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Evaluating the Effectiveness of Machine Learning Methods for Spam Detection

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dc.contributor.authorKontsewaya, Y.
dc.contributor.authorAntonov, E.
dc.contributor.authorArtamonov, A.
dc.contributor.authorАнтонов, Евгений Вячеславович
dc.contributor.authorАртамонов, Алексей Анатольевич
dc.date.accessioned2024-11-29T19:01:13Z
dc.date.available2024-11-29T19:01:13Z
dc.date.issued2021
dc.description.abstract© 2020 Elsevier B.V.. All rights reserved.Technological advances are accelerating the dissemination of information. Today, millions of devices and their users are connected to the Internet, allowing businesses to interact with consumers regardless of geography. People all over the world send and receive emails every day. Email is an effective, simple, fast, and cheap way to communicate. It can be divided into two types of emails: spam and ham. More than half of the letters received by the user - spam. To use Email efficiently without the threat of losing personal information, you should develop a spam filtering system. The aim of this work is to reduce the amount of spam using a classifier to detect it. The most accurate spam classification can be achieved using machine learning methods. A natural language processing approach was chosen to analyze the text of an email in order to detect spam. For comparison, the following machine learning algorithms were selected: Naive Bayes, K-Nearest Neighbors, SVM, Logistic regression, Decision tree, Random forest. Training took place on a ready-made dataset. Logistic regression and NB give the highest level of accuracy - up to 99%. The results can be used to create a more intelligent spam detection classifier by combining algorithms or filtering methods.
dc.format.extentС. 479-486
dc.identifier.citationKontsewaya, Y. Evaluating the Effectiveness of Machine Learning Methods for Spam Detection / Kontsewaya, Y., Antonov, E., Artamonov, A. // Procedia Computer Science. - 2021. - 190. - P. 479-486. - 10.1016/j.procs.2021.06.056
dc.identifier.doi10.1016/j.procs.2021.06.056
dc.identifier.urihttps://www.doi.org/10.1016/j.procs.2021.06.056
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85112628446&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/24532
dc.relation.ispartofProcedia Computer Science
dc.titleEvaluating the Effectiveness of Machine Learning Methods for Spam Detection
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume190
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