ÖzdemIr, CüneytAtas, MusaÖzer, Ahmet Bedri2024-12-242024-12-242013978-146735562-9https://doi.org10.1109/SIU.2013.6531457https://hdl.handle.net/20.500.12604/37312013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolat -- 98109In 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. © 2013 IEEE.trinfo:eu-repo/semantics/closedAccessArtificial immune algorithmsCreate a datasetCSCAFisherTurkish spam e-mailsConference ObjectN/A2-s2.0-8488089253710.1109/SIU.2013.6531457