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

• 作 者: Ya QIN Guo-wei SHEN Wen-bo ZHAO Yan-ping CHEN Miao YU Xin JIN
• 机 构: College of Computer Science and Technology, Guizhou University, Guiyang 550025, China“,”Guizhou Prov
• 出 处: 信息与电子工程前沿（英文版）
• 发表日期: 2019年6期
• 关键词: Network security entity Security knowledge graph (SKG) Entity recognition Featur

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat in-telligence 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 com-bined 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.