Institute of Computing Technology, Chinese Academy IR
Dynamic graph neural network-based fraud detectors against collaborative fraudsters | |
Ren, Lingfei1,2; Hu, Ruimin1,2,5; Li, Dengshi1,3; Liu, Yang4; Wu, Junhang1,2; Zang, Yilong1,2; Hu, Wenyi1,2 | |
2023-10-25 | |
发表期刊 | KNOWLEDGE-BASED SYSTEMS |
ISSN | 0950-7051 |
卷号 | 278页码:12 |
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. |
关键词 | Telecom fraud detection Collaborative fraud Semi -supervised learning Dynamic graph neural network |
DOI | 10.1016/j.knosys.2023.110888 |
收录类别 | SCI |
语种 | 英语 |
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 |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:001070959900001 |
出版者 | ELSEVIER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/21164 |
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.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan 430072, Peoples R China 2.Wuhan Univ, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan 430072, Peoples R China 3.Jianghan Univ, Sch Artificial Intelligence, Wuhan 430056, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, 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. |
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