Ethereum fraud behavior detection based on graph neural networks


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Since Bitcoin was first conceived in 2008, blockchain technology has attracted a large amount of researchers’ attention. At the same time, it has also facilitated a variety of cybercrimes. For example, Ethereum frauds, due to the potential for huge profits, occur frequently and pose a serious threat to the financial security of the Ethereum network. To create healthy financial environments, methods for automatically detecting and identifying Ethereum frauds are urgently needed in Ethereum system governance. To this end, this paper proposes a new framework to detect fraudulent transactions in Ethereum by mining Ethereum transaction records. Specifically, we obtain Ethereum addresses with fraud/legitimate labels through Web crawlers and then construct a transaction network according to the public transaction ledger. Then, a transaction behavior-based network embedding algorithm is proposed to extract node features for subsequent fraudulent transaction identification. Finally, we adopt the Graph Convolutional Neural Network model (GCN) to classify addresses into legal and fraudulent addresses. The experimental results show that the fraudulent transaction detection system can achieve an accuracy of 96% on fraud/legitimate record classification, which proves the effectiveness of the framework in the detection of Ethereum fraudulent transactions.
Ethereum,Fraud transaction detection,Graph neural networks,Trading behavior
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