Risk Assessment Methodology for Critical Infrastructure Protection

The European Programme for Critical Infrastructure Protection is the main vehicle for the protection of critical infrastructures in Europe. The Directive 2008/114/EC is the legislative instrument of this programme. Risk assessment is an important element that is mentioned throughout the Directive text. However, there is no harmonized methodology in Europe for the assessment of interconnected infrastructures. The present work describes such a methodology and its implementation for the assessment of critical infrastructures of European dimension. The methodology accounts for impact at asset level, evaluates the propagation of a failure at network level due to interdependencies and assess the economic impact of critical infrastructure disruption at national level.JRC.G.6-Security technology assessmen

A fuzzy dynamic inoperability input-output model for strategic risk management in global production networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that need to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed

Electricity Supply Interruptions: Sectoral Interdependencies and the Cost of Energy Not Served for the Scottish Economy

The power sector has a central role in modern economies and other interdependent infrastructures rely heavily upon secure electricity supplies. Due to interdependencies, major electricity supply interruptions result in cascading effects in other sectors of the economy. This paper investigates the economic effects of large power supply disruptions taking such interdependencies into account. We apply a dynamic inoperability input–output model (DIIM) to 101 sectors (including households) of the Scottish economy in 2009 in order to explore direct, indirect, and induced effects of electricity supply interruptions. We then estimate the societal cost of energy not supplied (SCENS) due to interruption, in the presence of interdependency among the sectors. The results show that the most economically affected industries, following an outage, can be different from the most inoperable ones. The results also indicate that SCENS varies with duration of a power cut, ranging from around £4300/MWh for a one-minute outage to around £8100/MWh for a three hour (and higher) interruption. The economic impact of estimates can be used to design policies for contingencies such as roll-out priorities as well as preventive investments in the sector

ROBUST DECISION-MAKING AND DYNAMIC RESILIENCE ESTIMATION FOR INTERDEPENDENT RISK ANALYSIS

When systems and subsystems are put under external shocks and duress, they suffer physical and economic collapse. The ability of the system components to recover and operate at new stable production levels characterizes resilience. This research addresses the problem of estimating, quantifying and planning for resilience in interdependent systems, where interconnectedness adds to problem complexity. Interdependence drives the behavior of sectors before and after disruptions. Among other approaches this study concentrates on economic interdependence because it provides insights into other levels of interdependence. For sectors the normalized losses in economic outputs and demands are suitable metrics for measuring interdependent risk. As such the inoperability input-output model enterprise is employed and expanded in this study to provide a useful tool for measuring the cascading effects of disruptions across large-scale interdependent infrastructure systems. This research defines economic resilience for interdependent infrastructures as an "ability exhibited by such systems that allows them to recover productivity after a disruptive event in a desired time and/or with an acceptable cost". Through the dynamic interdependent risk model resilience for a disrupted infrastructure is quantified in terms of its average system functionality, maximum loss in functionality and the time to recovery, which make up a resilience estimation decision-space. Estimating such a decision-space through the dynamic model depends upon the estimation of the rate parameter in the model. This research proposes a new approach, based on dynamic data assimilation methods, for estimating the rate parameter and strengthening post-disaster resilience of economic systems. The solution to the data assimilation problem generates estimates for the rate of resilient recovery that reflects planning considerations interpreted as commodity substitutions, inventory management and incorporating redundancies. The research also presents a robust optimization based risk management approach for strengthening interdependent static resilience estimation. There is a paucity of research dealing with quantification and assessment of uncertainties in interdependency models. The focus here is more on the extreme bounds of event and data uncertainties. The deterministic optimization becomes a robust optimization problem when extremes of uncertainties are considered. Computationally tractable robust counterparts to nominal problems are presented here. Also presented in this research is a discrete event simulation based queuing model for studying multi-modal transportation systems with particular focus on inland waterway ports. Such models are used for impact analysis studies of inland port disruptions. They can be integrated with the resilience planning methodologies to develop a framework for large-scale interdependent risk and recovery analysis

Infastructure Interdependencies Modeling and Analysis - A Review and Synthesis

The events of 9/11 and the occurrence of major natural disasters in recent years has resulted in increased awareness and renewed desire to protect critical infrastructure that are the pillars to maintaining what has become normal life in our economy. The problem has been compounded because the increased connectedness between the various sectors of the economy has resulted in interdependencies that allow for problems and issues with one infrastructure to affect other infrastructures. This area is now being investigated extensively after the Department of Homeland Security (DHS) prioritized this issue. There is now a vast extant of literature in the area of infrastructure interdependencies and the modeling of it. This paper presents a synthesis and survey of the literature in the area of infrastructure interdependency modeling methods and proposes a framework for classification of these studies. The framework classifies infrastructure interdependency modeling and analysis methods into four quadrants in terms of system complexities and risks. The directions of future research are also discussed in this paper

