A Machine Learning Algorithms for Detecting Phishing Websites: A Comparative Study

Abstract

 Phishing website attacks are a type of cyber-attack in which perpetrators create fraudulent websites
that mimic legitimate platforms, such as online banking or social media, with the intent of tricking unsuspecting
users into divulging sensitive information. This includes passwords, credit card details, usernames, and other
personal data. These phishing websites are designed to look authentic and often employ various techniques, such as
URL spoofing, social engineering, and email or text message phishing, to lure victims into revealing their
confidential information. Web apps are growing increasingly complex and difficult to identify at first glance,
especially when they use encryption and obfuscation techniques. In order to effectively detect and stop phishing web
applications from being uploaded to the server in real-time, machine learning must be developed. In addition to
including analyses for the machine learning algorithms for identifying web application-based assaults, the study
calibrates fresh analyses by executing machine learning algorithms and confirming the findings. The study uses
unique and categorized results from a machine learning dataset. As per the outcomes obtained from experimental
and comparative analyses of the applied classification algorithms, the random forest model demonstrated the highest
accuracy, achieving an impressive rate of 96.89%, followed by the decision tree model at 94.57%, and Extreme
Gradient Boosting (XG).