Optimizing anti-spam filters with evolutionary algorithms

dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorBasto-Fernandes, V.en
dc.contributor.authorRuano-Ordás, D.en
dc.contributor.authorMendez, J. R.en
dc.date.accessioned2016-11-29T11:09:48Z
dc.date.available2016-11-29T11:09:48Z
dc.date.issued2013-01-18
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractThis work is devoted to the problem of optimising scores for anti-spam filters, which is essential for the accuracy of any filter based anti-spam system, and is also one of the biggest challenges in this research area. In particular, this optimisation problem is considered from two different points of view: single and multiobjective problem formulations. Some of existing approaches within both formulations are surveyed, and their advantages and disadvantages are discussed. Two most popular evolutionary multiobjective algorithms and one single objective algorithm are adapted to optimisation of the anti-spam filters’ scores and compared on publicly available datasets widely used for benchmarking purposes. This comparison is discussed, and the recommendations for the developers and users of optimising anti-spam filters are provided.en
dc.explorer.multimediaNoen
dc.funderN/Aen
dc.identifier.citationYevseyeva I., Basto-Fernandes V., Ruano-Ordás D., Mendez J.R. (2013) Optimizing anti-spam filters with evolutionary algorithms. Expert Systems with Applications. 40, (10), pp. 4010-4021en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2013.01.008
dc.identifier.urihttp://hdl.handle.net/2086/13001
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidN/Aen
dc.publisherElsevieren
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectAnti-spam filtersen
dc.subjectMultiobjective optimisationen
dc.subjectEvolutionary computationen
dc.subjectGenetic algorithmsen
dc.titleOptimizing anti-spam filters with evolutionary algorithmsen
dc.typeArticleen

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