Advancing Cloud Security: A Comprehensive Survey of Machine Learning Approaches for Threat Detection and Data Protection

The swift expansion of cloud computing has revolutionized how businesses and individuals handle data storage, management, and processing, providing unparalleled flexibility, scalability, and cost efficiency. Nonetheless, this growing dependence on cloud services has brought about considerable security challenges, demanding sophisticated solutions to safeguard data confidentiality, integrity, and availability. Conventional security measures frequently prove inadequate in managing the intricate nature of cloud environments, thereby increasing interest in the application of machine learning (ML) techniques to enhance cloud security.This paper presents an extensive survey of contemporary ML approaches for cloud security, encompassing supervised, unsupervised, and reinforcement learning methods. The survey assesses the effectiveness of these techniques in various security areas, including threat detection, anomaly identification, intrusion prevention, and data protection. To demonstrate the practical effectiveness of these methods, the proposed approach in this study achieves a notable accuracy of 96%, with a mean absolute error (MAE) of 0.485 and a root mean square error (RMSE) of 0.203. These metrics underscore the proposed method’s capability to deliver precise and dependable security solutions.By evaluating the strengths and weaknesses of various ML strategies, this study aims to provide a thorough overview of the current state of cloud security and pinpoint future research opportunities in this developing field of the findings are discussed, and future research directions are proposed.