Improved Random Forest Algorithm using Chicken Swarm Optimization for Phishing Website Classification Model

Phishing is a type of online fraud which enables attackers to trick individuals into giving away confidential data like login credentials or financial data. A phishing website utilizes URL that comparable reasonable users thinking it legitimate, may comprise suspicious links forms has been developed for collecting sensitive from users. Machine learning (ML) can be utilized categorise websites as legitimate protect falling victim these attacks. There are several approaches using machine classification. This article focuses on the design Chicken Swarm Optimization with Improved Random Forest Website Classification (CSOIRF-PWC) technique. The CSOIRF-PWC technique aims discriminate and accurately. To execute this, presented approach initially performs normalization process. Next, classification takes place IRF classifier. For improving performance RF classifier, parameter tuning process performed through CSO algorithm, supports attaining improved performance. simulation values methodology investigated two datasets, outputs under diverse measures. comprehensive comparative outcomes emphasized enhanced system over other methodologies in terms