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.
1. Chapter 1: Introduction to IoT and IIoT security - p. 1 2. Chapter 2: Fundamentals of deep learning and transfer learning for IoT security - p. 17 3. Chapter 3: Edge computing: architecture and security - p. 57 4. Chapter 4: Deep transfer learning for intrusion and anomaly detection - p. 79 5. Chapter 5: Resource-efficient models for edge devices - p. 111 6. Chapter 6: Secure communication and privacy-preserving techniques in edge intelligence - p. 155 7. Chapter 7: Challenges and innovations in securing the future of IoT - p. 179 8. Chapter 8: Future trends and emerging technologies in IoT security - p. 209 9. Index - p. 241
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.
1. Chapter 1: Introduction to IoT and IIoT security - p. 1 2. Chapter 2: Fundamentals of deep learning and transfer learning for IoT security - p. 17 3. Chapter 3: Edge computing: architecture and security - p. 57 4. Chapter 4: Deep transfer learning for intrusion and anomaly detection - p. 79 5. Chapter 5: Resource-efficient models for edge devices - p. 111 6. Chapter 6: Secure communication and privacy-preserving techniques in edge intelligence - p. 155 7. Chapter 7: Challenges and innovations in securing the future of IoT - p. 179 8. Chapter 8: Future trends and emerging technologies in IoT security - p. 209 9. Index - p. 241
