Phishing Detection on Ethereum via Graph Neural Architecture Search of Transaction Subgraph

Abstract

With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.