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

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, short

Summary

A new Explainable AI (XAI)-driven framework has been developed for cyber risk analytics and model reliability assessment, targeting the intelligent governance of U.S. critical infrastructure. This framework addresses the increased cyber exposure of sectors like energy, healthcare, and transportation due to digital technology adoption. It utilizes the CICIDS2017 dataset to develop and test intrusion detection and cyber risk prediction models. Machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, are employed to identify malicious network activities and determine cyber risk levels. The integration of XAI techniques aims to enhance transparency, interpretability, and trust in cybersecurity decision-making. The framework's reliability and resilience are evaluated using performance measures such as accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate.

Key takeaway

For cybersecurity engineers managing U.S. critical infrastructure, you should consider integrating Explainable AI (XAI) into your intrusion detection systems. This framework demonstrates how XGBoost and SHAP can enhance transparency and reliability in cyber risk analytics, moving beyond traditional methods. Implementing XAI can improve trust in automated decision-making and strengthen your defense against advanced persistent threats. Evaluate model performance using metrics like ROC-AUC and F1 score to ensure robust protection.

Key insights

An XAI-driven framework enhances cyber risk analytics and intrusion detection for critical infrastructure by improving model transparency and reliability.

Principles

• AI-powered governance improves critical infrastructure efficiency.
• XAI integration boosts trust in cybersecurity decisions.
• Traditional cybersecurity often falls short against evolving threats.

Method

Develop intrusion detection and cyber risk models using XGBoost, Random Forest, and Decision Tree on CICIDS2017 data. Integrate XAI for transparency and assess reliability via accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate.

In practice

• Apply XGBoost for network intrusion detection.
• Use SHAP for model interpretability.
• Evaluate models with ROC-AUC and F1 score.

Topics

• Explainable AI
• Cyber Risk Analytics
• Intrusion Detection Systems
• Critical Infrastructure Security
• XGBoost
• SHAP
• CICIDS2017 Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.