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Ethereum fraud behavior detection based on graph neural networks

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Abstract

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.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (61972105), Joint Research Fund of Guangzhou and University (202201020181), and the Guangdong Province Key Research and Development Plan (2019B010137003), and in part by the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).

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Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong, China

    Runnan Tan, Qingfeng Tan & Peng Zhang

  2. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Qin Zhang

  3. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yushun Xie

  4. Zhejiang University, Hangzhou, Zhejiang, China

    Zhao Li

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  1. Runnan Tan
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  2. Qingfeng Tan
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Correspondence to Qingfeng Tan or Peng Zhang.

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Tan, R., Tan, Q., Zhang, Q. et al. Ethereum fraud behavior detection based on graph neural networks. Computing 105, 2143–2170 (2023). https://doi.org/10.1007/s00607-023-01177-7

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