# NLP-based Cross-Layer 5G Vulnerabilities Detection via Fuzzing Generated Run-Time Profiling

arXiv: https://arxiv.org/abs/2305.08226 · HTML: https://mlsanity.com/paper/2305.08226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems](https://mlsanity.com/paper/2108.03514/) (2021)
- [Radar Altimeter Redesign for Multi-Stage Interference Risk Mitigation in 5G and Beyond](https://mlsanity.com/paper/2305.05579/) (2023)
- [Berserker: ASN.1-based Fuzzing of Radio Resource Control Protocol for 4G and 5G](https://mlsanity.com/paper/2107.01912/) (2021)

## Built on by (most cited first, 1 of 1)

- [From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis](https://mlsanity.com/paper/2308.03277/) (2023)

## Abstract (arXiv)

The effectiveness and efficiency of 5G software stack vulnerability and unintended behavior detection are essential for 5G assurance, especially for its applications in critical infrastructures. Scalability and automation are the main challenges in testing approaches and cybersecurity research. In this paper, we propose an innovative approach for automatically detecting vulnerabilities, unintended emergent behaviors, and performance degradation in 5G stacks via run-time profiling documents corresponding to fuzz testing in code repositories. Piloting on srsRAN, we map the run-time profiling via Logging Information (LogInfo) generated by fuzzing test to a high dimensional metric space first and then construct feature spaces based on their timestamp information. Lastly, we further leverage machine learning-based classification algorithms, including Logistic Regression, K-Nearest Neighbors, and Random Forest to categorize the impacts on performance and security attributes. The performance of the proposed approach has high accuracy, ranging from  93.4 %  to  95.9 % , in detecting the fuzzing impacts. In addition, the proof of concept could identify and prioritize real-time vulnerabilities on 5G infrastructures and critical applications in various verticals.