  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.
