Dynamic Telemetry and Deep Neural Networks for Anomaly Detection in 6G Software-Defined Networks

Abstract

With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures. This article introduces a meticulously crafted solution designed explicitly for 6G software-defined networks (SDNs). The approach employs deep neural networks for anomaly detection within network traffic, contributing to a more robust security framework. Furthermore, the paper delves into the realm of network monitoring automation by harnessing dynamic telemetry, providing a specialized and forward-looking strategy to tackle the distinctive challenges inherent in SDN environments. In essence, our proposed solution aims to elevate the security and responsiveness of 6G mobile networks. By addressing the intricate challenges posed by next-generation network architectures, it seeks to fortify these networks against emerging threats and dynamically adapt to the evolving landscape of next-generation technology.