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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • Shakti Kundu,
  • Ravi Prakash Chaturvedi,
  • Annu Mishra,
  • Akshat Agrawal,
  • Sultan Mesfer Aldossary,
  • Manel Ayadi,
  • Kedir Botamo Adem,
  • Arshad Hashmi

摘要

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.