Design of Wireless Network Coverage Vulnerability Awareness Model under Virus Intrusion
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 as-sessment of coverage vulnerabilities was completed.Thirdly,D-S evidence theory was used to integrate with multi-source 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.
NETL
NSTL
万方数据
维普
