Reference : Federated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicula...
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/48006
Federated Learning-based Scheme for Detecting Passive Mobile Attackers in 5G Vehicular Edge Computing
English
Boualouache, Abdelwahab mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Engel >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Jul-2021
Annals of Telecommunications
Springer
Yes (verified by ORBilu)
International
1958-9395
1958-9395
[en] 5G Vehicular Edge Computing ; Machine Learning ; Federated learning ; Security ; Privacy ; Passive Attacker Detection
[en] Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95\% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations.
http://hdl.handle.net/10993/48006
H2020 ; 825496 - 5G-MOBIX - 5G for cooperative & connected automated MOBIility on X-border corridors
FnR ; FNR14891397 > Thomas Engel > 5G-INSIGHT > Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks > 01/02/2021 > > 2020

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
FL_for_Detecting_Passive_Mobile__Attackers_in_Vehicular_Edge_Computing.pdfAuthor postprint7.4 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.