A sequential method of tuning interval-type fuzzy relations for assessing the security of information systems

Abstract

Today, an urgent scientific and technical problem is the creation of systems for assessing the security of information systems from threats that can process fuzzy information. These systems allow you to determine what actions are effective to minimize and prevent threats. The fuzzy model is built on the basis of the Zadeh compositional inference rule, in which the information carrier is the matrix of fuzzy "threat-damage" relations connecting the vector of measures of the significance of threats and the vector of measures of significance of damage. When designing such systems based on fuzzy relations, it is necessary to determine a set of its parameters (WІ - a set of parameters that determine a system model based on fuzzy relations of type I and WІІ - a set of parameters that define a system model based on fuzzy relations of type II). To date, for tuning interval-type fuzzy systems, an independent method is used, which assumes the determination of the set WІІ from "zero", without using the results of tuning the set of parameters WІ, which leads to an increase in the setup time. The article proposes a sequential method for adjusting fuzzy relations of interval type, which firstly provides for the determination of a set of parameters of fuzzy relations of type I using a genetic-neural algorithm, and then, on their basis, adjustment of only additional parameters, which makes it possible to reduce the average tuning time of a fuzzy model and assess the effect of uncertainty on the accuracy of the assessment.