Unmasking the Hidden Threat: A Hierarchical Multi-Scale Graph Convolutional Network for Detecting Ethereum Phishing

Abstract

The rapid growth of Ethereum has enabled innovation in digital finance, smart contracts, and non-fungible tokens (NFTs) but it has also facilitated increasingly sophisticated phishing schemes. The existing fraud detection systems have limitations in addressing large-scale phishing involving multi-hop transaction patterns, rich edge metadata, extreme class imbalance, and hierarchical account organizations. This paper proposes a hierarchical multi-scale graph convolutional network (HMG-CN) that can: (1) adaptively fuse 1-3 hop convolutions to avoid over-smoothing, (2) perform edge-aware message passing conditioned on transaction attributes, (3) discover organizational structure via two-level hierarchical pooling, and (4) combine class-balanced focal loss with contrastive learning. In an experiment with a transaction graph with 805,327 nodes and 17.1 million directed edges derived from 47,123 labeled phishing addresses, HMG-CN outperformed classic machine learning (ML), graph embeddings, standard graph neural networks (GNNs), and recent phishing detectors across five different graph sizes. On 150,000 nodes, HMG-CN attained 0.943 F1 and 0.957 AUROC, exceeding the best baseline by 8.1% and 6.6%, respectively, uniquely improving its accuracy as the graph size increased. These results demonstrate that jointly modeling multi-scale structure, edges, and hierarchy yields scalable, accurate detection under low-prevalence conditions, providing a practical foundation for real-world blockchain security.