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.

翻译：恶意URL的激增已成为互联网安全的重大威胁，涵盖垃圾邮件、网络钓鱼、恶意软件及篡改攻击。传统检测方法难以跟上这些威胁不断演变的特性。实时检测恶意URL需要能够处理大规模数据集并识别新型攻击模式的高级技术。其核心挑战在于构建一个将高效特征提取与精准分类相结合的鲁棒模型。本文提出一种混合机器学习方法：结合基于自组织映射的径向移动优化（SOM-RMO）进行特征提取，并采用基于禁忌搜索优化的径向基函数网络（RBFN）进行分类。SOM-RMO能有效降维并突出关键特征，而经禁忌搜索优化的RBFN则以高精度实现URL分类。所提模型在检测各类恶意URL攻击中表现出优越性能。在基准数据集上，本方法取得了96.5%的准确率、95.2%的精确率、94.8%的召回率及95.0%的F1分数，显著优于传统方法。