Paper Title
Phishing Web Sites Features Classification Based on Extreme Learning Machine

Abstract
Some of the highly frequent as well as hazardous computer crimes assaults is phishing. The archival documents by people and companies to execute transactions are the target of these assaults. Phishing websites use a variety of indicators in both their text and data that is dependent on internet browsers. Phishing is an unique kind of networking attack in which the perpetrator makes a copy of a current Web page to trick users into providing private, economic, perhaps credential information towards what they believe is actual service provider's Web site (e.g., through employing carefully formulated e-mails or instant chats). The objective of this project is to classify 30 variables, comprising data from phishing websites, using Extreme Learning Machines (ELM) on a database at UC Irvine. Keywords - Random forest classifier, Adaboost classifier, Support vector classifier, Phishing.