UID:
almahu_9949226666502882
Format:
XXI, 257 p. 71 illus., 69 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783030890254
Series Statement:
Studies in Computational Intelligence, 997
Content:
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.
Note:
Chapter 1, Conceptualization of Security, Forensics, and Privacy of Internet of Things -- Chapter 2, Internet of Things, Preliminaries and Foundations -- Chapter 3, Internet of Things Security Requirements, Threats, Countermeasures -- Chapter 4, Digital Forensics in Internet of Things -- Chapter 5, Supervised Deep Learning for Secure Internet of Things -- Chapter 6, Unsupervised Deep Learning for Secure Internet of Things -- Chapter 7, Semi-supervised Deep Learning for Secure Internet of Things -- Chapter 8, Reinforcement Learning for Secure Internet of Things -- Chapter 9, Federated Learning for Privacy-Preserving Internet of Things -- Chapter 10, Challenges, Opportunities, and Future Prospects.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030890247
Additional Edition:
Printed edition: ISBN 9783030890261
Additional Edition:
Printed edition: ISBN 9783030890278
Language:
English
DOI:
10.1007/978-3-030-89025-4
URL:
https://doi.org/10.1007/978-3-030-89025-4
