Convergence of deep learning in cyber-IoT systems and security /
The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and...
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2023.
|
| Series: | Artificial intelligence and soft computing for industrial transformation
|
| Subjects: | |
| Online Access: | CONNECT |
| Summary: | The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. |
|---|---|
| Item Description: | Wiley EBA |
| Physical Description: | 1 online resource (xxi, 444 pages) : illustrations (some color). |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781119857686 9781119857679 1119857678 1119857686 |
