## 2 Digital Twins for Next-Generation Networks
### Digital Twins for Core Networks  
As for the core networks, DT also plays an important role in their evolution to the Next-generation network. As an example, [PERSON] et al. [39] propose a DT framework to enhance optical communication networks. The authors describe a generic framework composed of the physical layer, the data layer, the model layer, and the application layer. The approach includes the propositions of three separate Deep learning (DL) models to achieve different objectives. First, a fault management model that uses bidirectional Gated recurrent unit (GRU) [40] to predict faulty equipment and XGBoost [41] to make a fault diagnosis. Second, a flexible hardware configuration model to dynamically configure a programmable optical transceiver (POT) using DRL. Finally, a dynamic transmission simulation system based on bidirectional Long short-term memory (LSTM) [42] that could replace a traditional block-based optical transmission simulation system.  
Another approach proposed by [PERSON] et al. [43] involves building networks of digital twins to tackle problems in complex telecommunication networks with two specific examples in feedback loops and traffic monitoring. In the former case, the network of digital twins allows the developer the tracking of all changes made and intervention in the development of the telecommunication system. In the latter case, the network of digital twins enables the network operators to solve urgent problems in emergencies where the network traffic suddenly changes due to the dysfunction of some network elements.  
In another context, [PERSON] et al. [44] present a digital twin-enabled framework aimed at Distributed denial of service (DDoS) attack detection for autonomous core networks. Since existing DDoS solutions are insufficient for data centers and edge networks in terms of scalability, detection rates, and latency, the authors develop an online ML-based algorithm for effective DDoS detection. This algorithm processes the data that can be captured in real time with the support of the digital twin designed for the core network. Moreover, the Yet another next generation (YANG) model and automated feature selection are also involved in reducing the complexity of the provided data before feeding it to the ML algorithm. Finally, the authors evaluate and show the outperformance of their proposed system using two different DDoS attack datasets as the simulation of the core network.  
Lessons Learned  
DTs have emerged as valuable tools in Next-generation networks, offering advanced capabilities in network optimization, monitoring, and security across various domains, including core networks, access networks, edge networks, and vehicular networks. In the context of optimization, digital twins function as centralized controllers that collect data from the physical network and propose optimal policies. For monitoring, they operate as a closed-loop autonomous system, ensuring effective network surveillance. Additionally, this autonomous system coupling with intrusion detection further emphasizes their importance in ensuring network security. Overall, digital twins play a pivotal role in enhancing network performance and safeguarding network integrity.