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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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Official URL: http://dx.doi.org/10.1109/TII.2019.2952917

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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
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:
DateEvent
August 2020Published
11 November 2019Available
11 November 2019Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
61602168[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61972145[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61932010[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2018RS3040Xiangtan Universityhttp://dx.doi.org/10.13039/501100007930

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