Reinforced machine learning methods for testing quality of cyber threat prediction results

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dc.contributor.authorKachynskyi, A. B.
dc.contributor.authorTsebrinska, N. A.
dc.date.accessioned2020-10-15T14:15:27Z
dc.date.available2020-10-15T14:15:27Z
dc.date.issued2020
dc.description.abstractenThe article considered on machine learning methods with reinforcement to make decisions about evaluating the quality of a mathematical prediction model. Given the problems of cybersecurity specificity A/B testing algorithms, analysis of variance (ANOVA), as well as multi-armed bandit are presented. Features of their practical implementation are taken into account: data type and distribution function, sample size, knowledge about the dispersion of the general population, dependence and independence of observations. The cybersecurity problems solved with the help of these algorithms are discussed and the methods of their solution are suggested.uk
dc.format.pagerangePp. 15-21uk
dc.identifier.citationKachynskyi, A. B. Reinforced machine learning methods for testing quality of cyber threat prediction results / A. B. Kachynskyi, N. A. Tsebrinska // Theoretical and Applied Cybersecurity : scientific journal. – 2020. – Vol. 2, Iss. 1. – Pp. 15–21. – Bibliogr.: 16 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132020.1.209432
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/36788
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.sourceTheoretical and Applied Cybersecurity : scientific journal, 2020, Vol. 2, No. 1uk
dc.subjectreinforced machine learninguk
dc.subjecthypothesis testinguk
dc.subjectA/B testinguk
dc.subjectanalysis of varianceuk
dc.subjectmulti-armed bandituk
dc.subject.udc004.9uk
dc.titleReinforced machine learning methods for testing quality of cyber threat prediction resultsuk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 2, No. 1