Intelligent Automation Solutions for Network Management and Security in 5G Networks: A Study on AutoML and Digital Twins
Abstract
This thesis explores intelligent automation solutions, particularly Automated Machine Learning (AutoML) and Digital Twins (DTs), to enhance network management and security in Fifth-Generation (5G) networks. The first topic surveys Zero-touch network and Service Management (ZSM) systems, implementing an online AutoML pipeline to predict application throughput in Fourth Generation (4G) and 5G networks. Simulation results demonstrate the superiority of AutoML over traditional ML approaches, enabling ZSM to adapt to changing traffic patterns and, consequently, improving service quality and operational efficiency. The second topic implements an AutoML pipeline for multi-class network attack classification, incorporating real-time model updates using synthetic data within the DT environment. Uniform sampling for data generation is shown to offer faster recovery, lower overhead, and enhanced privacy. As for the generative models, variational autoencoders show lower overhead than generative adversarial models. However, they exhibit suboptimal performance when dealing with limited training samples, which can be attributed to their objective functions.