AI-Enhanced Microservices for Cybersecurity Monitoring Over Critical Digital Infrastructure with Predictive Machine Learning Capabilities
Keywords:
Cybersecurity, Microservices, Predictive Maintenance, Machine Learning, Anomaly Detection, Critical Infrastructure, AI-based Monitoring, Threat IntelligenceAbstract
Cybersecurity threats to critical digital infrastructure have become increasingly complex and dynamic, demanding scalable and intelligent monitoring systems. This paper explores the integration of microservices architecture with AI-driven predictive machine learning models to enhance threat detection, automate anomaly response, and enable real-time decision-making. The proposed system leverages containerized services that independently manage ingestion, analysis, prediction, and alerting—improving flexibility, fault isolation, and deployment efficiency. We review historical literature, introduce an architecture design, and evaluate AI model effectiveness in identifying key threat patterns.
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