Toward Quantifying Vulnerabilities in Critical Infrastructure Systems

Pravallika Devineni; Bill Kay; Hao Lu; Anika Tabassum; Supriya Chintavali; Sangkeun Matt Lee

doi:10.1109/BigData50022.2020.9377730

Toward Quantifying Vulnerabilities in Critical Infrastructure Systems

Pravallika Devineni, Bill Kay, Hao Lu, Anika Tabassum, Supriya Chintavali, Sangkeun Matt Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. Vulnerability in critical infrastructures (CIs) is defined as a measure of system susceptibility to threat scenarios. Quantifying vulnerability in CIs has not been adequately addressed in the literature. This paper presents ongoing research on how the authors model CIs as network-based models and propose a set of metrics to quantify vulnerability in CI systems. The size and complexity of the CIs make this a challenging task. These metrics could be used for planning and efficient decision-making during extreme events.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2884-2890
Number of pages	7
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9377730
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). ACKNOWLEDGMENT This material is based upon work supported by DOE’s Office of Cybersecurity, Energy, Security, and Emergency Response. This research used resources of CADES at ORNL, which is supported by the US Department of Energy’s (DOE’s) Office of Science under contract no. DE-AC05-00OR22725.

Keywords

critical infrastructure
interdependencies
metrics
risk analysis

Access to Document

10.1109/BigData50022.2020.9377730

Cite this

Devineni, P., Kay, B., Lu, H., Tabassum, A., Chintavali, S., & Lee, S. M. (2020). Toward Quantifying Vulnerabilities in Critical Infrastructure Systems. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 2884-2890). Article 9377730 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9377730

Devineni, Pravallika ; Kay, Bill ; Lu, Hao et al. / Toward Quantifying Vulnerabilities in Critical Infrastructure Systems. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 2884-2890 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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title = "Toward Quantifying Vulnerabilities in Critical Infrastructure Systems",

abstract = "Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. Vulnerability in critical infrastructures (CIs) is defined as a measure of system susceptibility to threat scenarios. Quantifying vulnerability in CIs has not been adequately addressed in the literature. This paper presents ongoing research on how the authors model CIs as network-based models and propose a set of metrics to quantify vulnerability in CI systems. The size and complexity of the CIs make this a challenging task. These metrics could be used for planning and efficient decision-making during extreme events.",

keywords = "critical infrastructure, interdependencies, metrics, risk analysis",

author = "Pravallika Devineni and Bill Kay and Hao Lu and Anika Tabassum and Supriya Chintavali and Lee, \{Sangkeun Matt\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",

year = "2020",

month = dec,

day = "10",

doi = "10.1109/BigData50022.2020.9377730",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2884--2890",

editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, \{Xiaohua Tony\} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

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Devineni, P, Kay, B, Lu, H , Tabassum, A , Chintavali, S & Lee, SM 2020, Toward Quantifying Vulnerabilities in Critical Infrastructure Systems. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9377730, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 2884-2890, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9377730

Toward Quantifying Vulnerabilities in Critical Infrastructure Systems. / Devineni, Pravallika; Kay, Bill; Lu, Hao et al.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2884-2890 9377730 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Toward Quantifying Vulnerabilities in Critical Infrastructure Systems

AU - Devineni, Pravallika

AU - Kay, Bill

AU - Lu, Hao

AU - Tabassum, Anika

AU - Chintavali, Supriya

AU - Lee, Sangkeun Matt

PY - 2020/12/10

Y1 - 2020/12/10

N2 - Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. Vulnerability in critical infrastructures (CIs) is defined as a measure of system susceptibility to threat scenarios. Quantifying vulnerability in CIs has not been adequately addressed in the literature. This paper presents ongoing research on how the authors model CIs as network-based models and propose a set of metrics to quantify vulnerability in CI systems. The size and complexity of the CIs make this a challenging task. These metrics could be used for planning and efficient decision-making during extreme events.

AB - Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. Vulnerability in critical infrastructures (CIs) is defined as a measure of system susceptibility to threat scenarios. Quantifying vulnerability in CIs has not been adequately addressed in the literature. This paper presents ongoing research on how the authors model CIs as network-based models and propose a set of metrics to quantify vulnerability in CI systems. The size and complexity of the CIs make this a challenging task. These metrics could be used for planning and efficient decision-making during extreme events.

KW - critical infrastructure

KW - interdependencies

KW - metrics

KW - risk analysis

UR - https://www.scopus.com/pages/publications/85103838461

U2 - 10.1109/BigData50022.2020.9377730

DO - 10.1109/BigData50022.2020.9377730

M3 - Conference contribution

AN - SCOPUS:85103838461

T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

SP - 2884

EP - 2890

BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

A2 - Saltz, Jeff

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Big Data, Big Data 2020

Y2 - 10 December 2020 through 13 December 2020

ER -

Devineni P, Kay B, Lu H , Tabassum A , Chintavali S , Lee SM. Toward Quantifying Vulnerabilities in Critical Infrastructure Systems. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2884-2890. 9377730. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9377730

Toward Quantifying Vulnerabilities in Critical Infrastructure Systems

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