AI framework enhances cyber risk analytics for US critical infrastructure

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]

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• CICIDS2017
• Decision Tree
• Explainable AI
• Random Forest
• SHAP
• U.S.
• XGBoost

• paper
• safety
• product

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