Research on traffic representation in network anomaly detection

SUN Jianwen; ZHANG Bin; CHANG Heyu

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

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Research on traffic representation in network anomaly detection

Comprehensive Reviews | 更新时间：2025-02-13

- Research on traffic representation in network anomaly detection
- Journal on Communications Vol. 46, Issue 1, Pages: 192-209(2025)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62276091)
- DOI：10.11959/j.issn.1000-436x.2025003
  CLC： TP393.08
- Received：25 September 2024，
  
  Revised：2024-11-19，
  
  Published：25 January 2025
- 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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