Abstract
Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain security, calling for the development of effective regulatory strategies. Nowadays network science has been widely used in modeling Ethereum transaction data, further introducing the network representation learning technology to analyze the transaction patterns. In this paper, we consider phishing detection as a graph classification task and propose an end-to-end Phishing Detection Graph Neural Network framework (PDGNN). Specifically, we first construct a lightweight Ethereum transaction network and extract transaction subgraphs of collected phishing accounts. Then we propose an end-to-end detection model based on Chebyshev-GCN to precisely distinguish between normal and phishing accounts. Extensive experiments on five Ethereum datasets demonstrate that our PDGNN significantly outperforms general phishing detection methods and scales well in large transaction networks.
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Acknowledgments
This work was partially supported by the National Key R &D Program of China under Grant 2020YFB1006104, by the Key R &D Programs of Zhejiang under Grant 2022C01018, by the National Natural Science Foundation of China under Grant 61973273, and by the Zhejiang Provincial Natural Science Foundation of China under Grant LR19F030001.
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Li, P., Xie, Y., Xu, X., Zhou, J., Xuan, Q. (2022). Phishing Fraud Detection on Ethereum Using Graph Neural Network. In: Svetinovic, D., Zhang, Y., Luo, X., Huang, X., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2022. Communications in Computer and Information Science, vol 1679. Springer, Singapore. https://doi.org/10.1007/978-981-19-8043-5_26
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DOI: https://doi.org/10.1007/978-981-19-8043-5_26
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