EthAegis: Featured graph based Fraud Detection in Ethereum Transactions

Abstract

Detecting fraudulent transactions in Ethereum is challenging due to the evolving tactics of malicious actors and the complexity of blockchain transactions. This work constructs a proximity-aware graph (PAG) for each Ethereum account, where each node is connected to its transactional neighbours within two hops to capture financial interactions effectively. For better performance, EthAegis integrates both local graphical features and global transactional properties of each proximity-aware graph (PAG).To effectively capture the graphical and transactional relationships between Ethereum accounts, Graph Sample and Aggregate (GraphSAGE) are deployed. It is a Graph Neural Network (GNN) based model that is optimised for inductive learning on large-scale graphs. It is observed that EthAegis could effectively capture hidden patterns and anomalies in Ethereum transactions by considering both the transactional and graphical characteristics. Experimental evaluations on real Ethereum transaction data demonstrate that this method significantly improves fraud detection accuracy while ensuring scalability, providing a robust and adaptive solution to secure blockchain transactions. The proposed model achieves an accuracy of 0.9908, precision of 0.9922, recall of 0.9894, and F1-score of 0.9908 on the Ethereum transactional dataset.