AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910023758643200 |
|---|---|
| author | Natanzi, Seyed Bagher Hashemi Mohammadi, Hossein Tang, Bo Marojevic, Vuk |
| author_facet | Natanzi, Seyed Bagher Hashemi Mohammadi, Hossein Tang, Bo Marojevic, Vuk |
| contents | Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.2% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms Natanzi, Seyed Bagher Hashemi Mohammadi, Hossein Tang, Bo Marojevic, Vuk Signal Processing Networking and Internet Architecture Systems and Control Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.2% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks. |
| title | AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms |
| topic | Signal Processing Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2601.18690 |