AI AND MACHINE LEARNING FOR CLOUD SECURITY A COMPREHENSIVE SURVEY OF IDS AND THREAT DETECTION METHODS

Abstract

Cloud computing becomes central to modern digital infrastructure, ensuring robust security has become paramount, particularly against sophisticated cyber threats targeting dynamic and multi-tenant environments. IDS struggle to meet the demands of cloud ecosystems due to scalability issues and limited adaptability. This study offers a thorough analysis of the use of machine learning and artificial intelligence techniques in enhancing IDS capabilities for cloud security. In order to better detect recognized and unidentified acts of violence, adjust to changing threats, and increase detection accuracy, the study examines unsupervised, supervised, and reinforcement models. It further investigates hybrid models that combine multiple learning paradigms for context-aware and scalable defense mechanisms. Deployment challenges in cloud environments, such as virtualization, real-time monitoring, and integration with orchestration tools, are also examined. Emphasis is placed on AI-driven IDS architectures that support elastic scaling, behavior-based threat analysis, and automated responses. The paper underscores the shift from reactive to proactive defense strategies enabled by intelligent systems. IDS solutions that are bright, flexible, and adaptable are essential as use of clouds increases. This review synthesizes current advancements and identifies future directions, including the development of lightweight models, enhanced explain ability, and secure AI frameworks tailored for cloud-based intrusion detection.