that can aid security analysists and deter attacks on unprotected targets by strategically revealing information to an attacker. However, recent research has shown that uncertainty in real-time information processing can have a negative impact on the effectiveness of the defense algorithm. The current research developed a new algorithm, dubbed Confusion Signaling, that aims to account for uncertainty in an abstracted insider attack scenario. The results of cognitive model simulations and a human behavioral experiment reveal interesting and unexpected reactions under uncertainty. We discuss the implications of these findings for signaling algorithms that aim to account for uncertainty using deceptive signaling for cybersecurity.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.109
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102738
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th 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 and Cyberpsychology for Defense
dc.subjectcognitive model
dc.subjectdeceptive signaling
dc.subjectinsider attack
dc.subjectinstance-based learning
dc.subjectuncertainty
dc.titleAccounting for Uncertainty in Deceptive Signaling for Cybersecurity
dc.type.dcmitext
prism.startingpage876

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