A Novel Lexicon Enhanced Named Entity Recognition Model for Network Security Domain

Named Entity Recognition (NER) technology serves as the foundation for solving many Natural Language Processing(NLP) problems, particularly in the domain of building knowledge graphs, where an effective NER model is crucial for creating efficient knowledge representations. However, when constructing knowledge graphs in the Industrial Internet Security(IIS) domain, security-related incidents, as significant entities, pose challenges for existing NER models. In this paper, we propose Security Lexicon Enhanced Bidirectional Encoder Representations from Transformers(SEBERT). SEBERT enhances semantic processing by integrating security lexicon information into one transformer layer of BERT, assigning vocabulary to each character in input sentences. Then we use a Residual-Knowledge-Arnold Network(RKAN) to optimize the semantic features learned from SEBERT, utilizing the properties of residual networks to learn identity mapping features without significantly increasing computational complexity. Finally, the Conditional Random Field (CRF) is used to learn the sequence information and dependencies of adjacent labels. Experimental results demonstrate that SEBERT-RKAN-CRF model achieves superior performance in recognizing entities in the IIS domain, with improvements of 1.99%, 3.34%, and 2.66% in accuracy rate, recall rate, and F1-score compared to the baseline model.