Infrastructure Network Resilience and Economic Impacts: Applications in Multi-Modal Freight Transportation

The US has defined a number of critical infrastructures, the disruption of which “would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters”. Among these critical infrastructures are transportation networks, which enable the flow of people and commodities, and recent reports suggest that many highways, bridges, and other transit assets in the US fall short of a state of good repair, potentially threatening the efficiency of the network. In 2013, 55 million tons of goods valued at more than $49.3 billion traversed the US freight transportation system each day, and freight tonnage and monetary value rose by 6.3 and 8.0 percent, respectively, over 2007 levels. Over the next 30 years, transportation’s contribution to the US gross domestic product is expected to grow to approximately$1.6 trillion. Given the potential for disruption by malevolent attacks, natural disasters, human-made accidents, or common failures, recent US planning documents focus on the criticality of transportation network preparedness. Emphasis has been placed on “securing and managing flows of people and goods” along transportation networks. The consequences of disruptions to critical infrastructures highlight the need to better understand resilience, or the ability to withstand the effects of and recover timely from a disruption. Particularly for critical infrastructures, the Infrastructure Security Partnership (2011) noted that a resilient infrastructure sector would “prepare for, prevent, protect against, respond or mitigate any anticipated or unexpected significant threat or event” and “rapidly recover and reconstitute critical assets, operations, and services with minimum damage and disruption.” As with any other critical infrastructure, resilience planning is important for multi-modal transportation networks due to their role in the economic vitality of states, regions, and the broader country. The functionality of this network is threatened by disruptive events that can disable the capacity of the network to enable flows of commodities in portions of nodes and links. This research creates a new paradigm with which to improve decision making for freight transportation network sustainment through an integrated duple of resilience and interdependent economic impact. Integrating a multi-commodity network flow formulation with an economic interdependency model, driven by publicly available data from Bureau of Economic Analysis and U.S. Department of Transportation, I have proposed a framework to quantify the multi-regional, multi-industry impacts of a disruption in the transportation network which has led to (i) defining a new measure of network component importance, (ii) planning for adaptive capacity through contingent rerouting, (iii) investing for absorptive capacity, and (iv) guiding network recovery and resilience. This work has been applied a multimodal freight transportation network in Oklahoma that connects the state to several regional trading states, enabling the flow of six important commodities that have interdependent effects on the Oklahoma economy (classified into 62 industry sectors)

BAYESIAN KERNEL METHODS FOR THE RISK ANALYSIS AND RESILIENCE MODELING OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection of critical infrastructures has recently garnered attention with an emphasis on analyzing the risk and improving the resilience of such systems. With the abundance of data, risk managers should be able to better inform preparedness and recovery decision making under uncertainty. It is important, however, to develop and utilize the necessary methodologies that bridge between data and decisions. The goal of this dissertation is to (i) predict the likelihood of risk, (ii) assess the consequences of a disruption, and (iii) inform preparedness and recovery decision making. This research presents a data-driven analysis of the risk and resilience of critical infrastructure systems. First, a new Bayesian kernel model is developed to predict the frequency of failures and a Beta Bayesian kernel model is deployed to model resilience-based importance measures. Bayesian kernel models were developed for Gaussian distributions and later extended to other continuous probability distributions. This research develops a Poisson Bayesian kernel model to accommodate count data. Second, interdependency models are integrated with decision analysis and resilience quantification techniques to assess the multi-industry economic impact of critical infrastructure resilience and inform preparedness and recovery decision making under uncertainty. Examples of critical infrastructure systems are inland waterways, which are critical elements in the nation’s civil infrastructure and the world’s supply chain. They allow for a cost-effective flow of approximately $150 billion worth of commodities annually across industries and geographic locations, which is why they are called “inland marine highways.” Aging components (i.e., locks and dams) combined with adverse weather conditions, affect the reliability and resilience of inland waterways. Frequent disruptions and lengthy recovery times threaten regional commodity flows, and more broadly, multiple industries that rely on those commodities. While policymakers understand the increasing need for inland waterway rehabilitation and preparedness investment, resources are limited and select projects are funded each year to improve only certain components of the network. As a result, a number of research questions arise. What is the impact of infrastructure systems disruptions, and how to predict them? What metrics should be used to identify critical components and determine the system’s resilience? What are the best risk management strategies in terms of preparedness investment and recovery prioritization? A Poisson Bayesian kernel model is developed and deployed to predict the frequency of locks and dams closures. Economic dynamic interdependency models along with stochastic inoperability multiobjective decision trees and resilience metrics are used to assess the broader impact of a disruption resulting in the closure of a port or a link of the river and impacting multiple interdependent industries. Stochastic resilience-based measures are analyzed to determine the critical waterway components, more specifically locks and dams, that contribute to the overall waterway system resilience. A data-driven case study illustrates these methods to describe commodity flows along the various components of the U.S. Mississippi River Navigation System and employs them to motivate preparedness and recovery strategies