This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models

Prof. Cleotilde (Coty) Gonzalez
Carnegie Mellon University ~ Social and Decision Sciences Department

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.


There is nothing here yet. Be the first to create a thread.

Cite this as:

Gonzalez, C. (2020, November). Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models. Paper presented at MathPsych at Virtual Psychonomics 2020. Via