基于强化学习的通信网络入侵自适应检测方法

An adaptive intrusion detection method for communication networks based on reinforcement learning

通信网络入侵检测是网络信息安全领域研究的重点。传统的通信网络入侵检测方法属于固定式检测,不能根据通信业务变化而自主升级,容易发生误判、漏判等问题。为此,提出了基于Q强化学习算法的通信网络入侵自适应监测方法。利用强化学习智能体与环境交互,不断更新智能体的特点,设计了自适应监测的方法。以该方法为核心,介绍了入侵检测系统设计,实际应用情况表明该方法较传统固定式检测方法具有更高的检测稳定性,对通信网络传输业务变化的适应性更强。

Communication network intrusion detection is an important research topic in network informa-tion security.The traditional intrusion detection method of communication network belonged to fixed detection,which cannot be upgraded independently according to the communication service change,is prone to misjudgment,omission and other problems.To this end,an adaptive intrusion detection method for communication network based on Q reinforcement learning algorithm is proposed.Based on the interact between reinforcement learning agent and the communication network environment to constantly update its strategy,the detection method can continuously improve the adaptability to the communication service.The design of intrusion detection system is introduced,which takes this method as the core.The practical application shows that this method has higher detection stability than the traditional fixed detection method and stronger adaptability to the change of communication network transmission service.