PhishingWebsite URL's Detection Using Machine Learning

Phishing attacks pose a significant threat to online security, tricking users into divulging sensitive information by mimicking legitimate websites. This paper addresses the critical problem of detecting phishing URLs, which are often the frontline of phishing campaigns. Traditional security measures struggle to keep pace with the sophistication and volume of phishing attacks, necessitating more proactive and dynamic solutions. To combat this issue, we propose a machine learning-based approach that leverages Logistic Regression (LR), Naive Bayes (NB), Decision Trees, KNN, Random Forest and Linear Support Vector Classifier (LSVC) models to enhance the accuracy of phishing URL detection. The results of our research indicate enhanced accuracy of the models in identifying phishing URLs.