Automated Detection of Phishing Website Using Machine Learning Techniques

Phishing is a major online security issue where attackers trick individuals into revealing sensitive data by creating fake websites. Such attacks are most common in financial and e-commerce platforms, and their frequency continues to grow rapidly. Though many research and models have been created to prevent this there are still phishing attacks going on. This study proposes a machine learning-based model that examines diverse URL and web content features to effectively identify phishing websites. The proposed model achieves high accuracy and scalability in identifying phishing websites. Different classification algorithms were tested, and their performance compared using metrics like precision, recall, and F1-score to determine the best model. The findings demonstrate that feature engineering and predictive modeling significantly improve detection rates, offering a reliable and adaptive solution to counteract phishing attacks.