Identifying vulnerabilities in critical infrastructures by network analysis

Lam, C. Y. Teresa; Lin, Jiaying; Sim, Mong Soon; Tai, Kang

doi:10.1504/ijcis.2013.054978

Cited by 14 publications

(7 citation statements)

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“…The elementary critical infrastructure network constructed in QGIS contains the geographical location of the nodes and links, which provides the possibility of extension to geographical area damage simulation in future work. The failure scenarios within the physical infrastructure network, comprising of one-node failures, two-node failures and three-node failures, are simulated by assuming different combinations of failed nodes and computing the resulting impact on all critical infrastructure sectors interconnected in the I/O model (Kizhakkedath et al, 2013;Lam et al, 2013;Tai et al, 2013). Figure 4 shows the graphical representation of the Netlogo program for 'one physical network' consisting of the telecommunication network and 'two physical networks' consisting of the telecommunication network and the electrical grid.…”

Section: Combining I/o Model and Physical Critical Infrastructure Netmentioning

confidence: 99%

Modelling critical infrastructure network interdependencies and failure

Lin

Tai

Kong

et al. 2019

IJCIS

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Critical infrastructures are fundamental systems that are interconnected with one another, and their interdependencies need to be modelled in order to analyse any vulnerabilities that exist in these infrastructure networks. In this work, a Leontief input-output model of the critical infrastructure systems and two physical critical infrastructure networks are developed. The input-output model serves to evaluate the interdependencies among the various infrastructure systems, while the physical infrastructure network describes the linkages within a single physical infrastructure system. This study examines the overall impact of any failures in the telecommunication infrastructure or electricity grid within the input-output interdependencies model systematically so as to evaluate the worst case scenarios based on the impact caused by one or more points of failure in the physical infrastructure.

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Section: Combining I/o Model and Physical Critical Infrastructure Netmentioning

confidence: 99%

Modelling critical infrastructure network interdependencies and failure

Lin

Tai

Kong

et al. 2019

IJCIS

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“…In the context of our problem, impact refers to the magnitude of the disruption in the network computed according to various metrics used in network theory such as, e.g. the giant component size 17,18 , using agent-based simulation 19,20,21,22 . Probability refers to the probability of the failures propagating/leading to the disruption.…”

Section: Optimization and Analysis Of Infrastructure Network Disrmentioning

confidence: 99%

Identifying Extreme Risks in Critical Infrastructure Interdependencies

Tai

Kizhakkedath²,

Lin³

et al. 2014

Proceedings of the International Symposium for Next Generation Infrastructure

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Critical infrastructures like our power generation facilities and water supply form highly interconnected networks that are mutually dependent and any failure can cascade through the network, resulting in devastating impact on health, safety and the economy. These catastrophic events/disruptions can be triggered by environmental accidents, geological/weather phenomena, disease pandemics, etc. The disruptions can be caused/exacerbated by their being unexpected, but they may actually be expected if relevant data have been accounted for. To help account for and thereby anticipate such disruptions, one way is to identify potential unforeseen interdependencies among infrastructure components that can lead to extreme disruptions upon some failure in the network. This paper shows how a simulation model for cascading failures and a risk analysis/optimization approach can be applied to search for unforeseen interdependencies and failure points that give rise to the highest risk in a network.

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“…Establish valuable interdependencies among entities can achieve better coordination in supply chain network [1-5], which can help to maintain the overall performance of the network. Moreover, in studying the dynamic network interdependencies in supply chain, the characteristics of contemporary business situations and the internal functional structure of supply chain network can be revealed [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Network Modeling Approach With Interdependent Agents for Network Coordination

Lam¹

2019

GEOMATE

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A good coordination in supply chain network can achieve competitive advantages in terms of performance and customer satisfaction. In this paper, a supply chain is modeled as a network with higher visibility and better coordination among the supply chain entities. The approach is an integration of network modeling and agent-based framework, which considers the coordination problem in supply chain network with multiple entities. A supply chain is firstly modeled as a network with a set of nodes for supply chain entities and links for interdependent connection among entities, interdependent agents are then introduced to link and evaluate the output information from preceding entities to the input information for the succeeding entities in a supply chain network. The interdependent agents address problems of autonomy and complexity that cause changes and disruptions in supply chain network, so the proposed modeling approach can adjust any deviation as well as can maximize the coordinating performance in the network.

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Identifying vulnerabilities in critical infrastructures by network analysis

Cited by 14 publications

References 0 publications

Modelling critical infrastructure network interdependencies and failure

Modelling critical infrastructure network interdependencies and failure

Identifying Extreme Risks in Critical Infrastructure Interdependencies

A Network Modeling Approach With Interdependent Agents for Network Coordination

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