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

2026-06-04 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Advanced, quick

Summary

A study proposes an Explainable AI (XAI)-driven framework for cyber risk analytics and intrusion detection, specifically designed for the intelligent governance of U.S. critical infrastructure. This framework addresses the heightened exposure of sectors like energy, healthcare, and transportation to advanced cyber adversaries, including Distributed Denial of Service (DDoS) attacks, botnets, ransomware, and Advanced Persistent Threats (APTs). Utilizing the CICIDS2017 dataset, the research develops intrusion detection and cyber risk prediction models based on machine learning classifiers such as XGBoost, Random Forest, and Decision Tree. The integration of XAI techniques aims to enhance transparency, interpretability, and trust in cybersecurity decision-making. Model reliability and resilience are assessed using various performance measures, including accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate, to support robust decision-making in dynamic network environments.

Key takeaway

For AI Security Engineers developing intrusion detection systems for critical infrastructure, you should integrate Explainable AI (XAI) techniques like SHAP with your machine learning models. This approach not only improves the accuracy of detecting threats such as DDoS and APTs but also provides crucial transparency and interpretability, fostering trust in automated cybersecurity decisions. Ensure your model reliability is rigorously assessed using a comprehensive suite of metrics, including ROC-AUC and F1 score, to validate its resilience against evolving cyber threats.

Key insights

Integrating XAI with ML-based intrusion detection enhances transparency and trust for critical infrastructure cyber governance.

Principles

• ML classifiers can detect diverse network malicious activities.
• XAI improves interpretability of cybersecurity decisions.
• Robust evaluation requires multiple performance metrics.

Method

Develops ML models (XGBoost, Random Forest, Decision Tree) on CICIDS2017 for intrusion detection and risk prediction, then integrates XAI for interpretability, assessing reliability via multiple performance measures.

In practice

• Apply XGBoost for network intrusion detection.
• Use SHAP values to explain model predictions.
• Evaluate IDS with ROC-AUC and F1 score.

Topics

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

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.