An integrated optimization model of network behavior victimization identification based on association rule feature extraction
The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-08-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023180/ |
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| _version_ | 1841533616829497344 |
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| author | Shengli ZHOU Linqi RUAN Rui XU Xikang ZHANG Quanzhe ZHAO Yuanbo LIAN |
| author_facet | Shengli ZHOU Linqi RUAN Rui XU Xikang ZHANG Quanzhe ZHAO Yuanbo LIAN |
| author_sort | Shengli ZHOU |
| collection | DOAJ |
| description | The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification. |
| format | Article |
| id | doaj-art-2452f68efcee43428907fbd5ccda4439 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-2452f68efcee43428907fbd5ccda44392025-01-15T02:58:13ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-08-013912914059561287An integrated optimization model of network behavior victimization identification based on association rule feature extractionShengli ZHOULinqi RUANRui XUXikang ZHANGQuanzhe ZHAOYuanbo LIANThe identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023180/The National Social Science Foundation of ChinaZhejiang Natural Science Foundation and Public Welfare Research ProgramMinistry of Public Security Science and Technology Plan Projectnetwork traffic analysis |
| spellingShingle | Shengli ZHOU Linqi RUAN Rui XU Xikang ZHANG Quanzhe ZHAO Yuanbo LIAN An integrated optimization model of network behavior victimization identification based on association rule feature extraction Dianxin kexue The National Social Science Foundation of China Zhejiang Natural Science Foundation and Public Welfare Research Program Ministry of Public Security Science and Technology Plan Project network traffic analysis |
| title | An integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| title_full | An integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| title_fullStr | An integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| title_full_unstemmed | An integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| title_short | An integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| title_sort | integrated optimization model of network behavior victimization identification based on association rule feature extraction |
| topic | The National Social Science Foundation of China Zhejiang Natural Science Foundation and Public Welfare Research Program Ministry of Public Security Science and Technology Plan Project network traffic analysis |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023180/ |
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