Machine<b> </b>Learning<b> </b>for<b> </b>Anomaly<b> </b>Detection<b> </b>in<b> </b>Cloud<b> </b>Data Warehousing:<b> </b>Anomaly<b> </b>detection<b> </b>techniques<b> </b>to<b> </b>identify unusual<b> </b>activities

As organizations increasingly rely on cloud data warehousing for efficient storage and analysis of vast datasets, securing these environments against emerging cyber threats becomes paramount. This paper explores the application of machine learning techniques for proactive anomaly detection in cloud data warehousing, with a focus on identifying unusual activities. The background highlights the growing importance of cloud-based systems and the motivation behind countering rising cyber threats. The study delves into the definition of anomaly detection, emphasizing its significance in protecting sensitive data and ensuring the integrity and availability of cloud data warehousing systems. The paper provides an in-depth overview of machine learning techniques, distinguishing between supervised and unsupervised learning and emphasizing the importance of feature selection and engineering. Common machine learning algorithms such as Isolation Forests, One-Class SVM, Autoencoders, and clustering algorithms are discussed, offering insights into their applicability for anomaly detection in the cloud.