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
📰 ArXiv cs.AI
Learn how to build an explainable AI-driven cyber risk analytics framework using XGBoost and SHAP for intelligent governance of critical infrastructure
Action Steps
- Build an XGBoost model for intrusion detection using historical network traffic data
- Configure SHAP to explain the model's predictions and identify key features contributing to the predictions
- Test the framework using a dataset of simulated cyber attacks on critical infrastructure
- Apply the framework to real-world critical infrastructure systems to assess model reliability and identify potential vulnerabilities
- Compare the performance of the XGBoost model with other machine learning algorithms for intrusion detection
Who Needs to Know This
Cybersecurity teams and critical infrastructure operators can benefit from this framework to improve their intrusion detection and model reliability assessment capabilities
Key Insight
💡 Explainable AI can improve the reliability and trustworthiness of cyber risk analytics models for critical infrastructure governance
Share This
🚨 Improve critical infrastructure cybersecurity with explainable AI-driven cyber risk analytics using XGBoost and SHAP 🚨
Key Takeaways
Learn how to build an explainable AI-driven cyber risk analytics framework using XGBoost and SHAP for intelligent governance of critical infrastructure
Full Article
Title: 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
Abstract:
arXiv:2606.05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a key part of the operation of critical infrastructure systems, including energy, healthcare, transportation, financial services, and communication infrastru
Abstract:
arXiv:2606.05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a key part of the operation of critical infrastructure systems, including energy, healthcare, transportation, financial services, and communication infrastru
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