TSMAE : A novel anomaly detection approach for Internet of Things time series data using memory-augmented autoencoder
Journal article
Gao, Honghao, Qiu, Binyang, Barroso, Ramón J. Durán, Hussain, Walayat, Xu, Yueshen and Wang, Xinheng. (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. pp. 1-15. https://doi.org/10.1109/TNSE.2022.3163144
Authors | Gao, Honghao, Qiu, Binyang, Barroso, Ramón J. Durán, Hussain, Walayat, Xu, Yueshen and Wang, Xinheng |
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Abstract | With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector. |
Keywords | anomaly detection; time-series classification; memory augmentation; autoencoder |
Year | 2022 |
Journal | IEEE Transactions on Network Science and Engineering |
Journal citation | pp. 1-15 |
Publisher | IEEE Computer Society |
ISSN | 2327-4697 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSE.2022.3163144 |
Scopus EID | 2-s2.0-85127470863 |
Page range | 1-15 |
Funder | National Key Research and Development Program of China |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | In press |
Publication dates | |
Online | 29 Mar 2022 |
Publication process dates | |
Deposited | 18 Jul 2023 |
Grant ID | 2020YFB1006003 |
https://acuresearchbank.acu.edu.au/item/8z52v/tsmae-a-novel-anomaly-detection-approach-for-internet-of-things-time-series-data-using-memory-augmented-autoencoder
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