基于深度学习的网络入侵检测系统综述  

Summary of network intrusion detection systems based on deep learning

作　　者：邓淼磊[1,2] 阚雨培 孙川川 徐海航 樊少珺 周鑫 DENG Miaolei;KAN Yupei;SUN Chuanchuan;XU Haihang;FAN Shaojun;ZHOU Xin(College of Information Science and Engineering,Henan University of Technology,Zhengzhou Henan 450001,China;Henan International Joint Laboratory of Grain Information Processing,Zhengzhou Henan 450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,郑州450001 [2]河南省粮食信息处理国际联合实验室,郑州450001

出　　处：《计算机应用》2025年第2期453-466,共14页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62276091);河南省科技攻关项目(232102210132)。

摘　　要：入侵检测系统(IDS)等安全机制已被用于保护网络基础设施和网络通信免受网络攻击。随着深度学习技术的不断进步,基于深度学习的IDS逐渐成为网络安全领域的研究热点。通过对文献广泛调研,详细介绍利用深度学习技术进行网络入侵检测的最新研究进展。首先,简要概述当前几种IDS;其次,介绍基于深度学习的IDS中常用的数据集和评价指标;然后,总结网络IDS中常用的深度学习模型及其应用场景;最后,探讨当前相关研究面临的问题,并提出未来的发展方向。Security mechanisms such as Intrusion Detection System(IDS)have been used to protect network infrastructure and communication from network attacks.With the continuous progress of deep learning technology,IDSs based on deep learning have become a research hotspot in the field of network security gradually.Through extensive literature research,a detailed introduction to the latest research progress in network intrusion detection using deep learning technology was given.Firstly,a brief overview of several IDSs was performed.Secondly,the commonly used datasets and evaluation metrics in deep learning-based IDSs were introduced.Thirdly,the commonly used deep learning models in network IDSs and their application scenarios were summarized.Finally,the problems faced in the current related research were discussed,and the future development directions were proposed.

关 键 词：网络安全 入侵检测 深度学习 异常检测 网络入侵检测系统 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]