CT-GCN+: a high-performance cryptocurrency transaction graph convolutional model for phishing node classification

Abstract

Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.