A Machine Learning-Based Framework for Malicious URL Detection in Cybersecurity

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.