用户隐式行为挖掘在抗信誉共谋中的应用研究

Detecting Collusive Fraudulent Online Transaction with Implicit User Behaviors

【目的】探究和验证用户隐式行为数据的挖掘方法及结果对信誉共谋攻击识别模型精度提升的效果。【方法】提出用户融合隐式行为分析的总体框架,提取隐式行为特征;设计两阶段综合特征选择方法,选择多个高辨别力的特征。【结果】利用电子商务中的大量数据实验验证了用户隐式行为挖掘在抗信誉共谋中的有效性,对共谋者的识别能力优于显式特征。【局限】攻击者和合法用户隐式数据规模仍需要进一步扩大。【结论】融入用户隐式行为挖掘可较大幅度提升信誉共谋识别模型的精度。

[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.