Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality. To address these challenges, Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues. With the ability to analyze vast amounts of data, and make high-accuracy predictions, ML can transform the way CSPs approach security. In this paper, we will explore some of the most recent research in the field of ML-based security in Cloud Computing. We will examine the features and effectiveness of a range of ML algorithms, highlighting their unique strengths and potential limitations. Our goal is to provide a comprehensive overview of the current state of ML in cloud security and to shed light on the exciting possibilities that this emerging field has to offer.

翻译：云计算（CC）正在重塑信息技术资源交付给用户的方式，使系统访问与管理兼具更高的成本效益和简化的基础设施。然而，随着云计算的发展，一系列安全风险也随之而来，包括对可用性、完整性和机密性的威胁。为应对这些挑战，云服务提供商（CSPs）越来越多地采用机器学习（ML）来减少识别和解决安全问题过程中对人工干预的需求。凭借分析海量数据和进行高精度预测的能力，机器学习能够变革云服务提供商处理安全问题的范式。本文将探讨云计算中基于机器学习的安全领域最新研究进展，分析多种机器学习算法的特性与有效性，突出其独特优势与潜在局限性。旨在全面概述机器学习在云安全领域的发展现状，并揭示这一新兴领域的广阔前景。