Method for calculating information protection in social networks in the conditions of fuzzy sets

Abstract

The current task of analyzing and managing a social network is to select such a composition of system elements and their
parameters that will ensure the possibility of achieving maximum functional efficiency under conditions of uncertainty. This article
studies the system for protecting a social network from network components under conditions of uncertainty. The main attention is
paid to data fuzzification, building fuzzy set models, assessing risks and the level of security of network objects. The proposed approach allows developing effective solutions for making management decisions in the cybersecurity context. An algorithm for building a tuple of protection parameters and modeling them using membership functions is presented. Methods for aggregating results and calculations using trapezoidal and triangular functions are separately considered. For this purpose, the following were compiled: a tuple of fuzzy sets from network components; its modeling was carried out; risk levels were calculated; network security levels, aggregation of results, membership functions. Trapezoidal and triangular methods were used to calculate parameters. The calculations are illustrated with graphic material. The results of the work can be used by network administrators, organizations and government agencies to improve the efficiency of cybersecurity management and protection of information resources.