INFORMATION PROCESSING AND OPTIMIZATION OF MACHINE LEARNING FRAMEWORKS IN CRITICAL INFRASTRUCTURES

Abstract

The accelerating deployment of machine learning (ML) frameworks across critical infrastructures - including energy grids, water distribution systems, transportation networks, and financial systems - is reshaping how societies manage complex, high-stakes operational environments. By enabling real-time anomaly detection, predictive maintenance, adaptive control, and intelligent resource allocation, ML technologies offer transformative improvements in operational resilience, efficiency, and threat response. This article provides a comprehensive analysis of how information processing pipelines and ML framework optimization are applied to critical infrastructure domains, examining the principal technologies involved - including federated learning, edge inference, adversarial robustness, and explainable AI - alongside their operational applications, demonstrated effectiveness, and the challenges that persist.
The study also addresses the security and reliability considerations inherent in deploying ML systems within environments where failures carry systemic societal consequences, including adversarial attacks on model inputs, distribution shift, and single-point computational failures. A comparative analysis of ML application domains is presented, evaluating performance levels and deployment barriers across sectors. The findings indicate that while ML holds extraordinary potential to reduce infrastructure downtime, anticipate cascading failures, and optimize cross-system resource flows, its sustainable integration requires hardened data pipelines, sector-specific validation protocols, and robust governance frameworks that balance operational agility with systemic security.