Secure framework for virtualized systems with data confidentiality protection

Benefits have been claimed by adopting virtualization techniques in many fields. It could significantly reduce the cost of managing systems, including critical systems used in cyber power grid. However, in such environments, multiple virtual instances run on the same physical machine concurrently, and reliance on logical isolation makes a system vulnerable to attacks. Virtual Machine Introspection techniques show effectiveness in building a more secure virtualized environment, since they simplify the process to acquire evidence for further analysis in this complex system. However, the VMI technique breaks down the borders of the segregation between multiple tenants, which might lead to the disclosure of cloud tenants' data. This potential threat becomes a concern for virtual instances running critical systems, and hence it should be avoided in a public cloud computing environment. The disclosure of data could happen easily due to compromised connections, both inside and outside of the cloud, and the misuse of the cloud administrator's authorization. Thus, in this thesis, we focus on building a secure framework, CryptVMI, to address the above concerns. Our approach maintains a client application on the user end to send queries to the cloud, as well as parse the results returned in a standard form. We also have a handler that cooperates with the introspection applications in the cloud infrastructure to process queries and return encrypted results. The introspection application is able to extract information reflecting the behaviors of the guest systems. It also demonstrates its ability to restore processes upon unexpected modification from the remote user. This work shows our design and implementation of this system, and the benchmark results prove that it does not incur much performance overhead