Learning Games for Defending Advanced Persistent Threats in Cyber Systems

Abstract

A cyber system may face to multiple attackers from diverse adversaries, who usually employ sophisticated techniques to both continuously steal sensitive data and avoid being detected by defense strategies. This continuous process is typically involved in an advanced persistent threat (APT). Since the game theory is an ideal mathematical model for investigating continuous decision making of competing players, it is broadly used to research the interaction between defenders and APT attackers. Although many researchers are now using the game theory to defend against APT attacks, most of the existing solutions are limited to single-defender, single-attacker scenarios. In the real world, threats by multiple attackers are not uncommon and multiple defenders can be put in place. Therefore, to overcome the limitation of the existing solutions, we develop a multiagent deep reinforcement learning (MADRL) method with a novel sampling approach. The MADRL method allows defenders to create strategies on the fly and share their experience with other defenders. To develop this method, we create a multidefender, multiattacker game model and analyze the equilibrium of this model. The results of a series of experiments demonstrate that, with MADRL, defenders can quickly learn efficient strategies against attackers.