以太坊上可解释的庞氏骗局检测
Explainable Ponzi Schemes Detection on Ethereum
AI总结：
针对以太坊庞氏骗局检测，提出一个优于现有方法的分类器，发布含4422个真实合约的带标签数据集，并利用可解释AI识别关键特征及其影响。
摘要：
区块链技术已被成功用于部署新的经济应用。然而，它也开始引起恶意行为者的兴趣，这些行为者通过实施诈骗来欺骗诚实用户并获取经济利益。庞氏骗局是最常见的诈骗类型之一。在此，我们提出一个用于检测以太坊上智能庞氏合约的分类器，该分类器可作为开发检测工具的基础。首先，我们发布了一个包含4422个真实世界智能合约的带标签数据集，以解决带标签数据不可用的问题。然后，我们展示了我们的分类器在考虑AUC作为指标时优于文献中提出的分类器。最后，我们确定了一个小而有效的特征集，以确保良好的分类质量，并使用可解释人工智能技术研究这些特征对分类的影响。
英文摘要：
Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious actors who deliver scams to deceive honest users and to gain economic advantages. Ponzi schemes are one of the most common scams. Here, we present a classifier for detecting smart Ponzi contracts on Ethereum, which can be used as the backbone for developing detection tools. First, we release a labelled data set with 4422 unique real-world smart contracts to address the problem of the unavailability of labelled data. Then, we show that our classifier outperforms the ones proposed in the literature when considering the AUC as a metric. Finally, we identify a small and effective set of features that ensures a good classification quality and investigate their impacts on the classification using eXplainable AI techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
