Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

Researchers present an XGBoost and SHAP-based intrusion detection framework for protecting U.S. critical infrastructure using explainable AI techniques. The study demonstrates how machine learning models combined with transparency mechanisms can enhance cybersecurity decision-making across energy, healthcare, transportation, and financial sectors.

Critical infrastructure protection faces unprecedented pressure as cyber threats evolve faster than traditional defense mechanisms can adapt. This research addresses a fundamental gap in cybersecurity governance by combining predictive machine learning with explainability frameworks that enable human oversight of automated security decisions. The use of XGBoost and Random Forest classifiers on the CICIDS2017 dataset establishes a practical foundation for detecting sophisticated attacks including DDoS, ransomware, and Advanced Persistent Threats. The integration of SHAP (SHapley Additive exPlanations) represents a critical advancement because security teams must understand why AI systems flag threats—particularly in regulated sectors where decision transparency is legally required and operationally essential.

The broader context reflects a sector-wide shift toward AI-augmented security after years of high-profile infrastructure breaches. Energy grids, financial networks, and healthcare systems increasingly deploy automated monitoring, creating both efficiency gains and new vulnerabilities. Traditional rule-based systems lack adaptability against novel attack patterns, while pure machine learning models often operate as uninterpretable black boxes that security personnel cannot trust or validate.

This framework addresses both challenges simultaneously. For infrastructure operators and security teams, explainable AI provides actionable threat intelligence with confidence metrics. For policymakers, it demonstrates how governance can incorporate AI while maintaining accountability. The comprehensive performance metrics (accuracy, precision, recall, F1-score, ROC-AUC) provide benchmarks for production deployment. However, real-world effectiveness depends on model generalization beyond the dataset and integration with existing security operations centers.

• →XGBoost with SHAP explainability enables interpretable threat detection for critical infrastructure security decisions.
• →Explainable AI frameworks address regulatory and operational requirements for transparency in automated cybersecurity systems.
• →Machine learning models show measurable advantages over traditional static defense mechanisms against evolving cyber threats.
• →Model reliability assessment through multiple performance metrics is essential before deploying AI systems in critical infrastructure.
• →Integration of interpretability techniques bridges the gap between AI accuracy and human-supervised security operations.