A Machine Learning-Based Framework for Malicious URL Detection in Cybersecurity
Tanjim Mahmud, G.M. Sakhawat Hossain, Md. Hasan Ali, Tanvir Hasan, Md. Faisal Bin Abdul Aziz, Mohammad Tarek Aziz, Mohammad Shahadat Hossain, Karl Andersson · 2025
Malicious URLs represent a significant cybersecurity threat, facilitating malware distribution and data theft. This paper explores a ML-based framework for malicious URL detection, providing a comparative analysis of DL and traditional ML approaches. Eight ML models─LR, SVM, DT, KNN, GNB, RF, XGBoost, and LightGBM─are benchmarked against three DL models: LSTM, BiLSTM, and GRU. The results reveal that traditional ML models, particularly RF, XGBoost, and LightGBM, achieve superior performance with accuracy scores of up to 92%, outperforming DL models, which achieve accuracy rates of 90%, 91%, and 88%, respectively. To further enhance detection performance, a stacking model combining these techniques is proposed, achieving a remarkable accuracy of 99.99%. This research underscores the potential of stacked models to significantly improve malicious URL detection, offering advanced solutions to strengthen cybersecurity frameworks for both individuals and organizations.