Adaptive network threat mitigation based on reinforcement learning
With the in-depth development of internet information technology,communication networks are constantly threatened by attacks and intrusions.A reinforcement learning algorithm is proposed for network adaptive threat mitigation.Based on the SDN frame-work,the use of reinforcement learning algorithms for network security management is studied.Based on the D3QN algorithm and im-proved its structure,the improved D3QN deep reinforcement learning method is used to learn and mitigate APT attacks,achieving adaptive control of network threats.Finally,the experimental results were evaluated and the convergence results of the improved algorithm model were provided,verifying the availability and effectiveness of the reinforcement learning method for adaptive network threat mitigation.
国家科技期刊平台
NSTL
万方数据
维普
