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An adaptive model for detecting vulnerabilities in the interfaces of unmanned vehicles based on dynamic assessment of information states of resources is proposed. The method is based on fuzzy logic and immunological principles. Rules classify objects belonging to several classes at the same time with different degrees of belonging. Resource state recognition is performed under conditions of a lack of a priori information about the properties of the intrusion source and the stochastic nature of the recognized events. To increase the level of reliability of vulnerability detection, the model makes adaptive dynamic tuning of decision-making rules for classifying the information state of unmanned vehicle resources.

Adaptive fuzzy model for detecting of vulnerabilities of unmanned vehicles interfaces based on evaluation of the information state of resources