ANOMALY DETECTION METHOD FOR KPIS IN APPLICATION SYSTEMS BASED ON MULTI-FEATURE FUSION
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.
KPIsAnomaly detectionFeature extractionVariational auto-encoderExtreme value theory
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