Spatio-Temporal Anomaly Detection for 5G-Clusters: A Multi-Scale Fuzzy Contrastive Learning Approach

Anomaly detection is an important data-mining task closely related to specific applications. In recent years, spatio-temporal data deriving from various networks has been collected from many real-world scenarios, but how to detect anomalies in such data is still an open problem. In this article, we propose a novel anomaly detection framework, Multi-scale Fuzzy Contrastive Anomaly Detection (MFCAD), by capturing anomalous patterns of data from multiple spatio-temporal scales so as to learn distinguishable feature representations. Different from conventional reconstruction- or prediction-based anomaly detection methods, this approach is not concerned with the consistency of the encoded representation of the implicit layer and the discriminability of the implicit layer of the anomalies. MFCAD implements a more discriminative representation using fuzzy contrastive learning and explicitly performs anomaly detection in the potential space by measuring the distance between outliers and implicit values. This proposed method has been applied for the anomaly detection of 5G mobile network clusters (5G-MNCs) in China Mobile. Furthermore, in order to validate the generalizability of the proposed method, it is further tested on public datasets and experiment results show the promising performance.