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Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities

Oliva, Gabriele and Esposito Amideo, Annunziata and Starita, Stefano and Setola, Roberto and Scaparra, Maria Paola (2019) Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities. In: Critical Information Infrastructures Security. CRITIS 2019. Lectures Notes in Computer Science. Lecture Notes in Computer Science, 11777 . Springer, Cham, Switzerland, pp. 57-68. ISBN 978-3-030-37669-7. E-ISBN 978-3-030-37670-3. (doi:10.1007/978-3-030-37670-3_5) (KAR id:80071)

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https://dx.doi.org/10.1007/978-3-030-37670-3_5

Abstract

Assessing critical infrastructure vulnerabilities is paramount to arrange efficient plans for their protection. Critical infrastructures are network-based systems hence, they are composed of nodes and edges. The literature shows that node criticality, which is the focus of this paper, can be addressed from different metric-based perspectives (e.g., degree, maximal flow, shortest path). However, each metric provides a specific insight while neglecting others. This paper attempts to overcome this pitfall through a methodology based on ranking aggregation. Specifically, we consider several numerical topological descriptors of the nodes’ importance (e.g., degree, betweenness, closeness, etc.) and we convert such descriptors into ratio matrices; then, we extend the Analytic Hierarchy Process problem to the case of multiple ratio matrices and we resort to a Logarithmic Least Squares formulation to identify an aggregated metric that represents a good tradeoff among the different topological descriptors. The procedure is validated considering the Central London Tube network as a case study.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-37670-3_5
Uncontrolled keywords: Critical infrastructures, Criticality analysis, Ranking aggregation, Analytic Hierarchy Process, Least squares optimisation
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Paola Scaparra
Date Deposited: 14 Feb 2020 16:04 UTC
Last Modified: 16 Feb 2021 14:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/80071 (The current URI for this page, for reference purposes)
Esposito Amideo, Annunziata: https://orcid.org/0000-0002-7284-9690
Scaparra, Maria Paola: https://orcid.org/0000-0002-2725-5439
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