Edge Intelligence Advanced Deep Transfer Learning for IoT Security
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- Taschenbuch
- eBook ausgewählt
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 154.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
16.01.2026
Herausgeber
Jawad Ahmad + weitereVerlag
Elsevier Science & Techn.Seitenzahl
250 (Printausgabe)
Dateigröße
14230 KB
Sprache
Englisch
EAN
9780443382987
Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.
- Examines the potential of edge computing and deep transfer learning, offering in-depth insights into how edge intelligence can be leveraged to enhance IoT and IIoT security
- Emphasizes the development of lightweight and resource-efficient models suitable for deployment on edge devices, ensuring that security measures can be effectively implemented without imposing undue computational burden or network overhead
- Presents practical examples, case studies, and implementation guidelines that demonstrate how advanced deep transfer learning techniques can be applied to address real-world security challenges in IoT and IIoT deployments
- Examines the potential of edge computing and deep transfer learning, offering in-depth insights into how edge intelligence can be leveraged to enhance IoT and IIoT security
- Emphasizes the development of lightweight and resource-efficient models suitable for deployment on edge devices, ensuring that security measures can be effectively implemented without imposing undue computational burden or network overhead
- Presents practical examples, case studies, and implementation guidelines that demonstrate how advanced deep transfer learning techniques can be applied to address real-world security challenges in IoT and IIoT deployments
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