基于机器学习的加密流量分类研究综述

付钰; 刘涛涛; 王坤; 俞艺涵

doi:10.11959/j.issn.1000-436x.2025006

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基于机器学习的加密流量分类研究综述

综述 | 更新时间：2025-02-13

- 基于机器学习的加密流量分类研究综述
- Survey of research on encrypted traffic classification based on machine learning
- 通信学报 2025年46卷第1期页码：167-191
- 作者机构：
  
  1.海军工程大学信息安全系，湖北武汉 430033
  2.信阳职业技术学院数学与信息工程学院，河南信阳 464000
  3.海军工程大学作战运筹与规划系，湖北武汉 430033
- 作者简介：
  
  [ "付钰（1982- ），女，湖北武汉人，博士，海军工程大学教授、博士生导师，主要研究方向为信息安全、人工智能。" ]
  [ "刘涛涛（1996- ），男，江西吉安人，海军工程大学博士生，主要研究方向为人工智能、信息处理、网络安全。" ]
  [ "王坤（1981- ），女，河南信阳人，海军工程大学博士生，信阳职业技术学院副教授，主要研究方向为信息安全、人工智能。" ]
  [ "俞艺涵（1992- ），男，浙江金华人，博士，海军工程大学讲师，主要研究方向为隐私保护、信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62102422);河南省科技攻关基金资助项目(242102211070)
- DOI：10.11959/j.issn.1000-436x.2025006
  中图分类号： TP393
- 收稿：2024-09-29，
  
  修回：2024-12-13，
  
  纸质出版：2025-01-25
- 稿件说明：
移动端阅览
付钰,刘涛涛,王坤等.基于机器学习的加密流量分类研究综述[J].通信学报,2025,46(01):167-191.

FU Yu,LIU Taotao,WANG Kun,et al.Survey of research on encrypted traffic classification based on machine learning[J].Journal on Communications,2025,46(01):167-191.
付钰,刘涛涛,王坤等.基于机器学习的加密流量分类研究综述[J].通信学报,2025,46(01):167-191. DOI： 10.11959/j.issn.1000-436x.2025006.

FU Yu,LIU Taotao,WANG Kun,et al.Survey of research on encrypted traffic classification based on machine learning[J].Journal on Communications,2025,46(01):167-191. DOI： 10.11959/j.issn.1000-436x.2025006.

摘要

加密流量分类是网络管理和安全防护的重要组成部分，不过当前网络流量环境复杂多变，致使传统的分类方法已基本失效。而机器学习，尤其是深度学习，凭借强大的特征提取能力已广泛应用于加密流量分类领域。为此，对机器学习驱动的加密流量分类最新成果进行系统性综述，首先将加密流量分类工作划分为数据采集与处理、特征提取与选择及流量分类与性能评估3个部分，分别对应加密流量分类中的数据获取、显著特征构建及模型的应用与验证；接着将这3个部分内容细分为流量采集、数据集构建、数据预处理、特征提取、特征选择、分类模型及性能评估7个阶段；然后分别对这7个阶段进行全面的归纳、总结与分析；最后详细分析当前工作所面临的挑战并展望加密流量分类未来的研究方向。

Abstract

Encrypted traffic classification was an important component of network management and security protection. However

the complexity and variability of the current network traffic environment rendered traditional classification methods largely ineffective. Machine learning

particularly deep learning

with its strong feature extraction capabilities

has been widely used in the field of encrypted traffic classification. To this end

a systematic review of the latest advancements in machine learning-driven encrypted traffic classification was provided. Firstly

the encrypted traffic classification work was roughly divided into three parts: data collection and processing

feature extraction and selection

and traffic classification and performance evaluation

which correspond to data acquisition

significant feature construction

and model application and validation in encrypted traffic classification. The content was further subdivided into seven stages: traffic collection

dataset construction

data preprocessing

feature extraction

feature selection

classification models

and performance evaluation. A comprehensive summary

synthesis

and analysis of these seven stages were then conducted. Finally

the challenges faced by current research were analyzed in detail

and the future research directions for encrypted traffic classification were prospected.

关键词

Keywords

references

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