Malware Clearance for Secure Commitment of OS-Level Virtual Machines

A virtual machine(VM) can be simply created upon use and disposed upon the completion of the tasks or the detection of error. The disadvantage of this approach is that if there is no malicious activity, the user has to redo all of the work in her actual workspace since there is no easy way to commit (i.e., merge) only the benign updates within the VM back to the host environment. In this work, we develop a VM commitment system called Secom to automatically eliminate malicious state changes when merging the contents of an OSlevel VM to the host. Secom consists of three steps: grouping state changes into clusters, distinguishing between benign and malicious clusters, and committing benign clusters. Secom has three novel features. First, instead of relying on a huge volume of log data, it leverages OS-level information flow and malware behavior information to recognize malicious changes. As a result, the approach imposes a smaller performance overhead. Second, different from existing intrusion detection and recovery systems that detect compromised OS objects one by one, Secom classifies objects into clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the false-positive rate when identifying malicious clusters, it simultaneously considers two malware behaviors that are of different types and the origin of the processes that exhibit these behaviors, rather than considers a single behavior alone as done by existing malware detection methods. We have successfully implemented Secom on the featherweight virtual machine system, a Windows-based OS-level virtualization system. Experiments show that the prototype can effectively eliminate malicious state changes while committing a VM with small performance degradation. Moreover, compared with the commercial antimalware tools, the Secom prototype has a smaller number of false negatives and thus can more thoroughly clean up malware side effects. In addition, the number of false positives of the Secom prototype is also lower than that achieved by the online behavior-based approach of the commercial tools.