Smart Predictive Modeling for Malicious URL Detection and Prevention- (Smart Predictive Modeling for Real-Time Malicious URL Detection and Prevention Using Advanced Machine Learning and Deep Learning Techniques)

The proliferation of malicious URLs poses significant threats to cybersecurity, enabling phishing attacks, malware distribution, and data breaches. Existing detection methods, including blacklists, heuristic rules, and traditional machine learning models, are limited by high false-positive rates, slow adaptability, and inadequate handling of evolving attack patterns. This paper presents a novel hybrid predictive modeling approach that combines deep learning with feature engineering to detect and prevent malicious URLs effectively. Our method leverages a deep neural network architecture enriched with engineered features such as lexical characteristics, URL entropy, and domain-based attributes to capture both structural and contextual patterns of malicious URLs. Experiments conducted on a large-scale dataset demonstrate a detection accuracy of 95%, outperforming conventional machine learning and deep learning-only approaches. Additional metrics, including precision (94%), recall (95%), and F1-score (94.5%), validate the robustness of the proposed system. Comparative analysis highlights the hybrid model’s superior capability to generalize across unseen