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Volume 15
Issue 6
10.3390/app15063396
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Article

In-Depth Analysis of Phishing Email Detection: Evaluating the Performance of Machine Learning and Deep Learning Models Across Multiple Datasets

by
Abeer Alhuzali
1,*,
Ahad Alloqmani
1,†,
Manar Aljabri
1,† and
Fatemah Alharbi
2
1
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Computer Science Department, College of Computer Science and Engineering, Taibah University, Yanbu 46522, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(6), 3396; https://doi.org/10.3390/app15063396
Submission received: 27 February 2025 / Revised: 16 March 2025 / Accepted: 17 March 2025 / Published: 20 March 2025
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Abstract

Phishing emails remain a primary vector for cyberattacks, necessitating advanced detection mechanisms. Existing studies often focus on limited datasets or a small number of models, lacking a comprehensive evaluation approach. This study develops a novel framework for implementing and testing phishing email detection models to address this gap. A total of fourteen machine learning (ML) and deep learning (DL) models are evaluated across ten datasets, including nine publicly available datasets and a merged dataset created for this study. The evaluation is conducted using multiple performance metrics to ensure a comprehensive comparison. Experimental results demonstrate that DL models consistently outperform their ML counterparts in both accuracy and robustness. Notably, transformer-based models BERT and RoBERTa achieve the highest detection accuracies of 98.99% and 99.08%, respectively, on the balanced merged dataset, outperforming traditional ML approaches by an average margin of 4.7%. These findings highlight the superiority of DL in phishing detection and emphasize the potential of AI-driven solutions in strengthening email security systems. This study provides a benchmark for future research and sets the stage for advancements in cybersecurity innovation.
Keywords: phishing email detection; cybersecurity; artificial intelligence (AI); deep learning (DL); machine learning (ML); spam filtering; threat detection; transformer models phishing email detection; cybersecurity; artificial intelligence (AI); deep learning (DL); machine learning (ML); spam filtering; threat detection; transformer models

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MDPI and ACS Style

Alhuzali, A.; Alloqmani, A.; Aljabri, M.; Alharbi, F. In-Depth Analysis of Phishing Email Detection: Evaluating the Performance of Machine Learning and Deep Learning Models Across Multiple Datasets. Appl. Sci. 2025, 15, 3396. https://doi.org/10.3390/app15063396

AMA Style

Alhuzali A, Alloqmani A, Aljabri M, Alharbi F. In-Depth Analysis of Phishing Email Detection: Evaluating the Performance of Machine Learning and Deep Learning Models Across Multiple Datasets. Applied Sciences. 2025; 15(6):3396. https://doi.org/10.3390/app15063396

Chicago/Turabian Style

Alhuzali, Abeer, Ahad Alloqmani, Manar Aljabri, and Fatemah Alharbi. 2025. "In-Depth Analysis of Phishing Email Detection: Evaluating the Performance of Machine Learning and Deep Learning Models Across Multiple Datasets" Applied Sciences 15, no. 6: 3396. https://doi.org/10.3390/app15063396

APA Style

Alhuzali, A., Alloqmani, A., Aljabri, M., & Alharbi, F. (2025). In-Depth Analysis of Phishing Email Detection: Evaluating the Performance of Machine Learning and Deep Learning Models Across Multiple Datasets. Applied Sciences, 15(6), 3396. https://doi.org/10.3390/app15063396

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