Safe side effects commitment for OS-level virtualization

  • Authors:

  • Zhiyong Shan;Xin Wang;Tzi-cker Chiueh;Xiaofeng Meng

  • Affiliations:

  • Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Stony Brook University, Beijing, China;Stony Brook University, Stony Brook, NY, USA;Stony Brook University, Stony Brook, NY, USA;Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing, China

  • Venue:
  • Proceedings of the 8th ACM international conference on Autonomic computing
  • Year:
  • 2011

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Abstract

A common application of virtual machines (VM) is to use and then throw away, basically treating a VM like a completely isolated and disposable entity. The disadvantage of this approach is that if there is no malicious activity, the user has to re-do 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 OS-level 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 Feather-weight Virtual Machine (FVM) 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 anti-malware 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 on-line behavior-based approach of the commercial tools.