基于强化学习的网络流量异常检测方法  

Network traffic anomaly detection method based on reinforcement learning

作　　者：唐科 TANG Ke(Chengdu College of University of Electronic Science and Technology of China,Chengdu 610017,China)

机构地区：[1]电子科技大学成都学院,成都610017

出　　处：《计算机应用文摘》2025年第3期144-146,共3页

摘　　要：随着网络安全问题的日益严峻,作为保障网络安全的重要手段之一,网络流量异常检测受到了广泛关注。基于统计和机器学习的传统网络流量异常检测方法在处理复杂网络环境时存在诸多局限性,因此文章提出了一种基于强化学习的网络流量异常检测方法,旨在提高检测的准确性和效率。通过构建强化学习模型,自动学习网络流量的特征和模式,实现了实时、准确的异常检测。With the increasingly severe network security problems,as one of the important means to ensure network security,network traffic anomaly detection has been widely concerned.Traditional network traffic anomaly detection methods such as statistics and machine learning have many limitations when dealing with complex network environments.Therefore,this paper proposes a network traffic anomaly detection method based on reinforcement learning to improve the accuracy and efficiency of detection.By building a reinforcement learning model,the characteristics and patterns of network traffic are automatically learned to achieve real-time and accurate anomaly detection.

关 键 词：网络流量 异常检测 强化学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]