GRNN-Based Detection of Eavesdropping Attacks in SWIPT-Enabled Smart Grid Wireless Sensor Networks

This paper proposes a novel Graph Recurrent Neural Network (GRNN) based approach for detecting eavesdropping attacks in smart grid wireless communication systems enabled by Simultaneous Wireless Information and Power Transfer (SWIPT). By leveraging the graph-centric nature of GRNNs, the proposed method effectively learns the topological structure and edge features of Wireless Sensor Networks (WSNs), enabling the detection of eavesdropping attacks in dynamic WSNs. This paper mathematically models the Channel State Information (CSI) under man-in-the-middle eavesdropping attacks based on Physical Layer Security (PLS) in SWIPT networks. Moreover, this paper sets up a real-world testbed to create training and testing datasets. The proposed GRNN model can handle large-scale complex topologies and dynamic eavesdropping networks, accurately detect eavesdropping behaviors, and enhance the security of information transmission in WSNs. Simulation results demonstrate that, compared with algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), and Gated Recurrent Unit (GRU), the proposed method exhibits stronger robustness under complex attack scenarios, achieving a detection accuracy of over 95%. This paper provides a novel and effective graph learning solution for smart grid wireless communication security, which is of great significance to ensure the stable and reliable operation of smart grids.