西华大学学报（自然科学版）2025，Vol.44Issue(5)：48-59,12.DOI:10.12198/j.issn.1673-159X.5868
基于并行时间通道注意力反馈网络的微服务异常多分类
Anomaly Multi-classification for Microservices Based on Parallel Time Channel Attention Feedback Network
摘要
Abstract
In microservice architectures,multivariate time series data constitutes a core component of system monitoring and operations maintenance,with anomaly types often exhibiting significant diversity and complexity.However,current research lacks sufficient studies on multi-class classification methods for anomalies.Therefore,this paper proposes a multi-class anomaly detection model for microservice systems based on a parallel time channel attention feedback network(PTCAFNet).The model uses temporal convo-lutional network(TCN)to extract long-term dependency features from multivariate time series,employs squeeze-and-excitation network(SENet)to extract global features,and adopts parallel channel attention mechanisms to separately capture global and local features,and obtains global anomaly patterns and fine-grained anomaly characteristics to enhance anomaly recognition capability and robustness.Through feed-back mechanisms(IM),it strengthens feature fusion,and improves channel relationship modeling and ro-bust feature representation.Experimental results demonstrate that PTCAFNet achieves average scores of 0.922 in Macro-F1 and 0.963 in Micro-F1,outperforming baseline models by approximately 36.9%and 3.3%respectively.关键词
多元时间序列/深度学习/异常多分类/通道注意力/微服务Key words
multivariate time series/deep learning/multi-class anomaly classification/channel attention/microservice分类
信息技术与安全科学引用本文复制引用
席庆辉,陈鹏,李曦,边昂..基于并行时间通道注意力反馈网络的微服务异常多分类[J].西华大学学报（自然科学版）,2025,44(5):48-59,12.基金项目
国家自然科学基金项目(62172062) （62172062）
四川省自然科学基金创新研究群体项目(2024NSFTD0008) （2024NSFTD0008）
四川省科技计划项目(2024ZHCG0016) （2024ZHCG0016）
四川省文化和旅游科研项目(2024YB19). （2024YB19）
