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Advanced persistent threats (APTs) are stealthy attacks which make use ofsocial engineering and deception to give adversaries insider access tonetworked systems. Against APTs, active defense technologies aim to create andexploit information asymmetry for defenders. In this paper, we study a scenarioin which a powerful defender uses honeynets for active defense in order toobserve an attacker who has penetrated the network. Rather than immediatelyeject the attacker, the defender may elect to gather information. We introducean undiscounted, infinite-horizon Markov decision process on a continuous statespace in order to model the defender's problem. We find a threshold ofinformation that the defender should gather about the attacker before ejectinghim. Then we study the robustness of this policy using a Stackelberg game.Finally, we simulate the policy for a conceptual network. Our results provide aquantitative foundation for studying optimal timing for attacker engagement innetwork defense.

Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats