Computer Science ›› 2020, Vol. 47 ›› Issue (7): 292-298.doi: 10.11896/jsjkx.190600156

• Information Security • Previous Articles     Next Articles

Network Representation Learning Algorithm Based on Vulnerability Threat Schema

HUANG Yi1,2, SHEN Guo-wei1,2, ZHAO Wen-bo1, GUO Chun1,2   

  1. 1 Department of Computer Science and Technology,Guizhou University,Guiyang 550025,China
    2 Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China
  • Received:2019-06-19 Online:2020-07-15 Published:2020-07-16
  • About author:HUANG Yi,born in 1997,postgraduate,is a member of China Computer Federation.Her main research interests include representation learning and network security.
    SHEN Guo-wei,born in 1986,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include cyberspace security and big data.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61802081),National Science and Technology Major Project of the Ministry of Science and Technology of Guizhou Province,China(20183001) and Guizhou Provincial Science and Technology Plan (20161052,20171051)

Abstract: Threat intelligence analysis can provide effective attack and defense information for network attack and defense,and fine-grained mining,that is,the relationship between security entities and entities in network threat intelligence data,is a hotspot of network threat intelligence analysis research.Traditional machine learning algorithms,when applied to large-scale network threat intelligence data analysis,face sparse,high-dimensional and other issues,and thus it is difficult to effectively capture network information.To this end,a network representation learning algorithm based on vulnerability threat schema——HSEN2vec for the classification of network security vulnerabilities is proposed.The algorithm aims to capture the structure and semantic information of the heterogeneous security entity network to the maximum extent,and obtains the low-dimensional vector representation of the security entity.In the algorithm,the structural information of the heterogeneous security entity network is obtained based on the vulnerability threat schema,and then modeled by the Skip-gram model,and the effective prediction is performed by the negative sampling technique to obtain the final vector representation.The experimental results show that in the national security vulnerability data,compared with other methods,the learning algorithm proposed in this paper improves the accuracy of vulnerability classification and other evaluation indicators.

Key words: Network representation learning, Heterogeneous security entity network, Threat schema, Vulnerability

CLC Number: 

  • TP393.0
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