Effective Classification of Phishing Web Pages Based on New Rules by Using Extreme Learning Machines

Year 2017, Volume: 2 Issue: 1, 15 - 36, 01.06.2017

Mustafa Kaytan Davut Hanbay

Abstract

Internet
is an essential part of our life. Internet users can beaffectedfrom different
types of cyber threats. Thus cyber threats may attack financial data, private
information, online banking and e-commerce. Phishing is a type of cyber threats
that is targeting to get private information such as credit cards information
and social security numbers. There is not a specific solution that can detect
whole phishing attacks. In this study, we proposed an intelligent model for
detecting phishing web pages based on Extreme Learning Machine. Types of web
pages are different in terms of their features. Hence, we must use a specific web
page features set to prevent phishing attacks. We proposed a model based on
machine learning techniques to detect phishing web pages.We have suggested some
new rules to have efficient features. The model has 30 inputs and 1 output. In
this application, the 10-fold cross-validation test has been performed. The
average classification accuracy was measured as 95.05%.

Keywords

Machine Learning, Extreme Learning Machine, Phishing, Information Security

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