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Dynamic graph neural network-based fraud detectors against collaborative fraudsters

文献类型：期刊论文

作者Ren, Lingfei2,3; Hu, Ruimin2,3,5; Li, Dengshi2,4; Liu, Yang1; Wu, Junhang2,3; Zang, Yilong2,3; Hu, Wenyi2,3
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2023-10-25
卷号278页码:12
ISSN号0950-7051
关键词Telecom fraud detection Collaborative fraud Semi -supervised learning Dynamic graph neural network
DOI10.1016/j.knosys.2023.110888
英文摘要Telecom fraud detection is a challenging task since the fact that fraudulent behaviors are hidden in the vast amount of telecom records. More concerning, the ongoing coronavirus pandemic (COVID-19) accelerated the use of mobile internet, providing more criminal opportunities for fraudsters. However, current telecom fraud detection mostly focuses on individual sequences representation, rarely noticing the collaboration of fraudsters, making it exhibit unsatisfactory performance in the face of gang crimes. To address this problem, we propose to extract collaborative networks from user call logs with an emphasis on unveiling collaborative fraud. We employ eight months of telecom datasets in China with 6,106 users and 5.0 million call logs between 1.25 million telephone recipients. Through our study, we find that the social structure of fraudsters evolute rapidly while the normal users remain stable relatively. In addition, we find that mining collaborative fraud strategies help to detect fraudsters with less distinct fraud characteristics. To this end, we propose a novel model named COllaborative-REsistant Dynamic Graph Neural Network (CORE-DGNN), to enhance the dynamic GNN aggregation process. Specifically, we first use co-recipients to obtain the collaborative network under each time slice. Then, we design a multi-frequency graph neural network to adaptively aggregate the features of node neighbors at different frequencies to address the problem of heterophily in collaborative networks. Finally, a self-attentive temporal convolutional network is designed to aggregate node embedding features across multiple time spans. Comprehensive experiments on two real-world telecom fraud datasets show that our approach outperforms several state-of-the-art algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.
资助项目National Nature Science Foundation of China[U22A2035] ; National Nature Science Foundation of China[U1803262] ; National Nature Science Foundation of China[U1736206] ; National Social Science Foundation of China[19ZDA113] ; Application Foundation Frontier Project of Wuhan Science and Technology Bureau[2020010601012288]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:001070959900001
源URL[http://119.78.100.204/handle/2XEOYT63/21163]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Ruimin
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China
2.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan 430072, Peoples R China
3.Wuhan Univ, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan 430072, Peoples R China
4.Jianghan Univ, Sch Artificial Intelligence, Wuhan 430056, Peoples R China
5.Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China
推荐引用方式
GB/T 7714		 Ren, Lingfei,Hu, Ruimin,Li, Dengshi,et al. Dynamic graph neural network-based fraud detectors against collaborative fraudsters[J]. KNOWLEDGE-BASED SYSTEMS,2023,278:12.
APA Ren, Lingfei.,Hu, Ruimin.,Li, Dengshi.,Liu, Yang.,Wu, Junhang.,...&Hu, Wenyi.(2023).Dynamic graph neural network-based fraud detectors against collaborative fraudsters.KNOWLEDGE-BASED SYSTEMS,278,12.
MLA Ren, Lingfei,et al."Dynamic graph neural network-based fraud detectors against collaborative fraudsters".KNOWLEDGE-BASED SYSTEMS 278(2023):12.

入库方式： OAI收割

来源：计算技术研究所

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