Chapter 15 - Computational modeling of decisions in cyber-security games in the presence or absence of interdependence information

Abstract

Cyber-attacks are increasing rapidly in the real world and these attacks cause widespread damage to cyber-infrastructure. Information about actions and consequences of opponents (interdependence information) is likely to influence the decision-making of defenders (analysts) and adversaries (hackers) during cyber-attacks; however, currently, little is known about the cognitive processes involved that would account for the role of interdependence information on the interaction between hackers and analysts. This chapter uses data from a published experiment involving the presence or absence of interdependence information about actions and payoffs of opponents in a simulated cyber-security game. We propose a computational cognitive model based upon Instance-Based Learning Theory (IBLT), a theory of decisions from experience, for explaining hackers’ and analysts’ decisions in the experiment. Results reveal that the model could account for participants’ decisions as hackers and analysts in the presence or absence of interdependence information. We discuss the implications of our results for computational modeling of decisions in the cyber world.