Academic Journal
Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network.
| Title: | Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network. |
|---|---|
| Authors: | Wei, Sizheng, Lee, Suan |
| Source: | PLoS ONE; 12/5/2025, Vol. 20 Issue 12, p1-41, 41p |
| Subject Terms: | Internet fraud, Graph neural networks, Data augmentation, Fraud investigation, Recurrent neural networks |
| Abstract: | This study proposes an advanced Internet fraud transaction detection method, the Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network (THG-OAFN), designed to address the increasingly severe fraud issues in EC. The method innovatively abstracts transaction data into a heterogeneous graph structure, captures temporal dynamic features through Gated Recurrent Unit (GRU), and fuses Graph Neural Network (GNN) to process static topological relationships. To address data imbalance, an improved Graph-based Synthetic Minority Oversampling Technique (GraphSMOTE) framework is introduced, maintaining the structural integrity of fraud clusters through k-hop topological constraints. Meanwhile, a multi-layer attention mechanism (including relationship fusion, neighborhood fusion, and information perception modules) is employed to achieve active fraud prevention. Experimental results show that THG-OAFN attains an area under the curve (AUC) of 96.56% (a 7.78% improvement over the best baseline). Moreover, it achieves a recall of 95.21% (a 6.29% improvement) and an F1-score of 94.72% (a 3.96% improvement) on the Amazon dataset. On the YelpChi dataset, these three metrics reach 90.43%, 89.51%, and 90.31%, respectively, remarkably outperforming existing GNN models. This achievement provides a deployable solution for dynamic fraud detection and active defense. Our code is available at https://github.com/wei4zheng/THG-OAFN. [ABSTRACT FROM AUTHOR] |
| Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19326203&ISBN=&volume=20&issue=12&date=20251205&spage=1&pages=1-41&title=PLoS ONE&atitle=Internet%20fraud%20transaction%20detection%20based%20on%20temporal-aware%20heterogeneous%20graph%20oversampling%20and%20attention%20fusion%20network.&aulast=Wei%2C%20Sizheng&id=DOI:10.1371/journal.pone.0337208 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Sizheng%22">Wei, Sizheng</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+Suan%22">Lee, Suan</searchLink> – Name: TitleSource Label: Source Group: Src Data: PLoS ONE; 12/5/2025, Vol. 20 Issue 12, p1-41, 41p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+fraud%22">Internet fraud</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Fraud+investigation%22">Fraud investigation</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study proposes an advanced Internet fraud transaction detection method, the Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network (THG-OAFN), designed to address the increasingly severe fraud issues in EC. The method innovatively abstracts transaction data into a heterogeneous graph structure, captures temporal dynamic features through Gated Recurrent Unit (GRU), and fuses Graph Neural Network (GNN) to process static topological relationships. To address data imbalance, an improved Graph-based Synthetic Minority Oversampling Technique (GraphSMOTE) framework is introduced, maintaining the structural integrity of fraud clusters through k-hop topological constraints. Meanwhile, a multi-layer attention mechanism (including relationship fusion, neighborhood fusion, and information perception modules) is employed to achieve active fraud prevention. Experimental results show that THG-OAFN attains an area under the curve (AUC) of 96.56% (a 7.78% improvement over the best baseline). Moreover, it achieves a recall of 95.21% (a 6.29% improvement) and an F1-score of 94.72% (a 3.96% improvement) on the Amazon dataset. On the YelpChi dataset, these three metrics reach 90.43%, 89.51%, and 90.31%, respectively, remarkably outperforming existing GNN models. This achievement provides a deployable solution for dynamic fraud detection and active defense. Our code is available at https://github.com/wei4zheng/THG-OAFN. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pone.0337208 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 41 StartPage: 1 Subjects: – SubjectFull: Internet fraud Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Fraud investigation Type: general – SubjectFull: Recurrent neural networks Type: general Titles: – TitleFull: Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Sizheng – PersonEntity: Name: NameFull: Lee, Suan IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 12 Text: 12/5/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19326203 Numbering: – Type: volume Value: 20 – Type: issue Value: 12 Titles: – TitleFull: PLoS ONE Type: main |
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