Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Yulei Wu*, Hong Ning Dai, Haina Tang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

86 Citations (Scopus)


The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries toward Industry 4.0. By connecting sensors, instruments, and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains, such as transportation, energy, and factory, as well as for dynamically evolving networks. This article provides a useful investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful data sets, challenges, and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy, and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

Original languageEnglish
Pages (from-to)9214-9231
Number of pages18
JournalIEEE Internet of Things Journal
Issue number12
Early online date2 Jul 2021
Publication statusPublished - 15 Jun 2022

Scopus Subject Areas

  • Information Systems
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

User-Defined Keywords

  • Anomaly detection
  • graph neural networks (GNNs)
  • Industrial Internet of Things (IIoT)
  • industry 4.0


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