Anomaly Detection and Automated Mitigation for Microservices Security with AI

Abstract

Microservices are becoming increasingly fundamental to modern scalable applications, yet their distributed nature makes them susceptible to complex cyber-attacks. Traditional security solutions, especially static, rule-based systems, fail to keep up with the dynamic threats presented by these architectures. This paper proposes an AI-based framework for real-time intrusion detection and automated mitigation within microservices. By leveraging unsupervised learning for anomaly detection and reinforcement learning for dynamic firewall adjustments and service isolation, the framework adapts to evolving threats autonomously. Experimental evaluations demonstrate that AI-driven security solutions can significantly enhance detection accuracy, reduce response times, and maintain system availability while minimizing downtime in real-world microservice environments. This paper discusses the framework's architecture, highlights its implementation, and presents results that validate the efficacy of AI-driven security strategies for microservices.