Random Time-Axis Traffic Perception Model to Ensure Security in Industrial Internet of Things Edge Devices
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.
Industrial Internet of Things(IIoT)edge devicesanomalous data trafficperception modelglobal correlation situation
万方数据
维普
