@conference {1499825,
	title = {Towards Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models },
	booktitle = {International Conference on Cognitive Modeling (ICCM), 2019},
	year = {2019},
	abstract = {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. While effective, the algorithms are optimized against
perfectly rational adversaries. In a laboratory experiment, we
pit humans against the defense algorithm in an online game
designed to simulate an insider attack scenario. Humans attack
far more often than predicted under perfect rationality.
Optimizing against human bounded rationality is vitally
important. We propose a cognitive model based on instancebased learning theory and built in ACT-R that accurately
predicts human performance and biases in the game. We show
that the algorithm does not defend well, largely due to its static
nature and lack of adaptation to the particular individual{\textquoteright}s
actions. Thus, we propose an adaptive method of signaling that
uses the cognitive model to trace an individual{\textquoteright}s experience in
real time, in order to optimize defenses. We discuss the results
and implications of personalized defense.
Keywords: cyber deception; cognitive models; instance-based
learning; knowledge-tracing; model-tracing},
	author = {Edward A. Cranford and Gonzalez, Cleotilde and Palvi Aggarwal and Cooney, Sarah and Tambe, Milind and Lebiere, Christian}
}
