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Classification of Websites Based on the Content and Features of Sites in Onion Space

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dc.contributor.authorKorolev, D.
dc.contributor.authorFrolov, A.
dc.contributor.authorBabalova, I.
dc.contributor.authorКоролев, Денис Вячеславович
dc.contributor.authorБабалова, Ирина Филипповна
dc.date.accessioned2024-11-25T18:00:32Z
dc.date.available2024-11-25T18:00:32Z
dc.date.issued2020
dc.description.abstract© 2020 IEEE.This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.
dc.format.extentС. 1680-1683
dc.identifier.citationKorolev, D. Classification of Websites Based on the Content and Features of Sites in Onion Space / Korolev, D., Frolov, A., Babalova, I. // Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. - 2020. - P. 1680-1683. - 10.1109/EIConRus49466.2020.9039347
dc.identifier.doi10.1109/EIConRus49466.2020.9039347
dc.identifier.urihttps://www.doi.org/10.1109/EIConRus49466.2020.9039347
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85082995842&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/20563
dc.relation.ispartofProceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
dc.titleClassification of Websites Based on the Content and Features of Sites in Onion Space
dc.typeConference Paper
dspace.entity.typePublication
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