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Title: Identification of Critical Infrastructure via PageRank

Abstract

Assessing critical infrastructure vulnerabilities is paramount to arranging efficient plans for their protection. Critical infrastructures are cyber-physical systems that can be represented as a network consisting of nodes and edges and highly interdependent in nature. Given the interdependent nature of critical infrastuctures, failure in one node may cause failure in many others resulting in a cascade of failures. In this paper, we propose a node criticality metric that uses Google’s PageRank algorithm to identify nodes that are likely to fail (are vulnerable), nodes whose failure may cascade to many other sites in the network (are important), and nodes that are both vulnerable and important (are critical). We then present a series of experiments to understand how protecting certain critical nodes can help mitigate massive cascading failures. Simulating failures in a real-world network with and without critical node protections demonstrates the importance of identifying critical nodes in an infrastructure network.

Authors:
 [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
  2. Virginia Tech, Blacksburg, VA
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1873839
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2021 IEEE International Conference on Big Data - Orlando, Florida, United States of America - 12/15/2021 9:00:00 AM-12/18/2021 9:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Kay, Bill, Lu, Hao, Devineni, Pravallika, Tabassum, Anika, Chinthavali, Supriya, and Lee, Sangkeun. Identification of Critical Infrastructure via PageRank. United States: N. p., 2021. Web. doi:10.1109/BigData52589.2021.9671620.
Kay, Bill, Lu, Hao, Devineni, Pravallika, Tabassum, Anika, Chinthavali, Supriya, & Lee, Sangkeun. Identification of Critical Infrastructure via PageRank. United States. https://doi.org/10.1109/BigData52589.2021.9671620
Kay, Bill, Lu, Hao, Devineni, Pravallika, Tabassum, Anika, Chinthavali, Supriya, and Lee, Sangkeun. 2021. "Identification of Critical Infrastructure via PageRank". United States. https://doi.org/10.1109/BigData52589.2021.9671620. https://www.osti.gov/servlets/purl/1873839.
@article{osti_1873839,
title = {Identification of Critical Infrastructure via PageRank},
author = {Kay, Bill and Lu, Hao and Devineni, Pravallika and Tabassum, Anika and Chinthavali, Supriya and Lee, Sangkeun},
abstractNote = {Assessing critical infrastructure vulnerabilities is paramount to arranging efficient plans for their protection. Critical infrastructures are cyber-physical systems that can be represented as a network consisting of nodes and edges and highly interdependent in nature. Given the interdependent nature of critical infrastuctures, failure in one node may cause failure in many others resulting in a cascade of failures. In this paper, we propose a node criticality metric that uses Google’s PageRank algorithm to identify nodes that are likely to fail (are vulnerable), nodes whose failure may cascade to many other sites in the network (are important), and nodes that are both vulnerable and important (are critical). We then present a series of experiments to understand how protecting certain critical nodes can help mitigate massive cascading failures. Simulating failures in a real-world network with and without critical node protections demonstrates the importance of identifying critical nodes in an infrastructure network.},
doi = {10.1109/BigData52589.2021.9671620},
url = {https://www.osti.gov/biblio/1873839}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {12}
}

Conference:
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