Abstract
Intrusion Detection Systems (IDSs) help in creating cyber situational awareness for defenders by providing recommendations. Prior research in simulation and game-theory has revealed that the presence and accuracy of IDS-like recommendations influence the decisions of defenders and adversaries. In the current paper, we present novel analyses of prior research by analyzing the sequential decisions of defenders and adversaries over repeated trials. Specifically, we developed computational cognitive models based upon Instance-Based Learning Theory (IBLT) to capture the dynamics of the sequential decisions made by defenders and adversaries across numerous conditions that differed in the IDS’s availability and accuracy. We found that cognitive mechanisms based upon recency, frequency, and variability helped account for adversarial and defender decisions better than the optimal Nash solutions. We discuss the implications of our results for adversarial-and-defender decisions in the cyber-world.