TRIMO: An Efficient Multimodal Misbehavior Detection Model in Vehicular Networks
Van-Linh Nguyen, Lan-Huong Nguyen, Wen-Pin Liu, Hao-En Ting, Xuan-Zhang Hu · 2024
Vehicular networks are expected to be the key technologies in the age of intelligent transportation and connected intelligence. However, by broadcasting false maneuver information in vehicular networks (emergency brake, merging/changing lane), an attacker can cause many vehicles to be disoriented or even crash in severe accidents. This work introduces a robust misbehavior detection scheme, namely TRIMO, by exploiting multimodal learning from various independent data sources (e.g., camera, joint radar and communications in the sixth-generation (6G) mobile networks). TRIMO can determine whether a car is lying about its sharing data with up to 92.7 percent accuracy by examining the consistency of data from numerous sources.