Abstract:
"
Phishing is one of the oldest type of cybercrimes which tricks people to obtain their sensitive
information such as usernames, passwords, and credit card information. Time to time the
attackers have changed their methods for phishing to avoid being detected by the security
protocols. If a phishing website is hosted under a legitimate domain or if the website content
contains only images, it would be able to avoid being detected by the security system
algorithms since there’s only a slight chance of identifying any suspicious elements from such
sites to make a prediction.
In this proposed machine learning approach to detect image-based web phishing attempts, the
main aim was to develop a system that would be able to detect phishing websites that uses
image-based attempts to avoid being detected. This image-based phishing detection system
has taken a novel approach by using combined sets of features to identify any suspicious
elements from the site such as URL, domain, content, and images. A brand new dataset has
been compiled with previously addressed element data in order to train a high accurate model
with the use of best fitting ml algorithm. In this case random forest algorithm has been selected
for the model classification after comparing with several other algorithms. Compiled dataset
was fed up with enough phishing and legitimate URL data from various source with unique
elements to maintain a higher accuracy.
A finalized prototype application has been developed based on the proposed solution for the
end users. The development was carried out in a Python environment since it goes well with
data science related projects. Before finalizing the project, the implemented system was tested
properly according to a certain criteria and evaluated with the guidance from domain and
technical experts in order to confirm that the taken approach was a success.
"
