Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abstract

In the rapidly evolving landscape of computing and networking, the concepts of cloud networks have gained significant prominence. Although the cloud network offers on-demand access to shared resources, anomalies pose potential risks to the integrity and security of cloud networks. However, protecting the cloud network against anomalies remains a challenge. Unlike traditional detection techniques, machine learning (ML) and deep learning (DL) offer new and adaptable methods for detecting anomalies in cloud networks. The objective of this study is to comprehensively explore existing ML /DL methods for detecting different anomalies based on distributed denial of service anomaly (DDoS) and intrusion detection systems (IDS) in cloud networks. The study seeks to address the gaps in anomaly detection for cloud networks, proposing potential solutions for anomaly detection in these cloud environments. The ultimate goal is to contribute valuable insights and practical solutions to enhance the security and reliability of cloud networks through effective anomaly detection by ML/ DL techniques. Methodologies for ML/DL are explained, along with their advantages, disadvantages, and respective approaches. In addition, a summary of the comparison between different ML/ DL models is also included.