基于深度学习的网络入侵检测综述  

Review of network intrusion detection based on deep learning

作　　者：王玉芳 杨怀洲[1] Wang Yufang;Yang Huaizhou(School of Computing,Xi’an Shiyou University,Xi’an 710065,China;School of Information Engineering,Shaanxi Institute of International Trade&Commerce,Xi’an 712046,China)

机构地区：[1]西安石油大学计算机学院,陕西西安710065 [2]陕西国际商贸学院信息工程学院,陕西西安712046

出　　处：《无线互联科技》2024年第7期122-124,共3页Wireless Internet Technology

摘　　要：文章对基于深度学习的入侵检测研究进行了综述。首先,分析当前网络安全面临的威胁和入侵检测面临的新的挑战以及入侵检测的基本原理。其次,从卷积神经网络、循环神经网络和生成式对抗网络3种模型出发,论述了基于深度学习的入侵检测技术,并对其他基于深度学习的入侵检测模型进行补充总结。最后,介绍了入侵检测数据集、存在问题与未来展望。This article provides a review of intrusion detection research based on deep learning.Firstly,analyze the current threats to network security and the new challenges faced by intrusion detection,as well as the basic principles of intrusion detection.In fact,starting from three models:CNN,RNN and GAN,this paper discusses intrusion detection technology based on deep learning,and supplements and summarizes other intrusion detection models based on deep learning.Finally,the intrusion detection dataset,existing problems,and future prospects were introduced.

关 键 词：网络安全 入侵检测 深度学习 机器学习 神经网络 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]