Vulnerability assessment of infrastructure networks by using hierarchical decomposition methods

Socioeconomic development and sustainability highly depend on the construction and operation of infrastructure networks. Therefore, robustness, reliability, and resiliency of infrastructure networks are vital to the economy, security and wellbeing of any country. When exposed to natural or man-made hazards, the estimation of the vulnerability and the extent of damage, potentially caused to infrastructure networks, is a complex and computationally expensive task. This paper presents a model that combines a systems approach with strategies for detecting community structures within networks to make vulnerability estimates. Then, by means of a clustering-based decomposition, a hierarchical representation of the network is derived. This is used to obtain information (i.e., evidence) at different levels of abstraction avoiding the complexity and computational cost of a full evaluation of the network. This simplified model of the network favors the efficient assessment of risk and vulnerability for decision-making regarding, for instance, resource allocation and risk mitigation. In this paper, the conceptual foundations and the practical implementation of the proposed model are presented and discussed. Furthermore, the applicability and the challenges of the model are presented through a practical application.

[1]  Nancy R. Mead,et al.  Survivable Network Systems: An Emerging Discipline , 1997 .

[2]  Susan Snedakar.  Vulnerability Assessment Tools , 2007 .

[3]  Baruch Barzel,et al.  Quantifying the connectivity of a network: the network correlation function method. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Somesh Jha,et al.  Survivability analysis of network specifications , 2000, Proceeding International Conference on Dependable Systems and Networks. DSN 2000.

[6]  Peter Checkland,et al.  Systems Thinking, Systems Practice , 1981 .

[7]  Ju An Wang,et al.  Vulnerability categorization using Bayesian networks , 2010, CSIIRW '10.

[8]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[9]  Jun Liu,et al.  Geographical constraints to range-based attacks on links in complex networks , 2008 .

[10]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[11]  Kay W. Axhausen,et al.  Vulnerability Assessment Methodology for Swiss Road Network , 2009 .

[12]  Mauricio Sánchez-Silva,et al.  Hierarchical infrastructure network representation methods for risk-based decision-making , 2013 .

[13]  Francesco Masulli,et al.  A survey of kernel and spectral methods for clustering , 2008, Pattern Recognit..

[14]  Laura Painton Swiler,et al.  A graph-based network-vulnerability analysis system , 1997, S&P 1998.

[15]  Jianbo Shi,et al.  Learning Segmentation by Random Walks , 2000, NIPS.

[16]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .

[17]  M G H Bell,et al.  Attacker–defender models and road network vulnerability , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[18]  Duminda Wijesekera,et al.  Scalable, graph-based network vulnerability analysis , 2002, CCS '02.

[19]  Salim Hariri,et al.  A Framework for Network Vulnerability Analysis , 2002, Communications, Internet, and Information Technology.

[20]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[21]  Massimo Marchiori,et al.  Vulnerability and protection of infrastructure networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[23]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[24]  I. A. Nazarova.  Solution methods for the vertex variant of the network system vulnerability analysis problem , 2009 .

[25]  S. Dongen.  Graph clustering by flow simulation , 2000 .

[26]  Sadaaki Miyamoto,et al.  LVQ Clustering and SOM Using a Kernel Function , 2005 .

[27]  L. Mattsson,et al.  Vulnerability : A model-based case study of the road network in Stockholm , 2007 .

[28]  Gábor Schuchmann,et al.  Road network vulnerability-evaluation of measures in ranking damages and developments , 2010 .