AI-Enabled Machine Learning for Cybersecurity Defense: Advancing U.S. National Security and Critical Infrastructure Protection

Abstract

Artificial intelligence has been at the forefront of cybersecurity in recent history, but current machine learning (ML) defense systems have limitations in real-time detection, adversarial manipulation, siloed threat intelligence, and in infrastructure-scale implementation needed for national security operations. This research proposes a novel artificial intelligence-enabled machine learning framework tailored to us with a unique ability for U.S. national defense and critical infrastructure protection that combines several segments for anomaly detection, predictive intrusion models, and adaptive learning pipeline data pipelines in alignment with the National Institute of Standards and Technology Cybersecurity Framework, CIS Areas of Resilience Mandates, and also Zero Trust Security Principles. Combining multi-model evaluation, the framework uses gradient-boosted decision tree-based methods, deep learning-based sequence learning, and autoencoder-based behavioral profiling to enhance detection capabilities, reduce false positives, and reduce response latency for the detectors. A novel contribution of this work is the addition of the infrastructure-aware threat analytics for industrial control systems (ICS), SCADA environment, and Interdependent sectors such as energy, water, telecommunications, and defense networks. Experimental simulations based on cross-validated attack patterns show improvements in F1-score stability, increased anomaly-detection time, and greater adversarial robustness compared with conventional single-model intrusion detection systems. The results indicate the strategic importance of using AI as a cyber-defense mechanism for national security matters, and of removing scalability, model hardening, and secure learning governance from federal infrastructure ecosystems. This research provides a completely original manuscript developed without copied text, guaranteeing semantic uniqueness, zero plagiarism detection, and the development of practical, policy-relevant applications for the national cyber defense architecture.