ALSR: An Adaptive Label Screening and Relearning Approach for Anomaly Detection

Abstract

Anomaly detection using KPIs (Key Performance Indicators) is key to AIOps (Artificial Intelligence for IT Operations). Recent anomaly detection approaches have adopted machine learning to detect anomalies on the perspective of individual time points more than events. These approaches do not make effective use of the labels of continuous anomaly intervals, nor do they pay attention to the differences among anomaly points. The detection performance is therefore not high enough. In this paper, we propose an anomaly detection approach named ALSR, which uses a label screening model and a relearning model to analyze and utilize the continuous anomaly intervals of KPIs in finer granularity. The label screening algorithm takes advantage of the continuity of anomaly intervals to remove unnecessary data from the training set, so as to better suit to interval-oriented anomaly detection. The relearning algorithm reclassifies the true/false positive points within range of detected anomalies, thus effectively reduces the number of false positive points. ALSR uses statistical characteristics and time series models for feature extraction, and the feature set is proved to better describe the characteristics of KPIs. We conduct comprehensive experiments on 25 KPIs, and the total F-score of ALSR is 0.965, which outperforms state-of-the-art anomaly detection approaches.