CLC number: TP393.08
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-06-11
Cited: 0
Clicked: 7352
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.
@article{title="A network security entity recognition method based on feature template and CNN-BiLSTM-CRF",
author="Ya Qin, Guo-wei Shen, Wen-bo Zhao, Yan-ping Chen, Miao Yu, Xin Jin",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="6",
pages="872-884",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800520"
}
%0 Journal Article
%T A network security entity recognition method based on feature template and CNN-BiLSTM-CRF
%A Ya Qin
%A Guo-wei Shen
%A Wen-bo Zhao
%A Yan-ping Chen
%A Miao Yu
%A Xin Jin
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 6
%P 872-884
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800520
TY - JOUR
T1 - A network security entity recognition method based on feature template and CNN-BiLSTM-CRF
A1 - Ya Qin
A1 - Guo-wei Shen
A1 - Wen-bo Zhao
A1 - Yan-ping Chen
A1 - Miao Yu
A1 - Xin Jin
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 6
SP - 872
EP - 884
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800520
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.
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