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

Boualouache, Abdelwahab; Djaidja, Taki Eddine Toufik; Senouci, Sidi‐Mohammed; Ghamri‐Doudane, Yacine; Brik, Bouziane; Engel, Thomas

doi:10.1109/vtc2022-spring54318.2022.9860373

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Specifically, intra-slice attacks in which the attacker(s) and the target(s) belong to the same V2X Network Slice (V2X-NS), while inter-slice attacks in which the attacker(s) and/or the target(s) belong to different V2X-NSs. The authors in [18] proposed a DL-based approach to detect V2X network slicing attacks, such as end-to-end from vehicles to the core network. However, the paper only focuses on intra-slice V2X attacks and does not consider inter-slice.…”

Section: Related Workmentioning

confidence: 99%

Multi-Process Federated Learning With Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Boualouache,

Jolfaei,

Engel

2024

IEEE Trans. Intell. Transport. Syst.

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Being part of the 6G ecosystem vision, Connected and Automated Vehicles (CAVs) will enjoy sophisticated tailored services offering road safety and entertainment for users. As one of the 6G cornerstones, Network Slicing (NS) allows the creation of various customized 6G-V2X (Vehicle-to-Everything) use cases on the same physical infrastructure. However, 6G-NS advances can open up breaches to cyber-attacks aiming to break 6G-V2X Network slices to inflict maximum damage on CAVs and their users. Crossing borders, where CAVs leave their V2X-NS (V2X Network Slice) in the Home Mobile Network Operator (H-MNO) toward a similar V2X-NS in the Visited MNO (V-MNO), is an attractive opportunity to exploit by attackers. Detecting and mitigating attacks, in this case, becomes a priority, confronted by NS requirements and MNOs not ready to share their private data. To this end, this paper proposes a 3GPP-compliant privacy preservation collaborative learning scheme for 6G-NS security, focusing on V2X-NS cross-border areas. Our scheme leverages multi-process Federated Learning (FL) architecture to build efficient V2X-NS security-related models while preserving 6G V2X-NS isolation. In addition, it uses differential privacy-enabled stacking to build up attack detection knowledge at the V2X-NSs and MNOs levels while ensuring privacy preservation. We conducted an experimental study on the 5G-NIDD dataset, which is one of the most realistic publicly available 5G datasets. Our results demonstrate that multi-process FL with stacking can deliver high accuracy while ensuring isolation between 6G-V2X-NSs and privacy preservation between H-MNO and V-MNO.

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Section: Related Workmentioning

confidence: 99%

Multi-Process Federated Learning With Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Boualouache,

Jolfaei,

Engel

2024

IEEE Trans. Intell. Transport. Syst.

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“…For example, one virtual network could be optimized for high-bandwidth video streaming, while another could be designed to prioritize low-latency communications for safety-critical applications. The connectivity needs are ever-increasing, so it is demanding rapid action towards consideration of the network capacity in the existing infrastructure, especially with the advent of the IoT [1]. Every few years, new technology emerges and adds traffic to the network.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Network Slicing in V2X Networks – A Comprehensive Review

Abood

Wang

et al. 2023

JAIT

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ABSTRACT- The rise of the internet of things (IoT) and autonomous systems has made connecting vehicles more critical. Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications. With the advent of 5G networks, vehicle-to-everything (V2X) networks are expected to be highly intelligent, reside on superfast, reliable, and low-latency connections. Network slicing, Machine Learning (ML), and Deep Learning (DL) are related to network automation and optimization in V2X communication. Machine Learning and Deep Learning (ML/DL) with network slicing aims to optimize the performance, reliability of the V2X network, personalized services, reduced costs, and scalability and enhance the overall driving experience. These advantages can ultimately lead to a safer and more efficient transportation system. However, existing Long-Term Evolution (LTE) systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels. Machine learning algorithms mitigate complexity levels, which can be highly instrumental in such vehicular communication systems. This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing, a different configuration of slicing, the requirements of slicing, and the ML algorithm used to control and manage to slice. This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases, focusing on V2X network slicing and considering efficient control and management. The enabler technologies are considered in light of the network requirements, particular configurations, and the underlying methods and algorithms, with a review of some critical challenges and possible solutions available. The paper concludes with a future roadmap by discussing some open research issues and future directions.

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A literature review on V2X communications security: Foundation, solutions, status, and future

Ying,

Wang,

Xiong

et al. 2024

IET Communications

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With the refinement and development of the Vehicle to Everything (V2X) concept, its security issues have gradually come to the fore, revealing many security risks and increasing security requirements for V2X, and many protective measures have likewise emerged. The article first introduces the development history of the past Internet of Vehicles(IoV), summarizes some common V2X security threats, surveys the security technologies used for V2X communication, and outlines the development of each technology and the proposed security protocols in the last 3 years. Due to the different advantages and disadvantages of previous protection schemes, the idea of using National Cryptography to supplement the security scheme or designing a new security scheme article based on the National Cryptography Algorithms((AKA SM algorithms) is proposed. The survey then introduces the SM2, SM3, SM9, and ZUC algorithms, describes the development and application of the SM commercial algorithm in recent years, and finally, statistics and introduces the part of the development process of the security protocols currently used in IoV regarding the SM algorithms and gives some application prospects.

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

Cited by 3 publications

References 13 publications

Multi-Process Federated Learning With Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Multi-Process Federated Learning With Stacking for Securing 6G-V2X Network Slicing at Cross-Borders

Intelligent Network Slicing in V2X Networks – A Comprehensive Review

A literature review on V2X communications security: Foundation, solutions, status, and future

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