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 [author.]
Contributor(s): Moustafa, Nour [author.] | Hawash, Hossam [author.]
Language: English Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., c2023Edition: First editionDescription: xvi, 368 pages : illustrations ; 23 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781119884149Subject(s): Internet of things -- Security measures -- Data processing | Deep learning (Machine learning)Additional physical formats: Online version:: Deep learning approaches for security threats in IoT environments.DDC classification: 004.678 A1354 2023 LOC classification: TK5105.8857 | .A255 2023Summary: "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.| Item type | Current location | Home library | Collection | Call number | Status | Date due | Barcode | Item holds |
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Sibalom | Sibalom CCS | Main-Circulation | 004.678 A1354 2023 (Browse shelf) | Available | UAMAIN 35818 |
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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.
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