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Graph attention and Kolmogorov-Arnold network based smart grids intrusion detection

  • 0School of Computer Science and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China. wuying1992@cqust.edu.cn.
Scientific Reports +

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March 14, 2025

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March 14, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces GraphKAN, a novel intrusion detection system for smart grids. GraphKAN enhances cyberattack detection accuracy by integrating physical and network data with advanced graph and learnable activation functions.

Area Of Science

  • Cybersecurity
  • Electrical Engineering
  • Computer Science

Background

  • The digital transformation of power systems increases cyberattack risks due to complexity and interconnectivity.
  • Traditional intrusion detection systems struggle to integrate physical power grid data and model complex attack patterns.
  • Existing graph neural network (GNN) methods often overlook physical device interactions and rely on fixed activation functions.

Purpose Of The Study

  • To develop an advanced intrusion detection framework for smart grids that accurately captures intricate device interactions and complex attack patterns.
  • To enhance the precision of intrusion detection in critical power infrastructure by integrating physical and network data.
  • To overcome the limitations of traditional GNNs in representing nonlinear attack behaviors.

Main Methods

  • Introduced GraphKAN, a novel framework combining Graph Attention Network (GAT) and Kolmogorov-Arnold Network (KAN).
  • Constructed a comprehensive graph representation including power, IT, and communication devices, with edges representing physical and logical dependencies.
  • Employed GAT with multi-head attention for dynamic node weighting and KAN with learnable B-spline activations for enhanced nonlinear feature expression.

Main Results

  • GraphKAN achieved high detection accuracies: 97.63% (binary), 98.66% (ternary), and 99.04% (37-class).
  • Demonstrated significant accuracy improvements over state-of-the-art models (e.g., 5.73% gain over GA-RBF-SVM).
  • Validated performance on datasets from Mississippi State University and Oak Ridge National Laboratory.

Conclusions

  • GraphKAN effectively enhances intrusion detection accuracy in smart grids by integrating physical and network information.
  • The framework shows robust performance in identifying complex cyberattack patterns.
  • The combination of GAT and KAN offers a powerful approach for securing critical power infrastructure.

Keywords:

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