生成式人工智能驱动下电信网络诈骗受害风险影响因素量化分析

周胜利; 徐睿; 陈庭贵; 汪邵杰

doi:10.11959/j.issn.1000-0801.2025163

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生成式人工智能驱动下电信网络诈骗受害风险影响因素量化分析

研究与开发 | 更新时间：2025-08-07

- 生成式人工智能驱动下电信网络诈骗受害风险影响因素量化分析
- Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence
- 电信科学 2025年41卷第7期页码：71-84
- 作者机构：
  
  1.浙江警察学院信息网络安全学院，浙江杭州 310053
  2.杭州电子科技大学网络空间安全学院，浙江杭州 310018
  3.浙江工商大学统计与数学学院，浙江杭州 310018
- 作者简介：
  
  [ "周胜利（1982- ），男，博士，浙江警察学院信息网络安全学院教授，杭州电子科技大学网络空间安全学院硕士生导师，主要研究方向为网络安全、网络空间治理。" ]
  [ "徐睿（2000- ），男，杭州电子科技大学网络空间安全学院硕士生，主要研究方向为网络安全、机器学习。" ]
  [ "陈庭贵（1979- ），男，浙江工商大学统计与数学学院教授、博士生导师，主要研究方向为网络舆情演化分析。" ]
  [ "汪邵杰（2004- ），男，浙江警察学院在读，主要研究方向为网络安全。" ]
- 基金信息：
  
  国家社会科学基金资助项目;The National Social Science Foundation of China(23BGL272)
- DOI：10.11959/j.issn.1000-0801.2025163
  中图分类号： D924.3
- 收稿日期：2025-04-02，
  
  修回日期：2025-07-02，
  
  纸质出版日期：2025-07-20
- 稿件说明：
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周胜利,徐睿,陈庭贵等.生成式人工智能驱动下电信网络诈骗受害风险影响因素量化分析[J].电信科学,2025,41(07):71-84.

ZHOU Shengli,XU Rui,CHEN Tinggui,et al.Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence[J].Telecommunications Science,2025,41(07):71-84.
周胜利,徐睿,陈庭贵等.生成式人工智能驱动下电信网络诈骗受害风险影响因素量化分析[J].电信科学,2025,41(07):71-84. DOI： 10.11959/j.issn.1000-0801.2025163.

ZHOU Shengli,XU Rui,CHEN Tinggui,et al.Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence[J].Telecommunications Science,2025,41(07):71-84. DOI： 10.11959/j.issn.1000-0801.2025163.

摘要

开展生成式人工智能驱动下电信网络诈骗的受害风险影响因素研究，对于揭示犯罪规律和提升技术防治能力具有重要的理论价值与实践意义。为此，依托真实AI诈骗案件数据开展模拟实验，将犯罪过程解构为伪造信息的生成、传播与影响3个阶段，从中提取生成式人工智能、数据流、数据包、网络行为和受害风险等潜变量，再结合结构方程模型理论构建分析框架，系统量化了不同要素对受害风险的影响路径与作用贡献度。研究结果表明，生成式人工智能对受害风险具有显著的直接效应，且在整体影响效应中占据主导地位；数据流与数据包特征的中介效应较弱，在影响路径中作用不显著。

Abstract

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.

关键词

Keywords

references

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