Research on Network Attack Detection and Defense Strategies Based on Deep Reinforcement Learning
Deep Reinforcement Learning(DRL),as a cutting-edge artificial intelligence technology,has begun to show powerful decision-making and optimization capabilities in more and more fields in recent years,especially when it is applied to solving complex,dynamic and uncertain environmental problems,Deep Reinforcement Learning undoubtedly has a huge advantage.In the current complex network environment,the diversification of network attack methods and the increasing complexity of technology,the traditional static,predefined defense strategy has been difficult to effectively respond to the rapidly changing network security threats.Therefore,in-depth exploration and implementation of a network defense mechanism that can dynamically adapt,flexibly adjust,and has the ability of continuous learning has gradually become a key topic in network security research.Based on this,this study focuses on the development of deep reinforcement learning technology,discusses the network attack detection and defense strategy based on deep reinforcement learning,for reference and reference only.
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