Multi-process Federated Learning with Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Being part of the 6G ecosystem vision, Connected and Automated Vehicles (CAVs) will enjoy sophisticated tailored services offering road safety and entertainment for users. As one of the 6G cornerstones, Network Slicing (NS) allows the creation of various customized 6G-V2X (Vehicle-to-Everything) use cases on the same physical infrastructure. However, 6G-NS advances can open up breaches to cyber-attacks aiming to break 6G-V2X Network slices to inflict maximum damage on CAVs and their users. Crossing borders, where CAVs leave their V2X-NS (V2X Network Slice) in the Home Mobile Network Operator (H-MNO) toward a similar V2X-NS in the Visited MNO (V-MNO), is an attractive opportunity to exploit by attackers. Detecting and mitigating attacks, in this case, becomes a priority, confronted by NS requirements and MNOs not ready to share their private data. To this end, this paper proposes a 3GPP-compliant privacy preservation collaborative learning scheme for 6G-NS security, focusing on V2X-NS cross-border areas. Our scheme leverages multi-process Federated Learning (FL) architecture to build efficient V2X-NS security-related models while preserving 6G V2X-NS isolation. In addition, it uses differential privacy-enabled stacking to build up attack detection knowledge at the V2X-NSs and MNOs levels while ensuring privacy preservation. We conducted an experimental study on the 5G-NIDD dataset, which is one of the most realistic publicly available 5G datasets. Our results demonstrate that multi-process FL with stacking can deliver high accuracy while ensuring isolation between 6G-V2X-NSs and privacy preservation between H-MNO and V-MNO.