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, it aims to identify the most effective classifier for detecting phishing from among forty different classifiers representing six learning strategies; secondly, it strives to discover which feature selection method works best for websites with phishing datasets. By analyzing two unique datasets related to Phishing and considering eight evaluation metrics. this study revealed that RandomForest, and RandomTree were superior in identifying phishing websites when compared with other approaches; likewise, GainRatioAttributeEval besides InfoGainAttributeEval had better performance than five alternative feature selection methods considered in this study.