  The proliferation of malicious URLs has become a significant threat to
internet security, encompassing SPAM, phishing, malware, and defacement
attacks. Traditional detection methods struggle to keep pace with the evolving
nature of these threats. Detecting malicious URLs in real-time requires
advanced techniques capable of handling large datasets and identifying novel
attack patterns. The challenge lies in developing a robust model that combines
efficient feature extraction with accurate classification. We propose a hybrid
machine learning approach combining Self-Organizing Map based Radial Movement
Optimization (SOM-RMO) for feature extraction and Radial Basis Function Network
(RBFN) based Tabu Search for classification. SOM-RMO effectively reduces
dimensionality and highlights significant features, while RBFN, optimized with
Tabu Search, classifies URLs with high precision. The proposed model
demonstrates superior performance in detecting various malicious URL attacks.
On a benchmark dataset, our approach achieved an accuracy of 96.5%, precision
of 95.2%, recall of 94.8%, and an F1-score of 95.0%, outperforming traditional
methods significantly.
