Framework for detecting outlier and database intrusions

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dc.contributor.authorKolomytsev, Mykhailo V.
dc.contributor.authorNosok, Svitlana O.
dc.date.accessioned2025-04-09T14:00:24Z
dc.date.available2025-04-09T14:00:24Z
dc.date.issued2024
dc.description.abstractThis paper presents a methodology and framework for detecting anomalies in the actions of relational database users, with a focus on insider threats. The architecture of the framework is described, including the choice of parameters for logging user behavior and the justification of the anomaly detection algorithm. An overview of the existing anomaly-detection solutions is provided. The proposed methodology for the functioning of the framework is outlined with recommendations on the choice of algorithm parameters. The analysis of insider actions in databases provides an original approach to anomaly detection and contributes to the field of information security.
dc.format.pagerangeP. 43-51
dc.identifier.citationKolomytsev, M. V. Framework for detecting outlier and database intrusions / Mykhailo V. Kolomytsev, Svitlana O. Nosok // Theoretical and Applied Cybersecurity: scientific journal. – 2024. – Vol. 6, No. 2. – P. 43-51. – Bibliogr.: 20 ref.
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132024.2.303507
dc.identifier.orcid0000-0002-0016-9346
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/73317
dc.language.isoen
dc.publisherIgor Sikorsky Kyiv Polytechnic Institute
dc.publisher.placeKyiv
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2
dc.subjectAnomaly detection
dc.subjectinsiders
dc.subjectmachine learning algorithm
dc.subject.udc004.67
dc.titleFramework for detecting outlier and database intrusions
dc.typeArticle

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2