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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Shiming He1,4, Meng Guo1, Bo Yang1, Osama Alfarraj2, Amr Tolba2, Pradip Kumar Sharma3, Xi’ai Yan4,*

1 School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410114, China
2 Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
3 Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3FX, UK
4 Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha, 410138, China

* Corresponding Author: Xi’ai Yan. Email: email

Computers, Materials & Continua 2023, 75(3), 5027-5047. https://doi.org/10.32604/cmc.2023.038551

Abstract

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators. Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework. To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly. Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators. Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

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Cite This Article

APA Style
He, S., Guo, M., Yang, B., Alfarraj, O., Tolba, A. et al. (2023). Fine-grained multivariate time series anomaly detection in iot. Computers, Materials & Continua, 75(3), 5027-5047. https://doi.org/10.32604/cmc.2023.038551
Vancouver Style
He S, Guo M, Yang B, Alfarraj O, Tolba A, Sharma PK, et al. Fine-grained multivariate time series anomaly detection in iot. Comput Mater Contin. 2023;75(3):5027-5047 https://doi.org/10.32604/cmc.2023.038551
IEEE Style
S. He et al., "Fine-Grained Multivariate Time Series Anomaly Detection in IoT," Comput. Mater. Contin., vol. 75, no. 3, pp. 5027-5047. 2023. https://doi.org/10.32604/cmc.2023.038551



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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