Identification of critical locations across multiple infrastructures for terrorist actions

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 2005.Includes bibliographical references (leaves 59-60).This paper discusses a possible approach to ranking geographic regions that can influence multiple infrastructures. Once ranked, decision makers can determine whether these regions are critical locations based on their susceptibility to terrorist acts. We identify these locations by calculating a value for a geographic region which represents the combined values to the decision makers of all the infrastructures crossing through that region. These values, as well as the size of the geographic regions, are conditional on a minor destructive threat of a given size, e.g,. a bomb that can affect objects within 15 feet of it. This approach first requires an assessment of the users of the system. During this assessment, each user is assigned a performance index (PI) based on the disutility of the loss of each infrastructure's resource via multi-attribute utility theory (MAUT). A Monte Carlo network analysis is then performed to develop importance measures (IM) for the elements of each infrastructure for their ability to service each user. We combine the IMs with the user PIs to a value that we call valued worth (VW) for each infrastructure's elements independently.(cont.) Then we use spatial analysis techniques within a Geographic Information System (GIS) to combine the VWs of each infrastructure's elements in a geographic area, conditional on the threat, into a total value we call geographic valued worth (GVW). The GVW is graphically displayed in the GIS system in a color scheme that shows the numerical ranking of these geographic areas. The map and rankings are then submitted to the decision makers to better allocate anti-terrorism resources. A case study of this methodology is preformed on the Massachusetts Institute of Technology's (MIT) campus. The results of the study show how the methodology can bring attention to areas that may be ignored through individual infrastructure analysis. The intersections of major infrastructures on the campus prove to be of the most importance to the stakeholders of the campus.by Sean Albert Patterson.S.M

NDM-515: AN ORIGINAL MODEL OF INFRASTRUCTURE SYSTEM RESILIENCE

Infrastructure systems of transportation, water supply, telecommunications, power supply, etc. are not isolated but highly interconnected and mutually coupled. Infrastructure interdependences can increase system vulnerability and produce cascading failures at the regional or national scales. Taking the advantage of network theory structure analysis, this paper models street, water supply network, power grid and information infrastructure as network layers that are integrated into a multilayer network. The infrastructure interdependences are detailed using five basic dependence patterns of network fundamental elements. Definitions of dynamic cascading failures and recovery mechanisms of infrastructure systems are also established. The main focus of the paper is introduction of a new infrastructure network resilience measure capable of addressing infrastructure system as well as network component (layer) interdependences. The new measure is based on infrastructure network performance, proactive infrastructure network resistance capacity and reactive infrastructure network recovery capacity. With three resilience features and corresponding network properties develops paper, this the of dynamic space new quantitative measure -time resilience and a resilience simulation model resilience and network properties three dimensions of use for infrastructure network assessments. The resilience model is applicable to any type of infrastructure and its application can improve the infrastructure planning, design and maintenance decision making

Quantitative maritime security assessment: a 2020 vision

Maritime security assessment is moving towards a proactive risk-based regime. This opens the way for security analysts and managers to explore and exploit flexible and advanced risk modelling and decision-making approaches in maritime transport. In this article, following a review of maritime security risk assessment, a generic quantitative security assessment methodology is developed. Novel mathematical models for security risk analysis and management are outlined and integrated to demonstrate their use in the developed framework. Such approaches may be used to facilitate security risk modelling and decision making in situations where conventional quantitative risk analysis techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in risk and uncertainty modelling technology are suggested with respect to maritime security risk quantification and management

Bulk Power Grid Risk Analysis: Ranking Infrastructure Elements According to their Risk Significance

Disruptions in the bulk power grid can result in very diverse consequences that include economic, social, physical, and psychological impacts. In addition, power outages do not affect all end-users of the system in the same manner. For these reasons, a risk analysis of bulk power systems requires more than determining the likelihood and magnitude of power outages; it must also include the diverse impacts power outages have on the users of the system. We propose a methodology for performing a risk analysis on the bulk power system. A power flow simulation model is used to determine the likelihood and extent of power outages when components within the system fail to perform their designed function. The consequences associated with these failures are determined by looking at the type and number of customers affected. Stakeholder input is used to evaluate the relative importance of these consequences. The methodology culminates with a ranking of each system component by its risk significance to the stakeholders. The analysis is performed for failures of infrastructure elements due to both random causes and malevolent acts

