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Use Cases

Use Case #1: Sustainability and reliability of 6G Slices and services


With the increasing number of connected devices and data-intensive applications in 6G edge-to-cloud networks, energy consumption is expected to increase substantially. At the same time, the number of threat actors and vulnerabilities is proliferating. Security is not optional in 6G, yet existing solutions are energy intensive. Finding sustainable and efficient energy solutions to power these networks while ensuring security-/trust-/sustainability-compliant provision of cloud-native services will be a significant challenge. Efficient optimisation techniques (including scalable AI) can be used to improve the network-user sustainability (in terms of energy efficiency and trust-compliance), by predicting and dynamically adapting to changes in traffic-workload patterns, user demand, and environmental conditions.


Use Case #2: Anti-jamming technologies for AVs


Autonomous vehicles (AVs) will rely heavily on 6G networks to communicate with other vehicles, infrastructure, and the cloud. However, the wireless links used by AVs are susceptible to various types of interference and jamming attacks, which can compromise the safety and reliability of the vehicle. Machine learning and AI can be used to detect, classify, and mitigate jamming attacks in real-time, by analysing signal patterns, adapting to changing signal environments and identifying anomalous behaviour. By leveraging the power of 6G networks and cutting-edge machine learning techniques, a way more safer and reliable future for AVs could be guaranteed.


Use Case #3: IoT security


The large-scale deployment of IoT devices in 6G networks presents significant security challenges, including the risk of distributed denial-of-service (DDoS) attacks, data breaches, and unauthorised access. Ensuring the security and privacy of IoT devices and their data in 6G networks will require advanced threat detection and mitigation mechanisms. Due to the limited computational capabilities most IoT devices cannot be extended with advanced security solutions. In these cases, the network infrastructure needs to provide this extra layer of functionality in a seamless way.


Use Case #4: Improving variability of network with continuous security


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 those of 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.