METHOD OF FORMING ASSOCIATIVE RULES FROM THE SIEM DATABASE BASED ON FUZZY SET THEORY AND LINGUISTIC TERMS

The article presents a method of forming fuzzy associative rules with weighted attributes from the database (DB) of the SIEM to supplement its knowledge base (KB) in order to more effectively detect cyber incidents that occur during the operation of special information and communication systems (SICS). The problems that reduce the effectiveness of the application of existing methods for solving the problem of forming associative rules based on the analysis of information located in the database of cyber protection systems are considered. An analysis of publications devoted to methods in which attempts were made to eliminate these problems was made. The basic idea of eliminating the shortcomings inherent in known methods is formulated, which consists in finding a compromise between reducing the time of the computing algorithm that implements the method in practice and reducing information losses as a result of its operation. An improved method of finding associative rules from SIEM databases is proposed, which is based on the theory of fuzzy sets and linguistic terms. The problem of finding fuzzy associative rules with weighted attributes is formulated. The mathematical apparatus th