One of the pressing areas that is developing in the field of information security is associated with the use of Honeypots (virtual decoys, online traps), and the selection of criteria for determining the most effective Honeypots and their further classification is an urgent task. The main products that implement virtual decoy technologies are presented. They are often used to study the behavior, approaches and methods that an unauthorized party uses to gain unauthorized access to information system resources. Online hooks can simulate any resource, but more often they look like real production servers and workstations. A number of fairly effective developments are known that are used to solve the problems of detecting attacks on information system resources, which are based on the apparatus of fuzzy sets. They showed the effectiveness of the appropriate mathematical apparatus, the use of which, for example, to formalize the approach to the formation of a set of reference values that will improve the process of determining the most effective Honeypots. For this purpose, many characteristics have been formed (installation and configuration process, usage and support process, data collection, logging level, simulation level, interaction level) that determine the properties of online traps. These characteristics became the basis for developing a method for the formation of standards of linguistic variables for further selection of the most effective Honeypots. The method is based on the formation of a Honeypots set, subsets of characteristics and identifier values of linguistic estimates of the Honeypot characteristics, a base and derived frequency matrix, as well as on the construction of fuzzy terms and reference fuzzy numbers with their visualization. This will allow classifying and selecting the most effective virtual baits in the future.

The results of the research aimed at the further development of detection models of cyber threats, as well as of common classes of anomalies and cyber-attacks in mission critical computer systems (MCCS) are presented. It is shown that one of the promising directions of synthesis of adaptive systems of detection and prevention of cyber-attacks is the application of models of logical procedures of detection, based on the coverage matrices of features of anomalies, threats and cyber-attacks within the known and new classes of the MCCS intrusions. The model of detection of cyber-attacks, anomalies and threats to MCCS was designed, which is based on the application of learning samples in the form of matrices of features and elementary classifiers for each of the modeled classes. The studies on minimization of the number of training samples, represented in a binary form of discerning features were carried out. The program "Threat Analyzer” was developed which allows automatic generation of dimensions of training matrix of features of anomalies, cyber threats, or cyber-attacks, without requiring the participation of experts. It is shown that for the object detection within known classes of cyber threats, attacks, anomalies, the usage in the training matrices of representative sets of 3-4 features long allows maximizing the effectiveness of the algorithm, reaching up to 98 %.

Applied results of scientific analysis should be the key focus of modern security research. A comparative analysis of research results obtained using different methods, as an applied task, forms a broader basis for interpreting the results and substantiating the conclusions. A social survey and expert opinion research were conducted to implement the general concept of strategic analysis of cybersecurity in Ukraine. Using the method based on determining the average value in a certain set of estimates, as well as the method based on the theory of fuzzy sets, the risks of spreading certain cyber threats in Ukraine were assessed. The results were compared. Although the use of different measurement methods led to some differences in quantitative risk indicators, the comparative analysis of the ratio of the level of different cyber threats did not change significantly. At the same time, the fuzzy set method provided more flexible interpretation of the results to characterize cyber threats in terms of their upward or downward trend. In general, the combined approach to cyber threat risk assessment can become an important risk management tool, as it takes advantage of different methods and allows for a deeper understanding of the current situation and the formation of more informed management decisions.

This article proposes a scientifically grounded approach to risk assessment for infrastructural and functional systems that underpin the development of digitally transformed regional territories under conditions of high threat dynamics and sociotechnical instability. The core methodology is based on modeling of multifactorial threats through the application of fuzzy set theory and logic–linguistic analysis, enabling consideration of parameter uncertainty, fragmented expert input, and the lack of a unified risk landscape within complex infrastructure environments. A special emphasis is placed on components of technogenic, informational, and mobile infrastructure that ensure regional viability across planning, response, and recovery phases. The results confirm the relevance of the approach for assessing infrastructure resilience risks in regional spatial–functional systems, which demonstrates the potential integration into sustainable development strategies at the level of regional governance, cross-sectoral planning, and cultural reevaluation of the role of analytics as an ethically grounded practice for cultivating trust, transparency, and professional maturity.