International Journal of Network Security & Its Applications (IJNSA)
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
Machine Learning for Network Intrusion Detection in Usa Critical Infrastructure: Challenges and Opportunities
Joy Selasi Agbesi1, Abigail Nanayaa Otchill2, Raymond Horlalie Tay3 and Noah K. Bamfo4, 1Ohio University, USA, 2Foundation and Support, United States, 3College of Engineering Northeastern University, United States, 4Consulting Network Engineer, United States
Abstract
The convergence of information technology and operational technology in United States critical infrastructure has created unprecedented efficiency gains while simultaneously expanding attack surfaces vulnerable to sophisticated cyber threats. This paper examines the application of machine learning to network intrusion detection in critical infrastructure, with particular emphasis on smart cities and power grid implementations. Through comprehensive analysis of current threat landscapes, technical approaches, and operational constraints, the study identifies key challenges impeding the deployment of machine learning-based security solutions, including data scarcity, class imbalance, concept drift, and adversarial robustness concerns.
The analysis reveals that while machine learning offers promising capabilities for detecting anomalous patterns and previously unknown attack vectors beyond traditional signature-based systems, successful implementation requires addressing fundamental tensions between real-time operational requirements and computational complexity, between model explainability and detection accuracy, and between privacy preservation and effective security monitoring. The paper examines specific vulnerabilities in smart grid architectures, municipal systems, and IoT-enabled infrastructure, demonstrating how heterogeneous device ecosystems and legacy system integration compound security challenges.
Furthermore, the study synthesizes emerging opportunities including ensemble detection approaches, physics-informed machine learning, transfer learning techniques, federated learning, explainable artificial intelligence, and collaborative threat intelligence sharing mechanisms. It proposes a framework for crosssector collaboration and outlines standardized evaluation methodologies essential for validating machine learning security solutions in safety-critical environments. The findings indicate that realizing the full potential of machine learning for infrastructure protection requires coordinated efforts spanning technology development, workforce capacity building, regulatory framework evolution, and sustained information sharing across stakeholder communities. This work contributes to the growing body of knowledge on securing increasingly interconnected critical infrastructure systems upon which modern society fundamentally depends.
Keywords
Machine learning, intrusion detection, smart cities, smart grid, IoT security, anomaly detection, operational technology, cybersecurity, federated learning, explainable AI
