Research on Trusted Startup of Virtual Machine Based on Non-interference Theory
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    Abstract:

    As a new type of high-value computing system, cloud computing has been widely used in various industries fields. Classified protection 2.0 also puts forward the requirement of dynamic trust verification for its application of active immune trusted computing technology. In the cloud computing mode, the virtual machine is the direct carrier for users to use cloud services, and its trusted startup is the basis for the trustworthiness of the virtual machine operating environment. However, since the virtual machine runs on the physical node in the form of process, its characteristics of startup process are high dynamic and unexpected interference between multiple virtual machine domains. But the existing trusted startup schemes of virtual machine have problems such as insufficient dynamic protection during virtual machine startup process and lack of elimination of unexpected interference between multiple virtual domains. To solve the above problems, this study proposes a scheme that research on trusted startup of virtual machine based on non-interference theory. Firstly, based on the non-interference theory, the run-time trusted theorem of virtual machine process is proposed. In addition, the definition of trusted launch of virtual machine is given and the judgement theorem of trusted boot of virtual machine is well proved. Then, according to the trusted startup theorem of virtual machine, the monitoring and control logic is designed based on system call, and the virtual machine startup process is actively measured and controlled. Finally, the experimental evaluation shows that the proposed scheme can effectively eliminate the unexpected interference between multiple virtual machines in complex cloud environment, ensure the dynamic credibility of virtual machine startup process, and greatly reduce the performance overhead.

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黄浩翔,张建标,袁艺林,王晓.基于无干扰理论的虚拟机可信启动研究.软件学报,2023,34(6):2959-2978

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History
  • Received:March 18,2021
  • Revised:June 07,2021
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  • Online: November 30,2022
  • Published: June 06,2023
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