Detect anomalies in cloud platforms by using network data: a review

Cloud computing is one of the utmost rapidly growing computing domains in today’s information technology ecosphere. Cloud computing links data and applications from various geographical locations over the internet. A large number of transactions and the secreted infrastructure in cloud computing systems have presented the research community with numerous challenges. Among these, maintaining cloud network security has emerged as a major challenge in the modern era. As well, detecting anomalous data has become a significant research area in the cloud computing domain. Anomaly detection (or outlier detection) is the identification of unusual or suspicious data that differs significantly from the majority of the data. Recently, machine learning methods have demonstrated their efficacy in anomaly detection approaches. The goal of this research study is to identify which machine learning algorithm is best suited for analyzing cloud network data on anomaly detection. This research study has led a systematic review by using scholarly articles which are published between 2017 and 2023. This review study has deliberated various techniques for anomaly detection on the cloud and different approaches for that.