Detection of Exploit Websites Using Machine Learning and Data Analysis

With the growing integration of internet integration into daily life, the prevalence of security threats such as phishing attacks has become a significant concern. Phishing websites aim to illicitly acquire sensitive user information, posing serious risks to online security. In response, machine learning methods have been investigated for the detection of such fraudulent websites. concentrates on developing a robust phishing website detection system, emphasizing efficiency, accuracy, and costeffectiveness. Four supervised classification models, namely K-Nearest Neighbour, Kernel Support Vector Machine, Decision Tree, and Random Forest Classifier, are employed and compared. Among these models, the Random Forest classifier demonstrates superior performance, achieving an impressive accuracy score of 96.82% on the selected dataset