Self-Healing Infrastructure for Security Services Using Anomaly-Aware Health Checks

High availability, security and resilience have never been more important than they are in the ever-increasing complexity and criticality of digital infrastructures. Traditional monitoring and alert systems are less capable of the early discovery of more sophisticated forms of anomalies or automated responses to events, further increasing the risk of system outages and potential security threats. This paper will see the researcher intensively analyse self-healing infrastructures that incorporate anomaly-sensitive health checks alongside AI-driven monitoring tools to enhance the provision of security services. The paper focuses on current trends, challenges and technologies that are founded on the detection of anomalies, the repair of autonomous systems, and the predictive maintenance of high-security infrastructures. The current paper builds on groundbreaking advances in the spheres of microservices, federated learning, IoT, and machine learning-driven architectures to provide the foundation for upcoming implementation strategies aimed at creating smart and self-healing architectures.