Improving transaction safety via anti-fraud protection based on blockchain
Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2163983 |
| _version_ | 1797684004490575872 |
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| author | Yong Ren Yan Ren Hongwei Tian Wei Song Yanhong Yang |
| author_facet | Yong Ren Yan Ren Hongwei Tian Wei Song Yanhong Yang |
| author_sort | Yong Ren |
| collection | DOAJ |
| description | Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy. |
| first_indexed | 2024-03-12T00:24:08Z |
| format | Article |
| id | doaj.art-783eb38dd1be43d9a43052ca328bc96f |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:08Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-783eb38dd1be43d9a43052ca328bc96f2023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2022.21639832163983Improving transaction safety via anti-fraud protection based on blockchainYong Ren0Yan Ren1Hongwei Tian2Wei Song3Yanhong Yang4Applied Technology College of Soochow UniversityApplied Technology College of Soochow UniversityApplied Technology College of Soochow UniversityApplied Technology College of Soochow UniversityApplied Technology College of Soochow UniversityFinancial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy.http://dx.doi.org/10.1080/09540091.2022.2163983blockchaindecentralisationgradient-boosting decision treetransaction protectionanti-fraud |
| spellingShingle | Yong Ren Yan Ren Hongwei Tian Wei Song Yanhong Yang Improving transaction safety via anti-fraud protection based on blockchain Connection Science blockchain decentralisation gradient-boosting decision tree transaction protection anti-fraud |
| title | Improving transaction safety via anti-fraud protection based on blockchain |
| title_full | Improving transaction safety via anti-fraud protection based on blockchain |
| title_fullStr | Improving transaction safety via anti-fraud protection based on blockchain |
| title_full_unstemmed | Improving transaction safety via anti-fraud protection based on blockchain |
| title_short | Improving transaction safety via anti-fraud protection based on blockchain |
| title_sort | improving transaction safety via anti fraud protection based on blockchain |
| topic | blockchain decentralisation gradient-boosting decision tree transaction protection anti-fraud |
| url | http://dx.doi.org/10.1080/09540091.2022.2163983 |
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