计算机工程与应用2025，Vol.61Issue(21)：61-80,20.DOI:10.3778/j.issn.1002-8331.2501-0218
基于深度学习的加密流量分类研究综述
Survey of Research on Encrypted Traffic Classification Based on Deep Learning
摘要
Abstract
With the rapid development of Internet technology,the types of network traffic have become complex and vari-able.At the same time,to ensure data security,a large amount of information is transmitted through encryption protocols.Under the encryption condition,the original information of the traffic is invisible,which makes traditional traffic classifi-cation methods no longer applicable.In this context,the encryption traffic classification technology has emerged.Its aim is to identify and distinguish the types and sources of encrypted traffic.Encryption traffic classification not only needs to correctly distinguish various known traffic for traffic control but also needs to identify unknown traffic that may carry malicious information for security protection.This paper introduces the research status of encryption traffic classification technology in a general environment,analyzing from three aspects:known traffic classification,unknown traffic classifica-tion,and data sets.It discusses the future development trends of encryption traffic classification technology in a general environment.Furthermore,it analyzes the research status of encryption traffic classification technology in an industrial internet environment,including the lack of public traffic data,diverse industrial protocols,and complex feature extraction.Finally,it looks forward to future research directions,including building high-quality data sets,optimizing feature extrac-tion methods,and improving the accuracy of unknown traffic detection.To achieve precise classification of encrypted traffic and ensure network service quality and security protection,it provides references and inspirations.关键词
加密流量/未知流量检测/深度学习/类不平衡/工业互联网Key words
encrypted traffic/unknown traffic detection/deep learning/data imbalance/industrial Internet分类
计算机与自动化引用本文复制引用
王影,王钢,高雲鹏,霍闯..基于深度学习的加密流量分类研究综述[J].计算机工程与应用,2025,61(21):61-80,20.基金项目
国家自然科学基金(62472237). （62472237）
