A trustworthy cybersecurity model for transparent cyberattack detection using Bald Eagle Search tuned XGBoost and explainable AI

The rapid escalation of sophisticated cyberattacks demands intrusion detection systems that not only achieve high accuracy but also provide transparency and fairness in decision-making, addressing critical gaps in current machine learning-based cybersecurity solutions. This study proposes a trustworthy intrusion detection framework that integrates Bald Eagle Search (BES) optimization with the XGBoost classifier and SHAP-based explainability to enhance both predictive performance and interpretability. The BES algorithm efficiently fine-tunes key XGBoost hyperparameters to overcome limitations of traditional models that struggle with complex, high-dimensional intrusion patterns. Evaluated on a real-world cybersecurity dataset, the BES-optimized XGBoost model achieves a substantially improved accuracy of 99.7%, outperforming baseline XGBoost (87.5%) and other meta-heuristic optimizers including GA, ACO, and PSO. SHAP visualizations further reveal feature-level contributions, ensuring transparent and accountable detection decisions, while fairness analysis highlights disparities across browser types, promoting responsible and bias-aware cybersecurity deployment. Overall, the proposed model offers a robust, interpretable, and ethically aligned solution for detecting modern cyber threats, contributing significantly to the advancement of transparent and high-performance intrusion detection systems.