Enhancing Cloud Security: A Comprehensive Review of Machine Learning Approaches

Cloud Computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to Machine Learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud Computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds.