计算机工程与应用2026，Vol.62Issue(10)：74-88,15.DOI:10.3778/j.issn.1002-8331.2510-0117
深度学习在网络入侵检测中的研究综述
Review of Deep Learning in Network Intrusion Detection
摘要
Abstract
Intrusion detection systems(IDS)and other security mechanisms play a critical role in safeguarding cyberspace and defending against malicious attacks.In recent years,deep learning technology has been widely applied and demon-strated significant advantages in the field of network intrusion detection,owing to its powerful capabilities for automatic feature extraction and learning complex patterns.Through a systematic review of the latest research literatures,this paper provides a detailed introduction to the research progress and application status of deep learning-based network intrusion detection technologies.Specifically,the research background and fundamental classification framework of IDS are outlined.A systematic summary and assessment is presented for the mainstream and emerging deep learning models applicable to network intrusion detection,such as recurrent neural networks and their variants,autoencoders,convolutional neural networks,generative adversarial networks,as well as emerging models like Transformer and graph convolutional networks.Furthermore,the deployment,adaptation,and associated challenges of deep learning-driven IDS in various specific application scenarios are analyzed,covering in-vehicle networks,unmanned aerial vehicle networks,smart grids,Internet of things,and software-defined networks.Based on existing research,key challenges currently faced in the field are discussed,such as data quality and imbalance,high-dimensional data processing,and real-time requirements.Future development trends are also explored and prospected.关键词
网络安全/入侵检测/深度学习/网络入侵检测系统Key words
network security/intrusion detection/deep learning/network intrusion detection system分类
信息技术与安全科学引用本文复制引用
赵鹏,王海凤,刘英华,张舒琦,赵昕晟,池志宏..深度学习在网络入侵检测中的研究综述[J].计算机工程与应用,2026,62(10):74-88,15.基金项目
内蒙古自治区自然科学基金(2025MS06003,2023LHMS06016) （2025MS06003,2023LHMS06016）
内蒙古自治区直属高校基本科研业务费项目(JY20240010). （JY20240010）
