The perception of data traffic in industrial Internet of Things(IIoT)edge devices plays a crucial role in ensuring production stability and supply chain security,and facilitates the maintenance and optimization of production process.In response to security issues such as background attacks on IIoT edge devices with stealth mechanisms and vulnerabilities in the backend of multi-production-line underlying device clusters,this paper proposes a random temporal axis traffic perception model for detecting sudden anomalies of edge data.The model synchronously perceives time windows of data traffic with the same temporal axis in multiple working cycles,calculates the average difference ratio of data traffic accumulations across different working cycle time windows to identifies anomalies of the data traffic.A comprehensive data traffic anomaly correlation situational awareness model is constructed for the IIoT of industrial clusters,based on residual matrices and average difference ratio matrices of data traffic in different working cycle time windows.The experiment demonstrates that the proposed model can effectively monitor security issues arising from backend vulnerabilities in IIoT edge devices in real-time.
关键词
工业互联网/边缘设备/异常数据流/感知模型/全域关联态势
Key words
Industrial Internet of Things(IIoT)/edge devices/anomalous data traffic/perception model/global correlation situation
维普
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