# Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment

arXiv: https://arxiv.org/abs/2507.17850 · HTML: https://mlsanity.com/paper/2507.17850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Key Focus Areas and Enabling Technologies for 6G](https://mlsanity.com/paper/2412.07029/) (2024)
- [Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration](https://mlsanity.com/paper/2307.09151/) (2023)
- [Tutorial on communication between access networks and the 5G core](https://mlsanity.com/paper/2112.04257/) (2021)

## Abstract (arXiv)

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.