Space Weather and Power Grids - A Vulnerability Assessment

Strong geomagnetic disturbances resulting from solar activity can have a major impact on ground-based infrastructures, such as power grids, pipelines and railway systems. The high voltage transmission network is particularly affected as currents induced by geomagnetic storms, so-called GICs, can severely damage network equipment possibly leading to system collapse. Therefore, increasing attention has been devoted to understanding the vulnerability of power grids to space weather conditions. In this study, we aim at analysing the vulnerability of power grids to extreme space weather. By means of complex network theory, we propose an analysis approach to understand how geomagnetically induced currents are driven through the power network based on its structural and physical characteristics. As a test network we used the Finnish power grid for which a study using network centrality measures was carried out to understand which components are the most critical for the system when exposed to an electric field of 1V/km. This information is helpful as the identification and ranking of critical components can help to identify where and how mitigation measures should be implemented to increase the system’s resilience to space weather impact. We have also subjected the grid to varying angles of the electric field. In addition, we have carried out a scoping study adding load flow to the GICs induced in the system. The preliminary results suggest that the benchmark system can resist GICs induced from high intensity electric fields. Moreover, the simplified network seems more prone to collapse if the electric field is oriented northward. Work is underway to further validate and expand our approach with the aim to eventually carry out a risk assessment of space weather impact on the power grid at EU level.JRC.G.5-Security technology assessmen

Incorporating geographic interdependencies into the resilience assessment of multimodal public transport networks

Severe weather events, such as snowfall, flooding and storms, may affect wide geographical areas and adversely impact discrete transport infrastructure networks (e.g. road, rail) at the same time, thus revealing the existence of geographic interdependencies between these networks. In this paper, we develop two accessibility-based measures to assess the impact of geographic interdependency on resilience based on the concepts of redundancy and substitutability, respectively. These measures are applied to the railway and long-distance bus networks in Scotland. Results reveal that the combined effect of redundancy and substitutability on the accessibility of locations offered by these discrete modes is reduced due to geographic interdependencies, with the extent of losses being positively associated with the spatial footprint of potential events. The results can be used to identify parts of the network where the potential impacts of geographic interdependencies are greatest, and thus require more in-depth scrutiny by network managers

Protection collaborative des réseaux logistiques

De nos jours la protection des réseaux logistiques représente un vrai défi. Dans la littérature, plusieurs travaux ont examiné ce problème et ont proposé des modèles pour la défense stratégique des réseaux logistiques. Habituellement, chaque réseau protège individuellement ses installations en utilisant ses propres moyens. Or, la protection collaborative de ces réseaux est susceptible d'engendrer une amélioration de l'utilité des stratégies de défense utilisées et une réduction des coûts de cette protection. Nous considérons, dans ce mémoire, un ensemble des réseaux logistiques qui ont accepté de collaborer pour améliorer leurs méthodes de protection. Tout d'abord, nous avons commencé par définir et appliquer un modèle de protection stratégique basé sur la théorie des jeux non coopératifs sur un ensemble de réseaux logistiques, afin de déterminer les stratégies de défenses qui seront adoptées individuellement par chaque réseau. Ce modèle a été ensuite étendu et modifié dans le cas où plusieurs réseaux logistiques décidèrent de collaborer pour améliorer l'utilité de leurs stratégies de défense. Ce modèle permet également, l'évaluation de l'utilité de cette coalition et la réduction des coûts générés grâce à la collaboration. Pour que cette collaboration soit durable et bénéfique pour tous les réseaux, nous devons allouer les coûts engendrés par cette coalition à tous les participants de façon équitable. Pour cela, nous appliquons plusieurs méthodes de partage des coûts issues de la théorie des jeux collaboratifs. Enfin, ces modèles seront appliqués pour le cas de deux réseaux logistiques

Towards Managing and Understanding the Risk of Underwater Terrorism

This dissertation proposes a methodology to manage and understand the risk of underwater terrorism to critical infrastructures utilizing the parameters of the risk equation. Current methods frequently rely on statistical methods, which suffer from a lack of appropriate historical data to produce distributions and do not integrate epistemic uncertainty. Other methods rely on locating subject matter experts who can provide judgment and then undertaking an associated validation of these judgments. Using experimentation, data from unclassified successful, or near successful, underwater attacks are analyzed and instantiated as a network graph with the key characteristics of the risk of terrorism represented as nodes and the relationship between the key characteristics forming the edges. The values of the key characteristics, instantiated as the length of the edges, are defaulted to absolute uncertainty, the state where there is no information for, or against, a particular causal factor. To facilitate obtaining the value of the nodes, the Malice spectrum is formally defined which provides a dimensionless, methodology independent model to determine the value of any given parameter. The methodology produces a meta-model constructed from the relationships between the parameters of the risk equation, which determines a relative risk value