Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (6): 17-28.DOI: 10.3778/j.issn.1002-8331.2107-0084

• Research Hotspots and Reviews • Previous Articles     Next Articles

Summary of Intrusion Detection Models Based on Deep Learning

ZHANG Hao, ZHANG Xiaoyu, ZHANG Zhenyou, LI Wei   

  1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • Online:2022-03-15 Published:2022-03-15



  1. 华北理工大学 人工智能学院,河北 唐山 063210

Abstract: With the continuous in-depth development of deep learning technology, intrusion detection model based on deep learning has become a research hotspot in the field of network security. This paper summarizes the commonly used data preprocessing operations in network intrusion detection. The popular intrusion detection models based on deep learning, such as convolutional neural network, long short-term memory network, auto-encode and generative adversarial networks, are analyzed and compared. The data sets commonly used in the research of intrusion detection model based on deep learning are introduced. It points out the problems of the existing intrusion detection models based on deep learning in data set timeliness, real-time, universality, model training time and other aspects, and the possible research focus in the future.

Key words: deep learning, network security, intrusion detection model

摘要: 随着深度学习技术的不断深入发展,基于深度学习的入侵检测模型已成为网络安全领域的研究热点。对网络入侵检测中常用的数据预处理操作进行了总结;重点对卷积神经网络、长短期记忆网络、自编码器和生成式对抗网络等当前流行的基于深度学习的入侵检测模型进行了分析和比较;并简单说明了基于深度学习的入侵检测模型研究中常用的数据集;指出了现有基于深度学习的入侵检测模型在数据集时效、实时性、普适性、模型训练时间等方面存在的问题和今后可能的研究重点。

关键词: 深度学习, 网络安全, 入侵检测模型