EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

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dc.authoridBilgen, Yusuf/0000-0001-6041-6129
dc.authoridKAYA, Mahmut/0000-0002-7846-1769
dc.contributor.authorBilgen, Yusuf
dc.contributor.authorKaya, Mahmut
dc.date.accessioned2024-12-24T19:33:32Z
dc.date.available2024-12-24T19:33:32Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractSpam messages have emerged as a significant issue in digital communication, adversely affecting users' mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency-Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl's Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.
dc.identifier.doi10.3390/app14219669
dc.identifier.issn2076-3417
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85208559508
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app14219669
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8186
dc.identifier.volume14
dc.identifier.wosWOS:001351005000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectspam classification
dc.subjectensemble learning
dc.subjectgru
dc.subjectmlp
dc.subjectautoencoder
dc.titleEGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection
dc.typeArticle

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