Edge-feature Modeling-based Topological Graph Neural Networks for Phishing Scams Detection on Ethereum

Abstract

Detecting phishing scams has become an important task in blockchain-based cryptocurrency applications. While many network representation learning-based approaches have been proposed for this task, they suffer from various issues including (1) the requirement of handcrafted features, which may not capture complex relationships and patterns in graph data, and/or (2) considering only node features while ignoring the more significant edge features, and/or (3) incapability of preserving complete network topology, which affects the generalization ability. In this paper, we propose a novel Edge-feature modeling-based Topological Graph Neural Network (ETGNN) to detect phishing scams on Ethereum, which avoids all aforementioned issues of existing approaches. Specifically, ETGNN involves two key components, one responsible for learning weighted features of nodes and edges in the Ethereum transaction graph, and the other responsible for incorporating global topological information of the graph using persistent homology. Finally, phishing scams are detected based on these two learned features. The experimental results demonstrate that ETGNN outperforms the state-of-the-art method with an improvement rate of 14.38% on F1-score.