【Author】
Farrugia, Steven; Ellul, Joshua; Azzopardi, George
【Source】EXPERT SYSTEMS WITH APPLICATIONS
【Abstract】The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 (+/- 0.006) with an average AUC of 0.994 (+/- 0.0007). The top three features with the largest impact on the final model output were established to be 'Time diffbetween first and last (Mins)', 'Total Ether balance' and 'Min value received'. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works. (C) 2020 Elsevier Ltd. All rights reserved.
【Keywords】Blockchain; Ethereum; Fraud detection; Machine learning; XGBoost
【摘要】加密货币最近的技术发展及其各自的利益被一系列通过网络运作的非法活动所掩盖，如洗钱、贿赂、网络钓鱼、欺诈等。在这项工作中，我们专注于以太坊网络，自其成立以来，已经见证了超过4亿次交易。使用2179个被以太坊社区标记为非法活动的账户和2502个正常账户，我们试图使用XGBoost分类器根据他们的交易历史来检测非法账户。通过10倍交叉验证，XGBoost的平均准确率为0.963(+/- 0.006)，平均AUC为0.994(+/- 0.0007)。对最终模型输出影响最大的前三个特征是“首末时间差(Mins)”、“总以太平衡”和“最小接收值”。基于这些结果，我们得出结论，所提出的方法在检测以太坊网络上的非法账户方面非常有效。我们的贡献是多方面的;首先，我们提出了一种有效的方法来检测以太坊网络上的非法账户;其次，我们提供了对最重要的特征的见解;第三，我们发布了编译后的数据集，作为今后相关工作的基准。(C) 2020年爱思唯尔有限公司保留所有权利。
【关键词】区块链;以太坊;欺诈检测;机器学习;XGBoost
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