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

Yiyu Dai et al.

International Journal of Global Energy Issues2026https://doi.org/10.1504/ijgei.2026.152150article
ABDC C
Weight
0.50

What the paper says

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-toend 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.

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https://doi.org/https://doi.org/10.1504/ijgei.2026.152150

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@article{yiyu2026,
  title        = {{Network security threat identification based on GNN-transformer fusion model in energy cyber systems}},
  author       = {Yiyu Dai et al.},
  journal      = {International Journal of Global Energy Issues},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijgei.2026.152150},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.