A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification
A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification
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DOI：
10.1109/access.2019.2921087
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Kentaroh Toyoda;P. Takis Mathiopoulos;T. Ohtsuki
中科院分区：
文献类型：
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作者：
Kentaroh Toyoda;P. Takis Mathiopoulos;T. Ohtsuki
比特币是迄今为止最受欢迎的去中心化加密货币之一。然而，它已被广泛报道，它可以用于投资骗局，这被称为高收益投资计划（高收益投资计划）。虽然从安全取证的角度来看，识别高收益操作者的比特币地址是非常重要的，但到目前为止，在公开的技术文献中还没有提出可靠地收集和识别这种比特币地址的系统方法。在本文中，介绍了一种新的方法，该方法有效地收集了大量的高收益操作员的比特币地址，并根据对他们的交易历史的新分析来识别它们。特别是，首次提出了一种基于刮取的方法，该方法能够从互联网上收集2,000多个高收益投运营商的比特币地址，从而提供大量的HYIP样本。其次，引入了一种有监督的机器学习技术，该技术对特定的比特币地址进行分类，无论是否属于高收益投操作员，并对其性能进行了评估。所提出的分类方法基于两种新方法，即缓解比特币价格波动影响的速率转换技术和在不牺牲分类性能的情况下减少计算量的采样技术。通过使用近30，000个真实的比特币地址，通过计算机模拟实验获得的广泛性能评估结果表明，所提出的方法实现了出色的性能，即，95%的HYIP地址可以被正确分类，同时保持误报率低于4.9%。为了进一步验证所提出的分类器检测高收益投操作员的比特币地址的能力，我们设计的分类器已经针对最近发布的高收益投地址列表进行了测试，通过实现93.75%的成功率来保持其出色的检测准确性。
Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators’ Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators’ Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators’ Bitcoin addresses from the Internet thus providing a large number of the HYIPs’ samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier’s ability to detect the HYIP operators’ Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.