EPSD-HOT: Ethereum Phishing Scam Detection by Higher-Order Topology

Abstract

In recent years, phishing scams have become one of the most rampant criminal activities on Ethereum, causing significant financial losses to investors and disruptions to the Ethereum ecosystem. Existing phishing scam detection methods typically model Ethereum transaction records as graphs, extracting features from paired nodes based on the topological relationships. However, these methods mostly focus on low-order relational aspects, neglecting higher-order structural information in the network. In this paper, we propose a new method — Ethereum Phishing Scam Detection by Higher-Order Topology (EPSD-HOT), which improves phishing scam detection performance by mining higher-order topological features from the network. We conduct experiments on a public dataset and a crawled real-world dataset, extracting ten subgraphs with distinct network characteristics. The experimental results show that the average AUC-ROC for the ten subgraphs is 0.9970, with improvements ranging from 0.0181 to 0.1658 compared to baseline methods. This indicates that our approach is highly robust and can effectively detect phishing scams across different subgraphs while overcoming the issue of class imbalance. By incorporating higher-order structural information into node features, this work offers new insights for enhancing phishing scam detection in Ethereum.