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AI framework enhances cyber risk analytics for US critical infrastructure

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for assessing cyber risks and model reliability in U.S. critical infrastructure. This framework utilizes machine learning classifiers like XGBoost, Random Forest, and Decision Tree to detect network intrusions and predict cyber risk levels. By integrating Explainable AI (XAI) techniques, the system aims to enhance transparency and trust in cybersecurity decision-making processes for sectors such as energy, healthcare, and transportation. AI

IMPACT Enhances cybersecurity for critical infrastructure by improving intrusion detection and risk prediction transparency.

RANK_REASON The cluster contains an academic paper detailing a new AI-driven framework for cybersecurity risk assessment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI framework enhances cyber risk analytics for US critical infrastructure

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The cluster contains an academic paper detailing a new AI-driven framework for cybersecurity risk assessment. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · B. M. Taslimul Haque, Md. Arifur Rahman, Md. Serajul Kabir Chowdhury Rubel, Md. Iqbal Hossan ·

    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: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…

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