Fraud De-Anonymization for Fun and Profit
Fraud De-Anonymization for Fun and Profit
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DOI：
10.1145/3243734.3243770
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Nestor Hernandez;Mizanur Rahman;Ruben Recabarren;Bogdan Carbunar
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文献类型：
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作者：
Nestor Hernandez;Mizanur Rahman;Ruben Recabarren;Bogdan Carbunar
在众包网站和专门的欺诈工作人员的帮助下，在线同行意见系统中搜索排名欺诈的持续存在表明，目前检测和过滤欺诈的方法效率低下。我们引入了一种欺诈去匿名化的方法来抑制搜索排名欺诈：将在线同行意见系统中的欺诈检测算法标记的用户帐户归因于众包网站中的人类工作人员，由他们控制这些帐户。我们将欺诈去匿名化建模为一个极大似然估计问题，并引入了一种无约束优化解Uoda。我们开发了一种基于图的深度学习方法来预测同一欺诈者对帐户对的所有权，并使用它来构建区分欺诈去匿名化(DDA)和假名欺诈者发现算法(PFD)。为了解决基础事实欺诈数据的缺乏及其对使用欺诈检测的在线系统的有害影响，我们提出了第一个防欺诈的欺诈去匿名化验证协议，将人类欺诈工作者转换为基础事实、性能评估先知。在对16名人类欺诈者进行的用户研究中，Uoda实现了91%的准确率。根据我们从其他23名诈骗犯那里收集的基本事实数据，我们的共同所有权预测器的表现远远超过了最先进的竞争对手，使DDA和PFD能够发现数十名新的诈骗犯，并将数千个可疑用户账户归因于现有的和新发现的诈骗犯。
The persistence of search rank fraud in online, peer-opinion systems, made possible by crowdsourcing sites and specialized fraud workers, shows that the current approach of detecting and filtering fraud is inefficient. We introduce a fraud de-anonymization approach to disincentivize search rank fraud: attribute user accounts flagged by fraud detection algorithms in online peer-opinion systems, to the human workers in crowdsourcing sites, who control them. We model fraud de-anonymization as a maximum likelihood estimation problem, and introduce UODA, an unconstrained optimization solution. We develop a graph based deep learning approach to predict ownership of account pairs by the same fraudster and use it to build discriminative fraud de-anonymization (DDA) and pseudonymous fraudster discovery algorithms (PFD). To address the lack of ground truth fraud data and its pernicious impacts on online systems that employ fraud detection, we propose the first cheating-resistant fraud de-anonymization validation protocol, that transforms human fraud workers into ground truth, performance evaluation oracles. In a user study with 16 human fraud workers, UODA achieved a precision of 91%. On ground truth data that we collected starting from other 23 fraud workers, our co-ownership predictor significantly outperformed a state-of-the-art competitor, and enabled DDA and PFD to discover tens of new fraud workers, and attribute thousands of suspicious user accounts to existing and newly discovered fraudsters.