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

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.

Keywords

• WSN;malware infection;spatiotemporal dynamics;cellular automaton;static Bayesian game