If You Can't Measure It, You Can't Improve It: Moving Target Defense Metrics

Stjepan Picek, Erik Hemberg, Una-May O'Reilly

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

We propose new metrics drawing inspiration from the optimization domain that can be used to characterize the effectiveness of moving target defenses better. Besides that, we propose a Network Neighborhood Partitioning algorithm that can help to measure the influence of MTDs more precisely. The techniques proposed here are generic and could be combined with existing metrics. The obtained results demonstrate how additional information about the effectiveness of defenses can be obtained as well as how network neighborhood partitioning helps to improve the granularity of metrics.
Original languageEnglish
Title of host publicationMTD'17 Proceedings of the 2017 Workshop on Moving Target Defense
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages115-118
Number of pages4
ISBN (Electronic)978-1-4503-5176-8
DOIs
Publication statusPublished - 2017
Event2017 Workshop on Moving Target Defense MTD'17 - Dallas, United States
Duration: 30 Oct 201730 Oct 2017

Workshop

Workshop2017 Workshop on Moving Target Defense MTD'17
CountryUnited States
CityDallas
Period30/10/1730/10/17

Keywords

  • Moving Target Defense
  • Metrics
  • Objective Space
  • Network Neighborhood Partitioning

Cite this

Picek, S., Hemberg, E., & O'Reilly, U-M. (2017). If You Can't Measure It, You Can't Improve It: Moving Target Defense Metrics. In MTD'17 Proceedings of the 2017 Workshop on Moving Target Defense (pp. 115-118). Association for Computing Machinery (ACM). https://doi.org/10.1145/3140549.3140558