Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence
Conducting research on the factors influencing victimization risks in generative AI (GAI) driven telecom network fraud holds significant theoretical and practical implications for areas such as summarizing patterns of criminal behavior and enhancing technological defense capabilities. For this purpo...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2025-07-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025163/ |
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| author | ZHOU Shengli XU Rui CHEN Tinggui WANG Shaojie |
| author_facet | ZHOU Shengli XU Rui CHEN Tinggui WANG Shaojie |
| author_sort | ZHOU Shengli |
| collection | DOAJ |
| description | Conducting research on the factors influencing victimization risks in generative AI (GAI) driven telecom network fraud holds significant theoretical and practical implications for areas such as summarizing patterns of criminal behavior and enhancing technological defense capabilities. For this purpose, simulation experiments were carried out based on real AI fraud case information. The criminal process was deconstructed into three stages: forged information generation, dissemination, and impact of forged information. Latent variables such as GAI, data flow, data packets, network behavior, and network risk were extracted. An analytical framework was then constructed by combining structural equation modeling theory to systematically quantify the influence paths and contribution degrees of different elements on victimization risk. The findings revealed that GAI had a significant direct effect on network risk, and the direct effect played a dominant role in the overall effect. The mediating effects of data flow and packet characteristics were weak, and its role in the influence path was not significant. |
| format | Article |
| id | doaj-art-af9c7f745a454d7eb66692bfb339380d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-af9c7f745a454d7eb66692bfb339380d2025-08-20T03:38:43ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-07-01417184120127950Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligenceZHOU ShengliXU RuiCHEN TingguiWANG ShaojieConducting research on the factors influencing victimization risks in generative AI (GAI) driven telecom network fraud holds significant theoretical and practical implications for areas such as summarizing patterns of criminal behavior and enhancing technological defense capabilities. For this purpose, simulation experiments were carried out based on real AI fraud case information. The criminal process was deconstructed into three stages: forged information generation, dissemination, and impact of forged information. Latent variables such as GAI, data flow, data packets, network behavior, and network risk were extracted. An analytical framework was then constructed by combining structural equation modeling theory to systematically quantify the influence paths and contribution degrees of different elements on victimization risk. The findings revealed that GAI had a significant direct effect on network risk, and the direct effect played a dominant role in the overall effect. The mediating effects of data flow and packet characteristics were weak, and its role in the influence path was not significant.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025163/generative artificial intelligencetelecom network fraudstructural equation modelquantitative analysis |
| spellingShingle | ZHOU Shengli XU Rui CHEN Tinggui WANG Shaojie Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence Dianxin kexue generative artificial intelligence telecom network fraud structural equation model quantitative analysis |
| title | Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| title_full | Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| title_fullStr | Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| title_full_unstemmed | Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| title_short | Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| title_sort | quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence |
| topic | generative artificial intelligence telecom network fraud structural equation model quantitative analysis |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025163/ |
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