Proactive Cloud Security with AI-Based Anomaly Detection and Self-Healing Systems
Abstract
Traditional reactive security approaches are insufficient for handling sophisticated cloud-based attacks. This paper presents a proactive cloud security framework that uses AI-based anomaly detection and self-healing mechanisms. The framework continuously monitors cloud workloads and network traffic, identifying deviations from normal patterns using machine learning models. Upon detecting a threat, the self-healing system automatically isolates affected components and restores services using backup instances. Performance tests demonstrate that the proposed framework reduces downtime and improves threat mitigation efficiency. The study highlights the importance of AI-driven proactive security in modern cloud architectures.
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