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Summary

The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mock up websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. Mostly, these methods rely on blacklisting and whitelisting techniques giving the attackers a chance to easily bypass the implemented security methods. Also these methods are very specific to certain phishing instances only and hence do not detect zero day attacks. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most phishing attacks have some common characteristics which can be identified by machine learning methods. This paper focuses on the use of machine learning methodology in conjunction with a browser extension that runs on the client side (browser) to effectively check all the URLs that the user visits and determine if those URLs are meant for phishing attacks. The browser extension then flags such URLs and warns the user of the possible phishing attack.

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