Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (5): 125-138    DOI: 10.11925/infotech.2096-3467.2018.0665
Current Issue | Archive | Adv Search |
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)
Download: PDF (1638 KB)   HTML ( 10
Export: BibTeX | EndNote (RIS)      

[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.

URL:     OR

[1] Black Hat World. 3-Way Feedback Exchange[OL]. (2010-05-26). .
[2] Black Hat World. eBay Feedback Change Anyone?[OL]. (2012-09-16). .
[3] Tsang S, Koh Y S, Dobbie G, et al.Detecting Online Auction Shilling Frauds Using Supervised Learning[J]. Expert Systems with Applications, 2014, 41(6): 3027-3040.
[4] Lian Q, Zhang Z, Yang M, et al.An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System[C]//Proceedings of the 27th International Conference on Distributed Computing Systems (ICDCS’07). 2007.
[5] You W, Liu L, Xia M, et al.Reputation Inflation Detection in a Chinese C2C Market[J]. Electronic Commerce Research and Applications, 2011, 10(5): 510-519.
[6] Zhang B, Zhang Q, Huang Z, et al.A Multi-Criteria Detection Scheme of Collusive Fraud Organization for Reputation Aggregation in Social Networks[J]. Future Generation Computer Systems, 2018, 79: 797-814.
[7] Chang W, Chang J.A Novel Two-Stage Phased Modeling Framework for Early Fraud Detection in Online Auctions[J]. Expert Systems with Applications, 2011, 38(9): 11244-11260.
[8] Almendra V.Finding the Needle: A Risk-based Ranking of Product Listings at Online Auction Sites for Non-delivery Fraud Prediction[J]. Expert Systems with Applications, 2013, 40(12): 4805-4811.
[9] Zhang Y, Bian J, Zhu W.Trust Fraud: A Crucial Challenge for China’s e-Commerce Market[J]. Electronic Commerce Research and Applications, 2013, 12(5): 299-308.
[10] 朱艳春, 刘鲁, 张巍. 在线信誉系统中的信任模型构建研究[J]. 控制与决策, 2007, 22(4): 413-417.
[10] (Zhu Yanchun, Liu Lu, Zhang Wei.Study of Trust Model in Online Reputation System[J]. Control and Decision, 2007, 22(4): 413-417.)
[11] 李瑞轩, 高昶, 辜希武, 等. C2C电子商务交易的信用及风险评估方法研究[J]. 通信学报, 2009, 30(7): 78-85.
[11] (Li Ruixuan, Gao Chang, Gu Xiwu, et al.Research on Credit Counting and Risk Evaluation for C2C E-Commerce[J]. Journal on Communications, 2009, 30(7): 78-85.)
[12] Zhai J, Cao Y, Yao Y, et al.Computational Intelligent Hybrid Model for Detecting Disruptive Trading Activity[J]. Decision Support Systems, 2017, 93: 26-41.
[13] 王忠群, 乐元, 修宇, 等. 基于模板用户信息搜索行为和统计分析的共谋销量欺诈识别[J]. 现代图书情报技术, 2015(11): 41-50.
[13] (Wang Zhongqun, Le Yuan, Xiu Yu, et al.Collusive Sales Fraud Detection Based on Users’ Information Search Behavior Template and Statistical Analysis[J]. New Technology of Library and Information Service, 2015(11): 41-50.)
[14] Yang Y F, Feng Q Y, Sun Y, et al.Dishonest Behaviors in Online Rating Systems: Cyber Competition, Attack Models, and Attack Generator[J]. Journal of Computer Science & Technology, 2009, 24(5): 855-867.
[15] Kamvar S D, Schlosser M T, Garcia-Molina H.The Eigentrust Algorithm for Reputation Management in P2P Networks[C]//Proceedings of the 12th International Conference on World Wide Web. ACM, 2003:640-651.
[16] Nejdl W, Olmedilla D, Winslett M.PeerTrust: Automated Trust Negotiation for Peers on the Semantic Web[C] // Proceedings of VLDB Workshop on Secure Data Management, 2004:118-132.
[17] Zhang B, Song Q, Yang T, et al. A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective[J]. Mobile Information Systems, 2016: Article ID 5185170.
[18] 甘早斌, 曾灿, 李开, 等. 电子商务下的信任网络构造与优化[J]. 计算机学报, 2012, 35(1): 27-37.
[18] (Gan Zaobin, Zeng Can, Li Kai, et al.Construction and Optimization of Trust Network in E-Commerce Environment[J]. Chinese Journal of Computers, 2012, 35(1): 27-37.)
[19] Lin J, Khomnotai L. Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes[J]. Entropy, 2016, 18(1): Article No.11.
[20] 郭洪海, 姜锦虎, 蔡涵. C2C电子社区成员信誉值的计算模型研究[J]. 管理学报, 2009, 6(8): 1056-1060.
[20] (Guo Honghai, Jiang Jinhu, Cai Han.Modeling for Reputation Computing in C2C Communities[J]. Chinese Journal of Management, 2009, 6(8): 1056-1060.)
[21] Zacharia G, Moukas A, Maes P.Collaborative Reputation Mechanisms for Electronic Marketplaces[J]. Decision Support Systems, 1999, 29(4): 371-388.
