Phishing URL Detection Using Machine Learning: A Comparative Study
Sahana Lokesh R · 2025
Phishing attacks are a prevalent form of cybercrime, targeting individuals and organizations by tricking users into visiting malicious websites. Traditional methods for detecting phishing attacks are often ineffective due to their inability to adapt to rapidly evolving phishing techniques. In this study, we compare multiple machine learning models to detect phishing URLs, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Naive Bayes, Decision Tree, and Random Forest. The experimental results indicate that the Random Forest classifier outperforms the other models, achieving an accuracy of 94.5%. This paper also discusses the implications of using different machine learning techniques for phishing URL detection.