Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (7): 357-362.doi: 10.11896/jsjkx.210900103

• Information Security • Previous Articles    

Network Security Situation Prediction Based on IPSO-BiLSTM

ZHAO Dong-mei1,2, WU Ya-xing1, ZHANG Hong-bin3   

  1. 1 College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China
    2 Hebei Key Laboratory of Network and Information Security,Hebei Normal University,Shijiazhuang 050024,China
    3 School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China
  • Received:2021-09-13 Revised:2021-12-09 Online:2022-07-15 Published:2022-07-12
  • About author:ZHAO Dong-mei,born in 1966,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer federation.Her main research interests include network information security and computer application.
    WU Ya-xing,born in 1997,postgra-duate.His main research interests include network and information security technology.
  • Supported by:
    National Natural Science Foundation of China(61672206),Central Guide Local Science and Technology Development Fund Project(216Z0701G),Key Research and Development Program of Hebei(20310701D) and Natural Science Foundation of Hebei Province(F2019205163).

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Abstract: Aiming at the complex network security situation prediction problem,a network security situation prediction model based on improved particle swarm optimization bidirectional long-short term memory(IPSO-BILSTM) network is proposed to improve the convergence speed and prediction accuracy.Firstly,in view of the lack of real situation value in the data set,a situation value calculation method based on attack influence is adopted for situation prediction.Secondly,to address the problems that particle swarm optimization(PSO) algorithm is prone to fall into local optima and unbalanced search capability,the inertia weights and acceleration factors are improved,and the improved particle swarm optimization(IPSO) algorithm has balanced global and local search capability and faster convergence speed.Finally,IPSO is used to optimize the parameters of bidirectional long short term memory(BiLSTM) network,so as to improve the prediction ability.Experimental results show that the fitting degree of IPSO-BiLSTM can reach 0.994 6,and the fitting effect and convergence speed are better than other models.

Key words: Bidirectional long-short term memory, Improved particle swarm optimization, Network security, Neural network, Situation prediction

CLC Number: 

  • TP393
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