Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.

Autor: Cranford EA; Department of Psychology, Carnegie Mellon University., Gonzalez C; Social and Decision Sciences Department, Carnegie Mellon University., Aggarwal P; Social and Decision Sciences Department, Carnegie Mellon University., Cooney S; USC Center for AI in Society, University of Southern California., Tambe M; Harvard Center for Research on Computation and Society, Harvard University., Lebiere C; Department of Psychology, Carnegie Mellon University.
Jazyk: angličtina
Zdroj: Topics in cognitive science [Top Cogn Sci] 2020 Jul; Vol. 12 (3), pp. 992-1011. Date of Electronic Publication: 2020 Jul 28.
DOI: 10.1111/tops.12513
Abstrakt: Recent research in cybersecurity has begun to develop active defense strategies using game-theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance-based learning cognitive model, built in ACT-R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.
(© 2020 Cognitive Science Society, Inc.)
Databáze: MEDLINE
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