Cyberwar game: A paradigm for understanding new challenges of cyberwar

Cyber-war is a growing form of threat to our society that involves multiple players executing simultaneously offensive and defensive operations. Given that cyber space is hyper dimensional and dynamic, human decision making must also incorporate numerous attributes and must be agile and adaptive. In this chapter, we review how computational models of human cognition can be scaled up from an individual model of a defender operating in a hostile environment, through a pair of models representing a defender and an attacker to multi-agents in a cyberwar. Following, we propose to study the decision making processes that drive the dynamics of cyber-war using a multi-agent model comprising of cognitive agents that learn to make decisions according to Instance-Based Learning Theory (IBLT). In this paradigm, the CyberWar game, assets and power are two key attributes that influence the decisions of agents. Assets represent the key resource that an agent is protecting from attacks while power represents technical prowess of an agent’s cyber security. All the agents share the same goal of maximizing their assets and they learn from experience to attack other agents and defend themselves in order to meet this goal. Importantly, they don’t learn by using predefined strategies, as many multiagent models do, but instead they learn from experience according to the situation and actions of others, as suggested by the IBLT’s process. This chapter contributes to current research by: proposing a novel paradigm to study behavior in cyber-war, using a well-known cognitive model of decisions from experience to predict what possible human behavior would be in a simulated cyber-war, and demonstrating novel predictions regarding the effects of power and assets, two main contributors to cyber-war.