A Review of Logistic Regression-Based Machine Learning for Real-Time Phishing URL Detection in Email Systems
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Phishing is one of the most persistent and evolving cybersecurity threats, targeting individuals, businesses, financial institutions, educational organizations, and government agencies through deceptive emails containing malicious URLs. Traditional detection techniques, such as blacklist-based and rule-based systems, are effective against known threats but often fail to identify newly generated, shortened, obfuscated, or previously unseen phishing URLs. This limitation has encouraged the adoption of machine learning for real-time phishing URL detection. This review presents a comprehensive, thematically organized analysis of phishing URL detection methods, with particular emphasis on lightweight machine learning approaches and Logistic Regression. It examines traditional detection techniques, supervised machine learning models, URL feature engineering, publicly available datasets, evaluation metrics, and real-time detection requirements, and it compares Logistic Regression against alternative algorithms on strength, limitation, and real-time suitability. The review identifies a research gap in developing an interpretable, lightweight, real-time phishing URL detection framework specifically for email systems, and it proposes a research framework, methodology, and set of hypotheses to address this gap through a Logistic Regression-based model evaluated for accuracy, latency, temporal generalization, and robustness against adversarial URL manipulation.
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