Phishing Detection Techniques: A review - Abdolrazzagh-Nezhad, Majid; Langarib, Nafise (Data Science: Journal of Computing and Applied Informatics, 2025)
Phishing remains one of the most pervasive and sophisticated threats to cybersecurity, exploiting human and system vulnerabilities to compromise sensitive information. This study systematically reviews and categorizes phishing detection techniques into four groups: anti-phishing tools, heuristic approaches, machine learning-based techniques, and metaheuristic algorithms. Each method is critically analyzed for its effectiveness, highlighting their strengths and limitations. The review identifies significant advancements in phishing detection, such as the adoption of hybrid techniques and real-time detection algorithms, while also addressing gaps, including handling zero-day phishing attacks and scalability in large datasets. The findings provide a roadmap for future research, encouraging the development of more robust, adaptive, and efficient solutions. This comprehensive analysis not only synthesizes the state-of-the-art in phishing detection but also lays the groundwork for designing next-generation defense mechanisms.