AI software selection for cybersecurity auditing using neutrosophic CRITIC CODAS

Sahin F., Menekse A., YILMAZ S.

Applied Soft Computing, cilt.179, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 179
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.asoc.2025.113295
  • Dergi Adı: Applied Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: AI software selection, CRITIC CODAS, Cybersecurity auditing, MCDM, Neutrosophic logic
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.