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Abdulmohsen Almalawi - SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention

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Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems Cyber-attacks on SCADA systemsthe control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory managementcan lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning. Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book: Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems Describes the relationship between main components and three generations of SCADA systems Explains the classification of a SCADA IDS based on its architecture and implementation Surveys the current literature in the field and suggests possible directions for future research SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.

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Table of Contents List of Tables Chapter 3 Chapter 4 Chapter 5 Chapter - photo 1
Table of Contents
List of Tables
  1. Chapter 3
  2. Chapter 4
  3. Chapter 5
  4. Chapter 6
  5. Chapter 7
List of Illustrations
  1. Chapter 1
  2. Chapter 2
  3. Chapter 3
  4. Chapter 4
  5. Chapter 5
  6. Chapter 6
  7. Chapter 7
Guide
Pages


Wiley Series On Parallel and Distributed Computing

Series Editor: Albert Y. Zomaya

A complete list of titles in this series appears at the end of this volume.

SCADA SECURITY: MACHINE LEARNING CONCEPTS FOR INTRUSION DETECTION AND PREVENTION
SCADA-BASED IDs SECURITY

Abdulmohsen Almalawi

King Abdulaziz University

Zahir Tari

RMIT University

Adil Fahad

Al Baha University

Xun Yi

RMIT University

SCADA Security Machine Learning Concepts for Intrusion Detection and Prevention - image 2

This edition first published 2021
2021 John Wiley & Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions .

The right of Abdulmohsen Almalawi, Zahir Tari, Adil Fahad, Xun Yi to be identified as the authors of this work has been asserted in accordance with law.

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Limit of Liability/Disclaimer of Warranty
In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability 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. 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. 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.

Library of Congress Cataloging-in-Publication Data:
Names: Almalawi, Abdulmohsen, author. | Tari, Zahir, author. | Fahad, Adil, author. | Yi, Xun, author.
Title: SCADA security : machine learning concepts for intrusion detection and prevention / Abdulmohsen Almalawi, King Abdulaziz University, Zahir Tari, RMIT University, Adil Fahad, Al Baha University, Xun Yi, Royal Melbourne Institute of Technology.

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