A Novel Methodology for HYIP Operators' Bitcoin Addresses Identification

【Author】 Toyoda, Kentaroh; Mathiopoulos, P. Takis; Ohtsuki, Tomoaki

【Source】IEEE ACCESS

【Abstract】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.

【Keywords】Bitcoin; blockchain analysis; forensics; data mining; HYIP (high yield investment programs)

【标题】一种新的HYIP运营商比特币地址识别方法

【摘要】比特币是迄今为止最流行的去中心化加密货币之一。但是，被广泛报道为“高收益投资计划(HYIP)”的投资诈骗手段。虽然从安全取证的角度来看，识别HYIP运营商的比特币地址是非常重要的，但到目前为止，在开放的技术文献中还没有提出可靠地收集和识别此类比特币地址的系统方法。本文介绍了一种新颖的方法，它有效地收集大量HYIP运营商的比特币地址，并根据其交易历史的新颖分析来识别它们。特别是首次提出了一种基于抓取的方法，该方法能够从互联网上收集2000多个HYIP运营商的比特币地址，从而提供了大量的HYIP样本。其次，引入了一种监督机器学习技术，对特定比特币地址是否属于HYIP运营商进行分类，并对其性能进行评估。本文提出的分类方法基于两种新的方法，即减缓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前提下减少计算量的抽样技术。利用近30,000个真实比特币地址，通过计算机仿真实验获得的大量性能评估结果表明，所提出的方法具有良好的性能，即95%的HYIP地址可以被正确分类，同时保持假阳性率低于4.9%。为了进一步验证所提出的分类器检测HYIP运营商比特币地址的能力，我们设计的分类器已针对最近发布的HYIP地址列表进行了测试，保持了良好的检测精度，达到93.75%的成功率。

【关键词】比特币;区块链分析;取证;数据挖掘;高收益投资项目

【发表时间】2019

【收录时间】2022-04-23

【文献类型】Article

【论文大主题】链上数据分析

【论文小主题】交易实体识别

【期刊级别】SCI二区

【影响因子】3.476

【翻译者】王佳鑫