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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 125-138    DOI: 10.11925/infotech.2096-3467.2018.0665
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Detecting Collusive Fraudulent Online Transaction with Implicit User Behaviors
Jiaming Liang1,Jie Zhao1(),Zhou Jianlong2,Zhenning Dong1
1(School of Management, Guangdong University of Technology, Guangzhou 510520, China)
2(Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia)
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[Objective] This paper explores new data mining method for implicit user behaviors, aiming to improve the precision of the model for collusive fraud detection. [Methods] First, we proposed a framework for implicit user behaviors analysis. Then, we designed a two-stage algorithm to select the needed implicit features. [Results] We examined our new model with massive data from an existing e-commerce platform and found that the proposed model was more effective than the existing ones. [Limitations] The size of our experimental dataset needs to be expanded. [Conclusions] Using implicit features is an effective way to improve the precision of the collusive fraud detection model.

Key wordsImplicit Behaviors      Feature Selection      Collusive Fraudulent Online Transaction      Attack Detection     
Received: 25 June 2018      Published: 03 July 2019

Cite this article:

Jiaming Liang,Jie Zhao,Zhou Jianlong,Zhenning Dong. Detecting Collusive Fraudulent Online Transaction with Implicit User Behaviors. Data Analysis and Knowledge Discovery, 2019, 3(5): 125-138.

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