A Neighborhood Reconstruction-Based Cyber Attack Detection Method for Smart Grid Security

The integration of advanced communication and information technologies in smart grids has led to enhanced efficiency and reliability but also introduced security vulnera-bilities, prompting the need for robust cyber attack detection methods. Traditional approaches struggle to capture evolving attack patterns and handle high-dimensional data, highlighting the necessity for more sophisticated approaches. A neighbor-hood reconstruction-based smart grid attack detection scheme based on subgraphs is proposed. By leveraging Graph Neural Networks (GNNs), the challenge of capturing complex inter-dependencies among grid nodes is addressed. This approach employs unsupervised learning principles, training the model solely on normal data and utilizing the reconstruction error of node features to detect attacks. Additionally, by subgraph sampling and feature suppression, the model's ability to utilize neighborhood information is enhanced, thereby further improving detection effectiveness. Simulation results on IEEE 30-bus and IEEE 118-bus power system demonstrate the feasibility of the method, achieving a detection accuracy of 96.67% and 97.46%, respectively.

I. Introduction

As an upgraded version of modern power systems, the smart grid integrates advanced communication and information technologies, aiming to enhance the efficiency, re-liability, and sustainability of the power system. By real-time monitoring, control, and optimization of power flow, the smart grid enables more flexible and intelligent energy distribution. However, the large-scale interaction and multilevel mapping of information and energy flows render the smart grid susceptible to security attacks. Attack behaviors propagated through information flow can lead to anomalies in energy flow, and even result in system collapse, posing an extremely serious threat to the security of the grid. Therefore, the reliability of information flow is of crucial importance in ensuring the safe flow of energy and stable operation of the system.