Malicious and benign websites classiﬁcation using machine learning methods

Lavreniuk, M.; Novikov, O.

Malicious and benign websites classiﬁcation using machine learning methods

dc.contributor.author	Lavreniuk, M.
dc.contributor.author	Novikov, O.
dc.date.accessioned	2020-10-15T14:32:07Z
dc.date.available	2020-10-15T14:32:07Z
dc.date.issued	2020
dc.description.abstracten	Nowadays 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.pagerange	Pp. 29-31	uk
dc.identifier.citation	Lavreniuk, 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.doi	https://doi.org/10.20535/tacs.2664-29132020.1.209434
dc.identifier.uri	https://ela.kpi.ua/handle/123456789/36790
dc.language.iso	en	uk
dc.publisher	Igor Sikorsky Kyiv Polytechnic Institute	uk
dc.publisher.place	Kyiv	uk
dc.source	Theoretical and Applied Cybersecurity : scientific journal, 2020, Vol. 2, No. 1	uk
dc.subject	cybersecurity	uk
dc.subject	malicious websites	uk
dc.subject	machine learning	uk
dc.subject	gradient-boosted decision trees	uk
dc.subject	neural networks	uk
dc.subject.udc	004.9	uk
dc.title	Malicious and benign websites classiﬁcation using machine learning methods	uk
dc.type	Article	uk

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