Abstract:

Ethereum faces increasingly complex fraud threats, while existing phishing detection methods mostly relied on static analysis of raw transaction networks, overlooked fine-grained dynamic transaction sequence patterns of accounts, and failed to fully exploit the synergistic benefits of structural and sequential features. Therefore, this paper proposed a TransG4Eth framework, which combined a transaction-level Transformer with a graph neural network to capture both transaction context information and account interaction relationships. The proposed method first constructed a Transformer-based transaction sequence encoder, which modeled the complete transaction history of accounts as fine-grained behavioral features. Second, it designed a contextual address recovery pre-training task, which guided the model to learn implicit interaction patterns among accounts in transaction sequences. Then, it constructed a global account interaction graph to explicitly characterize structural topology. Finally, it proposed an implicit-explicit dual learning framework, which achieved two-stage fusion at both the embedding level and the output level. Experimental results show that TransG4Eth significantly outperforms existing methods on real-world datasets, obtains an F1 Score of 85.99%.

Key words: blockchain, Ethereum, fraud detection, Transformer, graph representation learning