Accounting for Uncertainty in Deceptive Signaling for Cybersecurity

Date
2023-01-03
Authors
Cranford, Edward
Ou, Han-Ching
Gonzalez, Cleotilde
Tambe, Milind
Lebiere, Christian
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876
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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.
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Cyber Deception and Cyberpsychology for Defense, cognitive model, deceptive signaling, insider attack, instance-based learning, uncertainty
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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