PDHG: An Ethereum phishing detection approach via heterogeneous graph transformer

Abstract

Phishing scams have emerged as a significant threat within the Ethereum ecosystem. Cutting-edge Ethereum phishing scams detection techniques mostly treat accounts in Ethereum as homogeneous nodes in transaction graphs. Existing detection approaches model Ethereum transaction records as homogeneous transaction graphs and use graph representation learning for account classification. However, those approaches often overlook the heterogeneity between accounts and transactions, making it difficult to capture the diversity of interactions and features among accounts. In this paper, a heterogeneous graph transformer (HGT)-based phishing account identification approach called PDHG is proposed. Specifically, PDHG models the transaction network between accounts as a heterogeneous graph based on different attributes of Ethereum accounts, allowing for a more comprehensive description of the structure and behavioral patterns of the transaction network. To enhance the explainability, PDHG leverages PDHGexplainer as the explainer for the detection results. We compare PDHG with other existing detection models. The experimental results demonstrate that PDHG achieves an AUC score of 96.04 % and a recall score of 89.87 %, surpassing the state-of-the-art approaches.