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

Authors: Yiyu Dai; Junzheng Lu; Zesen Li; Jiawei Li; Mobina Rafieipour

Addresses: Information Technology Development and Management Office, Huaqiao University, Xiamen, 361021, China ' Information Technology Development and Management Office, Huaqiao University, Xiamen, 361021, China ' Information Technology Development and Management Office, Huaqiao University, Xiamen, 361021, China ' Information Technology Development and Management Office, Huaqiao University, Xiamen, 361021, China ' Electrical and Computer Engineering, University of Victoria, Victoria, V8P 5C2, Canada

Abstract: 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.

Keywords: energy network security threat identification; GNN-transformer; multi-head self-attention; spatio-temporal feature fusion; intrusion detection.

DOI: 10.1504/IJGEI.2026.152150

International Journal of Global Energy Issues, 2026 Vol.48 No.7, pp.64 - 84

Received: 01 Dec 2025
Accepted: 30 Jan 2026
Published online: 09 Mar 2026 *