网络异常检测中的流量表示研究

孙剑文; 张斌; 常禾雨

doi:10.11959/j.issn.1000-436x.2025003

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网络异常检测中的流量表示研究

综述 | 更新时间：2025-02-13

- 网络异常检测中的流量表示研究
- Research on traffic representation in network anomaly detection
- 通信学报 2025年46卷第1期页码：192-209
- 作者机构：
  
  信息工程大学密码工程学院，河南郑州 450001
- 作者简介：
  
  [ "孙剑文（1988- ），女，北京人，信息工程大学博士生、工程师，主要研究方向为网络流量异常检测、机器学习等。" ]
  [ "张斌（1969- ），男，河南南阳人，博士，信息工程大学教授、博士生导师，主要研究方向为信息系统安全等。" ]
  [ "常禾雨（1993- ），女，河南郑州人，博士，信息工程大学助理研究员，主要研究方向为人工智能安全、网络信息防御。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62276091)
- DOI：10.11959/j.issn.1000-436x.2025003
  中图分类号： TP393.08
- 收稿日期：2024-09-25，
  
  修回日期：2024-11-19，
  
  纸质出版日期：2025-01-25
- 稿件说明：
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孙剑文,张斌,常禾雨.网络异常检测中的流量表示研究[J].通信学报,2025,46(01):192-209.

SUN Jianwen,ZHANG Bin,CHANG Heyu.Research on traffic representation in network anomaly detection[J].Journal on Communications,2025,46(01):192-209.
孙剑文,张斌,常禾雨.网络异常检测中的流量表示研究[J].通信学报,2025,46(01):192-209. DOI： 10.11959/j.issn.1000-436x.2025003.

SUN Jianwen,ZHANG Bin,CHANG Heyu.Research on traffic representation in network anomaly detection[J].Journal on Communications,2025,46(01):192-209. DOI： 10.11959/j.issn.1000-436x.2025003.

摘要

针对网络异常检测中流量表示存在的信息丢失问题，从数据采集粒度入手分析不同流量表示的特征信息维度对异常检测性能的影响。首先，介绍了恶意异常检测中流量表示粒度间的协同与耦合关系，以及异常检测中的流量表示、特征学习和检测三环节间的耦合关系。然后，系统审视流量表示在网络异常检测中的发展轨迹，深入分析了流量表示形式、流量特征学习与流量表示在异常检测中的应用3个方面的国内外研究现状。最后，围绕流量表示在网络异常检测应用中协同耦合的发展趋势对未来研究进行展望。

Abstract

Aiming to address the problem of information loss in traffic representation for network anomaly detection

the impact of feature information dimension of different traffic representation on anomaly detection performance was analyzed from the perspective of data collection granularity. Firstly

the integrated collaboration between traffic representation granularity and the coupling among traffic representation

feature learning

and detection in malicious anomaly detection was introduced. Subsequently

the evolution of traffic representation in network anomaly detection was systematically reviewed

providing a comprehensive analysis of its forms

feature learning

and application in anomaly detection both globally and domestically. Finally

the future research directions revolving around the collaborative development trend of traffic representation in network anomaly detection were outlined.

关键词

Keywords

references

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