Abstract: In recent years, in order to protect the public privacy, a lot of traffic on the Internet is encrypted. The accuracy of traditional deep packet inspection and machine learning methods in the face of encrypted traffic has dropped significantly. With the application of deep learning automatic learning features, the encryption flow identification and classification technology based on deep learning algorithms have been rapidly developed. This article reviews these studies. First, this paper briefly introduces the application scenarios of encrypted traffic detection based on deep learning. Then, it summarizes and evaluates the existing works from three aspects: the use and construction of data sets, the detection model and the detection performance. The detection technology focuses on data preprocessing, unbalanced data set processing, neural network construction, real-time detection, etc. Finally, the problems in current research and future development directions and prospects are discussed, so as to provide some references for researchers in this field.

Key words: cyber security, encrypted traffic, classification, deep learning, machine learning