Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47784
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dc.contributor.authorCabrera-León, Ylermien_US
dc.contributor.authorGarcía Báez, Patricioen_US
dc.contributor.authorSuárez-Araujo, Carmen Pazen_US
dc.date.accessioned2018-11-23T16:23:00Z-
dc.date.available2018-11-23T16:23:00Z-
dc.date.issued2016en_US
dc.identifier.isbn978-989-758-201-1en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/47784-
dc.description.abstractSpam, or unsolicited messages sent massively, is one of the threats that affects email and other media. Its high volume generates substantial time and economic losses. A solution to this problem is presented: a hybrid anti-spam filter based on unsupervised Artificial Neural Networks (ANNs). It consists of two steps, preprocessing and processing, both based on different computation models: programmed and neural (using Kohonen SOM). This system has been optimized using, as a data corpus, ham from "Enron Email" and spam from two different sources: traditional (user's inbox) and spamtrap-honeypot. It has been proved that thematic categories can be found both in spam and ham words. 1260 system configurations were analyzed, comparing their quality and performance with the most used metrics. All of them achieved AUC > 0.90 and the best 204 AUC > 0.95, despite just using 13 attributes for the input vectors of the SOM, one for each thematic category. Results were similar to other researchers' over the same corpus, though they make use of different Machine Learning (ML) methods and a number of attributes several orders of magnitude greater. It was further tested with datasets not utilized during design, obtaining 0.77 < AUC < 0.96 with normalized data.en_US
dc.languageengen_US
dc.sourceIJCCI 2016 - Proceedings of the 8th International Joint Conference on Computational Intelligence, v. 3, p. 21-32en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherAnti-spamen_US
dc.subject.otherArtificial neural networksen_US
dc.subject.otherHamen_US
dc.subject.otherInverse category or class frequencyen_US
dc.subject.otherSelf-organizing maps (SOMs)en_US
dc.subject.otherSpamen_US
dc.subject.otherTerm frequencyen_US
dc.subject.otherThematic categoryen_US
dc.titleSelf-organizing maps in the design of anti-spam filters a proposal based on thematic categoriesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference8th International Joint Conference on Computational Intelligence, IJCCI 2016en_US
dc.identifier.doi10.5220/0006041400210032en_US
dc.identifier.scopus85006445776-
dc.identifier.isi000393153700001-
dc.contributor.authorscopusid57192423564-
dc.contributor.authorscopusid23476362100-
dc.contributor.authorscopusid6603605708-
dc.description.lastpage32en_US
dc.description.firstpage21en_US
dc.relation.volume3en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid33311301-
dc.contributor.daisngid32254292-
dc.contributor.daisngid9879072-
dc.description.numberofpages12en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Cabrera-Leon, Y-
dc.contributor.wosstandardWOS:Baez, PG-
dc.contributor.wosstandardWOS:Suarez-Araujo, CP-
dc.date.coverdateEnero 2016en_US
dc.identifier.conferenceidevents121027-
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptComputación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-5709-2274-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.fullNameCabrera León, Ylermi-
crisitem.author.fullNameGarcía Báez, Patricio-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.event.eventsstartdate09-11-2016-
crisitem.event.eventsenddate11-11-2016-
Appears in Collections:Actas de congresos
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