A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

PDF(785 KB)
PDF(785 KB)
Front. Inform. Technol. Electron. Eng ›› 2019, Vol. 20 ›› Issue (6) : 872-884. DOI: 10.1631/FITEE.1800520
Orginal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Author information +
History +

Abstract

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

Keywords

Network security entity / Security knowledge graph (SKG) / Entity recognition / Feature template / Neural network

Cite this article

Download citation ▾
Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN. A network security entity recognition method based on feature template and CNN-BiLSTM-CRF. Front. Inform. Technol. Electron. Eng, 2019, 20(6): 872‒884 https://doi.org/10.1631/FITEE.1800520

RIGHTS & PERMISSIONS

2019 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature
PDF(785 KB)

Accesses

Citations

Detail

Sections
Recommended

/