5G V2X Misbehavior Detection as Edge Core Network Function Based on AI/ML

As 5G Cellular Vehicle-to-Everything (C-V2X) technology takes the lead in V2X communication, it opens the possibility for telecommunication service providers to offer Vehicle-to-Network (V2N) services using their existing 5G network infrastructure. To enhance the security of 5G V2N services, in this paper we propose a novel collaborative V2X misbehavior detection system. This system would safeguard the V2X application servers (V2X ASs), deployed in the 5G edge network, from any malicious V2X position manipulation attacks. Our proposal includes two enhanced machine learning models. The first model utilizes historical data to conduct On-Road Plausibility Checks (ORPC), while the second model builds upon the first by enabling collaboration among edge detection nodes through the sharing of attack ratios for each vehicle. Our proposed models were tested using extensive 5G core-network emulations, yielding excellent results. The first model achieved a notable accuracy improvement from 73% to 91%, while the second model further enhanced the accuracy to an impressive 95%.