Authors:
- 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)
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Front Matter
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
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Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
Mohamed Abdel-Basset, Hossam Hawash
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School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, Australia
Nour Moustafa
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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