Behavior Path Analysis for Blockchain Fraud Detection Using Graph Neural Architectures

Abstract

With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoods. In addition, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is introduced to capture behavioral paths across block-level transactions, enabling accurate classification and prediction of abnormal accounts within blockchain networks. Experiments conducted on an Ethereum transaction dataset—containing approximately 3.6 million transaction records and 40,000 labeled addresses—show that the FraudGNN model significantly outperforms traditional methods such as Random Forest and Graph Convolutional Networks (GCN) in key metrics, achieving 91.2% precision, 87.5% recall, and an F1-score of 89.3%. In particular, the model demonstrates stronger generalization and reasoning capabilities when identifying previously unseen addresses, offering solid technical support for improving blockchain security systems.