Abstract
Ethereum is the largest blockchain system supporting Turing-complete smart contracts. In recent years, we have witnessed its boom and popularity in various applications. However, since users use pseudonyms in Ethereum, it is hard to know the true identity behind an account. Meanwhile, a large number of cyber-crimes in Ethereum emerged and have been reported. Therefore, it is an important task to analyze the transaction behavior of accounts in Ethereum and conduct account portraits based on the honest and public information which is transaction records. Although facing the anonymity challenge of blockchain, it makes this task possible that some Ethereum analysis platforms provide ground truth by classifying accounts into specific types. However, prior work tried to dig out features of one certain account type but lack of a comparative analysis of multi-class account types. In this paper, we model the partial Ethereum transaction data as a transaction network, then portray the characteristics of six types of accounts in Ethereum according to the obtained labels from both transaction statistics perspective and network structure perspective. Moreover, we adopt a Graph Convolutional Network (GCN)-based model to distinguish different kinds of accounts to verify the effectiveness of the properties we choose. The experimental results show that our model performs well in classifying various types of accounts in Ethereum.
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Acknowledgments
The work described in this paper is supported by the Guangdong Applied R&D Program (2015B010131006), the National Natural Science Foundation of China (61973325, U1811462).
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Xia, Y., Liu, J., Zheng, J., Wu, J., Su, X. (2021). Portraits of Typical Accounts in Ethereum Transaction Network. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_4
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DOI: https://doi.org/10.1007/978-981-16-7993-3_4
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