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数据分析与知识发现  2019, Vol. 3 Issue (5): 125-138    DOI: 10.11925/infotech.2096-3467.2018.0665
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
用户隐式行为挖掘在抗信誉共谋中的应用研究*
梁家铭1,赵洁1(),Jianlong Zhou2,董振宁1
1(广东工业大学管理学院 广州 510520)
2(悉尼科技大学工程与信息技术学院 新南威尔士 2007)
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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摘要 

【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。

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梁家铭
赵洁
Jianlong Zhou
董振宁
关键词 隐式行为特征选择信誉共谋攻击识别    
Abstract

[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
收稿日期: 2018-06-25     
基金资助:*本文系教育部人文社会科学研究规划项目“住宿共享平台双向信任计算研究”(项目编号: 18YJAZH137)、广东省自然科学基金项目“粗糙集和DS证据推理混合模型下抗信誉共谋攻击的行为信任研究”(项目编号: 2017A030313394)和2017年广东高校省级重大科研项目特色创新类项目(人文社会科学)“融合隐式和显式行为大数据挖掘下抗信誉共谋的行为信任研究”(项目编号: 2017WTSCX021)的研究成果之一
引用本文:   
梁家铭,赵洁,Jianlong Zhou,董振宁. 用户隐式行为挖掘在抗信誉共谋中的应用研究*[J]. 数据分析与知识发现, 2019, 3(5): 125-138.
Jiaming Liang,Jie Zhao,Zhou Jianlong,Zhenning Dong. Detecting Collusive Fraudulent Online Transaction with Implicit User Behaviors. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0665.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0665
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