Pramod Singh Rathore (editor) - Deep Learning Approaches to Cloud Security: Deep Learning Approaches for Cloud Security

Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106

Advances in Cyber Security

Series Editors: Rashmi Agrawal and D. Ganesh Gopal

Scope: purpose of this book series is to present books that are specifically designed to address the critical security challenges in today's computing world including cloud and mobile environments and to discuss mechanisms for defending against those attacks by using classical and modern approaches of cryptography, blockchain and other defense mechanisms. The book series presents some of the state-of-the-art research work in the field of blockchain, cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduates, and doctoral students who are working in the field of blockchain, cryptography, network security, and security and privacy issues in the Internet of Things (IoT). It will also be useful for faculty members of graduate schools and universities. The book series provides a comprehensive look at the various facets of cloud security: infrastructure, network, services, compliance and users. It will provide real-world case studies to articulate the real and perceived risks and challenges in deploying and managing services in a cloud infrastructure from a security perspective. The book series will serve as a platform for books dealing with security concerns of decentralized applications (DApps) and smart contracts that operate on an open blockchain. The book series will be a comprehensive and up-to-date reference on information security and assurance. Bringing together the knowledge, skills, techniques, and tools required of IT security professionals, it facilitates the up-to-date understanding required to stay one step ahead of evolving threats, standards, and regulations.

Publishers at Scrivener

Martin Scrivener ()
Phillip Carmical ()

Edited by

Pramod Singh Rathore

Vishal Dutt

Rashmi Agrawal

Satya Murthy Sasubilli

and

Srinivasa Rao Swarna

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

2022 Scrivener Publishing LLC

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Library of Congress Cataloging-in-Publication Data

ISBN 9781119760528

Cover image: Stockvault.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

This is Dr. Abhishek Kumar, Assistant Professor in Chitkara University, Himachal Pradesh. I have been involved in the research for more than 8 years with the authors of this book. This book is about a solution to these more intuitive problems. This solution is to allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.

This book is about how Deep Learning is the fastest growing field in computer science. Deep Learning algorithms and techniques are found to be useful in different areas like Automatic Machine Translation, Automatic Handwriting Generation, Visual Recognition, Fraud Detection, Detecting Developmental Delay in Children. However, applying Deep Learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of Deep Learning in these areas. It includes areas of detection, prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, approaches of Deep Learning such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems thereby bringing newer dimension.

This book shall help clarify understanding of certain key mechanism of technology helpful in realizing such system. Enables processing of very large dataset help with precise and comprehensive forecast of risk and delivers recommended action that improve outcome for consumer. It is a novel application domain of deep learning that is of prime importance to human civilization as a whole. This would be helpful for both professionals and students, with state-of-the art knowledge on the frontiers in information assurance. This book is a good step in that direction.

Dr. Abhishek Kumar

Assistant Professor

Abhishek Kumar || Assistant Professor, PhD, Senior Member (IEEE)

Chitkara University Research and Innovation Network (CURIN) Chitkara University, India

This book is organized into fifteen chapters. discusses the prevailing Biometric modalities, classification, and their working. It goes on to discuss the various approaches used for Facial Biometric Identification such as feature selection, extraction, face marking, and the nearest neighbor approach.

In we understand the cloud computing concept with Multi-Tenant Framework (MWF). In Multi-Tenant Framework, there is a requirement of privacy and security, a concept developed using Deep Learning. The goal is to find privacy requirements in many factors. Multi-tenancy based systems use the Deep Learning concept. The services of Multi-Tenant based systems are aggregated due to the dynamic environment of cloud computing. Three consistencies will maintain privacy policies using deep learning.