Beyond Type-1: Harnessing Fuzzy Logic Variants for Strengthening Trust Models in Cyber Defense

Abstract

The threat in computer systems has tended to change in intensity and methods, making traditional defense models ineffective in cyber systems' dynamic and uncertain nature. This paper focuses on trust models, which evaluate the credibility of different entities in a network to minimize dangers and guarantee protection. Thus, original trust assessment methods do not work here because cyber interactions are indefinite and ambiguous. Therefore, this paper recommends the inclusion of fuzzy logic variants into trust models to address such challenges in their implementation. Employing fuzzy logic such as fuzzy sets, fuzzy inference systems, and fuzzy clustering, we show that uncertainty and partial truth can be used effectively on the trust models. Several fuzzy logic methodologies in the study's context include simple fuzzy rules, traditional and non-traditional approaches such as fuzzy multi-criteria decision analysis, and adaptive fuzzy systems that define trust in complex and context-aware cyber environments. The results imply that incorporating fuzzy logic can enhance current trust models by providing a rich and flexible approach to cyber defense; it gives a course toward better security protection as contemporary threats evolve to be complex and unpredictable.