{
  "id": 1452,
  "url": "https://arxiv.org/abs/2606.05710v1",
  "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",
  "summary": "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 infrastructure, in order to improve efficiency and strategi",
  "authors": "B. M. Taslimul Haque, Md. Arifur Rahman, Md. Serajul Kabir Chowdhury Rubel, Md. Iqbal Hossan",
  "category": "research",
  "topics": "regulation,healthcare,military-security,transparency",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-06-04T05:05:14.000Z",
  "fetched_at": "2026-07-14T14:15:17.102Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1452",
  "original_url": "https://arxiv.org/abs/2606.05710v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}