Dynamic graph embedding-based anomaly detection on internet of things time series
- Authors
- Li, G.; Jung, Jason J.
- Issue Date
- Feb-2024
- Publisher
- John Wiley and Sons Inc
- Keywords
- anomaly detection; graph embedding; graph entropy; internet of things
- Citation
- Expert Systems, v.41, no.2
- Journal Title
- Expert Systems
- Volume
- 41
- Number
- 2
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/58356
- DOI
- 10.1111/exsy.13083
- ISSN
- 0266-4720
1468-0394
- Abstract
- Anomaly detection is critical in the internet of things (IoT) environment. To this issue, this study provides a novel approach for detecting anomalies in multivariate IoT time series. The proposed approach identified relationships between IoT time series to establish a dynamic graph and estimated the graph entropy to detect anomalies. The presented approach was applied to industrial IoT datasets. The results have shown that the presented method outperformed other models by 0.21 with respect to F1-score. In addition, we used three distinct algorithms to detect the anomalies from the multivariate IoT time series. According to the results, the local outlier factor approach outperformed the others by 0.18 with respect to F1-score. © 2022 John Wiley & Sons Ltd.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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