Analysis of Feature Selection and Phishing Website Classification Using Machine Learning

Phishing website detection is the task of
classifying websites as phishing or legitimate based on URL
parameters and certain behaviour of the site. In today’s world,
dependency on websites has become inevitable. With the
increase in website users population and the rise of the internet,
cyber-attacks have become a common thing. Attackers across
the globe target innocent users to steal their personal classified
information such as login credentials, credit or debit card
information, which may lead to serious monetary and identity
damage for the users. One of the main challenges with this
problem is the constant change in phishing URLs. Due to this,
there is a constant need to update the detection mechanism,
which may be extinct in a short period of time. Most of the
current phishing detection tools utilise the black box method,
where phishing URLs are stored and queried for verification.
This may not be an efficient way due to the constant change in
the URLs. In this study, a machine learning based approach is
proposed along with a feature selection method to select the
right set of features that may contribute to higher detection
accuracy. The proposed model is also aimed at being simple,
faster, and interpretable. Efficiency, accuracy, and model
execution time will be evaluated against the final model.