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Vol. 7, June, Issue 6

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DETECTION OF PHISHING WEBSITES USINGEXTREME LEARNING MACHINE BASED ON URL


Abstract

Abstract: Phishing is one kind of cyber-attack and at thesame time, it is most dangerous and common attack to acquire personalinformation, account details, organizational details, credit card details orpassword of a user to conduct transactions. Phishing websites look similar tothe appropriate ones which is difficult to differentiate between them. Themotive of this study is to perform Extreme Learning Machine (ELM) based ondifferent 30 features classification using Machine Learning approach. Most ofthe phishing URL’s use HTTPS to avoid getting detected. There are threeapproaches for detection of phishing websites. The first approach analyzingdifferent features of URL, second approach checking legitimacy of website andknowing where the website is hosted or not and it also check who are managingit, third approach checking genuineness of website.

Keywords—Phishing,Extreme Learning Machine, Features Classification, URL, Information Security.


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Computer Science ,Electronics, Electrical  Engineering Information Technology, Civil, Computer Science and Engineering , Mechanical, Mechanical-Sandwich Petroleum, Production Instrumentation & Control, Automobile ,Chemical, Electronics Instrumentation& Control, Electronics & Telecommunication  Submit paper at oaijse@gmail.com

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@AMITY SCHOOL OF ENGINEERING & TECHNOLOGY

Department of Civil Engineering, Amity University Haryana,




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