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Benchmarking Intrusion Detection Systems with Adaptive Provisioning of Virtualized Resources

  • Aleksandar MilenkoskiEmail author
  • K. R. Jayaram
  • Samuel Kounev
Chapter

Abstract

With the increasing popularity of virtualization, deploying intrusion detection systems (IDSes) in virtualized environments, for example, in virtual machines as virtualized network functions, has become an emerging practice. Modern virtualized environments feature on demand provisioning of virtualized processing and memory resources to virtual machines, dynamically adapting its intensity in order to meet resource demands. Such a provisioning may have a significant impact on many properties of an IDS deployed in a virtual machine, for example, on its attack detection accuracy. However, conventional metrics for quantifying IDS attack detection accuracy do not capture this impact, which may lead to inaccurate assessments of the IDS’s accuracy at detecting attacks. In this chapter, we discuss in detail on the impact of on demand provisioning of virtualized resources on IDS attack detection accuracy. Further, we discuss on relevant issues related to the use of conventional metrics for quantifying IDS attack detection accuracy. Finally, we present a preliminary metric and measurement methodologies, which allow for the accurate assessment of IDS attack detection accuracy taking on-demand resource provisioning into account.

Keywords

Virtual Machine False Positive Rate Intrusion Detection True Positive Rate Intrusion Detection System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

This work was funded by the German Research Foundation (DFG) under grant No. KO 3445/16-1. This research has been supported by the Research Group of the Standard Performance Evaluation Corporation (SPEC, http://www.spec.org, http://research.spec.org). The authors would like to thank Alexander Leonhardt for providing experimental data.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aleksandar Milenkoski
    • 1
    Email author
  • K. R. Jayaram
    • 2
  • Samuel Kounev
    • 1
  1. 1.University of WürzburgWürzburgGermany
  2. 2.Thomas J. Watson Research CenterNew York CityUSA

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