FMAD: Fusion-based Multimodal Abnormal Detection Scheme for Vehicular Communications

Connected and automated vehicles already are the main force in realizing the vision of intelligent transportation in smart cities. However, enabling broadband connectivity for vehicles brings up new threats of spreading fake information. By broadcasting false sharing data, an attack vehicle has the ability to cause nearby vehicles to get confused or possibly collide in catastrophic accidents. The present study presents a resilient fusion-based multimodal abnormal detection technique, referred to as FMAD. FMAD facilitates a fusion model based on Dempster- Shafer's theory to strengthen confidence in the final detection assessment of detection results from multiple vehicles. FMAD can determine whether a vehicle is spreading false maneuver information with up to 96.18 percent accuracy of confidence. Meanwhile, our method outperforms all existing approaches in terms of the reliability of the detection decision.