Because Of The Fast Expansion Of Internet Users, Phishing Attacks Have Become A Significant Menace Where The Attacker Poses As A Trusted Entity In Order To Steal Sensitive Data, Causing Reputational Damage, Loss Of Money, Ransomware, Or Other Malware Infections. Intelligent Techniques Mainly Machine Learning (ML) And Deep Learning (D L) Are Increasingly Applied In The Field Of Cyber Security Due To Their Ability To Learn From Available Data In Order To Extract Useful Insight And Predict Future Events. The Effectiveness Of Applying Such Intelligent Approaches In Detecting Phishing Web Sites Is Investigated In This Paper. We Used Two Separate Datasets And Selected The Highest Correlated Features Which Comprised Of A Combination Of Content-based, URL Lexical-based, And Domain-based Features. A Set Of ML Models Were Then Applied, And A Comparative Performance Evaluation Was Conducted. Results Proved The Importance Of Features Selection In Improving The Models' Performance. Furthermore, The Results Also Aimed To Identify The Best Features That Influence The Model In Identifying Phishing Websites. For Classification Performance, Random Forest (RF) Algorithm Achieved The Highest Accuracy For Both Datasets.
