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@article{181995,
        author = {Mohd Abdul Qayyum and Mohammed Abbad Mohiuddin and Dr. K.M Subramanian and Dr. Sridhar Gummalla},
        title = {A Comprehensive Survey on Machine Learning Approaches for Phishing Website Detection: Challenges and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {476-481},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181995},
        abstract = {Phishing attacks continue to pose significant cybersecurity threats, exploiting sophisticated social engineering techniques to deceive users into revealing sensitive information through malicious websites that mimic legitimate ones. Despite decades of research, these attacks remain highly effective due to their evolving nature and psychological manipulation tactics. This comprehensive survey examines the current state-of-the-art in phishing website detection, with particular emphasis on machine learning-based approaches. We systematically categorize detection methods into three primary categories: list-based, similarity-based, and machine learning-based techniques. Through extensive analysis of existing literature, we identify critical research gaps and propose future research directions. Our findings indicate that while machine learning approaches show promise in detecting zero-day phishing attacks, significant challenges remain in handling URL shortening services, feature engineering, and adapting to evolving attack vectors. This survey contributes to the cybersecurity community by providing a structured analysis of current detection methodologies and highlighting areas requiring immediate research attention.},
        keywords = {phishing detection, machine learning, cybersecurity, website classification, social engineering, feature engineering},
        month = {July},
        }