Rajdeep Chakraborty (editor) - Machine Learning Techniques and Analytics for Cloud Security (Advances in Learning Analytics for Intelligent Cloud-IoT Systems)

Scrivener Publishing
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Beverly, MA 01915-6106

Advances in Learning Analytics for Intelligent Cloud-IoT Systems

Series Editor: Dr. Souvik Pal and Dr. Dac-Nhuong Le

The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of cloud-IoT systems is becoming increasingly essential and intertwined. The capability of an intelligent system depends on various self-decision-making algorithms in IoT devices. IoT-based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of analytics reasoning and sense-making in big data, which is centered in the cloud and IoT-enabled environments. The series publishes volumes that are empirical studies, theoretical and numerical analysis, and novel research findings.

Submission to the series:

Please send proposals to Dr. Souvik Pal, Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishna Nagar, West Bengal, India.

E-mail:

Publishers at Scrivener

Martin Scrivener ()

Phillip Carmical ()

Edited by

Rajdeep Chakraborty

Anupam Ghosh

and

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

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

• 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.
• 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.
• 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.