Automatically Dismantling Online Dating Fraud
Automatically Dismantling Online Dating Fraud
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DOI：
10.1109/tifs.2019.2930479
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发表时间：
2019-05
影响因子：
6.8
通讯作者：
Guillermo Suarez-Tangil;M. Edwards;Claudia Peersman;G. Stringhini;A. Rashid;M. Whitty
中科院分区：
文献类型：
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作者：
Guillermo Suarez-Tangil;M. Edwards;Claudia Peersman;G. Stringhini;A. Rashid;M. Whitty
在西方，网络爱情诈骗是一种普遍的大众营销欺诈形式，但很少有研究对这一问题提出数据驱动的回应。在这种类型的骗局中，欺诈者制作虚假的个人资料，并手动与受害者互动。由于此类欺诈的特征以及约会网站的运作方式，传统的检测方法（例如，那些在垃圾邮件过滤中使用的）是无效的。在本文中，我们调查了这种形式的欺诈中使用的在线约会资料的原型，包括他们使用的人口统计数据，个人资料描述和图像，揭示了骗子为吸引受害者而部署的策略和受害者本身的特征。此外，为了应对约会欺诈造成的严重经济和心理伤害，我们开发了一个系统来检测在线约会平台上的浪漫骗子。本文介绍了第一个完整的描述系统自动检测这种欺诈行为。我们的目标是提供一个早期检测系统，以阻止浪漫骗子，因为他们创造了欺诈性的个人资料，或在他们与潜在的受害者接触之前。先前的研究表明，浪漫骗局的受害者在理想化的浪漫信念量表上得分很高。我们联合收割机了一系列结构化、非结构化和深度学习的特征，这些特征可以捕获这些信念，从而构建一个检测系统。我们的集成机器学习方法对省略配置文件细节具有鲁棒性，并且在保持验证集上具有高准确性（97%）。该系统能够为约会网站提供商和个人用户开发自动化工具。
Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.