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The article deals with an information security model developed by authors that is based on adaptive neuro-fuzzy system providing timely detection of unmasking features of the object of protection. A number of unmasking features of the object of study with fuzzy values are used as input variables of the considered model. The model combines the function of modeling fuzzy inference with the ability to train an artificial neural network. A set of fuzzy inference rules is generated ИНФОРМАЦИОННЫЕ СИСТЕМЫ Автоматизация процессов управления No 1 (59) 2020 39 directly from experimental data. The output result of the system is the value of the integral index in the interval [0,1] that describes the information content of the characteristic structure of the object under study with recommendations for reducing its information content. Evaluation of the correctness of the model was carried out by comparing experimental and obtained (forecast) results.

DEVELOPMENT OF A NEURO-FUZZY MODEL OF INFORMATION SECURITY BY IDENTIFYING UNMASKING FEATURES OF THE OBJECT OF PROTECTION