通信学报2025，Vol.46Issue(1)：192-209,18.DOI:10.11959/j.issn.1000-436x.2025003
网络异常检测中的流量表示研究
Research on traffic representation in network anomaly detection
摘要
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 ana-lyzed from the perspective of data collection granularity.Firstly,the integrated collaboration between traffic representa-tion granularity and the coupling among traffic representation,feature learning,and detection in malicious anomaly de-tection was introduced.Subsequently,the evolution of traffic representation in network anomaly detection was systemati-cally 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
异常检测/网络流量/流量表示形式/特征类型/多模态流量表示Key words
anomaly detection/network traffic/traffic representation form/feature type/multimodal traffic representation分类
信息技术与安全科学引用本文复制引用
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(1):192-209,18.基金项目
国家自然科学基金资助项目(No.62276091) The National Natural Science Foundation of China(No.62276091) （No.62276091）
