It has been demonstrated that technologies and methods of intelligent data analysis (IDA) in the educational domain, particularly based on the analysis of digital traces (DT) of students, offer substantial opportunities for analyzing student activities. Notably, the DT of students are generated both during remote learning sessions and during blended learning modes. By applying IDA methods to DT, one can obtain information that is beneficial for both the educator in a specific discipline and for the educational institution's management. Such information might pertain to various aspects of the functioning of the digital educational environment (DEE) of the institution, such as: the student's learning style; individual preferences; the amount of time dedicated to a specific task, among others. An algorithm has been proposed for constructing a process model in the DEE based on log analysis within the DEE. This algorithm facilitates the description of a specific process in the DEE as a hierarchy of foundational process elements. Additionally, a model based on cluster analysis methods has been proposed, which may prove beneficial for analyzing the registration logs of systemic processes within the university's DEE. Such an analysis can potentially aid in detecting anomalous behavior of students and other individuals within the university's DEE. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.