Graph Neural Networks for Anomaly Detection in Industrial Internet of Things
Graph Neural Networks for Anomaly Detection in Industrial Internet of Things
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DOI：
10.1109/jiot.2021.3094295
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发表时间：
2021-07
影响因子：
10.6
通讯作者：
Yulei Wu;Hongning Dai;Haina Tang
中科院分区：
文献类型：
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作者：
Yulei Wu;Hongning Dai;Haina Tang
工业物联网（IIoT）在传统工业向工业4.0的数字化转型中发挥着重要作用。通过将传感器、仪器和其他工业设备连接到互联网，工业物联网促进了数据收集、数据分析和自动化控制，从而提高了企业的生产力和效率，并带来了经济效益。由于工业物联网基础设施的复杂性，异常检测成为确保工业物联网成功的重要工具。由于工业物联网的性质，图级异常检测已经成为一种很有前途的方法，可以检测和预测许多不同领域的异常，如交通、能源和工厂，以及动态发展的网络。本文对图神经网络（gnn）在支持iiot的智能交通、智能能源和智能工厂中的异常检测进行了有用的研究。除了gnn支持的点异常、上下文异常和集体异常类型的异常检测解决方案外，还提供并讨论了三个确定的行业部门（即智能交通、智能能源和智能工厂）中每种异常类型的有用数据集、挑战和开放问题，这将对该领域的未来研究有用。为了演示GNN在具体场景中的应用，我们分别展示了智能交通、智能能源和智能工厂的三个案例研究。
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.