A Multi-objective Model for Misbehavior Detection in IoV

As the key element of the Intelligent Transportation System, Internet of Vehicles (IoV) is expected to reduce the traffic congestion and improve the road safety. For road safety, connected vehicles need to broadcast basic safety messages frequently, which contain location, speed, acceleration, steer...

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Bibliographic Details
Published inGLOBECOM 2022 - 2022 IEEE Global Communications Conference pp. 4395 - 4400
Main Authors Huang, Jiaqi, Lv, Jiahuan, Zhou, Zhiguo, Gyawali, Sohan, Qian, Yi
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.12.2022
Subjects
Ad hoc networks
data-centric
Global communication
Internet
Internet of Vehicles
Machine learning
misbehavior detection
Road safety
Safety
security
Wireless communication
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Summary:As the key element of the Intelligent Transportation System, Internet of Vehicles (IoV) is expected to reduce the traffic congestion and improve the road safety. For road safety, connected vehicles need to broadcast basic safety messages frequently, which contain location, speed, acceleration, steering information, etc. Due to the wireless environment and ad-hoc nature, the vehicular communications are vulnerable to various attacks. It is vital to protect the correctness of the exchanged messages, in which the misbehavior detection mechanisms play an important role. In this paper, we propose a data-centric misbehavior detection method based on a multi-objective learning model. Different from current machine learning based misbehavior detection system, our work exploits a new direction to improve the detection performance by maximizing the recall and specificity. To show the effectiveness of the proposed model, we conduct experiments on the VeReMi Dataset and compare the detection results with existing approaches.
DOI:10.1109/GLOBECOM48099.2022.10000959