Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908463716630528 |
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| author | Moreira, Rodrigo Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira |
| author_facet | Moreira, Rodrigo Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira |
| contents | The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functions (NFs) on User Equipment registration performance. Our findings highlight the necessity of diverse resource profiles to ensure Service-Level Agreement (SLA) compliance in large-scale 5G core deployments. Additionally, our analysis of packet capture approaches demonstrates the potential of kernel-based monitoring for scalable security threat defense. Finally, our empirical evaluation provides insights into the effective deployment of 5G NFs in complex scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment Moreira, Rodrigo Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira Cryptography and Security Artificial Intelligence The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functions (NFs) on User Equipment registration performance. Our findings highlight the necessity of diverse resource profiles to ensure Service-Level Agreement (SLA) compliance in large-scale 5G core deployments. Additionally, our analysis of packet capture approaches demonstrates the potential of kernel-based monitoring for scalable security threat defense. Finally, our empirical evaluation provides insights into the effective deployment of 5G NFs in complex scenarios. |
| title | Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2507.17850 |