eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks

doi:10.1145/3474379

CSpace > 应用数学研究所

	eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
	Zhang, Ge 1; Li, Zhao 2; Huang, Jiaming 2; Wu, Jia 1; Zhou, Chuan3 ; Yang, Jian 1; Gao, Jianliang 4
	2022-07-01
发表期刊	ACM TRANSACTIONS ON INFORMATION SYSTEMS
ISSN	1046-8188
卷号	40 期号:3 页码:29
摘要	With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of users. It is of great practical value to detect fraud behaviors on e-commerce platforms. However, the task is non-trivial, since the adversarial action taken by fraudsters. Existing fraud detection systems used in the e-commerce industry easily suffer from performance decay and can not adapt to the upgrade of fraud patterns, as they take already known fraud behaviors as supervision information to detect other suspicious behaviors. In this article, we propose a competitive graph neural networks (CGNN)-based fraud detection system (eFraudCom) to detect fraud behaviors at one of the largest e-commerce platforms, "Taobao"1. In the eFraudCom system, (1) the competitive graph neural networks (CGNN) as the core part of eFraudCom can classify behaviors of users directly by modeling the distributions of normal and fraud behaviors separately; (2) some normal behaviors will be utilized as weak supervision information to guide the CGNN to build the profile for normal behaviors that are more stable than fraud behaviors. The algorithm dependency on fraud behaviors will be eliminated, which enables eFraudCom to detect fraud behaviors in presence of the new fraud patterns; (3) the mutual information regularization term can maximize the separability between normal and fraud behaviors to further improve CGNN. eFraudCom is implemented into a prototype system and the performance of the system is evaluated by extensive experiments. The experiments on two Taobao and two public datasets demonstrate that the proposed deep framework CGNN is superior to other baselines in detecting fraud behaviors. A case study on Taobao datasets verifies that CGNN is still robust when the fraud patterns have been upgraded.
关键词	Online e-commerce platforms fraud detection system graph neural networks
DOI	10.1145/3474379
收录类别	SCI
语种	英语
资助项目	ARC DECRA Project[DE200100964]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000776450500006
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60240
专题	应用数学研究所
通讯作者	Li, Zhao
作者单位	1.Macquarie Univ, Dept Comp, N Ryde, NSW, Australia 2.Alibaba Grp, Hangzhou, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 4.Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Ge,Li, Zhao,Huang, Jiaming,et al. eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2022,40(3):29.
APA	Zhang, Ge.,Li, Zhao.,Huang, Jiaming.,Wu, Jia.,Zhou, Chuan.,...&Gao, Jianliang.(2022).eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks.ACM TRANSACTIONS ON INFORMATION SYSTEMS,40(3),29.
MLA	Zhang, Ge,et al."eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks".ACM TRANSACTIONS ON INFORMATION SYSTEMS 40.3(2022):29.