MTD CBITS: Moving Target Defense for Cloud-Based IT Systems

  • Alexandru G. Bardas
  • Sathya Chandran Sundaramurthy
  • Xinming Ou
  • Scott A. DeLoach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10492)

Abstract

The static nature of current IT systems gives attackers the extremely valuable advantage of time, as adversaries can take their time and plan attacks at their leisure. Although cloud infrastructures have increased the automation options for managing IT systems, the introduction of Moving Target Defense (MTD) techniques at the entire IT system level is still very challenging. The core idea of MTD is to make a system change proactively as a means to eliminating the asymmetric advantage the attacker has on time. However, due to the number and complexity of dependencies between IT system components, it is not trivial to introduce proactive changes without breaking the system or severely impacting its performance.

In this paper, we present an MTD platform for Cloud-Based IT Systems (MTD CBITS), evaluate its practicality, and perform a detailed analysis of its security benefits. To the best of our knowledge MTD CBITS is the first MTD platform that leverages the advantages of a cloud-automation framework (ANCOR) that captures an IT system’s setup parameters and dependencies using a high-level abstraction. This allows our platform to make automated changes to the IT system, in particular, to replace running components of the system with fresh new instances. To evaluate MTD CBITS’ practicality, we present a series of experiments that show negligible (statistically non-significant) performance impacts. To evaluate effectiveness, we analyze the costs and security benefits of MTD CBITS using a practical attack window model and show how a system managed using MTD CBITS will increase attack difficulty.

Notes

Acknowledgements

We would like to thank the reviewers for their valuable feedback and everyone involved in this research over the years, especially Rui Zhuang, Ali Ali, Simon Novelly, Ian Unruh, and Brian Cain. This work was supported by the Air Force Office of Scientific Research (FA9550-12-1-0106). Opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the agencies’ views.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alexandru G. Bardas
    • 1
  • Sathya Chandran Sundaramurthy
    • 2
  • Xinming Ou
    • 3
  • Scott A. DeLoach
    • 4
  1. 1.University of KansasLawrenceUSA
  2. 2.DataVisorMountain ViewUSA
  3. 3.University of South FloridaTampaUSA
  4. 4.Kansas State UniversityManhattanUSA

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