Abstract:
There are number of users who purchase products online and make payment through
various websites. There are multiple websites who ask user to provide sensitive data
such as username, password or credit card details etc. often for malicious reasons.
This type of websites is known as phishing website. In order to detect and predict
phishing website, we proposed an intelligent, flexible and effective system that is
based on using classification Data mining algorithm. We implemented classification
algorithm and techniques to extract the phishing data sets criteria to classify their
legitimacy. The phishing website can be detected based on some important
characteristics like URL and Domain Identity, security and encryption criteria in the
final phishing detection rate This project proposes a phishing detection plugin for
targeting chrome browser that can detect and warn the user about phishing web sites
in real-time using random forest classifier. One common approach is to make the
classification in a server and then let the plugin to request the server for result. Unlike
the old approach, this project aims to run the classification in the browser itself. The
classifying in the client-side browser has advantages like, better privacy, detection
is independent of network latency. This project is implemented in Javascript for it to
run as a browser plugin. Since javascript doesn’t have much ML libraries support
and considering the processing power of the client machines, the approach needs to
be made lightweight.
