Data and knowledge-driven named entity recognition for cyber security

Cybersecurity

article

Gao, Chen¹  Zhang, Xuan¹  Liu, Hui¹ 

[1] School of Software, Yunnan University;Key Laboratory of Software Engineering of Yunnan Province;Engineering research center of cyberspace

关键词: Cyber security;    Named entity recognition;    Attention mechanism;    Dictionary;    Deep learning;   

DOI  :  10.1186/s42400-021-00072-y

学科分类：社会科学、人文和艺术（综合）

来源: Springer

【 摘 要 】

Named Entity Recognition (NER) for cyber security aims to identify and classify cyber security terms from a large number of heterogeneous multisource cyber security texts. In the field of machine learning, deep neural networks automatically learn text features from a large number of datasets, but this data-driven method usually lacks the ability to deal with rare entities. Gasmi et al. proposed a deep learning method for named entity recognition in the field of cyber security, and achieved good results, reaching an F1 value of 82.8%. But it is difficult to accurately identify rare entities and complex words in the text.To cope with this challenge, this paper proposes a new model that combines data-driven deep learning methods with knowledge-driven dictionary methods to build dictionary features to assist in rare entity recognition. In addition, based on the data-driven deep learning model, an attention mechanism is adopted to enrich the local features of the text, better models the context, and improves the recognition effect of complex entities. Experimental results show that our method is better than the baseline model. Our model is more effective in identifying cyber security entities. The Precision, Recall and F1 value reached 90.19%, 86.60% and 88.36% respectively.

【 授权许可】

CC BY