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

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Jeffrey Pawlick, Thi Thu Hang Nguyen, Quanyan Zhu

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.