Detection of Malicious Websites using Machine Learning based on URL |
Author(s): |
| Kadwe Durgesh Abhay , Amrutvahini College of Engineering; Pawar Prasad, Amrutvahini College of Engineering; Shirode Nikhil, Amrutvahini College of Engineering; Daradi Kajol, Amrutvahini College of Engineering |
Keywords: |
| Phishing, Extreme Learning Machine, Features Classification, URL, Information Security |
Abstract |
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Phishing is one kind of cyber-attack and at the same time, it is most dangerous and common attack to acquire personal information, account details, organizational details, credit card details or password of a user to conduct transactions. Phishing websites look similar to the appropriate ones which is difficult to differentiate between them. The motive of this study is to perform Extreme Learning Machine (ELM) based on different 30 features classification using Machine Learning approach. Most of the phishing URL’s use HTTPS to avoid getting detected. There are three approaches for detection of phishing websites. The first approach analyzing different features of URL, second approach checking legitimacy of website and knowing where the website is hosted or not and it also check who are managing it, third approach checking genuineness of website. |
Other Details |
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Paper ID: IJSRDV8I20785 Published in: Volume : 8, Issue : 2 Publication Date: 01/05/2020 Page(s): 1292-1295 |
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