Local perception and BSM based misbehavior detection in Intelligent Transportation System

An intelligent transportation system aims to provide various traffic safety and navigation services, and mainly relies on local perception and vehicular communication technologies. However, the vehicular communication technologies can be a target of wide range of attacks including position falsification, Sybil and denial-of-service (DoS) attacks which can lead to disastrous traffic accidents and jams. As a viable solution, misbehavior detection systems can be used in vehicular networks. Different from other works, in this paper, we propose a misbehavior detection system that utilizes both local perception and basic safety messages (BSM). Our work shows the methodology for generating realistic vehicular network data sets that include both local perception and BSM. In addition, we compare and show that the propose scheme is better compared to the previous scheme utilizing only beacon information for accurately identifying misbehavior in intelligent transportation system.