Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (12): 28-31.doi: 10.16180/j.cnki.issn1007-7820.2020.12.006

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Network Security Situation Assessment Based on Multimodal Feature Fusion

LI Kang   

  1. Information Security Evaluation Center,Yunnan Nantian Electronics Information Co., Ltd., Kunming 650000,China
  • Received:2019-08-31 Online:2020-12-15 Published:2020-12-22
  • Supported by:
    Key Technology Matching Project of Yunnan Province(2004GP05)


In view of the problems of single information source and low early warning quality of network security monitoring equipment, a network security situation assessment method integrating multiple data sources is proposed in this study. The NSSA framework of network security situation is constructed by introducing Endsley model and agent theory. By using the idea of radial basis function neural network, the fusion of multi-source heterogeneous data is realized through eliminating redundant noise and irrelevant signals. Therefore, a network security situation assessment method with multi-modal feature fusion is proposed. The simulation results of MATLAB software show that compared with the traditional BP and RBF neural networks, the proposed network security situation assessment method has better learning ability and generalization ability.

Key words: network security, security situation, Endsley model, Agent theory, radial basis function neural network, NSSA framework, multimodal features, data fusion

CLC Number: 

  • TP393