Comprehensive review on intelligent security defences in cloud: Taxonomy, security issues, ML/DL techniques, challenges and future trends

Nowadays, machine learning and deep learning algorithms are used in recent studies as active security techniques instead of traditional ones to secure the cloud environment based on pre-trained data. In this paper, a literature review on machine and deep learning based defences against attacks and security issues in cloud computing is provided. A taxonomy of all different types of attacks and threats as per cloud security alliance (CSA) layers; and the general defences against cloud attacks is shown in this review as well as the reasons which let the traditional security techniques fail to satisfy the desired security level are discussed. Forty-two case studies are selected based on seven quality assessment standards and then, analyzed to answer seven research questions which help to protect cloud environments from various attacks, issues, and challenges. The analysis of case studies shows a description of the most common security issues in cloud; machine learning and deep learning models that are applied, datasets models, performance metrics, machine learning and deep learning based countermeasures and defences that are developed to prevent security issues. Finally, the future scope and open challenges in cloud computing security based on machine and deep learning are discussed as well.

Automated summary

This paper presents a literature review on the use of machine learning and deep learning algorithms as security techniques in cloud computing. It discusses different types of attacks and threats, the limitations of traditional security techniques, and the development of defenses based on machine learning and deep learning. The analysis of case studies provides insights into common security issues and countermeasures in cloud environments, and the paper also discusses future research directions and challenges in cloud computing security.