SCADA Security. Xun Yi

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SCADA Security - Xun Yi


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       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-BASED IDs SECURITY

       Abdulmohsen Almalawi

      King Abdulaziz University

       Zahir Tari

      RMIT University

       Adil Fahad

      Al Baha University

       Xun Yi

      RMIT University

      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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      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.

      Description: Hoboken, NJ, USA : Wiley, 2021. | Series: Wiley series on parallel and distributed computing | Includes bibliographical references and index.

      Identifiers: LCCN 2020027876 (print) | LCCN 2020027877 (ebook) | ISBN 9781119606031 (cloth) | ISBN 9781119606079 (adobe pdf) | ISBN 9781119606352 (epub)

      Subjects: LCSH: Supervisory control systems. | Automatic control–Security measures. | Intrusion detection systems (Computer security) | Machine learning.

      Classification: LCC TJ222 .A46 2021 (print) | LCC TJ222 (ebook) | DDC 629.8/95583–dc23

      LC record available at https://lccn.loc.gov/2020027876 LC ebook record available at https://lccn.loc.gov/2020027877

      Cover Design: Wiley

      Cover Image: © Nostal6ie/Getty Images

       To our dear parents

      FOREWORD

      In recent years, SCADA systems have been interfaced with enterprise systems, which therefore exposed them to the vulnerabilities of the Internet and to security threats. Therefore, there has been an increase in cyber intrusions targeting these systems and they are becoming an increasingly global and urgent problem. This is because compromising a SCADA system can lead to large financial losses and serious impact on public safety and the environment. As a countermeasure, Intrusion Detection Systems (IDSs) tailored for SCADA are designed to identify intrusions by comparing observable behavior against suspicious patterns, and to notify administrators by raising intrusion alarms. In the existing literature, there are three types of learning methods that are often adopted by IDS for learning system behavior and building the detection models, namely supervised, semisupervised, and unsupervised. In supervised learning, anomaly‐based IDS requires class labels for both normal and abnormal behavior in order to build normal/abnormal profiles. This type of learning is costly however and time‐expensive when identifying the class labels


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