This article presents the development of a modular decision support system (DSS) for cybersecurity, aimed at enhancing the protection of critical computer systems (CCS). The system is based on a fuzzy logic inference subsystem (FIS) model that utilizes data from sensors and SIEM systems to detect signs of threats, anomalies, and attacks through fuzzification of input values. A developed algorithm for forming a knowledge base of typical and emergency situations allows the system not only to effectively respond to known threats but also to analyze unforeseen situations. The application of the FIS module enables the creation of a multi-parameter image of CCS vulnerability, which ensures a more comprehensive and accurate assessment of their security.
Dmytro et al. (Fri,) studied this question.
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