A Review of Machine Learning-based Security in Cloud Computing - Comprehensive Overview

Aug 5, 2026

Cloud Computing (CC) reshapes how IT resources are delivered, enabling users to access and manage systems with greater cost-effectiveness and simpler infrastructure. At the same time, expanding CC introduces major security threats to availability, integrity, and confidentiality. Machine Learning (ML) is increasingly adopted by Cloud Service Providers (CSPs) to reduce human effort, learn from large datasets, and improve prediction accuracy. This review surveys recent ML-based approaches for cloud security, comparing algorithm features, effectiveness, strengths, and limitations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Cloud computing service and deployment models

Cloud Computing

Key characteristics and resource delivery

ML-based Security Review

Algorithm types (supervised, unsupervised, semi-supervised, reinforcement)

Common algorithms, features, and limitations

Conclusion and Future Research

A Review of Machine Learning-based Security in Cloud Computing Aptin Babaei, Parham M. Kebria, Member, IEEE, Mohsen Moradi Dalvand, and Saeid Nahavandi, Fellow, IEEE arXiv :2309 .04911v1 [ cs .CR] 10 Sep 2023 Abstract— Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality. To address these challenges, Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues. With the ability to analyze vast amounts of data, and make high-accuracy predictions, ML can transform the way CSPs approach security. In this paper, we will explore some of the most recent research in the field of ML-based security in Cloud Computing. We will examine the features and effectiveness of a range of ML algorithms, highlighting their unique strengths and potential limitations. Our goal is to provide a comprehensive overview of the current state of ML in cloud security and to shed light on the exciting possibilities that this emerging field has to offer. Index Terms—Cloud Computing, Machine Learning, Cloud Security. I. INTRODUCTION Cloud computing is a paradigm for delivering informa tion technology services through the internet, rather than through a direct connection to a server. This delivery model allows on-demand access to computing resources, such as storage, networking, software, analytics, and intelligence, without needing physical infrastructure. Typically, customers are charged for cloud computing services based on usage, rather than a fixed rate, and can be classified into three Service Model categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) . There are four deployment models for cloud computing: public, private, community, and hybrid. These models are comprised of various components, including providers, consumers, auditors, brokers, and carriers, each with distinct responsibilities [1] . As cloud computing continues to gain popularity and expand, security concerns regarding the confidentiality, integrity, and availability of user data remain a pressing issue. To mitigate these concerns, it is imperative that cloud service providers adopt state-of-the-art technology and techniques to A. Babaei, P. M. Kebria, and M. M. Dalvand are with the Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia (email: [a.babaei@deakin.edu.au](a.babaei@deakin.edu.au); [parham.kebria@deakin.edu.au](parham.kebria@deakin.edu.au); [mohsen.m.dalvand@deakin.edu.au](mohsen.m.dalvand@deakin.edu.au)). S. Nahavandi is with Swinburne University of Technology, Hawthorn Melbourne, VIC 3122, Australia, also with the Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134 USA (e-mail: [saeid.nahavandi@ieee.org](saeid.nahavandi@ieee.org)). prevent data loss and unauthorized access while ensuring the availability of their services [2] . In this paper, we aim to conduct a comprehensive review of cloud computing service and deployment models, with a focus on security challenges and attacks. Furthermore, we will examine the role of machine learning in enhancing cloud security. Machine learning, a subfield of artificial intelligence, allows systems to learn and improve from experience without explicit programming. It involves the development of algorithms and models that can identify patterns in data and make predictions or decisions without human intervention. Our objective is to thoroughly examine the various machine learning techniques employed to detect, prevent, and resolve cloud security vulnerabilities. De

FAQs

Why is security a central concern in cloud computing?

Cloud computing increases access to data and services over the internet, but security risks remain critical, particularly threats to confidentiality, integrity, and availability.

How does machine learning help cloud service providers improve security?

ML enables analysis of large datasets and supports high-accuracy predictions, helping CSPs identify and address security issues with less human intervention.

What types of machine learning algorithms are discussed for cloud security?

The review focuses on supervised, unsupervised, semi-supervised, and reinforcement learning approaches, describing their potential benefits for detecting, preventing, and resolving vulnerabilities.

Aug 5, 2026

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