1(School of Management, Guangdong University of Technology, Guangzhou 510520, China) 2(Faculty of Engineering & Information Technology, University of Technology Sydney, NSW 2007, Australia)
[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.
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.
(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.
(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.
(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.
(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.
(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.)
(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.
