Phishing Frauds Detection Based on Graph Neural Network on Ethereum

Abstract

Blockchain, as an emerging technology, has vulnerabilities that make the blockchain ecosystem rife with many criminal activities. However, existing technologies of phishing fraud detection heavily rely on shallow machine learning, leading to low detection precision. To solve this problem, in this paper, we construct a graph classification network model TransDetectionNet. Particularly, we propose a node embedding algorithm named Edge-sampling To Node Vector (Esmp2NVec) that can effectively extract the features hiding in the directed transaction network. Then, we use graph convolutional neural networks (GraphSAGE and GCN) to learn the topological space structure between nodes for further extraction of node features, where the nodes represent Ethereum accounts. To evaluate the method, a series of transaction data from the real Ethereum system is leveraged to train the graph classification model, and several experiments are designed to measure the phishing accounts identification performance comparison between our method and the other related works. The final results of those experiments show that our method can effectively detect phishing accounts from the Ethereum system.