Detection of Phishing Websites using Data Mining Techniques: A Review

TITLE	Detection of Phishing Websites using Data Mining Techniques: A Review
ABSTRACT	Phishing has emerged as one of the most widespread cyber threats targeting millions of users worldwide. Modern phishing attacks imitate legitimate websites to trick victims into revealing sensitive information such as passwords, banking details, and personal identity data. This review article examines existing techniques used for phishing detection, including email-based filtering, visual similarity analysis, fuzzy logic models, and machine-learning-based approaches. Special focus is given to data-mining techniques and the RIPPER rule-based classifier, which have demonstrated promising accuracy in detecting newly generated phishing URLs with no previous history. The article presents a comparative analysis of major methods, highlights challenges in zero-day phishing detection, and outlines future research directions for building more robust phishing detection systems.
AUTHOR	Fasseela Mol KJ, Shejin Mathulla Thomas, Smitha C Thomas PG Student, Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India Assistant Professor, Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India Professor, Department of Computer Science and Engineering, APJ Abdul Kalam Technological University, Kerala, India
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1206012
PDF	12_Detection of Phishing Websites using Data Mining Techniques A Review.pdf
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