# Automated Identification of Security-Relevant Configuration Settings Using NLP

arXiv: https://arxiv.org/abs/2209.08853 · HTML: https://mlsanity.com/paper/2209.08853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

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

- [Large Language Models Based Automatic Synthesis of Software Specifications](https://mlsanity.com/paper/2304.09181/) (2023)

## Abstract (arXiv)

To secure computer infrastructure, we need to configure all security-relevant settings. We need security experts to identify security-relevant settings, but this process is time-consuming and expensive. Our proposed solution uses state-of-the-art natural language processing to classify settings as security-relevant based on their description. Our evaluation shows that our trained classifiers do not perform well enough to replace the human security experts but can help them classify the settings. By publishing our labeled data sets and the code of our trained model, we want to help security experts analyze configuration settings and enable further research in this area.