To meet the complex demands of 6G use cases, 6G will require full automation of network and service management, while mitigating cybersecurity risks posed by its expanded threat landscape. The ROBUST-6G project is focused on developing an AI/ML-driven, zero-touch, end-to-end 6G security framework. ROBUST-6G prioritizes the security of distributed intelligence and promotes transparent, green, and sustainable AI/ML solutions to minimize energy consumption and foster trustworthy 6G networks. ROBUST-6G places a special emphasis on Physical Layer Security (PLS) as the first line of defense for wireless networks. ROBUST-6G aims to develop state-of-the-art techniques for anomaly detection, attack classification, and device authentication at the physical layer, creating a secure and efficient 6G ecosystem.
ROBUST-6G's main objectives:
1. Analysis of anticipated 6G architecture and scenarios, identification, and characterization of the threat landscape in an AI-driven 6G Networks.
2. To develop a holistic E2E 6G security architecture with inherent AI functionalities that will seamlessly integrate different functions in a heterogenous network environment.
3. Development of robust, sustainable (in terms of energy efficiency), explainable, effective (in terms of performance) and preserving privacy AI-driven security functionalities.
4. Automatic, zero-touch, security, and resource management for trusted and certified services among multiple stakeholders in distributed dynamic scenarios.
5. AI/ML-enabled smart techniques to detect and mitigate physical layer attacks on network and user devices and to propose novel physical layer security schemes for demanding scenarios, taking into account new radio technologies for 6G.
6. Validate the ROBUST-6G innovations through three use case scenarios.
