Biblio

The rate at which cyber-attacks are increasing globally portrays a terrifying picture upfront. The main dynamics of such attacks could be studied in terms of the actions of attackers and defenders in a cyber-security game. However currently little research has taken place to study such interactions. In this paper we use behavioral game theory and try to investigate the role of certain actions taken by attackers and defenders in a simulated cyber-attack scenario of defacing a website. We choose a Reinforcement Learning (RL) model to represent a simulated attacker and a defender in a 2×4 cyber-security game where each of the 2 players could take up to 4 actions. A pair of model participants were computationally simulated across 1000 simulations where each pair played at most 30 rounds in the game. The goal of the attacker was to deface the website and the goal of the defender was to prevent the attacker from doing so. Our results show that the actions taken by both the attackers and defenders are a function of attention paid by these roles to their recently obtained outcomes. It was observed that if attacker pays more attention to recent outcomes then he is more likely to perform attack actions. We discuss the implication of our results on the evolution of dynamics between attackers and defenders in cyber-security games.