Application of the Solution for Analysis of IT Systems Users Experience on the Example of Internet Bank Usage

Nikiforova, Oksana; Zabiniako, Vitaly; Kornienko, Jurijs; Gasparoviča-Asīte, Madara; Siliņa, Amanda

doi:10.1007/978-3-031-16072-1_52

Cited by 2 publications

(2 citation statements)

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“…To mitigate security risks, the monitoring of IS usage based on analyzing the behavioral models of users can be utilized. The company "ABC software" Ltd. has implemented this approach in the product e-StepControl [1], which is already applied for different problem domains and not only for IS users threats identification [10]. The conceptual scheme of the algorithm used in e-StepControl solution for identification of potentially malicious sessions with Markov chains is shown in Fig.…”

Section: Research Problem and Related Workmentioning

confidence: 99%

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Detecting and Identifying Insider Threats Based on Advanced Clustering Methods

Nikiforova,

Romanovs,

Zabiniako

et al. 2024

IEEE Access

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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.

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Section: Research Problem and Related Workmentioning

confidence: 99%

“…At present, this classification process relies on manual efforts. To improve efficiency and make better use of time and human resources, it becomes imperative to automate this procedure using formal algorithms [10].…”

Section: Research Problem and Related Workmentioning

confidence: 99%