人工智能对高等院校国企攻击影响的研究:结构方程建模方法

Khidzir Nzb, Ahmed Saam
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引用次数: 0

摘要

本研究采用人工启用社会工程攻击风险因素理论,确定干扰用户高等院校个人生产力对人工启用SoE攻击的影响。本文采用的五个自变量分别是威胁、脆弱性、评价、对策和个人干扰因素。并将其作为判定高校个人生产力干扰的指标。由于使用结构方程建模的多元回归-偏最小二乘(SEM-PLS)用于通过与AI相关的问卷调查来检查数据的收集,从而使SE攻击风险。结果表明,三个自变量对高校个人生产力有显著影响。事实上,本研究得出的结论是,人工智能对高校个人生产力干扰的首要影响因素是使国有企业能够攻击威胁、脆弱性、评估和对策等风险因素。本研究对于推动中国高等院校成为世界性的高等院校,不仅是一个入门性的研究,也是一个充满活力的认识。应用与计算数学学报[J] ., [J] .应用与计算数学[J] .中文信息学报,2011,31 (6):968 - 979
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Investigation of AI enabled SoE Attacking Impact in Higher Learning Institute: Structural Equation Modeling (SEM) Approach
Theory of artificial enabled social engineering attacking risk factors are employed in this study to determine the impact that disturbed the personal productivity of higher learning institute of the user towards the AI enable SoE attacking. Five independent variable which are threat, vulnerability, valuation, countermeasure and personal disturbance factors using in this paper. Moreover using as an indicator in determining disturbance of personal productivity in an higher learning institute. Since multiple regression by using Structural Equation Modelling –Partial Least Square (SEM-PLS) is used to examine the collection of data by a questionnaire which is relevant with AI enable SE attacking risk. And the resulting point out three independent variable significantly influences the personal productivity in higher learning institute. As a matter of fact this study concludes that the foremost influence factor on disturbance of personal productivity in higher learning institute towards the AI enables SoE attacking risk factors such as threat, vulnerability, valuation and countermeasure. This study contributes to introductory study but vibrant understanding in stimulating the higher learning institute to become a worldwide institution. Journal of Applied & Computational Mathematics J o u r n a l o f A pp lie d & Computional M th e m a t i c s ISSN: 2168-9679
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