Machine Learning for Cloud DDoS Attack Detection: A Systematic Review

Abstract

Cloud computing is an emerging technology that transfer the computing to providers through the internet. Cloud computing has numerous benefits such as cost saving, pay as you use and resources elasticity. Yet, cloud technology has various security concerns. Distributed Denial of Service (DDoS) attack represents one of the main cloud security challenges. Several machine learning approaches have been developed to handle cloud DDoS attack. Nevertheless, a common understanding of machine learning approaches for cloud Distributed Denial of Service (DDoS) attack is still missing. Furthermore, the increase of relative literature makes it difficult to manage and define state of the art and to recognize research emerging issues and gaps. This paper investigates research on machine learning approaches for cloud Distributed Denial of Service. This paper aims at carrying out a systematic review of the existing literature concerning cloud machine learning methods for DDoS in order to summarize the evidence regarding this issue.