Real-Time Anomaly Detection for Large-Scale Network Devices
Tao Lei, Shenglin Zhang, Junhua Kuang, Xiao‐Wei Guo, Canqun Yang · IEEE Transactions on Networking · 2025
With the booming of large-scale network devices, anomaly detection on multivariate time series (MTS), such as a combination of CPU utilization, average response time, and network packet loss, is important for system reliability. Although a collection of learning-based approaches have been designed for this purpose, our study shows that these approaches suffer from long initialization time for sufficient training data. Our previously proposed JumpStarter model stands as a MTS anomaly detection method characterized by its brief initialization time and commendable detection performance. However, it suffers from high computational cost and inappropriateness for periodic MTS. In this paper, we propose VersaGuardian, which introduces the Dynamic Mode Decomposition technique to MTS anomaly detection for diverse types of MTS in a rapidly initialized, computationally efficient manner. With real-world MTS datasets collected from three companies, our results show that VersaGuardian achieves an average F1 score of 94.42%, significantly outperforming the popular anomaly detection algorithms, with a much shorter initialization time of 20 minutes and detection time of 15.28 milliseconds.