计算机应用与软件2024，Vol.41Issue(8)：74-83,10.DOI:10.3969/j.issn.1000-386x.2024.08.011
基于多特征融合的应用系统监控指标异常检测方法
ANOMALY DETECTION METHOD FOR KPIS IN APPLICATION SYSTEMS BASED ON MULTI-FEATURE FUSION
摘要
Abstract
In order to solve the problems of existing KPIs anomaly detection methods,such as insufficient feature learning and fixed thresholds,we propose a anomaly detection method for KPIs in application systems based on multi-feature fusion.We used the 1D-convolutional neural network(1D-CNN)and stochastic recurrent neural network(SRNN)to extract data features,and introduced the squeeze-and-excitation(SE)block to highlight the key features of KPIs to optimize feature extraction and strengthen the classification effect.We used the variational auto-encoder(VAE)as the framework to calculate the reconstruction probability of data,and calculated the best anomaly threshold through the extreme value model to determine anomalies.Experimental results show that the proposed method can effectively detect outlier on two public datasets,with best Fl score of 92％,and has better performance than some advanced anomaly detection methods.关键词
监控指标/异常检测/特征提取/变分自编码器/极值理论Key words
KPIs/Anomaly detection/Feature extraction/Variational auto-encoder/Extreme value theory分类
信息技术与安全科学引用本文复制引用
曹钰聪,张俊..基于多特征融合的应用系统监控指标异常检测方法[J].计算机应用与软件,2024,41(8):74-83,10.基金项目
国家自然科学基金项目(61976032). （61976032）
