This paper explores the analysis of user behavior in information systems through audit records, creating a behavior model represented as a graph. The model captures actions over a specified period, facilitating real-time comparison to identify insider threats exploring anomalies detected in behavior models. "e-StepControl," developed by "ABC software" Ltd., incorporates this approach for monitoring user behavior in different business environments. The study proposes enhancing this solution with automatic user clustering, achieved by grouping individuals exhibiting similar behavior patterns using AI/ML algorithms. The research evaluates various clustering methods, discussing their suitability for grouping users based on their behavior. The subsequent step involves leveraging user class behavior models to identify anomalies by comparing an individual’s actions with the behavior model expected in their specific user group. This extension aims to enhance the system’s ability to detect potentially malicious activities, providing data security administrators with timely alerts in case of deviations from typical behavior.

Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.

Day-to-day working activities have been heavily altered by COVID-19 pandemic, forcing a transition from traditional on-site work to on-line telework across the whole world. It has become much harder to efficiently organise, guide and evaluate employee’s work. There are different factors that can influence “work from home” quality, and many of these affect such work negatively. A set of relevant methods and tools should be developed which could improve this situation. The goal of the study is to summarise related background of this problem and to propose an approach to overcoming this problem. To achieve the goal, design engineer’s work is evaluated in an appropriate environment (e.g., AutoCAD, etc.) using automated analysis and visualization of IS auditing data.

Insufficient user involvement, lack of user feedback, incomplete and changing user requirements are some of the critical reasons for the difficulty of IS usage, which could potentially reduce the number of customers. Under the previous authors’ research, the method for analysing the behaviour of IT system users was developed, which was intended to improve the usability of the system and thus could increase the efficiency of business processes. The developed method is based on the use of graph searching algo rithms, Markov chains and Machine Learning approach. This paper focuses on detailing of method output data in the context of definition of their importance based on expert evaluation and demonstration of visual presentation of different UX analysis situations. The paper briefly reminds the essence of the method, including both the input and output data sets, and, with the help of experts, evaluates the expected result in the context of their importance in UX analysis. It also introduces visualization prototype developed to obtain the output data, which allows verifying the input/output data transformation possibilities and expected data acquisition potential.

Analysis of user behavior models based on user session data can be conducted using clustering (or community detecting) algorithms that do not require a predefined number of clusters. An unknown number and quality of potential behavioral models, non-efficient utilization of memory and processing units, and a large amount of data are the main difficulties developers could meet. Besides, the suitability of clustering algorithms for the task highly depends on the characteristics of datasets and still requires additional research. The first step and the goal of this research is to analyze the capabilities of the clustering algorithms in order to determine more promising ones and techniques able to minimize computing resources while solving the task. During the research, the properties of algorithms were analyzed through a literature review and also experimentally. It was found that, although the algorithm’s suitability is theoretically proved, experiments could show unsatisfactory results. Therefore, a certain trade-off analysis and problem-specific modifications of original processing must be done. As a result, the clickstream clustering method of the Louvain clustering algorithm and the modified Longest Common Subsequence algorithm showed more appropriate results in detecting the well-clustered user behavior models based on user sessions data. Thus, these algorithms are suitable for the further development of the detecting method.

Estimation of work efficiency is one of the important tasks in company management and analysis of workers’ activities. The environment for determining the efficiency of workers is the use of one or more IS of any kind, within the framework of which the worker’s duties are accomplished. This process can be performed both from home (isolated from the work environment and co-workers) and in the office on-site. Another aspect is related to the fact that a worker can work both with only one IS, or with multiple specialised systems in parallel, which requires the simultaneous integration of several data sources and the parallel reading and analysis of information relevant to worker activities. The task of work efficiency estimation is especially complicated if the company’s activity domain is narrowly specific. In this case, solving such a task of working hour accounting and efficiency estimation is not a trivial accumulation of statistical data, but requires a dedicated method. The work of an engineering system designer is one of the examples of such specific activities. Therefore, exactly this domain was chosen as an application case of the method and metrics proposed in the paper.