Adaptive threat mitigation in SDN using improved D3QN

Chenghao Shi; Yu Wang; Yinghao Su; Ying Wan

doi:10.1117/12.2652679

28 September 2022 Adaptive threat mitigation in SDN using improved D3QN

Chenghao Shi, Yu Wang, Yinghao Su, Ying Wan

Proceedings Volume 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022); 1233911 (2022) https://doi.org/10.1117/12.2652679
Event: Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 2022, Chendu, China

Abstract

SDN provides network managers with a global network view and programmability, making it more convenient for network managers to carry out security management. In recent years, machine learning has innovated many fields of computer science. The reinforcement learning method has emerged as an effective solution to sequential decision-making and control problems. Its method is widely used in the field of automation control. The computer network is becoming more and more complex and changeable. In the face of the emerging technology of IoT and the threat of attack and intrusion, the demand for adaptive security control is increasingly urgent. The reinforcement learning method is a feasible solution to achieve adaptive control. Based on the SDN framework, this paper develops and uses reinforcement learning applications for SDN network security management. For the existing problems, we improve the structure of D3QN and use the improved D3QN deep reinforcement learning agent to learn and mitigate apt attacks. Finally, the experimental results are evaluated, and the convergence results of the improved algorithm model are given, which shows the availability of reinforcement learning methods for adaptive threat mitigation in the SDN environment.

Citation Download Citation

Chenghao Shi, Yu Wang, Yinghao Su, and Ying Wan "Adaptive threat mitigation in SDN using improved D3QN", Proc. SPIE 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 1233911 (28 September 2022); https://doi.org/10.1117/12.2652679

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