CLASSIFICATION OF TURKISH SPAM E-MAILS WITH ARTIFICIAL IMMUNE SYSTEM

dc.authoridOZDEMIR, Cuneyt/0000-0002-9252-5888
dc.authoridOzer, Ahmet Bedri/0000-0002-8005-7386
dc.contributor.authorOzdemir, Cuneyt
dc.contributor.authorAtas, Musa
dc.contributor.authorOzer, Ahmet Bedri
dc.date.accessioned2024-12-24T19:23:55Z
dc.date.available2024-12-24T19:23:55Z
dc.date.issued2013
dc.departmentSiirt Üniversitesi
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
dc.description.abstractIn this study, it is aimed to detect frequently encountered spam e-mails with artificial immune algorithms. Turkish spam and non-spam e-mail dataset are generated within the scope of the work. Fisher discriminant analysis (FDA) and Euclidean Distance (ED) are utilized in order to extract features from the turkish email dataset. In order to evaluate the classification accuracies, artificial immune algorithms with Bayes as a linear and artificial neural network as a non-linear classifiers are used. Various artificial immune algorithms, including AIRS1, AIRS2, AIRS2PARALLEL, CLONALG and CSCA are investigated. Among them, CSCA reveals the best classification accuracy of 86%. Furthermore, CSCA algorithm classifies spam emails with 81% and non-spam e-mails with 90% accuracies.
dc.identifier.isbn978-1-4673-5563-6
dc.identifier.isbn978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5751
dc.identifier.wosWOS:000325005300297
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectTurkish spam e-mails
dc.subjectartificial immune algorithms
dc.subjectcsca
dc.subjectfisher
dc.subjectCreate a dataset
dc.titleCLASSIFICATION OF TURKISH SPAM E-MAILS WITH ARTIFICIAL IMMUNE SYSTEM
dc.typeConference Object

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