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Reinforcement Learning-based Security Orchestration for 5G-V2X Network Slicing at Cross-borders
BOUALOUACHE, Abdelwahab; Amara Korba, Abdelaziz; Senouci, Sidi-Mohammed et al.
2023In Reinforcement Learning-based Security Orchestration for 5G-V2X Network Slicing at Cross-borders
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Keywords :
5G-V2X; Network Slicing; Security Orchestration; Machine learning; Reinforcement Learninng
Abstract :
[en] As part of the 5G, Connected and Automated Vehicles (CAVs) will benefit from Network Slicing (NS) in several tailored 5G-Vehicle-to-Everything (V2X) services running on the same physical infrastructure. However, the use of 5G-NS may also increase the risk of cyber-attacks that could compromise 5G-V2X network slices (5G-V2X-NSs) and cause significant harm to CAV's passengers. This risk is particularly high at cross-borders, where CAVs move from their Home Mobile Network Operator (H-MNO) to a Visited MNO (V-MNO), with similar 5G-V2X-NSs in place. Therefore, deploying security services to neutralize 5G-V2X NS threats in this scenario is mandatory. However, if H-MNO and V-MNO act independently, deploying these security services could be inefficient and may result in increased memory, processing, and network resource consumption. Thus, MNOs should collaborate to orchestrate their security services to neutralize 5G-V2X NS attacks and optimize their costs efficiently. In this context, this paper proposes a novel approach to enhance the security of 5G-V2X NS at cross-borders using Reinforcement Learning (RL) based security orchestration. Specifically, we trained and deployed an RL agent interacting with both H-MNO and V-MNO. The RL agent efficiently deploys security services to effectively remove threats, optimize resource utilization, and minimize the impact on 5G-V2X-NSs. The performance results show that the RL-based security orchestration neutralizes threats with an average success rate of almost 100%. Additionally, resource consumption is minimal at less than 8%, and the acceptable impact on 5G-V2X-NSs is negligible, averaging less than 12%.
Disciplines :
Computer science
Author, co-author :
BOUALOUACHE, Abdelwahab ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Amara Korba, Abdelaziz;  University of La Rochelle, France
Senouci, Sidi-Mohammed;  University of Borgogne, France
Ghamri-Doudane, Yacine;  University of La Rochelle, France
ENGEL, Thomas ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
yes
Language :
English
Title :
Reinforcement Learning-based Security Orchestration for 5G-V2X Network Slicing at Cross-borders
Publication date :
December 2023
Event name :
IEEE Global Communications Conference
Event organizer :
IEEE
Event place :
Kuala Lumpur, Malaysia
Event date :
4–8 December 2023
Audience :
International
Main work title :
Reinforcement Learning-based Security Orchestration for 5G-V2X Network Slicing at Cross-borders
Publisher :
IEEE
Peer reviewed :
Peer reviewed
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 11 December 2023

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