A National-Scale AI-Driven Cyber Defense Framework for Protecting U.S. Critical Infrastructure Against Nation-State Attacks

Abstract

The Cybersecurity and Infrastructure Security Agency (CISA) has predicted a 140% rise in high-impact attacks between 2022 and 2024, signalling an increase in nation-state adversary cyberattacks that affect US critical infrastructures. The sophistication, persistence and ever-evolving nature of nation-states' hacking has outgrown current cybersecurity, which primarily focuses on reactive and perimeter-based approaches. In this paper, we present and test a National-Scale AI-Driven Cyber Defense Framework (NAICDF) a multi-layer, intelligence-based framework with machine learning-based threat detection, federated learning for data sharing across critical infrastructure sectors, Zero Trust Architecture (ZTA) and automated response systems. We compare intrusion detection accuracy, mean time to respond (MTTR) and resilience with traditional approaches using incident reports of 847 confirmed nation-state intrusions in 11 critical infrastructure sectors (2020-2024), publicly available threat intelligence and simulation data from the National Cyber Exercise Program (NCEP) of the Cybersecurity and Infrastructure Security Agency (CISA). Results demonstrate the NAICDF achieves a 94.3% accuracy in detecting intrusions (2.1% false positives) and a 67% reduction in the mean time to respond (MTTR) when compared to conventional security operations center (SOC) systems. We also explore governance and public-private partnership models, and compatibility with existing policy frameworks such as CIRCIA (2022) and the National Cybersecurity Strategy (2023). The framework offers a policy-friendly, scalable model to secure our critical systems in the 21st century.