Beyond Intent: A Unified AI Framework for Self-Optimizing, Self-Securing, and Self-Healing Networks Using Generative AI, Federated Learning, and Neuromorphic Computing

This paper proposes a unified AI framework integrating generative AI, federated learning, and neuromorphic computing to enable self-optimizing, self-securing networks. Building on Bairy and Jorepalli’s foundational work in AI-driven Intent-Based Networking [1], we extend autonomous network capabilities through three novel components: (1) Generative adversarial networks (GANs) for syn thetic network policy generation, reducing configuration times by 41% compared to traditional SDN approaches; (2) Federated learning architecture for col laborative threat detection across multi-vendor environments, demonstrating 97.3% accuracy in identifying zero-day attacks; (3) Neuromorphic co-processors enabling energy-efficient edge AI for real-time traffic optimization. Our frame work achieves 94.2% intent recognition accuracy while reducing fault recovery times by 63% through explainable AI-driven decision trees. Experimental results from smart education deployments show 38% improvement in QoS parameters and 55% faster threat mitigation compared to existing systems. The study con cludes with a roadmap for implementing quantum-resistant AI algorithms in blockchain-secured autonomous networks, addressing critical challenges identified in prior IBN research.