Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeypots for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce a Markov decision process on a continuous state space in order to model the defender's problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.
The work most related to this one is REF , which introduces a continuous-state infinite-horizon Markov Decision Process (MDP) model where the defender decides when to eject the attacker from the network.
Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats
ArXiv
ArXiv
Computer Science