Machine Learning Methods in E-mail Spam Classification

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dc.contributor.author Świtalski, Piotr
dc.contributor.author Kopówka, Mateusz
dc.date.accessioned 2021-04-27T08:34:36Z
dc.date.available 2021-04-27T08:34:36Z
dc.date.issued 2019
dc.identifier.citation Studia Informatica : systemy i technologie informacyjne. Nr 23 (2019), s. 57-76 pl
dc.identifier.issn 1731-2264
dc.identifier.uri http://hdl.handle.net/11331/3490
dc.description.abstract Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section. pl
dc.language.iso en pl
dc.publisher Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego pl
dc.rights Uznanie autorstwa-Na tych samych warunkach 3.0 Polska *
dc.rights.uri http://creativecommons.org/licenses/by-sa/3.0/pl/ *
dc.subject Information technology and information technologies pl
dc.subject Machine learning pl
dc.subject Spam pl
dc.subject Informatyka i technologie informacyjne pl
dc.subject Uczenie się maszyn pl
dc.subject Spam pl
dc.title Machine Learning Methods in E-mail Spam Classification pl
dc.type Article pl


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Uznanie autorstwa-Na tych samych warunkach 3.0 Polska Except where otherwise noted, this item's license is described as Uznanie autorstwa-Na tych samych warunkach 3.0 Polska

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