AI-Powered Multi-Modal Routing Framework for Secure and Scalable Big Data Transfers in Area Networks

The more & increasing reliance on big data transfer in area network generates a corresponding need for adaptive routing protocols to provide security for the data flow and to optimize the network's performance. Traditional methods of routing usually fail to address the dynamic change in threats and conditions of the network, which leaves them with poor performance and more vulnerability to breaches. This paper presents an AI-based framework that synergizes several advanced techniques to overcome the process limitations outlined above. Our approach starts with Multi-Agent Deep Reinforcement Learning (MADRL), wherein distributed agents collaborate for optimizing zone-specific routing while proactively mitigating threats. Federated Graph Neural Networks (FedGNN) enable decentralized threat detection, thus preserving data privacy and improving the accuracy of anomaly detection by up to 60%. Bayesian Neural Networks (BNN) improves routing decisions with uncertainty quantification, which helps in robust path selection even in dynamic environments. Hybrid Optimization Framework combines Bayesian methods with Genetic Algorithms to find and refine secure high-performance paths leading to near-optimal configurations with minimal vulnerabilities. Ensemble Learning with Stacked Models combines the outputs of all models to provide holistic routing decisions, improving decision accuracy by 20–30%. The integration of these techniques offers an all-rounded balance between throughput, latency, and security. The impacts gathered from our findings include a great improvement in throughput up to 60%, a reduction in breach up to 50%, and improved network resilience. The proposed framework, therefore, represents a new paradigm for secure adaptive big data routing that solves major challenges in contemporary area networks, paving the way for scalable intelligent network management solutions.