深度学习在网络入侵检测中的研究综述OA

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.