Applied Sciences, Vol. 13, Pages 11349: Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model

Applied Sciences, Vol. 13, Pages 11349: Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model

Applied Sciences doi: 10.3390/app132011349

Authors: Tao Shi Zhuoliang Zou Jun Ai

The detection of anomalies in software systems has become increasingly crucial in recent years due to their impact on overall software quality. However, existing integrated anomaly detectors usually combine the results of multiple detectors in a clustering manner and do not consider the changes in data anomalies in the time dimension. This paper investigates the limitations of existing anomaly detection methods and proposes an improved integrated anomaly detection approach based on time windows and a voting mechanism. By utilizing multiple time windows, the proposed method overcomes the challenges of cumulative anomalies and achieves enhanced performance in capturing anomalies that accumulate gradually over time. Additionally, two hybrid models are introduced, based on accuracy and sensitivity, respectively, to optimize performance metrics such as AUC, precision, recall, and F1-score. The proposed method demonstrates remarkable performance, achieving either the highest or only a marginal 3% lower performance compared to the optimal model.