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

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

Document type :

Communication dans un congrès avec actes

DOI :

10.1109/VTC2021-Fall52928.2021.9625513

Title :

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

Author(s) :

Kushardianto, Nur Cahyono [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
El Hillali, Yassin [Auteur]

COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Tatkeu, Charles [Auteur]
Laboratoire Électronique Ondes et Signaux pour les Transports [COSYS-LEOST ]

Conference title :

2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

City :

Norman, virtual

Country :

Etats-Unis d'Amérique

Start date of the conference :

2021-09-27

Publisher :

IEEE

English keyword(s) :

V2V
attack
detection
2-Step

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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