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January 1, 2026International Journal of Global Energy IssuesOpen Access

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

Yiyu Dai Junzheng Lu Zesen Li Jiawei Li Mobina Rafieipour

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Key Points

The aim is to identify network security threats specifically within energy cyber systems using a novel approach.
Utilized a GNN (Graph Neural Network) and transformer fusion model for analysis.
Focused on energy cyber systems to assess specific vulnerabilities.
Employed data-driven techniques for threat detection.
Demonstrated enhanced accuracy in threat detection compared to traditional methods.
Identified key vulnerabilities in network security.
Provided insights into the effectiveness of the GNN transformer model fusion.

Implication

This research identifies network security threats in energy cyber systems using advanced modeling techniques, suggesting improved defenses.

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Cite This Study

Dai et al. (2026) studied this question.

synapsesocial.com/papers/699fe38b95ddcd3a253e778a https://doi.org/10.1504/ijgei.2026.10076525

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