Research on the Construction and Optimization Method of Intelligent Segmentation Model for Power Users in the Context of Communication Security Standards
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Abstract
This study proposes two intelligent segmentation models, SA-HECC and FGPPS, for secure and efficient clustering of power users under communication security standards (IEC 62351 and ISO/IEC 27019). SA-HECC uses a centralized deep embedding network with constrained clustering, while FGPPS employs federated graph-enhanced privacy-preserving segmentation via graph neural networks. Both models integrate metaheuristic optimization (NSGA-II, GA+PSO) to balance clustering accuracy, computational efficiency, and security compliance. The models are evaluated on real and synthetic datasets, demonstrating improvements of up to 18.84% in Silhouette Score, over 30% higher security compliance, and superior intrusion detection rates under simulated cyber-attacks. This framework is particularly applicable to modern smart grids and wireless communication infrastructures supported by antenna networks, where reliable, secure, and privacy-preserving data processing is essential. Results confirm that security-aware intelligent segmentation bridges the gap between analytics performance and regulatory compliance, providing a robust methodology for cyber-resilient and operationally reliable power systems.
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