Network security threat identification based on GNN-transformer fusion model in energy cyber systems

At present, energy network security threat identification still faces the problem that temporal and network relationships are difficult to fuse. To address this issue, this study proposes a fusion model using Graph Neural Network (GNN) and Transformer model. This model mainly includes the following parts: using Graph Attention Network (GAN) to mine the spatial relationships between energy nodes and control entities; and using Multi-Head Self-Attention (MHSA) to extract long-range time series of energy regulation data. By combining the above two methods, the model well completes end-to-end threat detection for energy communication networks. The above research results verify that the method of joint modelling of spatial and temporal information has certain effectiveness in the field of energy network security, which provides a new idea for constructing adaptive threat identification methods in localised energy regulation networks.