AI software selection for cybersecurity auditing using neutrosophic CRITIC CODAS

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-05-31 DOI:10.1016/j.asoc.2025.113295

Fatih Sahin , Akin Menekse , Selman Yilmaz

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引用次数: 0

Abstract

Today’s companies must develop creative solutions to counter the risks of cyberattacks that make it difficult to protect their valuable information in an increasingly complex digital world. In this context, cybersecurity audits have gained importance, and companies have become especially interested in artificial intelligence (AI)-based cybersecurity audit tools. On the other hand, the selection of AI software includes multiple criteria and alternatives, and decision experts may have uncertainty in their linguistic evaluations. In this study, a new neutrosophic CRiteria Importance Through Intercriteria Correlation (CRITIC) integrated COmbinative Distance-based ASsessment (CODAS) methodology is proposed for selecting AI software for cybersecurity auditing. The importance weights of the criteria are directly calculated with the CRITIC method, and the alternatives are ranked with the CODAS approach. The uncertainty of decision experts is modeled with neutrosophic sets through truth, indeterminacy, and falsity degrees. The study includes sensitivity analyses for criterion and decision expert weights, as well as a comparative study with rank correlation analysis. Implications and discussions, limitations, and future research avenues are also given in the study.

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使用中性CRITIC CODAS进行网络安全审计的人工智能软件选择

当今的企业必须开发出创造性的解决方案，以应对网络攻击带来的风险。在日益复杂的数字世界中，网络攻击使企业难以保护自己的宝贵信息。在这种背景下，网络安全审计变得越来越重要，公司对基于人工智能（AI）的网络安全审计工具特别感兴趣。另一方面，人工智能软件的选择包括多个标准和备选方案，决策专家在其语言评估中可能存在不确定性。在本研究中，提出了一种新的中性标准重要性通过标准间相关性（critical）综合组合距离评估（CODAS）方法，用于选择用于网络安全审计的人工智能软件。使用CRITIC方法直接计算标准的重要性权重，使用CODAS方法对备选方案进行排序。决策专家的不确定性通过真、不确定和假度的中性集来建模。该研究包括对准则和决策专家权重的敏感性分析，以及与等级相关分析的比较研究。在研究中也给出了启示和讨论、局限性和未来的研究途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.