Özet:

A method for assessing cybersecurity in special-purpose information systems was developed. Cybersecurity assessment was performed using decision trees, implemented using “IF-THEN” fuzzy rules, which are considered as common building blocks of the decision tree. This approach allows processing large amounts of data. The use of the decision tree allows increasing evaluation accuracy, is easy to set up and intuitive. Improvement of the efficiency of cybersecurity assessment (error reduction) was achieved using evolving neuro-fuzzy artificial neural networks. Training of evolving neuro-fuzzy artificial neural networks was carried out by learning not only the synaptic weights of the artificial neural network, type, parameters of the membership function, but also by reducing the dimensionality of the feature space. The efficiency of information processing was also achieved through training the architecture of artificial neural networks; taking into account the type of uncertainty of information to be assessed; working with both clear and fuzzy products, and reducing the feature space. This reduces the computational complexity of decision-making and eliminates the accumulation of learning errors of artificial neural networks. The computational complexity of the method is on average 2 million calculations less compared to the known ones, and after 2 epochs, the learning error decreases. Cybersecurity analysis in general occurs due to an advanced clustering procedure that allows working with both static and dynamic data. Testing of the proposed method was carried out. The increase in the efficiency of cybersecurity assessment of about 20–25 % in terms of information processing efficiency was revealed Author Biographies Serhii Drozdov, Air Force of Armed Forces of Ukraine Striletska str., 105, Vinnytsia, Ukraine, 21001 PhD, Commander

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