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Industrial control systems (ICSs) are facing increasing cyber-physicalattacks that can cause catastrophes in the physical system. Efficient anomalydetection models in the industrial sensor networks are essential for enhancingICS reliability and security, due to the sensor data is related to theoperational state of the ICS. Considering the limited availability of computingresources, this paper proposes a hybrid anomaly detection approach incloud-edge collaboration industrial sensor networks. The hybrid approachconsists of sensor data detection models deployed at the edges and a sensordata analysis model deployed in the cloud. The sensor data detection modelbased on Gaussian and Bayesian algorithms can detect the anomalous sensor datain real-time and upload them to the cloud for further analysis, filtering thenormal sensor data and reducing traffic load. The sensor data analysis modelbased on Graph convolutional network, Residual algorithm and Long short-termmemory network (GCRL) can effectively extract the spatial and temporal featuresand then identify the attack precisely. The proposed hybrid anomaly detectionapproach is evaluated using a benchmark dataset and baseline anomaly detectionmodels. The experimental results show that the proposed approach can achieve anoverall 11.19% increase in Recall and an impressive 14.29% improvement inF1-score, compared with the existing models.

Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks