HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks

Chen, Liangzhe; Xu, Xinfeng; Lee, Sangkeun (Matt); Duan, Sisi; Tarditi, Alfonso G.; Chinthavali, Supriya; Prakash, B. Aditya

doi:10.1145/3132847.3132867

Title: HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks

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Abstract

Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.

Authors:

^[1]; Xu, Xinfeng ^[2];

^[1];

^[1]; Prakash, B. Aditya ^[2]

ORNL
Virginia Tech, Blacksburg, VA

Publication Date:: 2017-11-01

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1423087

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: 2017 ACM on Conference on Information and Knowledge Management (CIKM) - Singapore, , Singapore - 11/6/2017 5:00:00 AM-11/10/2017 5:00:00 AM

Country of Publication:: United States

Language:: English

Citation Formats


                    Chen, Liangzhe, Xu, Xinfeng, Lee, Sangkeun, Duan, Sisi, Tarditi, Alfonso G., Chinthavali, Supriya, and Prakash, B. Aditya. HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks.  United States: N. p., 2017. 
        Web.  doi:10.1145/3132847.3132867.

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                    Chen, Liangzhe, Xu, Xinfeng, Lee, Sangkeun, Duan, Sisi, Tarditi, Alfonso G., Chinthavali, Supriya, & Prakash, B. Aditya. HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks.  United States.  https://doi.org/10.1145/3132847.3132867

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                    Chen, Liangzhe, Xu, Xinfeng, Lee, Sangkeun, Duan, Sisi, Tarditi, Alfonso G., Chinthavali, Supriya, and Prakash, B. Aditya. 2017.  
        "HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks".  United States.  https://doi.org/10.1145/3132847.3132867.  https://www.osti.gov/servlets/purl/1423087.

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@article{osti_1423087,

  title        = {HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks},

  author       = {Chen, Liangzhe and Xu, Xinfeng and Lee, Sangkeun and Duan, Sisi and Tarditi, Alfonso G. and Chinthavali, Supriya and Prakash, B. Aditya},

  abstractNote = {Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.},

  doi          = {10.1145/3132847.3132867},

  url          = {https://www.osti.gov/biblio/1423087},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2017},

  month        = {11}

}

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