Full Description
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 environnements will also find this book useful as a reference.
Contents
.- Theoretical Foundations.
.- Foundations of Mathematics and Informatics.
.- Foundations of Network Structure.
.- Non-Uniform Quasi-Static Networks.
.- Introduction of Non-Uniform Quasi-Static Network Structures.
.- Evaluation and Optimization of 2-D Non-Uniform Quasi-Static
Network Performance.
.- Evaluation and Optimization of 3-D Non-Uniform Quasi-Static
Network Performance.
.- Periodic Dynamic Networks.
.- Defining and Introducing Periodic Dynamic Network Structures.
.- Analysis and Evaluation of Periodic Dynamic Networks Properties
and Capabilities.
.- Optimization and Design of Periodic Dynamic Network Performance.
.- Random Networks.
.- Analysis of Random Network Structures.
.- Performance Analysis of Random Networks.
.- Performance Optimization of Random Networks.
