Deep learning approaches for security threats in IoT environments / Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.

By:

Abdel-Basset, Mohamed, 1985- [author.]

Contributor(s):

Material type: Text

TextPublisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]Edition: First editionDescription: 1 online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781119884170
1119884179

Subject(s):

Additional physical formats: Print version:: Deep learning approaches for security threats in IoT environments.DDC classification:

004.67/8 22

LOC classification:

TK5105.8857 .A255 2023eb

Online resources:

Click here to access online

Summary: "Deep Learning Approaches for Security Threats in IoT Environments discusses approaches and measures to ensure our IoT systems are secure. This book discusses important concepts of AI and IoT and applies vital approaches that can be used to protect our systems - these include supervised, unsupervised, and semi-supervised Deep Learning approaches as well as Reinforcement and Federated Learning for privacy-preserving. This book applies Digital Forensics to IoT and discusses problems that professionals may encounter when working in the field of IoT forensics, providing ways in which smart devices can solve cyber security issues. Aimed at readers within the cyber security field, this book presents the most recent challenges that are faced in deep learning when creating a secure platform for IoT systems and addresses the possible solutions, paving the way for a more secure future"-- Provided by publisher.

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Includes bibliographical references and index.

"Deep Learning Approaches for Security Threats in IoT Environments discusses approaches and measures to ensure our IoT systems are secure. This book discusses important concepts of AI and IoT and applies vital approaches that can be used to protect our systems - these include supervised, unsupervised, and semi-supervised Deep Learning approaches as well as Reinforcement and Federated Learning for privacy-preserving. This book applies Digital Forensics to IoT and discusses problems that professionals may encounter when working in the field of IoT forensics, providing ways in which smart devices can solve cyber security issues. Aimed at readers within the cyber security field, this book presents the most recent challenges that are faced in deep learning when creating a secure platform for IoT systems and addresses the possible solutions, paving the way for a more secure future"-- Provided by publisher.

Print version record.

John Wiley and Sons Wiley Online Library: Complete oBooks

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NLU Meghalaya Library

Online Public Access Catalogue (OPAC)

Deep learning approaches for security threats in IoT environments / Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.