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

张红; 沈士根; 吴小军; 曹奇英

doi:10.11959/j.issn.1000-0801.2019147

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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

研究与开发 | 更新时间：2024-06-05

- 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
- 电信科学 2019年35卷第6期页码：60-69
- 作者机构：
  
  1. 东华大学，上海 201620
  2. 绍兴文理学院，浙江绍兴 312000
- 作者简介：
  
  [ "张红（1984- ），女，东华大学计算机科学与技术学院博士生，主要研究方向为无线传感器网络、网络空间安全、博弈论。" ]
  [ "沈士根（1974- ），男，博士，绍兴文理学院计算机科学与工程系教授，主要研究方向为无线传感器网络、物联网、博弈论。" ]
  [ "吴小军（1978- ），男，东华大学信息科学与技术学院博士生，主要研究方向为无线传感器网络、智能系统。" ]
  [ "曹奇英（1960- ），男，博士，东华大学计算机科学与技术学院教授、博士生导师，主要研究方向为普适计算、智能信息处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61772018)
- DOI：10.11959/j.issn.1000-0801.2019147
  中图分类号： TP393
- 网络出版日期：2019-06，
  
  纸质出版日期：2019-06-20
- 稿件说明：
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张红, 沈士根, 吴小军, 等. 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[J]. 电信科学, 2019,35(6):60-69.

Hong ZHANG, Shigen SHEN, Xiaojun WU, et al. WSN malware infection model based on cellular automaton and static Bayesian game[J]. Telecommunications science, 2019, 35(6): 60-69.
张红, 沈士根, 吴小军, 等. 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[J]. 电信科学, 2019,35(6):60-69. DOI： 10.11959/j.issn.1000-0801.2019147.

Hong ZHANG, Shigen SHEN, Xiaojun WU, et al. WSN malware infection model based on cellular automaton and static Bayesian game[J]. Telecommunications science, 2019, 35(6): 60-69. DOI： 10.11959/j.issn.1000-0801.2019147.

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。

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.

关键词

Keywords

references

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