Survey on Machine Learning Based Anomaly Detection in Cloud Networks

As cloud computing becomes more popular and cyber threats become more sophisticated, ensuring the security of cloud networks has become a paramount concern for organizations worldwide. In this dynamic environment, traditional security measures are frequently insufficient, necessitating the deployment of new technologies such as machine learning-based anomaly detection systems. In the context of enhancing cloud network security, the goal of this survey research is to provide a comprehensive overview of the current state of machine learning-based anomaly detection systems. The article begins by discussing the challenges and threats that cloud networks face, highlighting the need for proactive security measures. It then delves into the various machine learning techniques and algorithms that have been employed for anomaly detection in cloud environments, covering methods for semi-supervised, supervised, and unsupervised learning. The advantages and limitations of each approach are analyzed, providing valuable insights into their practical implementation. This paper also explores practical application scenarios for anomaly detection systems in cloud networks that are based on machine learning, illustrating their effectiveness in identifying and mitigating security threats. It also discusses the key factors that influence the performance of these systems, such as data quality, feature engineering, and model selection. The survey article also covers the challenges and open research questions in the field, emphasizing the need for continuous innovation and adaptation in the face of evolving threats. The paper concludes with a set of best practices and recommendations for organizations looking to optimize their cloud network security through the implementation of machine learning-based anomaly detection systems. For security experts, researchers, and decision-makers looking to use machine learning approaches to improve the security of their cloud networks, the survey offers a thorough resource. It provides useful insights into the enhancement of cloud network security and acts as a road map for comprehending the state of anomaly detection in the cloud today.