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A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation
Hossain, Shajjad; Boualouache, Abdelwahab; Brik, Bouziane et al.
2023In A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation
Peer reviewed
 

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Keywords :
5G-V2X; Security; Deep learning; IDS; Knowledge Distillation; Network Slicing
Abstract :
[en] As the automotive industry grows, modern vehicles will be connected to 5G networks, creating a new Vehicular-to-Everything (V2X) ecosystem. Network Slicing (NS) supports this 5G-V2X ecosystem by enabling network operators to flexibly provide dedicated logical networks addressing use case specific-requirements on top of a shared physical infrastructure. Despite its benefits, NS is highly vulnerable to privacy and security threats, which can put Connected and Automated Vehicles (CAVs) in dangerous situations. Deep Learning-based Intrusion Detection Systems (DL-based IDSs) have been proposed as the first defense line to detect and report these attacks. However, current DL-based IDSs are processing and memory-consuming, increasing security costs and jeopardizing 5G-V2X acceptance. To this end, this paper proposes a lightweight intrusion detection scheme for 5G-V2X sliced networks. Our scheme leverages DL and Knowledge Distillation (KD) for training in the cloud and offloading knowledge to slice-tailored lightweight DL models running on CAVs. Our results show that our scheme provides an optimal trade-off between detection accuracy and security overhead. Specifically, it can reduce security overhead in computation and memory complexity to more than 50% while keeping almost the same performance as heavy DL-based IDSs.
Disciplines :
Computer science
Author, co-author :
Hossain, Shajjad;  University of Burgundy > Drive Lab
Boualouache, Abdelwahab ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Brik, Bouziane;  University of Burgundy > Drive Lab
Senouci, Sidi-Mohammed;  University of Burgundy > Drive Lab
External co-authors :
yes
Language :
English
Title :
A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation
Publication date :
May 2023
Event name :
IEEE ICC 2023 - IEEE International Conference on Communications
Event date :
from 28-05-2023 to 01-06-2023
Audience :
International
Main work title :
A Lightweight 5G-V2X Intra-slice Intrusion Detection System Using Knowledge Distillation
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR14891397 - Intelligent Orchestrated Security And Privacy-aware Slicing For 5g And Beyond Vehicular Networks, 2020 (01/04/2021-31/03/2024) - Thomas Engel
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since 25 May 2023

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