Fuzzy logic techniques for managing uncerainty in cybersecurity

Abstract

Abstract. Fuzzy logic, as a mathematical framework for handling uncertainty and incomplete data, is widely applied in risk management processes where traditional binary models fail to adequately capture the multidimensional and subjective nature of expert evaluations. The methodological foundation of the approach proposed by Swarnakar et al. [4] lies in the enhancement of the classical Kano model. This concept is employed to generate fuzzy assessments of project priority and impact, corresponding to the threat ranking or response strategy selection phase in classical risk management systems. Formally, if X denotes the universe of possible values for a given criterion , then a fuzzy set A is defined as: 𝐴 = {(𝑥, 𝜇𝐴(𝑥))|𝑥 ∈ 𝑋}, (1) where μA(x) is the membership function that reflects the degree of truth of the statement “x belongs to set A.” This allows for the preservation of nuanced expert judgments and prevents information loss associated with rigid categorization.