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

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

doi:10.1145/3140549.3140558

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

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

Cyber Security

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (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 language	English
Title of host publication	MTD'17 Proceedings of the 2017 Workshop on Moving Target Defense
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	115-118
Number of pages	4
ISBN (Electronic)	978-1-4503-5176-8
DOIs	https://doi.org/10.1145/3140549.3140558
Publication status	Published - 2017
Event	2017 Workshop on Moving Target Defense MTD'17 - Dallas, United States Duration: 30 Oct 2017 → 30 Oct 2017

Workshop

Workshop	2017 Workshop on Moving Target Defense MTD'17
Country/Territory	United States
City	Dallas
Period	30/10/17 → 30/10/17

Keywords

Moving Target Defense
Metrics
Objective Space
Network Neighborhood Partitioning

Access to Document

10.1145/3140549.3140558

Cite this

@inproceedings{c748d1762a7e40a0821fa295d90aa67e,

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

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.",

keywords = "Moving Target Defense, Metrics, Objective Space, Network Neighborhood Partitioning",

author = "Stjepan Picek and Erik Hemberg and Una-May O'Reilly",

year = "2017",

doi = "10.1145/3140549.3140558",

language = "English",

pages = "115--118",

booktitle = "MTD'17 Proceedings of the 2017 Workshop on Moving Target Defense",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "2017 Workshop on Moving Target Defense MTD'17 ; Conference date: 30-10-2017 Through 30-10-2017",

}

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 . Association for Computing Machinery (ACM), New York, NY, pp. 115-118, 2017 Workshop on Moving Target Defense MTD'17, Dallas, Texas, United States, 30/10/17. https://doi.org/10.1145/3140549.3140558

If You Can't Measure It, You Can't Improve It: Moving Target Defense Metrics. / Picek, Stjepan; Hemberg, Erik; O'Reilly, Una-May.
MTD'17 Proceedings of the 2017 Workshop on Moving Target Defense . New York, NY: Association for Computing Machinery (ACM), 2017. p. 115-118.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - If You Can't Measure It, You Can't Improve It

T2 - 2017 Workshop on Moving Target Defense MTD'17

AU - Picek, Stjepan

AU - Hemberg, Erik

AU - O'Reilly, Una-May

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Moving Target Defense

KW - Metrics

KW - Objective Space

KW - Network Neighborhood Partitioning

U2 - 10.1145/3140549.3140558

DO - 10.1145/3140549.3140558

M3 - Conference contribution

SP - 115

EP - 118

BT - MTD'17 Proceedings of the 2017 Workshop on Moving Target Defense

PB - Association for Computing Machinery (ACM)

CY - New York, NY

Y2 - 30 October 2017 through 30 October 2017

ER -

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

Abstract

Workshop

Keywords

Access to Document

Fingerprint

Cite this