[22] Chan P.A Non-Invasive Learning Approach to Building Web User Profiles[C]//Proceedings of ACM SIGKDD International Conference, 1999.
[23] 曹建勋, 刘奕群, 岑荣伟, 等. 基于用户行为的色情网站识别[J]. 计算机研究与发展, 2013, 50(2): 430-436.
[23] (Cao Jianxun, Liu Yiqun, Cen Rongwei, et al.Pornography Web Site Identification Based on User Behavior Analysis[J]. Journal of Computer Research and Development, 2013, 50(2): 430-436.)
[24] Maranzato R, Neubert M, Pereira A M, et al.Feature Extraction for Fraud Detection in Electronic Marketplaces[C]// Proceedings of the 2009 Latin American Web Congress. IEEE, 2009.
[25] Maranzato R, Pereira A, Neubert M, et al.Fraud Detection in Reputation Systems in e-Markets Using Logistic Regression and Stepwise Optimization[J]. ACM SIGAPP Applied Computing Review, 2010, 11(1): 14-26.
[26] 赵洁, 肖南峰, 钟军锐. 基于贝叶斯网络和行为日志挖掘的行为信任控制[J]. 华南理工大学学报:自然科学版, 2009, 37(5): 94-100.
[26] (Zhao Jie, Xiao Nanfeng, Zhong Junrui.Behaviour Trust Control Based on Bayesian Networks and User Behavior Log Mining[J]. Journal of South China University of Technology:Natural Science Edition, 2009, 37(5): 94-100.)
[27] 赵洁, 肖南峰, 钟军锐. Web使用挖掘在信任管理中的应用[J]. 计算机工程, 2009, 35(24): 33-35, 38.
[27] (Zhao Jie, Xiao Nanfeng, Zhong Junrui.Application of Web Usage Mining in Trust Management[J]. Computer Engineering, 2009, 35(24): 33-35, 38.)
[28] Levenshtein V I.Binary Codes Capable of Correcting Deletions, Insertions and Reversals[J]. Soviet Physics Doklady,1966, 10(8): 707-710.
[29] Zhao J, Lau R Y K, Zhang W, et al. Extracting and Reasoning About Implicit Behavioral Evidences for Detecting Fraudulent Online Transactions in e-Commerce[J]. Decision Support Systems, 2016, 86(C): 109-121.
[30] Dong F, Shatz S M, Xu H.Reasoning Under Uncertainty for Shill Detection in Online Auctions Using Dempster-Shafer Theory[J]. International Journal of Software Engineering & Knowledge Engineering, 2010, 20(7): 943-973.
[31] Panigrahi S, Kundu A, Sural S, et al.Credit Card Fraud Detection: A Fusion Approach Using Dempster-Shafer Theory and Bayesian Learning[J]. Information Fusion, 2009, 10(4): 354-363.
[1] Liang Jiaming, Zhao Jie, Zheng Peng, Huang Liushen, Ye Minqi, Dong Zhenning. Framework for Computing Trust in Online Short-Rent Platform Using Feature Selection of Images and Texts[J]. 数据分析与知识发现, 2021, 5(2): 129-140.
[2] Cheng Zhou,Hongqin Wei. Evaluating and Classifying Patent Values Based on Self-Organizing Maps and Support Vector Machine[J]. 数据分析与知识发现, 2019, 3(5): 117-124.
[3] Tingxin Wen,Yangzi Li,Jingshuang Sun. News Hotspots Discovery Method Based on Multi Factor Feature Selection and AFOA/K-means[J]. 数据分析与知识发现, 2019, 3(4): 97-106.
[4] Zhanglu Tan,Zhaogang Wang,Han Hu. Study on a Method of Feature Classification Selection Based on χ2 Statistics[J]. 数据分析与知识发现, 2019, 3(2): 72-78.
[5] Wen Tingxin,Li Yangzi,Sun Jingshuang. Extracting Text Features with Improved Fruit Fly Optimization Algorithm[J]. 数据分析与知识发现, 2018, 2(5): 59-69.
[6] Li Zhipeng,Li Weizhong. Feature Selection Based on Modified QPSO Algorithm[J]. 数据分析与知识发现, 2017, 1(7): 82-89.
[7] Zhang Yue,Wang Dongbo,Zhu Danhao. Segmenting Chinese Words from Food Safety Emergencies[J]. 数据分析与知识发现, 2017, 1(2): 64-72.
[8] Li Xiangdong,Ruan Tao,Liu Kang. Automatic Classification of Documents from Wikipedia[J]. 数据分析与知识发现, 2017, 1(10): 43-52.
[9] Lu Yonghe,Chen Jinghuang. Optimizing Feature Selection Method for Text Classification with Shuffled Frog Leaping Algorithm[J]. 数据分析与知识发现, 2017, 1(1): 91-101.
[10] Liu Hongguang,Ma Shuanggang,Liu Guifeng. Classifying Chinese News Texts with Denoising Auto Encoder[J]. 现代图书情报技术, 2016, 32(6): 12-19.
[11] Meng Yuan,Wang Hongwei. Evaluating Online Reviews Based on Text Content Features[J]. 现代图书情报技术, 2016, 32(4): 40-47.
[12] Li Xiangdong, Ba Zhichao, Huang Li. Allocation and Multi-granularity[J]. 现代图书情报技术, 2015, 31(5): 42-49.
[13] Xu Dongdong, Wu Shaobo. An Improved TF-IDF Feature Selection Based on Categorical Description[J]. 现代图书情报技术, 2015, 31(3): 39-48.
[14] Tan Xueqing, Zhou Tong, Luo Lin. A Text Classification Algorithm Based on the Average Category Similarity[J]. 现代图书情报技术, 2014, 30(9): 66-73.
[15] Gu Xiaoxue, Zhang Chengzhi. Using Content and Tags for Web Text Clustering[J]. 现代图书情报技术, 2014, 30(11): 45-52.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938