Comprehensive Framework for Collaborative Decision-Making in Evaluating Computer Network Security Using Interval Neutrosophic Information

Abstract

Computer network security (CNS) evaluation is crucial for protecting information 
assets and maintaining the stable operation of network systems. Through this evaluation, potential 
threats and vulnerabilities within the network can be systematically identified, effectively 
preventing possible cyber-attacks and data breaches. Additionally, network security evaluation helps 
enterprises and organizations allocate security resources appropriately, enhancing their ability to 
respond to emergencies and ensuring business continuity and data integrity. In summary, network 
security evaluation is one of the foundational tasks for achieving continuous, secure, and stable 
network operations. The CNS evaluation is multiple-attribute group decision making (MAGDM). 
Then, the generalized TODIM(GTODIM) approach has been developed to deal with MAGDM. The 
interval neutrosophic sets (INSs) are defined as an effective tool for representing uncertain data 
during the CNS evaluation. In this manuscript, the interval neutrosophic number generalized 
TODIM (INN-GTODIM) approach is proposed to put forward the MAGDM under INSs. Finally, 
numerical example study for CNS evaluation is introduced to validate the INN-GTODIM approach. 
The primary contributions of this study are outlined: (1) Deriving weight information through the 
application of average method, enhancing the robustness of the decision-making process; (1) 
Implementing the INN-GTODIM approach for MAGDM scenarios that utilize INSs, showcasing 
its adaptability to complex decision-making environments; (1) Applying the INN-GTODIM 
approach specifically to the domain of CNS evaluation, and comparing its performance against 
existing approaches to highlight its strengths and improvements; (4) Providing a detailed 
comparative analysis, which clearly demonstrates that the INN-GTODIM approach significantly 
enhances the effectiveness of CNS evaluations.  

DOI: 10.5281/zenodo.14039585