False Message Detection System for VANETs: A Reputation Evaluation Method Based on Voting Mechanism

Vehicular ad hoc networks (VANETs) enable vehicles to engage in vehicle-to-vehicle and vehicle-to-infrastructure communications to enhance driving safety. However, the open nature of the network and the uncertainty of information sources make VANETs vulnerable to false message attacks, potentially causing severe traffic accidents. Existing false-message detection systems suffer from high false alarm rates and high resource consumption. To address these challenges, we propose a false message attack detection system based on a software-defined network architecture that can efficiently and accurately detect false message attacks with minimal consumption of system resources. The system aims to detect emergency and normal beacon messages broadcast by vehicles, construct an automotive reputation evaluation system using the voting mechanism of the Byzantine consensus, and introduce an XGBoost-based traffic event classifier to improve classification accuracy. Experimental results show that the system can reliably assess vehicle reputation levels, effectively defend against conspiracy attacks, and perform well in intrusion detection.