AUTHOR=Alazaidah Raed , BaniSalman Mohammad , Alqawasmi Khaled E. , Abu Zaid Ali , Hazaimeh Yousuf , Alshraiedeh Fuad Sameh , Qumsiyeh Emma 
  
TITLE=Identifying key features for phishing website detection through feature selection techniques
  
JOURNAL=Frontiers in Computer Science
  
VOLUME=Volume 7 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1687867
  
DOI=10.3389/fcomp.2025.1687867
  
ISSN=2624-9898
  
ABSTRACT=Over the past few years, phishing has evolved into an increasingly prevalent form of cybercrime, as more people use the Internet and its applications. Phishing is a type of social engineering that targets users' sensitive or personal information. This paper seeks to achieve two main objectives: first, to identify the most effective classifier for detecting phishing among 40 classifiers representing six learning strategies. Secondly, it aims to determine which feature selection method performs best on websites with phishing datasets. By analyzing three unique datasets on phishing and evaluating eight metrics, this study found that Random Forest and Random Tree were superior at identifying phishing websites compared with other approaches. Similarly, GainRatioAttributeEval, along with InfoGainAttributeEval, performed better than the five alternative feature selection methods considered in this study.