A Review on Leveraging Machine Learning for Anomaly Detection in Cloud Cost Management

Cloud computing has revolutionized the way organizations manage and scale their IT infrastructure. However, with the increased reliance on cloud services, the need for effective cost management has become paramount. Anomalies in cloud cost data can indicate potential issues such as resource misconfigurations, security breaches, or inefficiencies, leading to unexpected financial burdens. This paper explores the application of machine learning (ML) techniques for anomaly detection in multi-cloud cost management. By leveraging supervised, unsupervised, and semi-supervised learning methods, this study aims to enhance the accuracy and efficiency of identifying cost anomalies. The paper also addresses key challenges such as high dimensionality, the dynamic nature of cloud environments, and scalability. Recent advancements in deep learning and hybrid models are discussed, providing insights into their potential for improving anomaly detection capabilities. Through comprehensive analysis and evaluation, this research contributes to the development of robust anomaly detection frameworks that can help organizations optimize their cloud expenditure and maintain financial control.