# Investigating cybersecurity incidents using large language models in latest-generation wireless networks

arXiv: https://arxiv.org/abs/2504.13196 · HTML: https://mlsanity.com/paper/2504.13196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection](https://mlsanity.com/paper/2405.11002/) (2024)
- [6G comprehensive intelligence: network operations and optimization based on Large Language Models](https://mlsanity.com/paper/2404.18373/) (2024)
- [System-level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN based Cellular Networks](https://mlsanity.com/paper/2402.06846/) (2024)

## Abstract (arXiv)

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems, synthesis of adversarial examples, execution of adversarial attacks on machine learning models, fine tuning of large language models for detecting adversarial attacks, explainability of decisions on detecting cybersecurity incidents based on the prompts technique. Scientific novelty: A binary classification of data poisoning attacks was performed using large language models, and the possibility of using large language models for investigating cybersecurity incidents in the latest generation wireless networks was investigated. The result of research: Fine-tuning of large language models was performed on the prepared data of the emulated wireless network segment. Six large language models were compared for detecting adversarial attacks, and the capabilities of explaining decisions made by a large language model were investigated. The Gemma-7b model showed the best results according to the metrics Precision = 0.89, Recall = 0.89 and F1-Score = 0.89. Based on various explainability prompts, the Gemma-7b model notes inconsistencies in the compromised data under study, performs feature importance analysis and provides various recommendations for mitigating the consequences of adversarial attacks. Large language models integrated with binary classifiers of network threats have significant potential for practical application in the field of cybersecurity incident investigation, decision support and assessing the effectiveness of measures to counter information security threats.