LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature

Autor:	Würsch, Maxime, Kucharavy, Andrei, David, Dimitri Percia, Mermoud, Alain
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 24 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.07110 Zobrazit plný text záznamu View this record from Arxiv