@conference {1500251,
	title = {Learning about Cyber Deception through Simulations: Predictions of Human Decision Making with Deceptive Signals in Stackelberg Security Games },
	booktitle = {Annual meeting of the Cognitive Science Society (CogSci)},
	year = {2018},
	abstract = {To improve cyber defense, researchers have developed
algorithms to allocate limited defense resources optimally.
Through signaling theory, we have learned that it is possible to
trick the human mind when using deceptive signals. The
present work is an initial step towards developing a
psychological theory of cyber deception. We use simulations
to investigate how humans might make decisions under various
conditions of deceptive signals in cyber-attack scenarios. We
created an Instance-Based Learning (IBL) model of the
attacker decisions using the ACT-R cognitive architecture. We
ran simulations against the optimal deceptive signaling
algorithm and against four alternative deceptive signal
schemes. Our results show that the optimal deceptive algorithm
is more effective at reducing the probability of attack and
protecting assets compared to other signaling conditions, but it
is not perfect. These results shed some light on the expected
effectiveness of deceptive signals for defense. The implications
of these findings are discussed.},
	author = {Edward A. Cranford and Lebiere, Christian and Gonzalez, Cleotilde and Cooney, Sarah and Vayanos, Phebe and Tambe, Milind}
}
