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Convergence of deep learning in cyber-IoT systems and security / edited by Rajdeep Chakraborty [and three others].

Contributor(s): Material type: TextTextSeries: Artificial intelligence and soft computing for industrial transformationPublisher: Hoboken, NJ : John Wiley & Sons, Inc., 2023Copyright date: �2023Description: 1 online resource (xxi, 444 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119857679
  • 1119857678
  • 9781119857686
  • 1119857686
Subject(s): Additional physical formats: Print version:: No titleDDC classification:
  • 006.3/1 23/eng/20221115
LOC classification:
  • Q325.73 .C68 2023
Online resources: 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.
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Includes bibliographical references and index.

Description based on online resource; title from digital title page (viewed on November 15, 2022).

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.

John Wiley and Sons Wiley Online Library: Complete oBooks

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