Unveiling Key Features for Phishing Website Detection through Feature Selection

Raed Alazaidah¹Mohammad BaniSalman¹Khaled E. Alqawasmi¹Ali Abu Zaid¹Yousuf Hazaimeh¹Fuad Sameh Alshraiedeh¹Emma Qumsiyeh^2*

• ¹Faculty of Information Technology, Zarqa University, Zarqa, Jordan
• ²Faculty of Engineering and Information Technology, Palestine Ahliya University, Bethlehem, Palestine

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 identify which feature selection method performs best on websites with phishing datasets. By analyzing three unique datasets related to Phishing and evaluating eight metrics, this study found that Random Forest and Random Tree were superior at identifying phishing websites compared with other approaches. Likewise, GainRatioAttributeEval, along with InfoGainAttributeEval, performed better than the five alternative feature selection methods considered in this study.