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Communication Dans Un Congrès Année : 2022

Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

Résumé

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.

Dates et versions

hal-03977619 , version 1 (07-02-2023)

Identifiants

Citer

Abdelwahab Boualouache, Taki Eddine Toufik Djaidja, Sidi-Mohammed Senouci, Yacine Ghamri-Doudane, Bouziane Brik, et al.. Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks. 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), Jun 2022, Helsinki, Finland. pp.1-5, ⟨10.1109/VTC2022-Spring54318.2022.9860373⟩. ⟨hal-03977619⟩
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