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.

利用大语言模型（LLMs）从网络安全相关文本中提取相关实体，以识别发展趋势和监测新兴趋势，研究结果表明 LLMs 的知识实体并不完全反映网络安全的上下文，但名词提取器在此领域具有潜力。通过统计分析，开发了一种名词提取器，可从领域中提取特定且相关的复合名词。通过测试模型在 LLM 领域中识别趋势，观察到一些限制，但它能够提供有关新兴趋势演变的有希望的结果。