Advancements in Metaverse Security: Phishing Website Detection Through Optimal Feature Selection and Random Forest Classifier

A. V. Senthil Kumar Hindusthan College of Arts & Science, India https://orcid.org/0000-0002-8587-7017 Pavithra Sivakumar Hindusthan College of Arts & Science, Coimbatore, India Ankita Chaturvedi IIS University (Deemed), India https://orcid.org/0000-0002-0739-5792 Ismail Bin Musirin Universiti Teknologi Mara, Malaysia Venkata Shesha Giridhar Akula Sphoorthy Engineering College, India R. V. Suganya VISTAS, India G. Vanishree ICFAI Business School, India https://orcid.org/0009-0000-1335-2919 Rajani H. Pillai Mount Carmel College, India G. Jagadamba Siddaganga Institute of Technology, India https://orcid.org/0000-0002-7379-7925 Gaganpreet Kaur Chitkara University, India Asadi Srinivasulu University of Newcastle, Australia Uma N. Dulhare Muffakham Jah College of Engineering and Technology, India https://orcid.org/0000-0002-4736-4472 Advancements in Metaverse Security Phishing Website Detection Through Optimal Feature Selection and Random Forest Classifier

This chapter proposes a novel approach for detecting phishing websites within the metaverse, leveraging the Optimal Feature Selection and the Random Forest classifier. This framework addresses the critical challenge of safeguarding users from deceptive tactics in virtual environments. By analyzing website characteristics and identifying the most informative features, the proposed method enhances the accuracy and efficiency of phishing detection in the metaverse, contributing to a more secure and trustworthy virtual landscape. The chapter delves into the methodology, including the chosen feature selection technique and the Random Forest classifier, followed by implementation details, experimental results evaluating the model's performance, and a discussion on the implications for future metaverse security research
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