Virtualized Security Function Placement for Security Service Chaining in Cloud

Wu, Hongjing; Zhang, Yan; Yang, Huiran; Yu, Guangxi; Cao, Jiuyue

doi:10.1109/padsw.2018.8644566

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…The authors of [10] proposed a placement scheme of virtual security network functions considering both performance optimization and security aspects. The authors of [11] introduced a potential resolution to the issue of placing virtualized security functions in security service chaining within cloud environments. The authors of [12] proposed a mathematical model for optimally placing virtualized network functions at the edge, considering network security and operator's best practices.…”

Section: Related Workmentioning

confidence: 99%

“…Building upon their previous work, the authors [18] proposed an RL-based detection model that processes V2X data broadcast by vehicles as time series at the roadside units. However, the studies in [10][11][12][13] are based on traditional optimization techniques, which have difficulty scaling for NP-Hard problems since their calculation time grows exponentially, rendering them unsuitable for real-time use. In addition, the works in [14][15][16] did not consider security service orchestration in the context of 5G-V2X and network slicing.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning-Based Security Orchestration for 5G-V2X Network Slicing at Cross-Borders

Boualouache,

Korba,

Senouci

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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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%.

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Section: Related Workmentioning

confidence: 99%