Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce

Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce
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提取和推理隐含行为证据以检测电子商务中的欺诈性在线交易

DOI:
10.1016/j.dss.2016.04.003
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发表时间:
2016-06
影响因子:
7.5
通讯作者:
Tang Deyu
Tang Deyu
中科院分区:
计算机科学1区
文献类型:
--
作者:
Zhao Jie;Lau Raymond Y. K.;Zhang Wenping;Zhang Kaihang;Chen Xu;Tang Deyu

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随着电子商务在全球范围内的爆炸性增长,电子商务中的串通欺诈交易攻击也越来越受到人们的关注。我们的研究工作的主要贡献是设计了一个新颖的检测框架,该框架可以推理隐含的在线用户行为来检测合谋欺诈交易。基于从世界上最大的电子商务平台之一收集的真实交易和用户行为数据,我们的实验结果证实了所提出的检测框架可以达到83%的平均真阳性检测率,而虚警率保持在2.4%。据我们所知,这是针对电子商务中的欺诈交易进行的规模最大的研究之一。我们研究的管理启示是,电子商务平台的管理者可以应用我们的框架来检测和防止欺诈性交易攻击,从而在不断扩大的电子商务世界中维护公平的电子交易。
With the explosive growth of e-Commerce worldwide, there are also growing concerns about collusive fraudulent transaction attacks in e-Commerce. The main contribution of our research work is the design of a novel detection framework that can reason about implicit online user behavior for detecting collusive fraudulent transactions. Based on real transactional and user behavioral data collected from one of the largest e-Commerce platforms in the world, our experimental results confirm that the proposed detection framework can achieve an average true positive detection rate of 83% while the false alarm rate is kept at as low as 2.4%. To the best of our knowledge, this is one of the largest scale studies toward the detection of fraudulent transactions in e-Commerce. The managerial implication of our study is that administrators of e-Commerce platforms can apply our framework to detect and prevent fraudulent transaction attacks, and hence fair electronic trading is upheld in the ever expanding e-Commerce world.
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