Deep Learning Approaches for Security Threats in IoT Environments (PDF)
(Sprache: Englisch)
Deep Learning Approaches for Security Threats in IoT Environments
An expert discussion of the application of deep learning methods in the IoT security environment
In Deep Learning Approaches for Security Threats in IoT Environments, a team of...
An expert discussion of the application of deep learning methods in the IoT security environment
In Deep Learning Approaches for Security Threats in IoT Environments, a team of...
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Deep Learning Approaches for Security Threats in IoT Environments
An expert discussion of the application of deep learning methods in the IoT security environment
In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation.
This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues.
Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They'll also find:
* A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy
* Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks
* In-depth examinations of the architectural design of cloud, fog, and edge computing networks
* Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks
Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.
An expert discussion of the application of deep learning methods in the IoT security environment
In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation.
This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues.
Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They'll also find:
* A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy
* Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks
* In-depth examinations of the architectural design of cloud, fog, and edge computing networks
* Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks
Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.
Autoren-Porträt von Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash
Mohamed Abdel-Basset, PhD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE.Nour Moustafa, PhD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia.
Hossam Hawash is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt.
Bibliographische Angaben
- Autoren: Mohamed Abdel-Basset , Nour Moustafa , Hossam Hawash
- 2022, 1. Auflage, 384 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1119884152
- ISBN-13: 9781119884156
- Erscheinungsdatum: 18.11.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Größe: 18 MB
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