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Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches

Year 2025, Volume: 7 Issue: 2, 189 - 205, 31.08.2025

Esma Nisa Candan Rehnüma Küçükilhan Alperen Eroğlu

Abstract

Spam has surged due to increased email and social media use, posing a critical challenge in effectively detecting and classifying this growing volume without causing harm to systems. This paper presents a holistic strategy to analyze and reveal the most efficient approaches for detecting and classifying e-mails as spam or ham by using Turkish and English datasets. We use two different datasets generated in different languages in addition to conjunctively generated new datasets. We make a comparative study to find out the best spam mail detection approaches based on our enhanced machine learning and deep learning methods. We also bring ensemble and hybrid learning methods together as a new approach for spam mail detection. We utilize natural language processing, and improved learning algorithms with optimized feature selection approaches and preprocessing. We compare various methods commonly used in the literature which are Multinomial Naive Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Voting classifier, and Stacking classifier as machine learning algorithms, and Long Short Term Memory, Bidirectional Long Short Term Memory, Bidirectional Encoder Representations from Transformers as deep learning algorithms. We split the datasets as train data and test data with the 80:20 ratios in addition to 5-fold cross-validation for each model. We also optimize the hyperparameters of our models by using Grid Search. The ensemble method based on machine learning approaches provides the best performances which are the percentage of 99.9% for the English Enron dataset, and the hybrid ensemble approach based on simple average yields the best accuracy value of 98.43% for the Turkish dataset from UCI and Kaggle.

Keywords

English Datasets Ensemble Learning Hybrid Learning Turkish Datasets Spam Mail

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Türkçe ve İngilizce Dillerinde Spam Posta Tespiti: Bireysel, Toplu ve Hibrit Yaklaşımları İçeren Yapay Zeka Tabanlı Tekniklerin Bütünsel Bir Çalışması

Year 2025, Volume: 7 Issue: 2, 189 - 205, 31.08.2025

Esma Nisa Candan Rehnüma Küçükilhan Alperen Eroğlu

Abstract

Artan e-posta ve sosyal medya kullanımı nedeniyle spam sayısı artmış ve bu durumun sistemlere zarar vermeden etkili bir şekilde tespit edilmesi ve sınıflandırılması konusunda kritik bir zorluk oluşturmuştur. Bu makale, Türkçe ve İngilizce veri kümelerini kullanarak e-postaları spam veya ham olarak tespit etmek ve sınıflandırmak için en etkili yaklaşımları analiz etmek ve ortaya çıkarmak için bütünsel bir strateji sunmaktadır. Birleşik olarak oluşturulan yeni veri kümelerine ek olarak, farklı dillerde oluşturulan iki farklı veri kümesi kullanılmaktadır. Gelişmiş makine öğrenmesi ve derin öğrenme yaklaşımlarını temel alarak en iyi spam posta algılama yöntemlerini sunmak için karşılaştırmalı bir çalışma yapılmaktadır. Ayrıca yeni bir yaklaşım olarak spam posta tespiti için toplu ve hibrit öğrenme yöntemleri bir araya getirilmiştir. Optimize edilmiş özellik seçimi yaklaşımları ve ön işleme ile doğal dil işlemeyi ve geliştirilmiş öğrenme algoritmaları kullanılmaktadır. Literatürde yaygın olarak kullanılan Multinomial Naive Bayes, Destek Vektör Makinesi, Lojistik Regresyon, K-En Yakın Komşular, Karar Ağacı, Rastgele Orman, Oylama sınıflandırıcısı ve makine öğrenme algoritmaları olarak Yığınlama Sınıflandırıcısı ile Uzun Kısa Süreli Bellek, Çift Yönlü yöntemlerini karşılaştırmaktayız. Uzun Kısa Süreli Bellek, Transformatörlerden Çift Yönlü Kodlayıcı Gösterimleri ise derin öğrenme algoritmaları olarak kullanılmaktadır. 5 kat çapraz doğrulamaya ek olarak, veri kümeleri her model için 80:20 oranlarıyla eğitim verileri ve test verileri olarak bölünmüştür. Izgara Arama tekniği kullanılarak modellerin hiper parametreleri de optimize edilmektedir. Makine öğrenmesi yaklaşımlarına dayalı toplu öğrenme yöntemi, İngilizce Enron veri seti için %99,9 ile en iyi performansı sağlarken, basit ortalamaya dayalı hibrit toplu öğrenme yaklaşımı, UCI ve Kaggle'dan Türkçe veri seti için %98,43 ile en iyi doğruluk değerini vermektedir.

