Multi-Criteria Voting Algorithm for Misbehavior Detection in VANET Beacon Messages

Abstract

The exchange of beacon messages plays an important role in the operation of vehicles in VANETs. The data transmitted through these messages can be subject to deliberate manipulation from cyberattacks or accidental distortion caused by faulty sensors. To address this challenge, a Multi-Criteria Voting mechanism was developed by integrating three independent algorithms: Threshold, Cooperative, and LA-DETECTS. The purpose of this mechanism is to improve the efficiency of detecting misbehavior. The proposed mechanism is based on the principle of consensus, whereby a vehicle is classified as malicious only if at least two algorithms confirm the presence of manipulation, thus reducing reliance on a single, potentially biased source. Simulations conducted using an integrated environment (OMNET++, Veins, and SUMO) demonstrate the efficiency of the developed mechanism in a complex urban scenario. It outperforms existing single-criterion algorithms by achieving an optimal balance between detection accuracy and false positive reduction, recording a peak F1-Score of 69.02 % and a Precision of 56.05%.