Understanding Internet topology: principles, models, and validation

Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet

Topological robustness of the global automotive industry

The manufacturing industry is characterized by large-scale interdependent networks as companies buy goods from one another, but do not control or design the overall flow of materials. The result is a complex emergent structure with which companies connect to each other. The topology of this structure impacts the industry’s robustness to disruptions in companies, countries, and regions. In this work, we propose an analysis framework for examining robustness in the manufacturing industry and validate it using an empirical dataset. Focusing on two key angles, suppliers and products, we highlight macroscopic and microscopic characteristics of the network and shed light on vulnerabilities of the system. It is shown that large-scale data on structural interdependencies can be examined with measures based on network science

Measures of Critical Infrastructure Vulnerability to Destructive Events

RÉSUMÉ : Les infrastructures critiques sont les actifs physiques qui fournissent aux sociétés modernes les services et besoins nécessaires pour le bon fonctionnement d’activités sociales et économiques essentielles. L'importance de ces infrastructures complexes est largement reconnue et la nécessité de protéger ces réseaux contre des événements destructifs—intentionnels ou accidentels—attire l'attention de plusieurs chercheurs et experts en sécurité. Il est également bien reconnu que le coût et les efforts associés à la protection totale représentent un énorme défi. La société réalisera son plus grand retour sur investissement en identifiant, en priorisant et en protégeant stratégiquement les actifs les plus vulnérables de son portefeuille d'infrastructures. Cela implique la nécessité d'une méthodologie de sélection permettant de cibler les actifs les plus cruciaux et de mesurer efficacement la vulnérabilité globale d'un réseau donné, ce qui nous permettra d'évaluer les niveaux de risque actuels et d'évaluer les améliorations techniques proposés. Le travail à suivre tente de mesurer la robustesse des systèmes d'infrastructures critiques en utilisant une approche basée sur les conséquences, évaluant la fonctionnalité de ces réseaux suite à la survenance d'un événement destructeur. Pour ce faire, des applications empiriques de deux approches différentes—une première utilisant la méthodologie fondée sur la théorie des réseaux et une deuxième méthodologie, proposé pour la première fois, fondée sur l'entropie—ont été réalisées sur les réseaux de transport d'électricité des quatre plus grandes provinces canadiennes en utilisant l'information disponible dans le domaine public. Notre enquête sur les similitudes entre ces deux méthodologies distinctes n’a fourni aucune similarité définitive lors de la comparaison de la vulnérabilité des provinces, mesurée selon les différentes approches, mais a éclairée des avenues prometteuses pour de la recherche future.----------ABSTRACT : Critical infrastructure systems are the physical assets that provide modern societies with the fundamental resources required to conduct essential economic and social operations, from power and electricity to drinking water and telecommunications. The crucial importance of these vast, complex and ubiquitous infrastructures is widely acknowledged and as such, the necessity to protect these networks from destructive events—both intentional and accidental—has garnered the attention of researchers and security experts alike. Similarly, it is also well recognized that the cost and effort associated with total protection presents an enormous challenge. Society will achieve its greatest return on investment by correctly identifying, prioritizing and protecting the most vulnerable assets in its infrastructure portfolio. This implies the need for a screening methodology by which we can target the most crucial assets, and effective metrics with which to gauge the vulnerability of a given network as a whole, allowing us to assess risk levels and evaluate proposed or completed engineering changes. The following work studies the robustness of critical infrastructure systems using a consequence-based framework, assessing the functionality of networks conditional on some destructive event having taken place. In order to do so, empirical applications of two different approaches—the network theory-based methodology and a novel entropy-based methodology—were carried out on the electrical transmission networks of the four largest Canadian provinces, using information available in the public domain. Our attempt to investigate the similarities between the separate methodologies failed to provide any meaningful consistencies when comparing provinces’ robustness according to the different grading schemes, but did provide promising avenues for future research