Machine Learning-Driven Cyber Defense: Enhancing U.S. Critical Infrastructure Resilience

The rising speed, intensity and complexity of cyberattacks is a major challenge to the resilience of the U.S. critical infrastructure such as energy systems, transport, healthcare, water and financial systems. These sectors increasingly depend upon interconnected digital technologies, so their attack surface is becoming increasingly large and they are subject to the more sophisticated persistent threats, ransomware campaigns and state-sponsored cyber operations. Conventional cybersecurity mechanisms - which are largely based on static rules, signature-based detection and manual intervention are increasingly ineffective in detecting novel, stealthy and rapidly evolving attacks in real-time. Machine learning (ML) has become a revolutionary method for proactive cyber defense, which allows systems to learn from large and diverse pieces of data, recognize complicated patterns of attacks, and dynamically adapt to new types of threats. ML-based methods facilitate round-the-clock surveillance, threat anomalies detection, predictive threat intelligence, and automated response, which is a major improvement compared to the conventional reactive security design. However, despite increasing adoption, existing research is fragmented, usually focused on isolated algorithms or single sector application and pay little attention to aspects relating to infrastructure-wide resilience, integration in operations, and policy relevance. The present research paper provides an analytical and conceptual synthesis of machine learning-based approaches to cyber defense as a means to increase the resiliency of the U.S. critical infrastructure. In the methodology, a comprehensive review of the latest ML techniques is combined with the analysis of comparative performance under typical infrastructure situations. The major contributions are a coherent cyber defense framework, the evaluation of the effectiveness of the ML models in detecting intrusions and risk elimination, and the evaluation of the implications of such models on the national security and infrastructure regulation. The results guide policy makers, operators of infrastructures and cybersecurity practitioners on how to use ML to build resilient and adaptive ecosystems of cyber defenses that are future resistant.