A Power Monitoring Network Attack Detection Mechanism based on Graph Convolutional Neural Network

The rapid development of information technology makes the network environment more complex and changeable, and also brings more complex network attack methods, which makes the power monitoring network face more challenges. Under such circumstances, deep learning techniques are increasingly becoming an important part of the attack detection field. Deep learning technology can make self-adaptive judgments on different network attack methods, realize the detection of network attacks, and then improve the accuracy of attack detection technology. This paper builds a power monitoring network attack detection system based on the graph convolutional neural network (GCNN). Our proposed method uses a graph convolutional neural network as a generator to generate a generation error (GE) graph, and implements block level anomaly detection through the convolutional layer and maximum pooling layer of the mean filter, enabling it to avoid the problem of insufficient detection accuracy in traditional frame level anomaly detection. Experimental results that the proposed method can accurately identify network attacks and reduce the false alarm rate of the system, thereby ensuring the smooth operation of the power monitoring network.