In the digital age, the proliferation of malicious
phishing URLs poses a significant threat to online
security. While conventional machine learning algorithms
have been employed to combat this menace, our research
pioneers the use of ensemble methods, including XGBoost
and Random Forest, for phishing URL detection. Our
methodology involves collection of the data, preprocessing
it then feature extraction followed by model training,
evaluation and comparison. Notably, our results reveal
the superior accuracy of ensemble methods in
distinguishing phishing URLs from legitimate ones. These
findings underscore the potential of ensemble methods as
a game-changing asset in the battle against cyber threats,
promising enhanced online security and the protection of
sensitive user information.
Keywords : Social Engineering, Phishing URLs, Cyber Security, Machine Learning.