Ethereum Phishing Scam Account Detection Method Based on Global Graph Transformer

Abstract

Existing Ethereum phishing scam detection methods based on graph neural networks focus on subgraph sampling while ignoring the global structure of the transaction network. When applied to graph structures, the sequence length of Transformers grows exponentially with the number of hops, leading to convergence issues for Transformer-based methods when applied to large-scale Ethereum data. To address these issues, this paper proposes a global graph transformer for Ethereum account detection, (GGTEAD). GGTEAD integrates node global attention extracted via dimensionality reduction algorithms with node local attention to strengthen transactional relationships between nodes. By introducing effective edge weighting strategies and anomaly scoring mechanisms, it significantly enhances detection capabilities for minority phishing accounts. This addresses the challenge of poor detection performance for phishing scam accounts within large-scale, heterogeneous Ethereum networks, providing valuable insights for IoT-blockchain integrated environments. The experimental findings, derived from two authentic Ethereum datasets (MulDiGraph and B4E) demonstrate that GGTEAD attains F1 scores of 84.36% and 71.22%, respectively, signifying enhancements of 5.53% and 2.88% over the prevailing state-of-the-art methods ScamSweeper and Bert4eth.