The rapid development and application of new digital technologies has, on the one hand, opened up new opportunities for more efficient management of technological and business processes. On the other hand, this leads to a significant increase in security threats, increasing the vulnerability of businesses and organisations to cybercriminals. In recent years, the rapid growth of incidents of various kinds has shown that traditional approaches to information security (IS) are insufficient. Consequently, software product information security risk assessment has become an important task for most organisations. Several models have been proposed to help different enterprises deal with the challenges of building information security. This paper proposes a new hierarchical structured model for information security risk assessment using fuzzy logic. A new method for information security risk assessment of software is also described using the example of automated control systems or enterprise resource planning (ERP) systems (using learning management systems as an example). The proposed new risk assessment model has been software implemented using fuzzy logic in the form of 15 fuzzy machines. In a series of experiments, we have scrutinised the information security risk assessment of various software products. The proposed method should solve the problem of flexible risk assessment.

In connection with the spread of ICT in education there is an acute issue about getting a qualitative education. Therefore, various methods of determining the quality of education are developed and applied. In this paper, we propose a fuzzy model of performance evaluation of students through the establishment of performance. And we will try to prove the advantages of the use of fuzzy logic in the evaluation of students’ knowledge

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

This paper proposes the creation of an intelligent system for assessing the levels of professional competencies of students in e-learning. Mathematical models and methods for assessing the formation of professional competencies of students at the level of disciplines, modules and the entire educational program are built. Algorithms for calculating the assessment of levels of competency in the discipline, module, and educational program are developed. These estimates can be used in the formation of inference rules for making decisions on changing the structure and content of disciplines, modules and educational programs.