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Frontiers of Information Technology & Electronic Engineering >> 2019, Volume 20, Issue 6 doi: 10.1631/FITEE.1800520

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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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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