Reference : Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehi...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/46911
Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks
English
Hawlader, Faisal mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
Boualouache, Abdelwahab mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
Faye, Sébastien mailto [Luxembourg Institute of Science and Technology, Luxembourg > IT for Innovative services]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Jun-2021
The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security)
Hawlader, Faisal mailto
Boualouache, Abdelwahab mailto
Faye, Sébastien mailto
Engel, Thomas mailto
Yes
No
International
The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security)
4-23 June 2021
[en] Vehicular Networks ; Security ; Ma- chine Learning-based Misbehavior Detection Systems
[en] Position falsification attacks are one of the most dangerous internal attacks in vehicular networks.
Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been
proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy.
http://hdl.handle.net/10993/46911
H2020 ; 825496 - 5G-MOBIX - 5G for cooperative & connected automated MOBIility on X-border corridors

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