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Название: Machine Learning Techniques and Analytics for Cloud Security
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119764090
isbn:
Jyotsna Kumar Mandal
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76225-6
Cover images: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Our objective in writing this book was to provide the reader with an in-depth knowledge of how to integrate machine learning (ML) approaches to meet various analytical issues in cloud security deemed necessary due to the advancement of IoT networks. Although one of the ways to achieve cloud security is by using ML, the technique has long-standing challenges that require methodological and theoretical approaches. Therefore, because the conventional cryptographic approach is less frequently applied in resource-constrained devices, the ML approach may be effectively used in providing security in the constantly growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues for effective intrusion detection and zero-knowledge authentication systems. Moreover, these algorithms can also be used in applications and for much more, including measuring passive attacks and designing protocols and privacy systems. This book contains case studies/projects for implementing some security features based on ML algorithms and analytics. It will provide learning paradigms for the field of artificial intelligence and the deep learning community, with related datasets to help delve deeper into ML for cloud security.
This book is organized into five parts. As the entire book is based on ML techniques, the three chapters contained in “Part I: Conceptual Aspects of Cloud and Applications of Machine Learning,” describe cloud environments and ML methods and techniques. The seven chapters in “Part II: Cloud Security Systems Using Machine Learning Techniques,” describe ML algorithms and techniques which are hard coded and implemented for providing various security aspects of cloud environments. The four chapters of “Part III: Cloud Security Analysis Using Machine Learning Techniques,” present some of the recent studies and surveys of ML techniques and analytics for providing cloud security. The next three chapters in “Part IV: Case Studies Focused on Cloud Security,” are unique to this book as they contain three case studies of three cloud products from a security perspective. These three products are mainly in the domains of public cloud, private cloud and hybrid cloud. Finally, the two chapters in “Part V: Policy Aspects,” pertain to policy aspects related to the cloud environment and cloud security using ML techniques and analytics. Each of the chapters mentioned above are individually highlighted chapter by chapter below.
Part I: Conceptual Aspects of Cloud and Applications of Machine Learning
– Chapter 1 begins with an introduction to various parameters of cloud such as scalability, cost, speed, reliability, performance and security. Next, hybrid cloud is discussed in detail along with cloud architecture and how it functions. A brief comparison of various cloud providers is given next. After the use of cloud in education, finance, etc., is described, the chapter concludes with a discussion of security aspects of a cloud environment.
– Chapter 2 discusses how to recognize differentially expressed glycan structure of H1N1 virus using unsupervised learning framework. This chapter gives the reader a better understanding of machine learning (ML) and analytics. Next, the detailed workings of an ML methodology are presented along with a flowchart. The result part of this chapter contains the analytics for the ML technique.
– Chapter 3 presents a hybrid model of logistic regression supported by PC-LR to select cancer mediating genes. This is another good chapter to help better understand ML techniques and analytics. It provides the details of an ML learning methodology and algorithms with results and analysis using datasets.
Part II: Cloud Security Systems Using Machine Learning Techniques
– Chapter 4 shows the implementation of a voice-controlled real-time smart informative interface design with Google assistance technology that is more cost-effective than the existing products on the market. This system can be used for various cloud-based applications such as home automation. It uses microcontrollers СКАЧАТЬ