Using Localized Attacks with Probabilistic Failures to Model Seismic Events over Physical-Logical Interdependent Networks

BACHMANN-ESPINOZA, IVANA FRANCISCA; BUSTOS-JIMENEZ, JAVIER ALEJANDRO; Ribeiro, P; Silva F.; Mendes, JF; Laureano, R

Abstract

Natural catastrophes can affect different structures with varying intensities depending on the global and local characteristics of the event. For example, for earthquakes we have global characteristics such as the depth, magnitude, and type (interface or intraslab). Whereas soil conditions, and the hypocentral distance are local characteristics. Here we study the robustness against seismic events of physical-logical interdependent networks used to represent Internet-like systems. To do this we present a novel type of localized attack: Localized Attacks with Probabilistic Failures (LAPF). We use LAPF to model seismic events as Seismic Attacks (SA). We compare the effect of seismic attacks with the effect of localized attacks. To generate these seismic attacks we use real data from earthquakes registered in Chile. We find that seismic attacks can result in catastrophic system failure, and can cause more damage than localized attacks by damaging a smaller fraction of nodes in the physical network. The results also show that catastrophic damage can be prevented by simply adding more interlinks between the logical network and the physical network. We found that seismic attacks that resulted in the loss of more than half of the logical network are related to the removal of logical bridge nodes during the cascading failure, suggesting that the robustness of physical-logical interdependent networks may be improved by identifying and protecting these types of nodes.

Más información

Título según WOS: Using Localized Attacks with Probabilistic Failures to Model Seismic Events over Physical-Logical Interdependent Networks
Título según SCOPUS: Using Localized Attacks with Probabilistic Failures to Model Seismic Events over Physical-Logical Interdependent Networks
Título de la Revista: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen: 13197
Editorial: Springer Science and Business Media Deutschland GmbH
Fecha de publicación: 2022
Página final: 14
Idioma: English
DOI:

10.1007/978-3-030-97240-0_1

Notas: ISI, SCOPUS