Autonomous network defense in cloud data center environments based on reinforcement learning

Jianping Zhao; Yichuan Wang; Huan Wang; Hua Li; Yunlong Tang

doi:10.1117/12.3032677

12 July 2024 Autonomous network defense in cloud data center environments based on reinforcement learning

Jianping Zhao, Yichuan Wang, Huan Wang, Hua Li, Yunlong Tang

Proceedings Volume 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) ; 131850H (2024) https://doi.org/10.1117/12.3032677
Event: International Conference on Communication, Information and Digital Technologies, 2024, Wuhan, China

Abstract

The internal networks of cloud data centers are vast in scale, rich in data value, and high in traffic, imposing stringent demands on network security. Manual security operations for such network resources are highly resource-intensive and prone to errors. To address this, this paper proposes an autonomous network defense method for internal networks of cloud data centers based on reinforcement learning. We construct a leader-follower game model for network attack and defense, depicting the NP-hard interactive behavior in network attack and defense; we design a reinforcement learning defense agent that autonomously iterates learning, continuously feedback control, and adapts to environmental changes, training to solve for autonomous network defense strategies. Experiments demonstrate that our reinforcement learning agent acquires an excellent autonomous network defense capability, effectively resisting network attacks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianping Zhao, Yichuan Wang, Huan Wang, Hua Li, and Yunlong Tang "Autonomous network defense in cloud data center environments based on reinforcement learning", Proc. SPIE 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) , 131850H (12 July 2024); https://doi.org/10.1117/12.3032677

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