TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder

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Gao, H, Qiu, B, Duran Barroso, RJ, Hussain, Walayat ORCID: 0000-0003-0610-4006, Xu, Y and Wang, X (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
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