<![CDATA[Cyber threat detection failures stem from the complexity of psychological exploitation tactics leveraging target anxiety and the limitations of traditional models in analyzing non-linear correlations within the structural features of malicious web URLs. To improve classification performance and computational efficiency in feature engineering, a sequential hybrid framework is developed by combining the spatial feature extraction capabilities of RGB Color Mapping image tensors via a Convolutional Neural Network (CNN) with tree-based boosting classifiers (XGBoost and LightGBM). Experiments are executed using a public dataset containing 11,055 observations, applying an 80:10:10 data splitting schema and isolated MinMaxScaler normalization to eliminate data leakage risks. The evaluation results demonstrate that the hybrid CNN + XGBoost model achieves the highest performance, with a validation accuracy of 97.83% and an F1-score of 98.06%, outperforming the replicated State-of-the-Art Random Forest baseline accuracy (97.38%). The metric stability confirms that the proposed architecture possesses robust generalization capabilities for implementation as a near real-time cyber defense system.]]>
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