NATWORK

The 6G network architecture will be highly dynamic and heterogeneous, consisting of different types of devices, access technologies, and services. Ensuring continuous security in such a complex and dynamic environment is a major challenge. Moreover, highly mobile and dynamic payloads, such as drones, robots or vehicles, present unique challenges for 6G security. Machine learning and AI can be used to a) provide real-time security analysis and adaptation by learning from past security incidents, predicting future threats, and adapting security measures to changing conditions and b) provide real-time situational awareness and dynamic defence, by predicting the movement of payload, identifying potentials threats, and adapting security measures to protect against them.

The dynamic nature of 6G networks, characterised by diverse devices, applications, and services, requires a high degree of variability and adaptability to ensure optimal network performance and service quality. This use case will explore ways to improve the variability of 6G networks while maintaining continuous security monitoring. Additionally, it will focus on developing effective moving target defence (MTD) and software-defined (SDR) based payload mobility mechanisms that can shift and evolve over time. NATWORK will improve the variability of the 6G network by developing strategies to continuously monitor and adjust security measures. The project will explore using AI and machine learning algorithms to detect anomalies in network traffic and on network or security functions execution patterns and proactively adjust security measures accordingly to protect against emerging threats. Moreover, it will explore the use of SDR techniques to protect the mobility of payloads in 6G networks.