Keywords

Hibrit Öğrenme İngilizce Veri Setleri Toplu Öğrenme Türkçe Veri Setleri Spam Mail

References

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  • N. Saidani, K. Adi, MS. Allili, A semantic-based classification approach for an enhanced spam detection. Computer & Security. 94 (2020), 101716. doi:10.1016/j.cose.2020.101716
  • B. Feng, Q. Fu, M. Dong, D. Guo, Q. Li, Multistage and elastic spam detection in mobile social networks through deep learning, IEEE Network. 32(4) (2018), 15-21. doi:10.1109/MNET.2018.1700406
  • A. Karim, S. Azam, B. Shanmugam, K. Kannoorpatti, M. Alazab, A comprehensive survey for intelligent spam email detection, IEEE Access. 7 (2019), 168261-168295. doi:10.1109/ACCESS.2019.2954791
  • S. Gibson, B. Issac, L. Zhang, SM. Jacob, Detecting spam email with machine learning optimized with bio-inspired metaheuristic algorithms, IEEE Access. 8 (2020), 187914-187932. doi:10.1109/ACCESS.2020.3030751
  • S. Rapacz, P. Chołda, M. Natkaniec, A method for fast selection of machine-learning classifiers for spam filtering, Electronics. 10(17) (2021), 2083. doi:10.3390/electronics10172083
  • S. Magdy, Y. Abouelseoud, M. Mikhail, Efficient spam and phishing email filtering based on deep learning, Computer Networks. 206 (2022), 108826. doi:10.1016/j.comnet.2022.108826
  • F. Ozen, R. Ortac Kabaoglu, T. V. Mumcu, Deep Learning Based Temperature and Humidity Prediction, Necmettin Erbakan University Journal of Science and Engineering. 5(2) (2023). 219-229. doi:10.47112/neufmbd.2023.20
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  • A. Pektaş, O. İnan, Application of Tree Seed Algorithm on Clustering Problems, Necmettin Erbakan University Journal of Science and Engineering. 4(1) (2022), 1-10. doi:10.47112/neufmbd.2022.8
  • F. Rustam, N. Saher, A. Mehmood, E. Lee, S. Washington, I. Ashraf, Detecting ham and spam emails using feature union and supervised machine learning models, Multimedia Tools and Applications. 82 (2023), 26545–26561. doi: 10.1007/s11042-023-14814-2
  • E. E. Eryılmaz, D. Ö. Şahin, E. Kılıç, Türkçe İstenmeyen E-postaların Farklı Öznitelik Seçim Yöntemleri Kullanılarak Makine Öğrenmesi Algoritmaları ile Tespit Edilmesi, Türkiye Bilişim Vakfı-Bilgisayar Bilimleri ve Mühendisliği Dergisi. 13 (2) (2020), 57-77.
  • M. A. Shaaban, Y. F. Hassan, S. K. Guirguis, Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text, Complex & Intelligent Systems. 8(6) (2022), 4897-4909. doi:10.1007/s40747-022-00741-6
  • S. Kaddoura, O. Alfandi, N. Dahmani, A spam email detection mechanism for English language text emails using a deep learning approach, In: 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), IEEE, Bayonne, France, 2020: 193-198. doi:10.1109/WETICE49692.2020.00045
  • T. Toma, S. Hassan, M. Arifuzzaman, An analysis of supervised machine learning algorithms for spam email detection, In: International Conference on Automation, Control, and Mechatronics for Industry 4.0 (ACMI), IEEE, Rajshahi, Bangladesh, 2021: 1-5. doi:10.1109/ACMI53878.2021.9528108
  • C. M. Shaik, N. M. Penumaka, S. K. Abbireddy, V. Kumar, S. Aravinth, Bi-LSTM and conventional classifiers for email spam filtering, In: Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), IEEE, Coimbatore, India, 2023: 1350-1355. doi:10.1109/ICAIS56108.2023.10073776
  • K. Debnath, N. Kar. Email spam detection using deep learning approach, In: International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), IEEE, Faridabad, India, 2022: 37-41. doi:10.1109/COM-IT-CON54601.2022.9850588
  • A. R. Yeruva, D. Kamboj, P. Shankar, U. S. Aswal, A. K. Rao, C. Somu, E-mail spam detection using machine learning—KNN, In: 5th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, Uttar Pradesh, India, 2022: 1024-1028. doi:10.1109/IC3I56241.2022.10072628
  • N. Ahmed, R. Amin, H. Aldabbas, D. Koundal, B. Alouffi, T. Shah, Machine learning techniques for spam detection in email and IoT platforms: Analysis and research challenges, Security and Communication Networks. (1) (2022), 1-19. doi:10.1155/2024/7538203
  • Z. B. Siddique, M. A. Khan, I. U. Din, A. Almogren, I. Mohiuddin, S. Nazir, Machine learning-based detection of spam emails, Scientific Programming. (1) (2021), 1-11. doi:10.1155/2021/6508784
  • Z. Hassani, V. Hajihashemi, K. Borna, I. S. Dehmajnoonie, A classification method for E-mail spam using a hybrid approach for feature selection optimization, Journal of Sciences, Islamic Republic of Iran. 31(2), (2020), 165-173.
  • A. Sheneamer, Comparison of deep and traditional learning methods for email spam filtering, International Journal of Advanced Computer Science and Applications (IJACSA). 12 (1) (2021), 560-565. doi: 10.14569/IJACSA.2021.0120164
  • S. Zavrak, S. Yilmaz, Email spam detection using hierarchical attention hybrid deep learning method, Expert Systems with Applications. 233 (2023), 120977. doi: 10.1016/j.eswa.2023.120977
  • G. Hnini, J. Riffi, M. A. Mahraz, A. Yahyaouy, H. Tairi, MMPC-RF: a deep multimodal feature-level fusion architecture for hybrid spam E-mail detection, Applied Sciences. 11(24) (2021), 11968. doi: 10.3390/app112411968
  • A. I. Taloba, S. S. Ismail, An intelligent hybrid technique of decision tree and genetic algorithm for e-mail spam detection, In: IEEE 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), IEEE, Cairo, Egypt, 2019: 99-104. doi:10.1109/ICICIS46948.2019.9014756
  • K. Meena, S. R. Upadhyaya, A Privacy-Preserving machine learning ensemble for spam detection, In: IEEE 5th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, Coimbatore, India, 2023: 255-259.
  • M. Sumathi, S. Raja, Machine learning algorithm-based spam detection in social networks, Social Network Analysis and Mining. 13(1) (2023), 104. doi:10.1007/s13278-023-01108-6
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There are 57 citations in total.

