An analysis model for detecting misbehaviors in anonymous cryptocurrency
【Author】 Huang, Shiyong; Yang, Xin; He, Langyue; Hao, Xiaohan; Ren, Wei
【Source】COMPUTER STANDARDS & INTERFACES
【影响因子】3.721
【Abstract】In online shopping, consumers often rely on information such as sales, reviews or ratings to inform their decision making. Such preferences or user behaviors can be subjected to manipulation. For example, a merchant can artificially inflate product sales by paying a click farm. Specifically, the click farm will recruit a number of non-genuine buyers to purchase the products. After the purchases have been made, the buyers will either refund the product minus the commission or no product exchange actually takes place and these buyers are paid a commission for their role in the activity. Increasingly due to the popularity of cryptocurrency, such as bitcoin, such payment mechanisms are used in such activities. Hence, in this paper, we seek to detect click farm transactions using cryptocurrency. Specifically, we propose three models to capture click farm operations, and based on the models we design three algorithms to detect anonymous click farm transactions. Extensive analysis demonstrates that our model achieves a high accuracy rate in detecting anonymous click farm transactions, without incurring expensive computational costs.
【Keywords】Cryptocurrency; Click farm; Anonymous; Blockchain
【发表时间】2023 JAN
【收录时间】2022-08-15
【文献类型】实验仿真
【主题类别】
区块链治理-技术治理-异常/非法交易识别
wangjiaxin
这篇关于异常交易检测,https://doi.org/10.1016/j.csi.2022.103669,发表在《COMPUTER STANDARDS & INTERFACES》,本文主要检测使用加密货币的点击农场(click farm,一个阴暗的互联网产业)交易。具体地说,提出了三种捕获点击农场行为的模型,并在此基础上设计了三种检测匿名点击场交易的算法。大量的分析表明模型在检测匿名点击场交易时实现了很高的准确率。
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