Comparative Study of Phishing URL Detection Using Artificial Intelligence Algorithms

Phishing is one of the most common attacks in the field of cybersecurity. Different Machine Learning and Artificial Neural Network techniques were used to detect phishing URLs. We used three datasets with different features and we implemented three Machine learning algorithms (Decision Trees, Random Forests and XGBoost) and three Artificial Neural Network algorithms (MLS, CNN and LSTM). The results we obtained demonstrate that XGBoost and Random Forests have the best performance and a low training time compared to the other candidates.