Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.

中文翻译：

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以太坊交易网络中的网络钓鱼诈骗检测

区块链吸引了越来越多的研究，并且在不同领域有许多令人耳目一新的实现方式。加密货币作为其代表实施，因网络钓鱼诈骗而遭受经济损失。在我们的工作中，账户和交易被视为节点和边，因此网络钓鱼账户的检测可以建模为节点分类问题。相应地，我们提出了一种基于图卷积网络和自动编码器的检测方法来精确区分网络钓鱼帐户。对来自以太坊的不同大规模真实世界数据集的实验表明，与相关方法相比，我们提出的模型始终表现出有希望的结果。