Comparison of the techniques decision tree and MLP for data mining in SPAMs detection to computer networks
dc.contributor.author | Costa, Kelton | |
dc.contributor.author | Ribeiro, Patricia | |
dc.contributor.author | Camargo, Atair | |
dc.contributor.author | Rossi, Victor | |
dc.contributor.author | Martins, Henrique | |
dc.contributor.author | Neves, Miguel | |
dc.contributor.author | Fabris, Ricardo | |
dc.contributor.author | Imaisumi, Renato | |
dc.contributor.author | Papa, Joao Paulo [UNESP] | |
dc.contributor.institution | College of Technology of São Paulo State | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.date.accessioned | 2022-04-29T07:13:09Z | |
dc.date.available | 2022-04-29T07:13:09Z | |
dc.date.issued | 2013-12-31 | |
dc.description.abstract | Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify these unusual traffic patterns. This research aims to use data mining techniques in order to correctly identify these anomalies. Weka is a collection of machine learning algorithms for data mining tasks - was used to identify and analyse anomalies of a data set called SPAMBASE in order to improve this environment. © 2013 IEEE. | en |
dc.description.affiliation | College of Technology of São Paulo State, Bauru | |
dc.description.affiliation | Department of Computing UNESP University Paulista State, Bauru | |
dc.description.affiliationUnesp | Department of Computing UNESP University Paulista State, Bauru | |
dc.format.extent | 344-348 | |
dc.identifier | http://dx.doi.org/10.1109/INTECH.2013.6653725 | |
dc.identifier.citation | 2013 3rd International Conference on Innovative Computing Technology, INTECH 2013, p. 344-348. | |
dc.identifier.doi | 10.1109/INTECH.2013.6653725 | |
dc.identifier.scopus | 2-s2.0-84891051187 | |
dc.identifier.uri | http://hdl.handle.net/11449/227401 | |
dc.language.iso | eng | |
dc.relation.ispartof | 2013 3rd International Conference on Innovative Computing Technology, INTECH 2013 | |
dc.source | Scopus | |
dc.subject | Anomalies | |
dc.subject | Artificial Neural Networks | |
dc.subject | Computer networks | |
dc.subject | Data Mining | |
dc.title | Comparison of the techniques decision tree and MLP for data mining in SPAMs detection to computer networks | en |
dc.type | Trabalho apresentado em evento | pt |
dspace.entity.type | Publication | |
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relation.isDepartmentOfPublication.latestForDiscovery | 872c0bbb-bf84-404e-9ca7-f87a0fe94e58 | |
relation.isOrgUnitOfPublication | aef1f5df-a00f-45f4-b366-6926b097829b | |
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unesp.campus | Universidade Estadual Paulista (UNESP), Faculdade de Ciências, Bauru | pt |
unesp.department | Computação - FC | pt |