Petrenko T. Methods and models of adaptive expert systems of recognition of cyber attacks on the basis of clustering of features

The dissertation contains the results of researches aimed at further development of methods and models for adaptive systems of recognition of cyber attacks on the basis of clusterization of the implementation of features. A structural scheme of an expert system for information security capable of self-education is proposed. On the basis of the analysis of available scientific publications, it was found that the complexity of application to intelligent recognition systems of target cyber attacks of the formalized apparatus of analysis and synthesis is that a specific information complex and their subsystems of information security consist of heterogeneous elements that are described using different models. The method of teaching the expert system is improved, which is an iterative procedure for finding the global maximum of the information condition of functional efficiency, and, unlike the existing one, prevents possible cases of one object acquisition of objects of recognition of basic realizations of signs of observation objects, as well as errors during the task of making decisions in the course of machine learning procedures. Further development of simulation models for the composite construction of intelligent detection systems for cyber attacks by simultaneously optimizing control tolerances during the analysis of recognition objects, allowing them to conduct research, to select rational methods of counteraction and neutralization of consequences, to analyze more complex and previously unknown types of cyber attacks on critical information systems.