Enhanced Phishing Website Detection Through Effective Feature Selection With Time‐Varying Mirrored S‐Shaped Transfer Function

ABSTRACT The increasing use of websites and the increasing sophistication of cybercrimes have highlighted the need for effective prevention mechanisms. Phishing attacks represent a significant cybersecurity threat, as they exploit human weaknesses and deceptive web‐based tactics to achieve unauthorized access to sensitive user information. Due to the evolving and increasingly sophisticated methodologies cybercriminals use, there is an urgent demand for advanced and innovative approaches to detect and mitigate these threats accurately. In this paper, a novel approach is proposed by incorporating a time‐varying mirrored S‐shaped transfer function into three prominent binary optimization algorithms: Binary Grey Wolf Optimization (TVBGWO), Binary Particle Swarm Optimization (TVBPSO), and Binary Harris Hawk Optimization (TVBHHO). The proposed models are rigorously evaluated using a publicly available phishing website dataset. Using a time‐varying mirrored S‐shaped transfer function for feature selection enables metaheuristic algorithms to improve the balance between exploration and exploitation, thus enhancing their ability to classify phishing websites accurately. The Friedman test confirms statistically significant differences among the compared methods (, ). The Wilcoxon Rank‐Sum test is applied to determine the difference in the performance of the time‐varying methods among the standard BGWO, BHHO, and BPSO algorithms in this study. TV‐based variants of BPSO and BGWO showed a solid and balanced performance, achieving the highest test accuracy (90.3% and 90.2%) and F1‐scores (0.91%). Proposed models achieve promising high accuracy and F1‐score performance in detecting phishing websites using fewer features and suggesting their potential.