A Human-Centered Situation Security Framework Based on Multi-Sensor Data Fusion for Abnormal Behavior Recognition in the Industrial Internet

To guarantee the robust security of the industrial internet, we propose a human-centered situational security framework that comprehensively considers multi-sensor industrial elements. Constructing an industrial spatial morphism mechanism allows for in-depth analysis of abnormal behaviors. Through the acquisition and effective analysis of multi-sensor data in industrial scenarios, a human-centered situation security is established, the computational complexity of the abnormal behavior identification model is reduced, while the accuracy and efficiency of identification are improved. This method has been verified for its generality in the processing data of metallic materials in 5G sensor networks. The experimental results demonstrate that the industrial situational triplets, validated through ISA-95 standard testing, achieve an average coverage rate of 99.6% in industrial scenarios. Extensive experiments on the Gas dataset and a real-world 5G-enabled production line demonstrated that the proposed method achieved mean precisions of 97.35% and 97.41%, respectively. Compared with the non-vectorized data, the processing time for 5G data and EdgeIIoT data decreased by 79.4 s and 52.9 s, respectively. The ablation study confirms that incorporating the intention component (T) yields a 13.92-percentage-point F1-score improvement over the E + A configuration. Through multi-dimensional performance validation, the security analysis framework for multi-sensor data in industrial scenarios enhances the identification performance of abnormal behaviors in the industrial internet and has excellent generalization capabilities.