Identifying, understanding, and analyzing critical infrastructure interdependencies
TL;DR: A conceptual framework for addressing infrastructure interdependencies is presented that could serve as the basis for further understanding and scholarship in this important area and is used to explore the challenges and complexities of interdependency.
Abstract: The notion that our nation's critical infrastructures are highly interconnected and mutually dependent in complex ways, both physically and through a host of information and communications technologies (so-called "cyberbased systems"), is more than an abstract, theoretical concept. As shown by the 1998 failure of the Galaxy 4 telecommunications satellite, the prolonged power crisis in California, and many other recent infrastructure disruptions, what happens to one infrastructure can directly and indirectly affect other infrastructures, impact large geographic regions and send ripples throughout the national a global economy. This article presents a conceptual framework for addressing infrastructure interdependencies that could serve as the basis for further understanding and scholarship in this important area. We use this framework to explore the challenges and complexities of interdependency. We set the stage for this discussion by explicitly defining the terms infrastructure, infrastructure dependencies, and infrastructure interdependencies and introducing the fundamental concept of infrastructures as complex adaptive systems. We then focus on the interrelated factors and system conditions that collectively define the six dimensions. Finally, we discuss some of the research challenges involved in developing, applying, and validating modeling and simulation methodologies and tools for infrastructure interdependency analysis.
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TL;DR: In this paper, the authors develop a framework for understanding the robustness of interacting networks subject to cascading failures and present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks.
Abstract: Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures ('concurrent malfunction') is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.
3,651 citations
03 Jun 2015
TL;DR: This document provides guidance on how to secure Industrial Control Systems, including Supervisory Control and Data Acquisition systems, Distributed Control Systems (DCS), and other control system configurations such as Programmable Logic Controllers (PLC), while addressing their unique performance, reliability, and safety requirements.
Abstract: This document provides guidance on how to secure Industrial Control Systems (ICS), including Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and other control system configurations such as Programmable Logic Controllers (PLC), while addressing their unique performance, reliability, and safety requirements The document provides an overview of ICS and typical system topologies, identifies typical threats and vulnerabilities to these systems, and provides recommended security countermeasures to mitigate the associated risks
1,351 citations
Cites background from "Identifying, understanding, and ana..."
...critical infrastructure is often referred to as a “system of systems” because of the interdependencies that exist between its various industrial sectors as well as interconnections between business partners [8] [9]....
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...米国の重要インフラは、よく「複数のシステムからなるシステム」と呼ばれるが、理由は多種多 様な業界・部門が相互に依存し合い、ビジネスパートナー同士が相互に関わり合っているからで ある[8] [9]。重要インフラは、物理的にも多数の情報・通信技術面でも、高度に相互連携し、 複雑に相互依存し合っている。...
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01 Jan 2011
TL;DR: In this paper, a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions is presented.
Abstract: This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].
1,296 citations
Journal Article•
TL;DR: The National Academy of Sciences founded The National Academies Press (NAP) with the goal of publishing reports of all four national academies as mentioned in this paper, which publishes more than 200 books from the fields of science, engineering and medicine and offers more than 4000 titles in PDF on its website.
Abstract: The National Academy of Sciences founded The National Academies Press (NAP) with the goal of publishing reports of all four national academies. Annually, NAP publishes more than 200 books from the fields of science, engineering and medicine and offers more than 4000 titles in PDF on its website (http://www.nap.edu/) free of charge.
1,241 citations
Cites background from "Identifying, understanding, and ana..."
...Outages can be caused by disruptions to natural gas production and delivery, commercial communications infrastructure, and transportation systems, among other critical infrastructures (Figure 4.5) (Rinaldi et al., 2001)....
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TL;DR: In this article, a general framework for modelling the percolation properties of interacting networks is presented, and the first results drawn from its study are drawn from their study are presented.
Abstract: Aspects concerning the structure and behaviours of individual networks have been studied intensely in the past decade, but the exploration of interdependent systems in the context of complex networks has started only recently. This article reviews a general framework for modelling the percolation properties of interacting networks and the first results drawn from its study.
1,077 citations
References
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01 Jan 1999
TL;DR: This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.
