生成式人工智能驱动下电信网络诈骗风险演化实证研究

周胜利; 徐睿; 陈庭贵; 汪邵杰; 王镇波

doi:10.11959/j.issn.1000-0801.2025135

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生成式人工智能驱动下电信网络诈骗风险演化实证研究

研究与开发 | 更新时间：2025-06-03

- 生成式人工智能驱动下电信网络诈骗风险演化实证研究
- Empirical study on the evolution of telecom fraud risks driven by artificial intelligence generated content
- 电信科学 2025年41卷第5期页码：149-165
- 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、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，浙江警察学院信息网络安全学院在读，主要研究方向为网络安全。" ]
  [ "王镇波（1987- ），男，博士，浙江警察学院信息网络安全学院讲师，主要研究方向为人工智能和智能交通。" ]
- 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.1000-0801.2025135
  中图分类号： D631.2
- 收稿：2025-03-09，
  
  修回：2025-05-14，
  
  纸质出版：2025-05-20
- 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风险演化实证研究[J].电信科学,2025,41(05):149-165.

ZHOU Shengli,XU Rui,CHEN Tinggui,et al.Empirical study on the evolution of telecom fraud risks driven by artificial intelligence generated content[J].Telecommunications Science,2025,41(05):149-165.
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风险演化实证研究[J].电信科学,2025,41(05):149-165. DOI： 10.11959/j.issn.1000-0801.2025135.

ZHOU Shengli,XU Rui,CHEN Tinggui,et al.Empirical study on the evolution of telecom fraud risks driven by artificial intelligence generated content[J].Telecommunications Science,2025,41(05):149-165. DOI： 10.11959/j.issn.1000-0801.2025135.

摘要

利用知识图谱与事理图谱技术对生成式人工智能驱动下的电信网络诈骗案件进行实证研究，可以更直观地回溯受骗过程中风险的演化方式，对新型电信网络诈骗反制预警具有重要意义。基于利用生成式人工智能实施的电信网络诈骗案件数据，首先，进行语义角色标注和依存句法分析；然后，通过事件元素识别和事件关系抽取，构建案件知识图谱和事理图谱；最后，结合数理统计和图谱技术分析电信网络诈骗风险演化的关键环节和演化模式。研究表明，嫌疑人借助生成式人工智能技术能更有效地利用证真偏差现象，博取受害人的信任；生成式人工智能驱动下电信网络诈骗风险的演化模式可分为3类，长链型演化模式反映了案件中的完整风险事件及事件间的演化过程，星型和复合型演化模式反映了同类案件中存在的不同风险行为模式及核心风险事件节点，能为制定更加科学合理的电信网络诈骗治理对策提供理论依据。

Abstract

The application of knowledge graph and eventic graph technologies in the empirical study of telecom fraud cases driven by artificial intelligence generated content (AIGC) allows for a more intuitive tracing of the evolution of risks during the victimization process

which is of great significance for countering and early warning of new types of telecom fraud. Based on data from telecom fraud cases implemented using AIGC

semantic role labeling and dependency parsing were firstly performed during data preprocessing. Then

event element recognition and event relationship extraction were constructed to construct knowledge graphs and eventic graphs of the cases. Finally

the key stages and patterns of the evolution of telecom fraud risks were analyzed by combining mathematical statistics with graph technologies. The research revealed that

suspects using AIGC were able to more effectively exploit the phenomenon of confirmation bias to gain the victim’s trust. The evolution patterns of telecom fraud risks driven by AIGC were categorized into three types: the long-chain type evolution pattern systematically identifies complete risk events and their inter-event evolutionary trajectories within cases

while investigation of the star-shaped and composite type evolution patterns enable recognition of divergent risk behavioral patterns and localization of core risk event nodes across homogeneous case clusters

there by establishing a theoretical foundation for developing scientifically rational governance strategies in telecom fraud countermeasures.

关键词

Keywords

references

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