云服务器虚拟机通信串口数据安全监控仿真

Security monitoring and simulation of cloud server virtual machine communication serial port data

云服务器虚拟机通信串口数据数量巨大,包含的数据类型非常多,其中不乏风险数据。由于通信串口数据包含大量的冗余信息和噪声,有效提取与安全性直接相关的特征变得尤为困难,导致对其安全性监控精度降低。因此,提出基于遗传算法与径向基函数融合算法(Genetic Algorithm Radial Basis Function,GA-RBF)的云服务器虚拟机通信串口数据安全性监控方法。在对通信串口数据完成采集与预处理后,搭建自编码器,提取数据中包含的安全要素,利用灰色关联度分析法筛选出其中的安全性因子。引入GA-RBF算法,构建通信串口数据安全性监控训练模型,通过计算精度训练函数、复杂度函数和收敛速度函数后,得到适应度函数,选取最优适应度,完成对通信串口数据安全性的精准监控。实验结果表明,所提方法在通信串口数据安全性监控中表现出色,达到了最高监控精度,且结果与实际值相符,有效提升了监控效能。

Generally,the communication serial port data of cloud server virtual machines is huge,containing a wide range of data types,including risk data.Due to the presence of a large amount of redundant information and noise in communication serial port data,effectively extracting features directly related to security becomes particularly challenging.In this article,a method of monitoring communication serial port data security of the cloud server virtual machine based on the Genetic Algorithm Radial Basis Function(GA-RBF) algorithm was proposed.After collecting and preprocessing communication serial port data,we constructed an autoencoder and extracted the security elements contained in the data.Then,we used the grey relational analysis to screen out the security factors.Moreover,we introduced the GA-RBF algorithm to build a training model for communication serial port data security monitoring.After calculating the accuracy training function,complexity function,and convergence speed function,we got the fitness function.Finally,we selected the optimal fitness to achieve precise monitoring for the security of communication serial port data.Experimental results show that the proposed method performs excellently in monitoring communication serial port data security,achieving the highest monitoring accuracy.And the result is consistent with actual values.Therefore,the method effectively enhances the monitoring performance.