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CLC number: TP393.08

On-line Access: 2019-07-08

Received: 2018-08-31

Revision Accepted: 2019-03-11

Crosschecked: 2019-06-11

Cited: 0

Clicked: 6500

Citations:  Bibtex RefMan EndNote GB/T7714


Ya Qin


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.6 P.872-884


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

Author(s):  Ya Qin, Guo-wei Shen, Wen-bo Zhao, Yan-ping Chen, Miao Yu, Xin Jin

Affiliation(s):  College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; more

Corresponding email(s):   qyamail@163.com, gwshen@gzu.edu.cn

Key Words:  Network security entity, Security knowledge graph (SKG), Entity recognition, Feature template, Neural network

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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[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 872-884.

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T1 - A network security entity recognition method based on feature template and CNN-BiLSTM-CRF
A1 - Ya Qin
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A1 - Miao Yu
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J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1800520

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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