Journal
EXPERT SYSTEMS
Volume -, Issue -, Pages -Publisher
WILEY
DOI: 10.1111/exsy.13083
Keywords
anomaly detection; graph embedding; graph entropy; internet of things
Funding
- National Research Foundation of Korea [NRF2020R1A2B5B01002207]
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This study presents a novel approach for anomaly detection in IoT time series data and achieves better performance compared to other models on industrial IoT datasets.
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.
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