Details

Primary Language English
Subjects Information Security Management, Deep Learning, Natural Language Processing
Journal Section Articles
Authors

Esma Nisa Candan This is me 0000-0002-9746-3495

Rehnüma Küçükilhan This is me 0009-0009-3930-6502

Alperen Eroğlu 0000-0002-1780-7025

Publication Date August 31, 2025
Submission Date September 25, 2024
Acceptance Date November 14, 2024
Published in Issue Year 2025 Volume: 7 Issue: 2

Cite

APA Candan, E. N., Küçükilhan, R., & Eroğlu, A. (2025). Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 7(2), 189-205.
AMA Candan EN, Küçükilhan R, Eroğlu A. Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches. NEJSE. August 2025;7(2):189-205.
Chicago Candan, Esma Nisa, Rehnüma Küçükilhan, and Alperen Eroğlu. “Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-Based Techniques Including Individual, Ensemble and Hybrid Approaches”. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 7, no. 2 (August 2025): 189-205.
EndNote Candan EN, Küçükilhan R, Eroğlu A (August 1, 2025) Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 7 2 189–205.
IEEE E. N. Candan, R. Küçükilhan, and A. Eroğlu, “Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches”, NEJSE, vol. 7, no. 2, pp. 189–205, 2025.
ISNAD Candan, Esma Nisa et al. “Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-Based Techniques Including Individual, Ensemble and Hybrid Approaches”. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 7/2 (August2025), 189-205.
JAMA Candan EN, Küçükilhan R, Eroğlu A. Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches. NEJSE. 2025;7:189–205.
MLA Candan, Esma Nisa et al. “Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-Based Techniques Including Individual, Ensemble and Hybrid Approaches”. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 7, no. 2, 2025, pp. 189-05.
Vancouver Candan EN, Küçükilhan R, Eroğlu A. Spam Mail Detection in Turkish and English Languages: A Holistic Study of AI-based Techniques including Individual, Ensemble and Hybrid Approaches. NEJSE. 2025;7(2):189-205.
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