Malicious and benign websites classification using machine learning methods

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dc.contributor.authorLavreniuk, M.
dc.contributor.authorNovikov, O.
dc.date.accessioned2020-10-15T14:32:07Z
dc.date.available2020-10-15T14:32:07Z
dc.date.issued2020
dc.description.abstractenNowadays web surfing is an integral part of the life of the average person and everyone would like to protect his own data from thieves and malicious web pages. Therefore, this paper proposes a solution to the discrimination of malicious and benign websites problem with desirable accuracy. We propose to utilize machine learning methods for classification malicious and benign websites based on URL and other host-based features. State-of-the-art gradient-boosted decision trees are proposed to use for this task and they have been compared with well-known machine learning methods as random forest and multilayer perceptron. It was shown that all machine learning methods provided desirable accuracy which is higher than 95% for solving this problem and proposed gradient-boosted decision trees outperforms random forest and neural network approach in this case in terms of both overall accuracy and f1-score.uk
dc.format.pagerangePp. 29-31uk
dc.identifier.citationLavreniuk, M. Malicious and benign websites classification using machine learning methods / M. Lavreniuk, O. Novikov // Theoretical and Applied Cybersecurity : scientific journal. – 2020. – Vol. 2, Iss. 1. – Pp. 29–31. – Bibliogr.: 13 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132020.1.209434
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/36790
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.subjectcybersecurityuk
dc.subjectmalicious websitesuk
dc.subjectmachine learninguk
dc.subjectgradient-boosted decision treesuk
dc.subjectneural networksuk
dc.subject.udc004.9uk
dc.titleMalicious and benign websites classification using machine learning methodsuk
dc.typeArticleuk

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