Toward Personalized Deceptive Signaling for CyberDefense Using Cognitive Models


Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Sarah Cooney, Milind Tambe, and Christian Lebiere. 7/30/2020. “Toward Personalized Deceptive Signaling for CyberDefense Using Cognitive Models.” Topics in Cognitive Science, 12, 3, Pp. 992-1011. Publisher's Version


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.
See also: 2020
Last updated on 07/30/2020