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This book examines how advanced artificial intelligence models can enhance the security of next-generation networks, including IoT, edge computing and cyber-physical systems. It explains improved AI-driven techniques for detecting anomalies, predicting vulnerabilities, and simulating cyber-attacks, while ensuring transparency. By integrating theoretical foundations with real-world applications such as intrusion detection, federated learning, predictive analytics, and mobile edge computing security, the book presents actionable frameworks and case studies. It serves as a comprehensive reference for researchers, engineers, and cyber security professionals seeking to build secure, reliable, and resilient modern network infrastructures. This book targets researchers working in cyber security and AI as well as advanced-level students focused on AI, networking, and security. Professionals working in IoT and edge computing environments will also find this book useful as a reference.
This book examines how advanced artificial intelligence models can enhance the security of next-generation networks, including IoT, edge computing and cyber-physical systems. It explains improved AI-driven techniques for detecting anomalies, predicting vulnerabilities, and simulating cyber-attacks, while ensuring transparency. By integrating theoretical foundations with real-world applications such as intrusion detection, federated learning, predictive analytics, and mobile edge computing security, the book presents actionable frameworks and case studies. It serves as a comprehensive reference for researchers, engineers, and cyber security professionals seeking to build secure, reliable, and resilient modern network infrastructures. This book targets researchers working in cyber security and AI as well as advanced-level students focused on AI, networking, and security. Professionals working in IoT and edge computing environments will also find this book useful as a reference.
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