Deep Reinforcement Learning based Adaptive Moving Target Defense

Published in 2020 Conference on Decision and Game Theory for Security, 2020

Recommended citation: Eghtesad T., Vorobeychik Y., Laszka A. (2020) Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense. In: Zhu Q., Baras J.S., Poovendran R., Chen J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science, vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_4 https://taahaaa.ir/files/eghtesad2020adversarial.pdf

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Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by continuously changing the attack surface of a system (e.g., changing host or network configurations), thereby increasing the adversary’s uncertainty and attack cost. To maximize the impact of MTD, a defender must strategically choose when and what changes to make, taking into account both the characteristics of its system as well as the adversary’s observed activities. Finding an optimal strategy for MTD presents a significant challenge, especially when facing a resourceful and determined adversary who may respond to the defender’s actions. In this paper, we propose a multi-agent partially-observable Markov Decision Process model of MTD and formulate a two-player general-sum game between the adversary and the defender. To solve this game, we propose a multi-agent reinforcement learning framework based on the double oracle algorithm. Finally, we provide experimental results to demonstrate the effectiveness of our framework in finding optimal policies.

Recommended citation: Eghtesad T., Vorobeychik Y., Laszka A. (2020) Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense. In: Zhu Q., Baras J.S., Poovendran R., Chen J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science, vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_4