SAFE-ADVENT

Wireless communication technology is a vital enabler for~5G vehicle-to-everything (5G-V2X) communications, which remain vulnerable to different types of cyberattacks, such as distributed denial of service (DDoS).~\mbox{5G-V2X} networks demand enhanced mechanisms for DDoS detection, which would call for sophisticated artificial intelligence \mbox{(AI)-based} approaches. However, one main concern is data privacy when using \mbox{AI-based} solutions. Secured peer-to-peer (P2P) federated learning (FL) techniques could overcome this challenge by training detection models locally while sharing model parameters for secure aggregation to preserve privacy and bypass the centralized FL's single point of failure. However, securing P2P FL with secure average computation (SAC) or encryption/decryption would scale poorly and incur a high communication cost as the number of FL clients grows. In such a context, we propose \mbox{SAFE-ADVENT}, a novel, secure P2P FL strategy that significantly reduces the communication cost, incorporating transfer learning and client selection within the P2P FL system to detect DDoS attacks. We validate the generalization capability of \mbox{SAFE-ADVENT} using data collected from our~\mbox{5G-V2X} testbed. We demonstrate our method's superior performances, in terms of accuracy, robustness, and cost, across different scenarios with unbalanced and balanced dataset distributions, compared to existing benchmarks.