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The use of augmented reality-enabled scenarios in cybersecurity teaching is proposed in the article to respond to new requirements for the rapid adoption of new technologies and profound knowledge of cybersecurity issues by professionals. Implementation of project-type activities based on real cybersecurity issues in application fields of cyber-physical systems is suggested to improve the competence forming. A use-case of agricultural cyber-physical system of systems is discussed as a viable example of augmented reality-enabled prototyping of cybersecurity risk-aware architecture. The necessary steps are analysis of general and business-specific tasks on cybersecurity, creation of a list of competencies, formalized in educational standards and curricula, development of gaming scenarios for the formation of hard and soft skills, development of the scenario management system for AR interfaces. The system using AR tools can be easily adapted to different cybersecurity training activities. Industrial cyber-physical systems may be vulnerable due to insecure wireless connectivity, lack of encryption, inadequate access policy. The project-based learning complex is focused on the implementation of a data acquisition, storage and processing platform for new sensor networks and instruments. Representing all the diverse information on different layers will be greatly improved by use of the developed holographic projection AR tools.

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The problem of optimization of software system architecture choice is considered taking into account changes of requirements in iterative technologies of design and reengineering. The solution is offered on the base of correction of alternatives' characteristics which were accounted on previous iteration or modification of existing software system during reengineering. This method prevents recalculations for evaluation and selection process. The method of pairwise substitution is applied for alternatives' characteristics correction. Its concept lies in compensation on supremacy of criterion change. Multicriteria optimization of substitution is carried out using nonlinear scalar convolution what improves validity of selected decision.