Deep Learning Techniques for IoT Security and Privacy
(Sprache: Englisch)
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...
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Klappentext zu „Deep Learning Techniques for IoT Security and Privacy “
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.Inhaltsverzeichnis zu „Deep Learning Techniques for IoT Security and Privacy “
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.
Bibliographische Angaben
- Autoren: Mohamed Abdel-Basset , Nour Moustafa , Hossam Hawash , Weiping Ding
- 2021, 1st ed. 2022, XXI, 257 Seiten, 69 farbige Abbildungen, Masse: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3030890244
- ISBN-13: 9783030890247
Sprache:
Englisch
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