Abnormal behavior detection in industrial control systems based on CNN

Abstract

With the widespread application of Internet of Things technology in industrial control systems, abnormal behavior detection has become a key task to ensure the safety and stable operation of the system. We propose a multi-branch convolutional fusion neural network method to improve the accuracy and efficiency of abnormal behavior detection in Internet of Things industrial control systems. This method achieves efficient detection of abnormal behavior in video data by combining ResNet152 for spatial feature extraction and GRU for temporal feature extraction. Unlike traditional methods, this method adopts a multi-branch structure, which can simultaneously capture multi-scale feature information, significantly enhancing the richness of feature expression and the accuracy of detection. Experimental results show that on the UCF-Crime dataset, the accuracy of this method reaches 85.76%, which is significantly better than that of traditional methods. In addition, on the larger UCF-101 dataset, the accuracy of this method reaches 92.21%, further verifying its excellent generalization performance. Compared with the C3D network, this method improves the accuracy by nearly 6% while maintaining a high processing speed. These results show that the proposed method has great potential in practical applications.

Keywords

• IoT industrial control systems
• Abnormal behavior detection
• Multi-branch CNN
• Time series feature extraction