Quality and Diversity Balanced Neighbor Selection against Eclipse Attack in Blockchain System

Blockchain technology has gained widespread adoption across diverse applications; however, its peer-to-peer network architecture remains susceptible to eclipse attacks via malicious neighbor infiltration. Existing defense mechanisms typically rely either on historical data to detect attacks post hoc or on diverse neighbor selection to prevent them. These approaches, however, exhibit critical limitations: detection-based strategies are inherently reactive, while diversity-based selection lacks rigorous quantitative models to characterize the differences between neighbors. To bridge this gap, this paper proposes integrating data analysis techniques directly into the neighbor selection process. Specifically, the proposed method dynamically evaluates peers’ block propagation performance and quantifies inter-peer differences using Wasserstein distance metrics. This enables the application of determinantal point processes (DPPs) to select an optimal set of high-performing and diverse peers as neighbors. Empirical evaluation utilizing Bitcoin network data demonstrates that the proposed scheme simultaneously achieves adaptive neighbor selection and robust protection against eclipse attacks.