Paper Info
Title
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data
Abstract
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).
Year
DOI
Venue
2021
10.1109/TII.2020.3022432
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Anomaly detection,feature representation,industrial big data (IBD),long short-term memory (LSTM),variational Bayes
Journal
17
Issue
ISSN
Citations 
5
1551-3203
12
PageRank 
References 
Authors
0.58
0
5
Name
Order
Citations
PageRank
Xiaokang Zhou122525.50
Yiyong Hu2120.58
Wei Liang3676.75
Jianhua Ma41401148.82
Jin, Q.523333.40