Generative AI in Adaptive Networking: Pioneering Real-Time Solutions to Address Scalability, Security, and Efficiency Challenges

Abstract

The rapid growth of data traffic, the proliferation of connected devices, and the advent of emerging technologies such as 5G and IoT have introduced significant challenges in network management. Traditional approaches to networking struggle to keep pace with the demands for scalability, security, and efficiency. Generative AI, which includes models like Generative Adversarial Networks (GANs), Reinforcement Learning (RL), and Variational Autoencoders (VAEs), offers an innovative solution to these challenges. This paper explores how generative AI can be applied to adaptive networking to enhance network scalability, improve security, and optimize resource efficiency in real-time. By leveraging AI-driven solutions, networks can dynamically respond to changing conditions, mitigate threats, and optimize performance. This paper provides a comprehensive analysis of these AI models, their applications in adaptive networking, and the challenges and opportunities they present for future network architectures.