Assignment on Phishing Website Detection Using Machine Learning

Running head: PHISHING WEBSITE DETECTION USING MACHINE LEARNING
PHISHING WEBSITE DETECTION USING MACHINE LEARNING
Name of the Student
Name of the University
Author Note

PHISHING WEBSITE DETECTION USING MACHINE LEARNING1
Introduction
In recent days, there has been a rise in phishing activity on multiple websites; due to
this reason, many people have lost their vital data resulting in loss of a lump sum money after
accessing an affected site. For this sole reason, ‘Phishing Website Detection with Machine
Learning’ post has been chosen to get a better understanding of how this phishing activity can
be detected using machine learning applications.
Discussion
The ‘Phishing Website Detection with Machine Learning’ blog post has been chosen
because of its simplicity in the describing process. Apart from the simplicity part, the
described method in attaining the Machine Learning application is quite useful as well
compared to any other posts available on the internet. With the rise of phishing activity over
the internet, many people are entering their confidential information believing that they are
entering the details on a genuine website. In the end, they (the people entering their valuable
information) end up losing a lot of money from their accounts (in most cases). Due to this
sole reason, there is a need to identify and separate the genuine websites from a pool of
various spoofed websites. Upon identifying the genuine from the spoofed websites, this
problem can be avoided and the selected post has done this simplistically and easily by
avoiding most of the complicated processes as much as possible.
The selected post has made the use of raw HTML codes to detect whether the website
is spoofed or not because for an attacker, s/he can easily use SSL to make the website URL
look like a genuine one. However, it is hard to obfuscate the site codes for preventing a
system from detecting its HTML codes. As per the post, initially, Byte Pair Encoding (BPE)
is used to generate tokens from any site’s HTML codes (for implementing this, tokenizer can
be found from the GitHub repository with instructions). Then TFIDF (term frequency,