TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder
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Gao, Honghao 
ORCID: 0000-0001-6861-9684, Qiu, Binyang, Barroso, Ramon J Duran ORCID: 0000-0003-1423-1646, Hussain, Walayat ORCID: 0000-0003-0610-4006, Xu, Yueshen ORCID: 0000-0001-7210-0543 and Wang, Xinheng ORCID: 0000-0001-8771-8901
(2022)
TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder.
IEEE Transactions on Network Science and Engineering.
p. 1.
ISSN 2327-4697
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| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/44073 |
| DOI | 10.1109/TNSE.2022.3163144 |
| Official URL | https://ieeexplore.ieee.org/document/9744555 |
| Subjects | Current > FOR (2020) Classification > 3503 Business systems in context Current > Division/Research > VU School of Business |
| Keywords | chronological order, contextual data points, transmission devices |
| Citations in Scopus | 66 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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