【Author】
Bartoletti, Massimo; Pes, Barbara; Serusi, Sergio
【Source】2018 CRYPTO VALLEY CONFERENCE ON BLOCKCHAIN TECHNOLOGY (CVCBT)
【Abstract】Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.
【Keywords】Bitcoin; data mining; fraud detection
【摘要】比特币在2009年推出后不久就被网络罪犯所采用，他们利用比特币的匿名性实施几乎无法追踪的诈骗。在比特币上运作的典型骗局之一是所谓的庞氏骗局。这是一种欺诈性投资，用加入该计划的新用户投资的资金偿还用户，当不再可能找到新的投资时，就会崩溃。尽管在许多国家都是非法的，但如今在比特币上的庞氏骗局正在激增，它们不断吸引新的受害者，这些人被掠夺了数百万美元。我们应用数据挖掘技术来检测与庞氏骗局相关的比特币地址。我们的出发点是一个真实世界庞氏骗局特征的数据集，我们通过在比特币区块链上分析用于执行骗局的交易来构建这个数据集。我们使用此数据集对各种机器学习算法进行实验，并通过标准验证协议和性能指标评估其有效性。我们试验过的最好的分类器可以识别数据集中的大多数庞氏骗局，假阳性的数量很低。
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