Reinforcement Learning-Based DoS Mitigation in Software Defined Networks

Vishnupriya, A.

doi:10.1007/978-981-13-0212-1_41

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…At present, the work of using reinforcement learning methods to solve network threat mitigation is minimal. Vishnupriya et al [4] proposed a direct learning strategy using reinforcement learning method under SDN. They set a dynamic threshold for the packets of each port according to the network status to mitigate denial of service (DOS) attacks.…”

Section: Related Workmentioning

confidence: 99%

Adaptive threat mitigation in SDN using improved D3QN

Shi

Wang²,

Su³

et al. 2022

Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022)

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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.

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Section: Related Workmentioning

confidence: 99%