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.
作者
王玉芳
杨怀洲
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)
出处
《无线互联科技》
2024年第7期122-124,共3页
Wireless Internet Technology
关键词
网络安全
入侵检测
深度学习
机器学习
神经网络
cyber security
intrusion detection
deep learning
machine learning
neural network