Accounting for Uncertainty in Deceptive Signaling for Cybersecurity Cranford, Edward Ou, Han-Ching Gonzalez, Cleotilde Tambe, Milind Lebiere, Christian 2022-12-27T18:55:39Z 2022-12-27T18:55:39Z 2023-01-03
dc.description.abstract Deceptive 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.extent 10
dc.identifier.doi 10.24251/HICSS.2023.109
dc.identifier.isbn 978-0-9981331-6-4
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Cyber Deception and Cyberpsychology for Defense
dc.subject cognitive model
dc.subject deceptive signaling
dc.subject insider attack
dc.subject instance-based learning
dc.subject uncertainty
dc.title Accounting for Uncertainty in Deceptive Signaling for Cybersecurity
dc.type.dcmi text
prism.startingpage 876
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