Reference : Federated Learning-based Inter-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/10993/54376
Federated Learning-based Inter-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) >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Sep-2022
2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall)
Boualouache, Abdelwahab mailto
Engel, Thomas mailto
IEEE
Yes
International
2022 IEEE 96th Vehicular Technology Conference: (VTC2022-Fall)
26-29 September 2022
[en] 5G-V2X ; Network Slicing ; Security ; Network Slicing ; Machine learning ; Misbehaving Detection Systems
[en] As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X) technology, NS enables various isolated 5G-V2X networks with different requirements such as autonomous driving and platooning. This combination has generated new attack surfaces against Connected and Automated Vehicles (CAVs), leading them to road hazards and putting users' lives in danger. More specifically, such attacks can either intra-slice targeting the internal service within each V2X Network Slice (V2X-NS) or inter-slice targeting the cross V2X-NSs and breaking the isolation between them. However, detecting such attacks is challenging, especially inter-slice V2X attacks where security mechanisms should maintain privacy preservation and NS isolation. To this end, this paper addresses detecting inter-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and Deep learning (DL) together with Federated learning (FL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) over V2X-NSs. Our privacy preservation scheme is hierarchical and supports FL-based collaborative learning. It also integrates a game-theory-based mechanism to motivate FL clients (CAVs) to provide high-quality DL local models. We train, validate, and test our scheme using a publicly available dataset. The results show our scheme's accuracy and efficiency in detecting inter-slice V2X attacks.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/54376
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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