Tools of the neuro-fuzzy model of information risk management in national security

Abstract

The rapid development of information technology has strengthened the importance of the information risk management system. Integrated systems for storing and processing information, its transmission channels, as well as the information itself, are strategically essential objects of national security. The growing volumes of statistical data, as well as the traditional uncertainty and incompleteness of information on the nature of potential threats, determine the need to use new approaches for risk analysis. The neuro-fuzzy model considered in the article is based on the advantages of fuzzy logic and artificial neural networks. The proposed neuro-fuzzy network is adapted for continuous risk analysis and iterative implementation of the analysis stage. It eliminates the disadvantages of the fuzzy logical model and takes full advantage of neural networks. This system copes well with large volumes of information since there is a direct correlation between the amount of data and the speed of network learning. The data provided by the network at the output is expressed in understandable terms and sufficient to make a balanced and reasoned decision on information risk management.