any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
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 and sensors in smart home design which can be connected through cloud database. Security concerns are also discussed in this chapter.
– Chapter 5 discusses a neoteric model of a cryptosystem for cloud security by using symmetric key and artificial neural network with Mealy machine. A cryptosystem is used to provide data or information confidentiality and a state-based cryptosystem is implemented using Mealy machine. This chapter gives a detailed algorithm with results generated using Lenovo G80 with processor Intel® Pentium® CPU B950@210GHz and RAM 2GB and programming language Turbo C, DebC++ and disc drive SA 9500326AS ATA and Windows 7 Ultimate (32 Bits) OS.
– Chapter 6 describes the implementation of an effective intrusion detection system using ML techniques through various datasets. The chapter begins with a description of an intrusion detection system and how it is beneficial for cloud environment. Next, various intrusion attacks on cloud environment are described along with a comparative study. Finally, a proposed methodology of IDS in cloud environment is given along with implementation results.
– Chapter 7 beautifully describes text-based sentiment analysis for cloud security that extracts the mood of users in a cloud environment, which is an evolving topic in ML. A proposed model for text-based sentiment analysis is presented along with an experimental setup with implementation results. Since text-based sentiment analysis potentially identifies malicious users in a cloud environment, the chapter concludes with applications of this method and implementation for cloud security.
– Chapter 8 discusses zero-knowledge proof (ZKP) for cloud, which is a method for identifying legitimate users without revealing their identity. The ZKP consist of three parts: the first is ticket generator, the second is user, and the third is verifier. For example, to see a movie in a theater we purchase ticket. So, the theater counter is the ticket generator; and while purchasing a ticket here we generally don’t reveal our identifying information such as name, address or social security number. We are allowed to enter the theater when this ticket is verified at the gate, so, this is the verifier algorithm. This chapter also discusses ZKP for cloud security.
– Chapter