DREAM: Dual foREcAsting Model for Network Anomaly Detection
Résumé
In this paper, we address the challenge of anomaly detection in multivariate time series data related to environments requiring stringent guarantees, notably in 5G and beyond 5G systems promising high reliability and quality. Traditional approaches to anomaly detection, including supervised, unsupervised, and self-supervised methods often struggle with the diversity and unpredictability of real-world anomalies. To overcome these limitations, we propose a novel dual forecasting model approach, DREAM (Dual foREcAsting Model Anomaly Detection), which leverages both normal and unlabeled data to enhance detection accuracy. Our approach involves training two distinct models: one on normal behavior and the other on mixed behavior, and then comparing their outputs to identify anomalies. We also introduce new evaluation method, addressing the shortcomings of traditional point-wise evaluation. Our experiments, with multiple networked system datasets, demonstrate that DREAM outperforms traditional forecasting-based approaches, including when used in a hybrid manner with traditional forecasting algorithms.
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