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Performance Analysis of Single and Multi-objective Approaches for the Critical Node Detection Problem

Faramondi, L. and Oliva, G. and Setola, R. and Pascucci, F. and Esposito Amideo, Annunziata and Scaparra, Maria Paola (2017) Performance Analysis of Single and Multi-objective Approaches for the Critical Node Detection Problem. In: Sforza, Antonio and Sterle, Claudio, eds. Optimization and Decision Science: Methodologies and Applications. Springer Proceedings in Mathematics & Statistics . Springer, pp. 315-324. ISBN 978-3-319-67307-3. E-ISBN 978-3-319-67308-0. (doi:10.1007/978-3-319-67308-0_32) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:64447)

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Critical infrastructures are network-based systems which are prone to various types of threats (e.g., terroristic or cyber-attacks). Therefore, it is paramount to devise modelling frameworks to assess their ability to withstand external disruptions and to develop protection strategies aimed at improving their robustness and security. In this paper, we compare six modelling approaches for identifying the most critical nodes in infrastructure networks. Three are well-established approaches in the literature, while three are recently proposed frameworks. All the approaches take the perspective of an attacker whose ultimate goal is to inflict maximum damage to a network with minimal effort. Specifically, they assume that a saboteur must decide which nodes to disable so as to disrupt network connectivity as much as possible. The formulations differ in terms of the attacker objectives and connectivity metrics (e.g., trade-off between inflicted damage and attack cost, pair-wise connectivity, size and number of disconnected partitions). We apply the six formulations to the IEEE24 and IEEE118 Power Systems and conduct a comparative analysis of the solutions obtained with different parameters settings. Finally, we use frequency analysis to determine the most critical nodes with respect to different attack strategies.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-67308-0_32
Uncontrolled keywords: Critical infrastructures, Network vulnerability, Critical node detection problem
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Paola Scaparra
Date Deposited: 15 Nov 2017 14:53 UTC
Last Modified: 19 Sep 2023 15:03 UTC
Resource URI: (The current URI for this page, for reference purposes)

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Esposito Amideo, Annunziata.

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Scaparra, Maria Paola.

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