Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis
Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis
复制标题
DOI：
10.1109/glocom.2017.8254420
复制
发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
Kentaroh Toyoda;T. Ohtsuki;P. Mathiopoulos
中科院分区：
文献类型：
--
作者：
Kentaroh Toyoda;T. Ohtsuki;P. Mathiopoulos
尽管比特币是最成功的去中心化加密货币之一，但最近的研究表明，它可以被用作 HYIP（高收益投资计划）等欺诈活动。为了识别此类不良活动，获取与欺诈相关的比特币地址非常重要。到目前为止，此类活动的识别是基于将比特币地址与图挖掘程序相关联。在本文中，我们采用不同的方法通过分析交易模式来识别与 HYIP 相关的比特币地址。特别是，基于对比特币 HYIP 活动的单独检查，我们提出了一些可以从交易中提取的特征。特别是，一个称为模式的有符号整数被分配给每个交易，并且每个模式的频率被计算为关键特征。通过评估超过 1,500 个标记的比特币地址的分类性能，结果表明，约 83% 的 HYIP 地址被正确分类，同时误报率保持在 4.4% 以下。
Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.