Vajda Dániel László, Do Tien Van, Bérczes Tamás, Farkas Károly
Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Magyar tudósok krt. 2, 1117, Budapest, Hungary.
Faculty of Informatics, University of Debrecen, Kassai út 26, 4028, Debrecen, Hungary.
Sci Rep. 2024 Oct 7;14(1):23288. doi: 10.1038/s41598-024-72982-z.
Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro. The novelty of the proposed algorithms lies in exploiting the periodic nature of data in anomaly detection. Our proposed algorithms apply a variational mode decomposition technique to find and extract periodic components from the original data before using Long Short-Term Memory neural networks to detect anomalies in the remainder time series. Furthermore, our methods include advanced techniques to eliminate prediction errors and automatically tune operational parameters. Extensive numerical results show that the proposed algorithms achieve comparable performance in terms of Precision, Recall, F-score, and MCC metrics while outperforming most of the state-of-the-art anomaly detection approaches in terms of initialisation delay and detection delay, which is favourable for practical applications.
在遥测系统中，快速准确的异常检测至关重要，因为它能帮助操作人员针对异常行为采取适当行动。然而，最近的技术虽然准确，但处理实时数据的速度不够快。有必要减少异常检测时间，这促使我们提出两种新算法，即AnDePeD（周期性数据异常检测器）和AnDePed Pro。所提出算法的新颖之处在于在异常检测中利用数据的周期性。我们提出的算法应用变分模态分解技术，在使用长短期记忆神经网络检测剩余时间序列中的异常之前，从原始数据中找到并提取周期性成分。此外，我们的方法包括消除预测误差和自动调整操作参数的先进技术。大量数值结果表明，所提出的算法在精度、召回率、F值和MCC指标方面取得了可比的性能，同时在初始化延迟和检测延迟方面优于大多数现有异常检测方法，这有利于实际应用。
