Reference : Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/53010
Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks
English
Boualouache, Abdelwahab mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Djaidja, Taki Eddine Toufik mailto []
Senouci, Sidi-Mohammed mailto []
Ghamri-Doudane, Yacine mailto []
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
25-Aug-2022
Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks
Yes
2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
19-22 June 2022
[en] 5G-V2X ; Network Slicing ; Security ; Deep Learning ; Intra-slice attack detection
[en] Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered
security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks.
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/53010
10.1109/VTC2022-Spring54318.2022.9860373
https://ieeexplore.ieee.org/document/9860373
FnR ; FNR14891397 > Thomas Engel > 5G-INSIGHT > Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks > 01/04/2021 > 31/03/2024 > 2020

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