Security monitoring and simulation of cloud server virtual machine communication serial port data
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 intro-duced 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.There-fore,the method effectively enhances the monitoring performance.
Communication serial port dataSafety factorsConvergence rate functionOptimal fitnessData matrix
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