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Frontiers of Information Technology & Electronic Engineering

2019, Volume 20,  Issue 6, Pages 872-884
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    A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

    1. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
    2. Guizhou Provincial Key Laboratory of Public Big Data, Guiyang 550025, China
    3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
    4. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China

    Available online:2019-08-01
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    10.1631/FITEE.1800520
    Cite this article
    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.

    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
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