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

• Published in: Metaverse Security Paradigms
• Main Author: Kumar A.V.S.; Sivakumar P.; Chaturvedi A.; Musirin I.B.; Giridhar Akula V.S.; Suganya R.V.; Vanishree G.; Pillai R.H.; Jagadamba G.; Kaur G.; Srinivasulu A.; Dulhare U.N.
• Format: Book chapter
• Language: English
• Published: IGI Global 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205530972&doi=10.4018%2f979-8-3693-3824-7.ch014&partnerID=40&md5=bff1eece24920488dff5bccffd00396f

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. © 2024, IGI Global. All Right Reserved.