Deep Learning Techniques for IoT Security and Privacy
Abdel-Basset, Mohamed, 1985-
Deep Learning Techniques for IoT Security and Privacy by Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding - 1st edition 2022 - 1 recurso en línea (XXI, 257 páginas) 71 ilustraciones, 69 ilustraciones a color - Studies in Computational Intelligence 997 1860-9503 .
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.
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.
9783030890254
10.1007/978-3-030-89025-4 doi
Aprendizaje automático
Internet de los objetos
Q325.5 / 2022 EB
Deep Learning Techniques for IoT Security and Privacy by Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash, Weiping Ding - 1st edition 2022 - 1 recurso en línea (XXI, 257 páginas) 71 ilustraciones, 69 ilustraciones a color - Studies in Computational Intelligence 997 1860-9503 .
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.
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.
9783030890254
10.1007/978-3-030-89025-4 doi
Aprendizaje automático
Internet de los objetos
Q325.5 / 2022 EB