Fuzzy logic allows for the creation of systems that handle vague boundaries, moving beyond binary true or false values. By assigning a degree of membership to sets, this approach enables more nuanced decision-making in information security, particularly when evaluating vulnerability risks.
The author demonstrates the application of fuzzy logic using the EPSS (Exploit Prediction Scoring System), which estimates the probability of a vulnerability being exploited within 30 days. By defining membership functions—such as triangular, trapezoidal, Gaussian, and sigmoid—the system can process complex inputs that traditional binary logic fails to categorize effectively.
The process involves four key steps: fuzzification, rule evaluation, aggregation, and defuzzification. Using the Mamdani inference method, the system calculates the strength of rules based on input variables like EPSS, impact, and age. This allows for a smooth transition between risk levels, preventing the 'gaps' often found in rigid classification models.
Practical implementation requires careful management of rule bases to ensure completeness and consistency. With a limit of 3-4 inputs per block, the author suggests using cascaded blocks to manage complexity, as the number of rules grows exponentially with the number of inputs and terms.
