LSTM Learning with Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

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.

Publication DOI: https://doi.org/10.1109/TII.2019.2952917
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 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. Funding: HuXiang Youth Talent Program; National Natural Science Foundation of China.
Publication ISSN: 1941-0050
Full Text Link:
Related URLs: https://ieeexpl ... cument/8896029/ (Publisher URL)
PURE Output Type: Article
Published Date: 2020-08
Published Online Date: 2019-11-11
Accepted Date: 2019-11-01
Authors: Wu, Di
Jiang, Zhongkai
Xie, Xiaofeng
Wei, Xuetao
Yu, Weiren (ORCID Profile 0000-0002-1082-9475)
Li, Renfa

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