Graph Neural Network for Ethereum Fraud Detection

Currently, the blockchain technology has been widely applied to various industries, and has attracted wide attention. However, because of its unique anonymity, digital currency has become a haven for all kinds of cyber crimes. It has been reported that Ethereum frauds provide huge profits, and pose a serious threat to the financial security of the Ethereum network. To create a desired financial environment, an effective method is urgently needed to automatically detect and identify Ethereum frauds in the governance of the Ethereum system. In view of this, this paper proposes a method for detecting Ethereum frauds by mining Ethereum-based transaction records. Specifically, web crawlers are used to capture labeled fraudulent addresses, and then a transaction network is reconstructed based on the public transaction book. Then, an amount-based network embedding algorithm is proposed to extract node features for identifying fraudulent transactions. At last, the graph convolutional network model is used to classify addresses into legal addresses and fraudulent addresses. The experimental results show that the system for detecting fraudulent transactions can achieve the accuracy of 95%, which reflects the excellent performance of the system for detecting Ethereum fraudulent transactions.