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The book explores next-generation frameworks for building resilient, intelligent, and self-optimizing network systems. It focuses on how AI-driven analytics and large-scale data processing can enhance cybersecurity, automate threat detection, and optimize resource allocation across dynamic infrastructures. The content integrates theoretical foundations with practical architectures, including adaptive routing, anomaly prediction, distributed trust management, and privacy-preserving data fusion. Designed for researchers, engineers, and postgraduate students, the book bridges the gap between network security engineering and emerging data-centric intelligence. Through detailed models and analytical perspectives, it outlines how AI and big data technologies transform secure communication, enabling scalable and self-healing networks suitable for industrial, governmental, and IoT-driven ecosystems.