Research And Implementation Of Virtual Machine Security Key Technology On Cloud Environment

As a new computing model,cloud computing has been widely studied and applied with its convenient,fast,low cost and other characteristics.At the same time,cloud computing is also faced with a wide range of challenges,and security issues are particularly important.As the core technology of cloud computing,virtualization technology supports the entire cloud computing virtualization services,its security is essential to cloud computing.Virtual machine as the main manifestation of virtualization and the basic unit of cloud computing,in the entire cloud platform plays an important role.And the virtual machine carrying a large number of services,as a very attractive target,by more and more attackers attack.As a result,virtual machine security becomes an important aspect of virtualization security.In order to improve the security of virtual machine and ensure the security of cloud platform,this thesis mainly has done the following research:(1)A dynamic monitoring model based on cloud environment security status is proposed.In view of the problem that the fixed monitoring mode in the traditional cloud platform can not make timely changes with the security status of the system,it is difficult to capture the security events of the system in time.This thesis according to the changing trend of the security state of the cloud environment,the monitoring frequency is dynamically adjusted to improve the monitoring efficiency and enhance system security.The safety assessment of cloud environment is evaluated by BP neural network,and the appropriate evaluation index is selected to establish the evaluation model.According to the evaluation results of the evaluation model,the safety state changing curve is constructed,and the monitoring frequency is adjusted according to the curve changing trend.The experimental results show that compared with the traditional fixed monitoring frequency,the algorithm can adjust the monitoring frequency according to the system security changed result,and the frequency has some correlation with the system security state.(2)An anomaly detection model of virtual machine based on process call sequence is proposed.In view of the low detection accuracy of the traditional detection model and the high time complexity of the existing improved model,this thesis improves the HMM model by BP neural network and balance the contradiction between the two.The service in the virtual machine exists in the form of a process.The sequence of the process reflects the activity information of the process in the system.Through the detection of the process sequence,the security state of the system can be effectively determined.The BP neural network is used to preprocess the process sequence to solve the required probabilities of the model and reduce the parameter adjustment process of the model training.The experimental results show that compared with the traditional model,the improved model improves the accuracy of the test results,and reduces the complexity of the model compared with the existing improved model.(3)Finally,based on the Openstack design of the cloud environment virtual machine security system cloud platform,in the platform to add a dynamic monitoring module and security protection module to verify the proposed algorithm model,and the system operation was demonstrated.