Dynamic graph embedding-based anomaly detection on internet of things time series
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, G. | - |
dc.contributor.author | Jung, Jason J. | - |
dc.date.accessioned | 2022-07-28T05:40:41Z | - |
dc.date.available | 2022-07-28T05:40:41Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.issn | 0266-4720 | - |
dc.identifier.issn | 1468-0394 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/58356 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | John Wiley and Sons Inc | - |
dc.title | Dynamic graph embedding-based anomaly detection on internet of things time series | - |
dc.type | Article | - |
dc.identifier.doi | 10.1111/exsy.13083 | - |
dc.identifier.bibliographicCitation | Expert Systems, v.41, no.2 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000814596200001 | - |
dc.identifier.scopusid | 2-s2.0-85132417679 | - |
dc.citation.number | 2 | - |
dc.citation.title | Expert Systems | - |
dc.citation.volume | 41 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | graph embedding | - |
dc.subject.keywordAuthor | graph entropy | - |
dc.subject.keywordAuthor | internet of things | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial IntelligenceComputer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.