JCSE, vol. 19, no. 4, pp.117-134, 2025

DOI: http://dx.doi.org/10.5626/JCSE.2025.19.4.117

A Comparative Statistical Analysis of Machine Learning Models for Suspicious Email Detection

Alaa Sheta, Nastaran Davudi Pahnehkolaee, Malik Braik, Abdel Karim M. Baareh, Walaa H. Elashmawi, and Emad S. Othman
Department of Computer Science, Southern Connecticut State University, New Haven, CT, USA Department of Computer Engineering, Islamic Azad University Malard Campus, Malard, Iran Computer Science Department, Al-Balqa Applied University, Salt, Jordan Department of Applied Science, Ajloun University College, Al-Balqa Applied University, Ajloun, Jordan Department of Computer Science, Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt Department of Management Information System, AL-Shorouk Academy, Cairo, Egypt

Abstract: The increasing volume of suspicious emails, commonly known as spam, has created a critical need for more reliable and robust anti-spam filters. These suspicious emails can be dangerous and can lead to the loss of personal information, underscoring the necessity for an effective spam filtering system. The application of machine learning methods has enhanced system security and improved the detection of suspicious messages. This research evaluates the effectiveness of seven machine learning algorithms for classifying suspicious email messages: random forest, support vector machine, artificial neural network, decision tree, gradient boosting classifier, and k-nearest neighbor. The primary focus of this evaluation is the accuracy achieved by each algorithm in identifying spam emails. Our analysis revealed that the random forest algorithm outperformed the other evaluated algorithms in terms of accuracy for spam email classification, achieving a remarkable 95%. The accuracy percentages of the various methods ranged from 88% to 93%.

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