Abstract: 1. Introduction 2. Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks 3. Division of Labor and Task Allocation 4. Cemetery Organization, Brood Sorting, Data Analysis, and Graph Partitioning 5. Self-Organization and Templates: Application to Data Analysis and Graph Partitioning 6. Nest Building and Self-Assembling 7. Cooperative Transport by Insects and Robots 8. Epilogue
5,822 citations
"Identifying, understanding, and ana..." refers background in this paper
...Complex adaptive systems do not require strong central control for emergent behaviors to arise [ 15 ]....
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...This additional complexity exhibited by a system as a whole, beyond the simple sum of its parts, is called emergent behavior and is a hallmark of CASs [ 15 ]....
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Book•
12 May 2000
TL;DR: Harnessing Complexity as mentioned in this paper is a step-by-step guide to understand the processes of variation, interaction, and selection that are at work in all organizations and how to use their own paradigm of "bottom up" management, the Complex Adaptive System-whether in science, public policy, or private commerce.
Abstract: A state-of-the-art guide to the new field of complexity-the tool leaders use to understand how people and organizations adapt in a world of rapid change.. Harnessing Complexity will be indispensable to anyone who wants to better comprehend how people and organizations can adapt effectively in the information age. This book is a step-by-step guide to understanding the processes of variation, interaction, and selection that are at work in all organizations. The authors show how to use their own paradigm of "bottom up" management, the Complex Adaptive System-whether in science, public policy, or private commerce. This simple model of how people work together will change forever how we think about getting things done in a group."Harnessing Complexity distills the managerial essence of current research on complexity.A very valuable contribution to the emerging theory of competition and competitive advantage. "-C.K. Prahalad, University of Michigan, coauthor of Competing for the Future"A brilliant exposition that demystifies both the theory and use of Complex Adaptive Systems."-John Seely Brown, Xerox Corporation and Palo Alto Research Center
1,304 citations
"Identifying, understanding, and ana..." refers background in this paper
...All of the aforementioned critical infrastructures have one property in common—they are all complex collections of interacting components in which change often occurs as a result of learning processes; that is, they are complex adaptive systems (CASs) [ 14 ]....
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...One effective way to investigate CASs is to view them as populations of interacting agents [ 14 ]....
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Book•
01 Jan 1998
TL;DR: This book clearly shows how to quantify risk and construct probability in conjunction with real-world decision-making problems, including a host of institutional, organizational, political, and cultural considerations.
Abstract: Integrates the art and science of risk analysis. Clearly shows how to quantify risk and construct probability in conjunction with real-world decision-making problems, including a host of institutional, organizational, political, and cultural considerations. Presents basic concepts as well as advanced material, avoiding higher mathematics whenever possible. Incorporates numerous examples and case studies to illustrate the analytical methods under discussion. An accompanying web site will offer 200 example problems and case studies.
899 citations
"Identifying, understanding, and ana..." refers background or methods in this paper
...Hierarchical holographic modeling (HHM) has also been used successfully for modeling linkages among infrastructures and their external environments [ 27 ]....
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...Recently, some progress has been made in integrating disparate model types and analyzing multiple infrastructures [20]-[ 27 ]....
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TL;DR: In this article, a Leontief-based infrastructure input-output model is developed to enable an accounting of the intraconnectedness within each critical infrastructure as well as the interconnectedness among them.
Abstract: Wassily Leontief received the 1973 Nobel Price in Economics for developing what came to be known as the Leontief input-output model of the economy. Leontief's model enables understanding the interconnectedness among the various sectors of an economy and forecasting the effect on one segment of a change in another. A Leontief-based infrastructure input-output model is developed here to enable an accounting of the intraconnectedness within each critical infrastructure as well as the interconnectedness among them. The linear input/output model is then generalized into a generic risk model with the former as the first-order approximation. A preliminary study of the dynamics of risk of inoperability is discussed, using a Leontief-based dynamic model. Several examples are presented to illustrate the theory and its applications.
570 citations
"Identifying, understanding, and ana..." refers methods in this paper
...Other approaches, such as those based on the Leontief economic model, compute flows of commodities and shared risks among sectors [ 26 ]....
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