Quantitative analysis of influencing factors of victimization risk of telecom network fraud driven by generative artificial intelligence - ZHOU Shengli; XU Rui; CHEN Tinggui; WANG Shaojie (Dianxin kexue)
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.