Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

Xiaokang Zhou, Yiyong Hu, Wei Liang*, Jianhua Ma, Qun Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

146 Citations (Scopus)


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).

Original languageEnglish
Article number9195000
Pages (from-to)3469-3477
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number5
Publication statusPublished - 2021 May


  • Anomaly detection
  • feature representation
  • industrial big data (IBD)
  • long short-term memory (LSTM)
  • variational Bayes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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