Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash

Deep Learning Techniques for IoT Security and Privacy

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 997
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-89024-7
• 1st ed. 2022
• Seitenzahl: 280
• Erscheinungstermin: 6. Dezember 2021
• Englisch
• Abmessung: 241mm x 160mm x 21mm
• Gewicht: 588g
• ISBN-13: 9783030890247
• ISBN-10: 3030890244
• Artikelnr.: 62540744

Inhaltsangabe

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.