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

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.

Produktdetails

• Autoren: Mohamed Abdel-Basset, Weiping Ding, Hossa Hawash, Hossam Hawash, Nour Moustafa
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 07.12.2022
• EAN: 9783030890278
• ISBN: 978-3-0-3089027-8
• Seiten: 257
• Abmessung: 155 mm x 15 mm x 235 mm
• Illustration: XXI, 257 p. 71 illus., 69 illus. in color.
• Serie: Studies in Computational Intelligence
• Thema: Naturwissenschaften, Medizin, Informatik, Technik > Technik > Allgemeines, Lexika