TY - GEN
T1 - Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense
AU - Eghtesad, Taha
AU - Vorobeychik, Yevgeniy
AU - Laszka, Aron
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85098272888
UR - https://www.scopus.com/inward/citedby.url?scp=85098272888&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64793-3_4
DO - 10.1007/978-3-030-64793-3_4
M3 - Conference contribution
AN - SCOPUS:85098272888
SN - 9783030647926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 58
EP - 79
BT - Decision and Game Theory for Security - 11th International Conference, GameSec 2020, Proceedings
A2 - Zhu, Quanyan
A2 - Baras, John S.
A2 - Poovendran, Radha
A2 - Chen, Juntao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Conference on Decision and Game Theory for Security, GameSec 2020
Y2 - 28 October 2020 through 30 October 2020
ER -