COMPARISON OF MACHINE LEARNING TECHNIQUES IN PHISHING WEBSITE CLASSIFICATION

Abstract: Phishing is one among the luring strategies utilized by phishing artist in the aim of abusing the personal details of unsuspected clients. Phishing website is a counterfeit website with similar appearance, but changed destination. The unsuspected client post their information thinking that these websites originate from trusted financial institutions. New antiphishing techniques rise continuously, yet phishers come with new strategy by breaking all the antiphishing mechanisms. Hence there is a need for productive mechanism for the prediction of phishing website. This paper described comparison in classification of phishing websites using different Machinelearning algorithms. Random Forest (RF), C4.5, REP Tree, Decision Stump, Hoeffding Tree, Rotation Forest and MLP were used to determine which method provides the best results in phishing websites classification. All instances are categorized as 1 for “Legitimate”, 0 for “Suspicious” and 1 for “Phishy”. Results show that RF with REP Tree show the best performance on this dataset for classification of phishing websites