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LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT
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Wu, Di, Jiang, Zhongkai, Xie, Xiaofeng, Wei, Xuetao, Yu, Weiren and Li, Renfa (2020) LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT. IEEE Transactions on Industrial Informatics, 16 (8). pp. 5244-5253. doi:10.1109/TII.2019.2952917
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WRAP-LSTM-learning-Bayesian-Gaussian-anomaly-IoT-Yu-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2101Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TII.2019.2952917
Abstract
The data generated by millions of sensors in Industrial Internet of Things (IIoT) is extremely dynamic, heterogeneous, and large scale. It poses great challenges on the real-time analysis and decision making for anomaly detection in IIoT. In this paper, we propose a LSTM-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in IIoT. In a nutshell, the LSTM-NN builds model on normal time series. It detects outliers by utilising the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of Gaussian Naive Bayes model through the predictive error. Empirical studies demonstrate our solution outperforms the best-known competitors, which is a preferable choice for detecting anomalies.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > QA Mathematics | |||||||||||||||
| Divisions: | Faculty of Science > Computer Science | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Gaussian processes, Anomaly detection (Computer security), Internet of things, Machine learning | |||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Industrial Informatics | |||||||||||||||
| Publisher: | IEEE | |||||||||||||||
| ISSN: | 1551-3203 | |||||||||||||||
| Official Date: | August 2020 | |||||||||||||||
| Dates: |
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| Volume: | 16 | |||||||||||||||
| Number: | 8 | |||||||||||||||
| Page Range: | pp. 5244-5253 | |||||||||||||||
| DOI: | 10.1109/TII.2019.2952917 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Publisher Statement: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| RIOXX Funder/Project Grant: |
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