Abstract
The rapid proliferation of the Power Internet of Things (PIoT) has given rise to severe network security threats, with eavesdropping attacks emerging as a paramount concern. Traditional eavesdropping detection methods struggle to adapt to complex and dynamic attack patterns, necessitating the exploration of more intelligent and efficient anomaly localization approaches. This paper proposes an innovative method for eavesdropping node localization based on Kolmogorov-Arnold Networks (KANs). Leveraging the powerful ability of KANs to approximate arbitrary nonlinear functions, this method constructs an end-to-end mapping from heterogeneous node features to eavesdropping locations through flexible combinations of spline functions. To address the challenges of real-world power grid environments, this paper designs optimization strategies such as adaptive grid refinement and hierarchical sparsity regularization, further enhancing the model's robustness and interpretability. Extensive simulations and experiments on real power grid data demonstrate that the proposed method significantly outperforms traditional machine learning and mainstream deep learning approaches in terms of localization accuracy, generalization ability, and computational efficiency. This paper provides new perspectives and tools for intelligent power grid information security in IoT environments, holding significant innovative value in both theory and practice.