Phishing web sites features classification based on extreme learning machine

Abstract

Phishing are one of the most common and most dangerous attacks among cybercrimes. The aim of these attacks is to steal the information used by individuals and organizations to conduct transactions. Phishing websites contain various hints among their contents and web browser-based information. The purpose of this study is to perform Extreme Learning Machine (ELM) based classification for 30 features including Phishing Websites Data in UC Irvine Machine Learning Repository database. For results assessment, ELM was compared with other machine learning methods such as Support Vector Machine (SVM), Naïve Bayes (NB) and detected to have the highest accuracy of 95.34%.