Telecommunications Science ›› 2019, Vol. 35 ›› Issue (6): 60-69.doi: 10.11959/j.issn.1000-0801.2019147

• research and development • Previous Articles     Next Articles

WSN malware infection model based on cellular automaton and static Bayesian game

ZHANG Hong1,SHEN Shigen2,WU Xiaojun1,CAO Qiying1   

  1. 1 Donghua University,Shanghai 201620,China
    2 Shaoxing University,Shaoxing 312000,China
  • Revised:2019-05-12 Online:2019-06-20 Published:2019-06-20
  • Supported by:
    The National Natural Science Foundation of China(61772018)

Abstract:

The theoretical model for the malware infection in wireless sensor networks (WSN) based on cellular automaton and static Bayesian game was studied.Firstly,the malware infection model of WSN based on cellular automaton was built.Secondly,the malware infection dynamics in WSN was predicted based on the static Bayesian game,through which malware and WSN systems would determine their optimal actions by Bayesian Nash equilibrium (BEN).Then the BEN was applied to the malware infection model to study the spatiotemporal dynamics characteristics of malware infection.Research results show that the proposed model can effectively predict the infection dynamics propagation process of malware in WSN,and the evolution trend of sensor nodes in various states with time,which are of significance for people to formulate measures to reduce the propagation speed of malware.

Key words: WSN, malware infection, spatiotemporal dynamics, cellular automaton, static Bayesian game

CLC Number: 

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