UML-CI: A reference model for profiling critical infrastructure systems

Bagheri, Ebrahim; Ghorbani, Ali A.

doi:10.1007/s10796-008-9127-y

Cited by 34 publications

(26 citation statements)

References 23 publications

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“…In recent decades, research was carried out in applied science on cataloguing, analysing and modelling the interdependences in critical infrastructure as well as modelling cascading failures in coupled critical infrastructure networks 40,[42][43][44][45][46][47][48] . However, no systematic mathematical framework, such as percolation theory, is currently available for adequately addressing the consequences of disruptions and failures occurring simultaneously in interdependent critical infrastructures.…”

Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

Networks formed from interdependent networks

et al. 2011

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Complex networks appear in almost every aspect of science and technology. Although most results in the field have been obtained by analysing isolated networks, many real-world networks do in fact interact with and depend on other networks. The set of extensive results for the limiting case of non-interacting networks holds only to the extent that ignoring the presence of other networks can be justified. Recently, an analytical framework for studying the percolation properties of interacting networks has been developed. Here we review this framework and the results obtained so far for connectivity properties of 'networks of networks' formed by interdependent random networks.T he interdisciplinary field of network science has attracted a great deal of attention in recent years . This development is based on the enormous number of data that are now routinely being collected, modelled and analysed, concerning social 31-39 , economic 14,36,40,41 , technological 40,42-48 and biological 9,13,49,50 systems. The investigation and growing understanding of this extraordinary volume of data will enable us to make the infrastructures we use in everyday life more efficient and more robust.The original model of networks, random graph theory, was developed in the 1960s by ErdAEs and Rényi, and is based on the assumption that every pair of nodes is randomly connected with the same probability, leading to a Poisson degree distribution. In parallel, in physics, lattice networks, where each node has exactly the same number of links, have been studied to model physical systems. Although graph theory is a well-established tool in the mathematics and computer science literature, it cannot describe well modern, real-life networks. Indeed, the pioneering 1999 observation by Barabasi 2 , that many real networks do not follow the ErdAEs-Rényi model but that organizational principles naturally arise in most systems, led to an overwhelming accumulation of supporting data, new models and computational and analytical results, and to the emergence of a new science, that of complex networks.Complex networks are usually non-homogeneous structures that in many cases obey a power-law form in their degree (that is, number of links per node) distribution. These systems are called scale-free networks. Real networks that can be approximated as scale-free networks include the Internet 3 , the World Wide Web 4 , social networks 31-39 representing the relations between individuals, infrastructure networks such as those of airlines 51 , networks in biology 9,13,49,50 , in particular networks of proteinprotein interactions 10 , gene regulation and biochemical pathways, and networks in physics, such as polymer networks or the potentialenergy-landscape network. The discovery of scale-free networks led to a re-evaluation of the basic properties of networks, such as their robustness, which exhibit a drastically different character than those of ErdAEs-Rényi networks. For example, whereas homogeneous ErdAEs-Rényi networks are extremely vulnerable to random fa...

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Section: Figure 1 | Schematic Demonstration Of First-and Second-ordermentioning

confidence: 99%

Networks formed from interdependent networks

et al. 2011

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“…The inhomogeneous network structure can be considered to be effectively homogeneous with an effective infection probability given by the denominator in the second term in Eq. (6). The smaller contrast makes the effect of the small clusters less important, leading to a mixed phase with finite and non-monotonic ρ over the whole range of p. This is schematically shown in Fig.…”

Section: Qualitative Treatment Phase Diagram and Physical Picturementioning

confidence: 91%

Suppressed epidemics in multirelational networks

Wang

Cai

et al. 2015

Phys. Rev. E

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A two-state epidemic model in networks with links mimicking two kinds of relationships between connected nodes is introduced. Links of weights w1 and w0 occur with probabilities p and 1 − p, respectively. The fraction of infected nodes ρ(p) shows a non-monotonic behavior, with ρ drops with p for small p and increases for large p. For small to moderate w1/w0 ratios, ρ(p) exhibits a minimum that signifies an optimal suppression. For large w1/w0 ratios, the suppression leads to an absorbing phase consisting only of healthy nodes within a range pL ≤ p ≤ pR, and an active phase with mixed infected and healthy nodes for p < pL and p > pR. A mean field theory that ignores spatial correlation is shown to give qualitative agreement and capture all the key features. A physical picture that emphasizes the intricate interplay between infections via w0 links and within clusters formed by nodes carrying the w1 links is presented. The absorbing state at large w1/w0 ratios results when the clusters are big enough to disrupt the spread via w0 links and yet small enough to avoid an epidemic within the clusters. A theory that uses the possible local environments of a node as variables is formulated. The theory gives results in good agreement with simulation results, thereby showing the necessity of including longer spatial correlations.

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“…Then, the next step for any MA&S activity is the "conceptualization" of the inspected systems and to build formal representations. In [18] the authors propose UML extensions (meta-models) in order to define the different aspects of an infrastructure organization and behaviour as ownership and management, structure and organization, resources, risk and relationship. The CEML language proposed in [19] is a graphical modelling language allowing domain experts to build formally grounded models related to crisis and emergency scenarios.…”

Section: Clusteringmentioning

confidence: 99%

Phenomenological Simulators of Critical Infrastructures

Tofani

D’Agostino

Martí

2016

Managing the Complexity of Critical Infrastructures

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The objective of this chapter is to introduce and discuss the main phenomenological approaches that have been used within the CI M&S area. Phenomenological models are used to analyse the organizational phenomena of the society considering its complexity (finance, mobility, health) and the interactions among its different components. Within CI MA&S, different modelling approaches have been proposed and used as, for example, physical simulators (e.g. power flow simulators for electrical networks). Physical simulators are used to predict the behaviour of the physical system (the technological network) under different conditions. As an example, electrical engineers use different kind of simulators during planning and managing of network activities for different purposes: (1) power flow simulators for the evaluation of electrical network configuration changes (that can be both deliberate changes or results from of the effects of accidents and/or attacks) and contingency analysis, (2) real time simulators for the design of protection devices and new controllers. For the telecommunication domain one mat resort to network traffic simulators as for example ns2/ns3 codes that allow the simulation of telecommunication networks (wired/wireless) at packet switching level and evaluate its performances. Single domains simulators can be federated to analyse the interactions among different domains. In contrast, phenomenological simulators use more abstract data and models for the interaction among the different components of the system. The chapter will describe the main characteristic of some of the main simulation approaches resulting from the ENEA and UBC efforts in the CIP and Complexity Science field.

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UML-CI: A reference model for profiling critical infrastructure systems

Cited by 34 publications

References 23 publications

Networks formed from interdependent networks

Networks formed from interdependent networks

Suppressed epidemics in multirelational networks

Phenomenological Simulators of Critical Infrastructures

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