Phishing URL Detection: Leveraging Machine Learning for Improved Security Measures

The persistence of phishing assaults as a serious threat to internet users underscores the significance of efficient detection systems. We explore the field of machine learning algorithms for phishing URL identification in this research study. In order to find efficient detection techniques, this work examines the importance of feature selection, model evaluation, and comparative analysis. Several supervised learning methods are trained and assessed using a dataset of 11,054 URLs with 30 features. The results show that Random Forest, Multilayer Perceptron, and Gradient Boosting Classifier all perform admirably, with Random Forest reaching an efficiency of 97.4%. By highlighting important phishing URL indications, feature importance analysis helps create reliable detection models. In the end, incorporating cutting-edge machine learning methods presents a viable solution to improve digital security defenses against changing cyberthreats.