A scalable hybrid computational intelligence framework with bio inspired optimization for high dimensional malicious URL inference

The increasing complexity and scale of internet infrastructure demand computational frameworks capable of performing accurate, scalable, and interpretable inference over high-dimensional network data. Conventional detection strategies often struggle to maintain robustness and efficiency when confronted with heterogeneous feature spaces and rapidly evolving threat patterns. This study presents a scalable hybrid computational intelligence framework that integrates discriminative statistical modeling, gradient-based inference, and bio-inspired meta-heuristic optimization to address large-scale malicious URL detection. The proposed framework couples Linear and Quadratic Discriminant Analysis with a categorical gradient-boosted inference engine, while automated parameter exploration is conducted using the Mother Optimization Algorithm and the Osprey Optimization Algorithm. A large-scale dataset consisting of 63,191 URLs, described by both application-layer and network-layer attributes, is employed to rigorously evaluate the framework's performance. Statistical robustness is evaluated through exploratory distribution assessment (Shapiro-Wilk), nonparametric hypothesis testing (Kruskal-Wallis), pairwise model comparison, and cross-validation-based performance consistency. These procedures provide quantitative support for model comparison and feature relevance under non-Gaussian conditions. Model transparency and reproducibility are further strengthened using SHAP-based feature attribution to quantify the influence of individual variables. Results demonstrate that the bio-inspired optimized models achieves superior performance, attaining an accuracy of 96.35%, precision of 96.54%, recall of 96.35%, F1-score of 96.40%, and specificity of 96.36%. These findings indicate that the synergistic integration of hybrid discriminative intelligence and bio-inspired optimization significantly enhances inference capability in real-world URL classification dataset with moderate-dimensional features. Beyond cybersecurity, the proposed framework offers a transferable and computationally efficient paradigm for high-dimensional classification and decision-making tasks across engineering systems and data-intensive scientific applications.