2-Step Prediction for Detecting Attacker in Vehicle to Vehicle Communication

Archive ouverte : Communication dans un congrès

Kushardianto, Nur Cahyono | El Hillali, Yassin | Tatkeu, Charles

Edité par HAL CCSD ; IEEE

International audience. Smart vehicles can be more adaptive to the road condition by exchange information between the vehicle. They can avoid traffic congestion, dangerous obstacles, even traffic accident earlier. This technology is closely related to the safety of the driver, therefore it must receive special attention. V2V communication has the potential to threaten interference and even attacks. There have been many studies that have focused on finding solutions to deal with these disorders. The first step is to strengthen the system's ability to detect attacks on V2V. On the other hand, the development of Machine Learning (ML) looks very promising to support this goal. In the proposed scheme, a 2-Step Prediction for detecting attackers is used. This system is using two classifiers ML from two modified training datasets. We show that the proposed scheme can improve the attack detection performance compared to one detection step.

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