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October 8, 2025Open Access

Comparative Analysis of Machine Learning Algorithms for Phishing Email Detection

Key Points

All evaluated machine learning models effectively differentiate between secure and phishing emails, enhancing cybersecurity.
SVM achieved perfect accuracy in phishing email detection, demonstrating its superior performance compared to other models.
Evaluation measures included F1-score, recall, accuracy, and precision across various machine learning algorithms.
These findings underscore the importance of advanced technologies in countering evolving cyber threats.

Abstract

Nowadays,The danger of cyberattacks grows as technology develops, requiring more advanced detection and prevention methods. With an emphasis on e-mail phishing detection, the study explores the use of machine learning (ML) to improve cybersecurity measures. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and CatBoost are among the ML models that are assessed to determine how well they can differentiate between secure and phishing e-mails. F1-score, recall, accuracy, and precision are among the evaluation measures. The results show that all models perform well, with SVM showing perfect accuracy. These findings highlight the importance of cutting-edge technologies in strengthening cybersecurity defenses against changing cyber threats.

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