摘要
为了及时发现病毒入侵风险,保证无线网络安全,设计一种基于改进遗传神经网络的无线网络覆盖漏洞感知模型。考虑多径衰落效应,构建无线网络信号传播模型,计算信号传输路径损耗,采用地理信息系统获取可视化无线网络覆盖图;融合贝叶斯理论和属性攻击图,通过后验概率更新静态贝叶斯攻击图属性状态,实现覆盖漏洞动态风险评估;使用D-S证据理论融合多源数据,运用蚁群算法实施数据寻优,以过往与现阶段网络安全状态为基础,建立无线网络覆盖漏洞感知模型,凭借改进遗传神经网络优化模型感知准确度。仿真结果表明,所提方法在不同节点密度与感知半径下均具备良好的漏洞感知精度及效率,可广泛应用于现实场景。
In order to Identify the risk of virus intrusion in time and ensure the security of wireless networks,a model of sensing coverage vulnerability of wireless networks based on improved genetic neural networks was designed.Firstly,a propagation model of the wireless network signal was constructed by considering the multipath fading effect.Secondly,the loss of signal on the transmission path was calculated,and then the geographic information system was used to obtain the visual wireless network coverage.After integrating Bayesian theory with attribute attack graph,the attribute state of static Bayesian attack graph was updated through the posterior probability,so that dynamic risk assessment of coverage vulnerabilities was completed.Thirdly,D-S evidence theory was used to integrate with multisource data.Meanwhile,the ant colony algorithm was adopted to optimize data.Based on the past and current network security status,a model of sensing coverage vulnerability of wireless networks was built.Finally,the improved genetic neural network was used to optimize the model and sense the accuracy.Simulation results show that the proposed method has good sensing accuracy and efficiency under different node densities and sensing radii,so it can be widely used in real scenes.
作者
代琪怡
刘维
DAI Qi-yi;LIU Wei(Chengdu College,University of Electronic Science and Technology of China,Chengdu Sichuan 611731,China;School of Electrical and Electronic Engineering,Chongqing University of Science and Technology,Chongqing 400054,China)
出处
《计算机仿真》
2024年第1期433-437,共5页
Computer Simulation
基金
电子科技大学成都学院2021年国腾创投基金项目(GTJG-04)。
关键词
病毒入侵
无线网络
覆盖漏洞
感知模型
改进遗传神经网络
Virus invasion
Wireless network
Coverage vulnerabilities
Sensing model
Improving genetic neural network
