A Novel Machine Learning Approach for Malicious URL Classification

Exponential rise in cyber threats has made malicious URL detection an essential component of modern cybersecurity systems. Malicious URLs are frequently used to initiate phishing attacks, spread malware, or deface legitimate websites, posing severe risks to users and organizations. In this research, we propose a hybrid machine learning approach for the efficient classification of URLs into four categories: benign, phishing, malware, and defacement. The system leverages both traditional machine learning algorithms and advanced deep learning techniques. Lexical features such as URL length, the number of special characters, and HTTPS presence are extracted and used to train classifiers including Decision Tree, Random Forest, and XGBoost. In parallel, a Character-level Convolutional Neural Network (Char-CNN) is developed to process raw URL strings, enabling automatic feature extraction at the character level.A labeled dataset containing over 599,359real-world URLs is used for training and evaluation. The models are assessed using standard performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the Char-CNN model achieves superior accuracy compared to traditional models, while Random Forest and XGBoost demonstrate robust and interpretable performance. The combination of feature-based and character-level models ensures both high detection accuracy and adaptability to evolving attack patterns. The proposed system can be effectively integrated into real-time web security applications to detect and block malicious URLs with high reliability.