Design of an AI-driven Network Digital Twin for advanced 5G-6G network management

• Main Authors: Karamchandani, Amit, Sanz, Mario, Burgaleta, Angela, De La Cal, Luis, Mozo, Alberto, Moreno, Jose Ignacio, Pastor, Antonio, Lopez, Diego R.
• Format: Conference Proceeding
• Language: English
• Subjects: 5G network; artificial intelligence; cybersecurity; Digital twins; HTTP; Machine learning; network digital twin; Solid modeling; Telecommunication traffic; Training; Virtual reality
• ISSN: 2374-9709
• DOI: 10.1109/NOMS59830.2024.10575106

The Network Digital Twin (NDT) developed in the B5GEMINI project is presented in this article, highlighting its architecture, objectives, functionalities, and practical applications. The design of the NDT architecture is detailed, including the establishment of the foundational infrastructure, developed as part of B5GEMINI-INFRA. The integration of artificial intelligence techniques for network management tasks within B5GEMINI-AIUC is illustrated with relevant use cases, such as the detection of cybersecurity attacks and the simulation and optimization of virtual reality applications, to demonstrate the usefulness and potential of the proposed NDT solution. The platform enables controlled experimentation and data collection for training Machine Learning (ML) models, addressing challenges associated with realistic network traffic datasets and cybersecurity experiments without disrupting live networks. The infrastructure supporting the NDT allows for creating virtual scenarios, isolating traffic between experiments, on-demand traffic generation, and capture, ensuring repeatability and enabling evaluation of different detection and mitigation tools under identical conditions. Additionally, an in-depth use case focusing on ML-based detection of a simulated denial of service attack through DNS over HTTPS within a 5G network framework showcases the NDT's potential to provide a secure environment for testing and validating ML-based solutions without disrupting live networks.