  The cybersecurity landscape evolves rapidly and poses threats to
organizations. To enhance resilience, one needs to track the latest
developments and trends in the domain. It has been demonstrated that standard
bibliometrics approaches show their limits in such a fast-evolving domain. For
this purpose, we use large language models (LLMs) to extract relevant knowledge
entities from cybersecurity-related texts. We use a subset of arXiv preprints
on cybersecurity as our data and compare different LLMs in terms of entity
recognition (ER) and relevance. The results suggest that LLMs do not produce
good knowledge entities that reflect the cybersecurity context, but our results
show some potential for noun extractors. For this reason, we developed a noun
extractor boosted with some statistical analysis to extract specific and
relevant compound nouns from the domain. Later, we tested our model to identify
trends in the LLM domain. We observe some limitations, but it offers promising
results to monitor the evolution of emergent trends.
