基于元胞自动机和静态贝叶斯博弈的WSN恶意程序传染模型  

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

作　　者：张红[1] 沈士根[2] 吴小军[1] 曹奇英[1] ZHANG Hong;SHEN Shigen;WU Xiaojun;CAO Qiying(Donghua University,Shanghai 201620,China;Shaoxing University,Shaoxing 312000,China)

机构地区：[1]东华大学,上海201620 [2]绍兴文理学院,浙江绍兴312000

出　　处：《电信科学》2019年第6期60-69,共10页Telecommunications Science

基　　金：国家自然科学基金资助项目(No.61772018)~~

摘　　要：基于元胞自动机理论和静态贝叶斯博弈,研究无线传感器网络(WSN)中恶意程序的传染模型。首先,依据元胞自动机理论,建立了WSN中恶意程序的传染模型。然后,基于静态贝叶斯博弈预测恶意程序的传染行为,得到博弈双方基于贝叶斯纳什均衡(Bayesian Nash equilibrium,BNE)确定的最优行动,并将其应用到上述传染模型,从而揭示恶意程序在WSN中的传染动力学特征。研究结果表明,该模型能揭示恶意程序在WSN中的传染行为,得到各种状态传感节点的数量随时间变化的动态演化趋势,对有效抑制WSN恶意程序传染有理论指导意义。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.

关 键 词：无线传感器网络 恶意程序传染 时空动力学 元胞自动机 静态贝叶斯博弈 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]