A deep learning mechanism to detect phishing URLs using the permutation importance method and SMOTE-Tomek link

What is it about?

This paper proposes an intelligent mechanism to detect phishing URLs. The proposed system is based on the permutation importance method to select the most relevant URL features and the SMOTE-Tomek link method to solve the problem of an unbalanced dataset. In addition, the XGBoost classifier and four deep learning models—CNN, LSTM, and two hybrid models (CNN-LSTM and LSTM-CNN)—are employed to classify URLs as phishing or legitimate and to compare their performance. The mechanism was tested in various scenarios, and the experimental results outperformed relevant comparisons, demonstrating the successful functioning of the proposed phishing detection mechanism. Finally, our phishing detection mechanism was implemented as a web application to enhance its usability for web users.

Featured Image