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Using Machine Learning to Analyze Network Traffic Anomalies

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dc.contributor.authorKhudoyarova, A.
dc.contributor.authorBurlakov, M.
dc.contributor.authorKupriyashin, M.
dc.contributor.authorКуприяшин, Михаил Андреевич
dc.date.accessioned2024-11-29T15:24:18Z
dc.date.available2024-11-29T15:24:18Z
dc.date.issued2021
dc.description.abstract© 2021 IEEE.In this paper, we study the application of machine learning methods, as well as spectral and statistical methods for real time network traffic anomaly detection. We determine the strengths and weaknesses of the existing methods and compare them in terms of efficiency. We then build a system of criteria to ensure the most efficient anomaly detection while meeting the specified system performance and resource consumption requirements. As a result, we suggest a set of the most effective anomaly detection methods as well as recommendations on the underlying system architecture.
dc.format.extentС. 2344-2348
dc.identifier.citationKhudoyarova, A. Using Machine Learning to Analyze Network Traffic Anomalies / Khudoyarova, A., Burlakov, M., Kupriyashin, M. // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 2344-2348. - 10.1109/ElConRus51938.2021.9396246
dc.identifier.doi10.1109/ElConRus51938.2021.9396246
dc.identifier.urihttps://www.doi.org/10.1109/ElConRus51938.2021.9396246
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104780669&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709802080
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/23979
dc.relation.ispartofProceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
dc.titleUsing Machine Learning to Analyze Network Traffic Anomalies
dc.typeConference Paper
dspace.entity.typePublication
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