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Deep Learning Techniques for IoT Security and Privacy

  • Book
  • © 2022

Overview

Authors:
  1. Mohamed Abdel-Basset

    1. Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

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  2. Nour Moustafa

    1. School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, Australia

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  3. Hossam Hawash

    1. Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

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  4. Weiping Ding

    1. School of Information Science and Technology, Nantong University, Nantong, China

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  • Presents a Machine Learning Approach to Conducting Digital Forensics
  • Contains state-of-the-art research and shows how to teach hands-on incident response and digital forensic courses
  • Covers the applications of digital forensics and artificial intelligence in operating systems

Part of the book series: Studies in Computational Intelligence (SCI, volume 997)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxi
    Download chapter PDF

  2. Introduction Conceptualization of Security, Forensics, and Privacy of Internet of Things: An Artificial Intelligence Perspective

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 1-35

  3. Internet of Things, Preliminaries and Foundations

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 37-65

  4. Internet of Things Security Requirements, Threats, Attacks, and Countermeasures

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 67-112

  5. Digital Forensics in Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 113-130

  6. Supervised Deep Learning for Secure Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 131-166

  7. Unsupervised Deep Learning for Secure Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 167-180

  8. Semi-supervised Deep Learning for Secure Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 181-202

  9. Deep Reinforcement Learning for Secure Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 203-213

  10. Federated Learning for Privacy-Preserving Internet of Things

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 215-228

  11. Challenges, Opportunities, and Future Prospects

    • Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding
    Pages 229-257
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Keywords

About this book

This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.

Authors and Affiliations

  • Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

    Mohamed Abdel-Basset, Hossam Hawash

  • School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, Australia

    Nour Moustafa

  • School of Information Science and Technology, Nantong University, Nantong, China

    Weiping Ding

Bibliographic Information

  • Book Title: Deep Learning Techniques for IoT Security and Privacy

  • Authors: Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-89025-4

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-89024-7Published: 06 December 2021

  • Softcover ISBN: 978-3-030-89027-8Published: 07 December 2022

  • eBook ISBN: 978-3-030-89025-4Published: 05 December 2021

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XXI, 257

  • Number of Illustrations: 2 b/w illustrations, 69 illustrations in colour

  • Topics: Data Engineering, Computational Intelligence, Artificial Intelligence

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