%0 Journal Article %A ZHANG Hao %A ZHANG Xiaoyu %A ZHANG Zhenyou %A LI Wei %T Summary of Intrusion Detection Models Based on Deep Learning %D 2022 %R 10.3778/j.issn.1002-8331.2107-0084 %J Computer Engineering and Applications %P 17-28 %V 58 %N 6 %X 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2107-0084