Email spam detection using hierarchical attention hybrid deep learning method

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dc.authoridZavrak, Sultan/0000-0001-6950-8927en_US
dc.authorscopusid55364954900en_US
dc.authorscopusid57200277426en_US
dc.authorwosidZavrak, Sultan/C-6702-2014en_US
dc.contributor.authorZavrak, Sultan
dc.contributor.authorYilmaz, Seyhmus
dc.date.accessioned2024-08-23T16:04:46Z
dc.date.available2024-08-23T16:04:46Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractEmail is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Considering the escalating number of spam emails, it has become crucial to devise effective strategies for spam detection. To tackle this challenge, this article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them.en_US
dc.identifier.doi10.1016/j.eswa.2023.120977
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85166668741en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.120977
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14360
dc.identifier.volume233en_US
dc.identifier.wosWOS:001040353200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHierarchical Attentional Hybrid Neuralen_US
dc.subjectNetworksen_US
dc.subjectEmail spam detectionen_US
dc.subjectNatural Language Processingen_US
dc.subjectFastTexten_US
dc.subjectAttention mechanismsen_US
dc.subjectIntrusion Detectionen_US
dc.subjectDetection Modelen_US
dc.subjectNetworken_US
dc.subjectClassificationen_US
dc.titleEmail spam detection using hierarchical attention hybrid deep learning methoden_US
dc.typeArticleen_US

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