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

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

Type de document :

Communication dans un congrès avec actes

DOI :

10.1109/VTC2021-Fall52928.2021.9625513

Titre :

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

Auteur(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 ]

Titre de la manifestation scientifique :

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

Ville :

Norman, virtual

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2021-09-27

Éditeur :

IEEE

Mot(s)-clé(s) en anglais :

V2V
attack
detection
2-Step

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL

2-Step Prediction for Detecting Attacker ... BibTeX CSV Excel RIS

2-Step Prediction for Detecting Attacker ...

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