Identification of Critical Infrastructure via PageRank

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3685-3690
Number of pages6
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

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