Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{206996,
        author = {Annappa S S and Asha S},
        title = {Classification of Features for detecting Phishing Web Sites based on Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3626-3631},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206996},
        abstract = {Phishing is one of the most widespread forms of cybercrime and poses a significant threat to individuals and organizations by attempting to steal sensitive information such as usernames, passwords, banking credentials, credit card details, and personal identification data. Attackers typically create fraudulent websites that closely resemble legitimate ones, misleading users into revealing confidential information. As phishing techniques continue to evolve, developing an effective and reliable detection mechanism remains a challenging task due to the dynamic nature of website characteristics and attack strategies. This study focuses on the detection of phishing websites using machine learning techniques. The proposed approach utilizes the Phishing Websites dataset available from the UCI Machine Learning Repository, which consists of 30 website-related features representing various characteristics of web pages. These features are analyzed to distinguish phishing websites from legitimate ones. To improve phishing detection, an Extreme Learning Machine (ELM) classifier is employed and its performance is evaluated against other well-known machine learning algorithms, including Naïve Bayes (NB) and Artificial Neural Networks (ANN). Experimental results demonstrate that the ELM-based model achieves superior classification performance, attaining an accuracy of 89.3%, thereby outperforming the comparative methods. The findings indicate that machine learning techniques, particularly Extreme Learning Machine, provide an efficient and practical solution for identifying phishing websites and enhancing cybersecurity.},
        keywords = {Extreme Learning Machine (ELM), Phishing Website Detection, Machine Learning, Information Security, Cybersecurity, Feature Classification, Website Classification, UCI Machine Learning Repository.},
        month = {July},
        }