Interconnected Critical Infrastructures Research Articles

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

An interconnectivity framework for analyzing and demarcating real-time operations across critical infrastructures and over time

Models and modeling of interconnected critical infrastructures are on the rise. A different framework is provided here for analyzing and distinguishing interconnected operations in real time and over time. Our framework treats interconnected operations as the unit and level of analysis rather than as methodologically distinct operations of conceptually separate infrastructures. We focus on interconnectivity configurations, shifts, connected system control variables, and changing performance standards across important shifts. Implications of the conceptual and methodological refocusing are drawn for policy and management. Our framework allows for a new understanding of reliability in relation to interconnectivity management, including the identification of potential errors in assessing and acting upon interconnectivity shifts. These include failing to recognize when changes in communication patterns need to follow from interconnectivity shifts and neglecting the operational importance of improvisations where system control variables (like electricity frequency or water pressure) overlap or are shared. Larger implications for interinfrastructural and societal safety are drawn throughout.

Measuring Resilience in Smart Infrastructures: A Comprehensive Review of Metrics and Methods

In today’s world, the safety, economic prosperity, and social well-being of nations depend heavily on highly interconnected critical infrastructures. These infrastructures encompass power networks, natural gas systems, communication networks, water treatment facilities, and transportation systems. Gaining insight into the behavior of these infrastructures, particularly during stress or attacks, has become crucial for both the private and public sectors. Ensuring an adequate level of functionality during emergencies, such as disasters, is also a priority, which can be attained by enhancing infrastructure resilience. Resilience metrics and models play a significant role in understanding the complex interplay between the behaviors and operational characteristics of interdependent critical infrastructures. Additionally, these models and metrics must demonstrate the interdependencies among infrastructures to provide a more comprehensive representation of infrastructure resilience. This paper reviews, categorizes, and presents resilience metrics and models for Smart Interdependent Critical Infrastructures (Smart ICIs). This paper provides a comprehensive evaluation of various resilience models and measurements tailored specifically for interdependent critical smart infrastructures. It includes the essential terminology and definitions related to the resilience of Smart ICIs, investigates the universally recognized phases and capabilities of resilience, and examines the various types of failures that could potentially affect Smart ICIs.

Disaster Management in Smart Cities

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

Assessing the Effects of Forest Fires on Interconnected Critical Infrastructures under Climate Change. Evidence from South France

The present work introduces a case study on the climate resilience of interconnected critical infrastructures to forest fires, that was performed within the framework on H2020 EU-CIRCLE project (GA 653824). It was conducted in South France, one of the most touristic European regions, and also one of the regions at the highest forest fire risk that is projected to be amplified under future climate conditions. The case study has been implemented through a co-creation framework with local stakeholders, which is critical in moving beyond physical damages to the infrastructures, introducing the elements of infrastructure business continuity and societal resilience. Future forest fires extremes are anticipated to impact the interconnections of electricity and transportation networks that could further cascade to communities throughout South France. The work highlighted the benefits of enhancing co-operation between academia, emergency responders, and infrastructure operators as a critical element in enhancing resilience through increased awareness of climate impacts, new generated knowledge on fire extremes and better cooperation between involved agencies.

Resilience Modeling of Interdependent Traffic-Electric Power System Subject to Hurricanes

Highly interconnected critical infrastructure systems (CISs) are the backbone of the well-being of the modern societies. It is crucial to account for the interdependencies among CISs in assessing the community resilience. This study presents a framework for resilience assessment of an interdependent traffic-electric power system subject to hurricanes. The traffic and electric power systems are both characterized using the flow-based mathematical representation, and three types of interdependency are captured. A modeling procedure coupled with the Monte Carlo simulation is presented to assess the resilience of both systems and to integrate different improvement strategies into corresponding stages of resilience. The traffic and electric power systems in Centerville, considering three improvement strategies, are studied to demonstrate the proposed framework. The results show that interdependencies can significantly affect the resilience of both systems, leading to different and the most effective strategies for each system. While hardening hurricane-vulnerable components and adopting a better repair schedule, both improve resilience of the electric power system significantly; adopting a better repair schedule works best for the resilience of the traffic system. The equal repair mode can achieve the resilience improvement in a balanced fashion. The results also highlight the importance of repair scheduling to the resilience improvement of these two interdependent systems when the available recovery resources are limited. Due to the interdependencies, the faster the functionality of traffic system can recover, the more efficiently the repair activities can be conducted, and thus, the greater resilience of both systems can be achieved.

Enhancing resilience of interdependent traffic-electric power system

Characterizing the interdependencies among highly interconnected critical infrastructure systems with adequate details is critical in devising cost-effective resilience improvement strategies. This study presents a bi-level, stochastic, and simulation-based decision-making framework for prioritizing mitigation and repair resources to maximize the expected resilience improvement of an interdependent traffic-electric power system under budgetary constraints. The upper level seeks to find the optimal resource allocation plan to maximize the expected attainable functionality gain. The lower level characterizes the functionalities of the traffic and electric power systems considering three types of interdependencies based on network flow analysis methods. The dynamic traffic assignment algorithm, rather than the static traffic assignment algorithm, is used in order to capture more realistic traffic dynamics in the congested urban roadway networks. Uncertainties in disruptions, traffic demands, and costs of mitigation and repair actions are also considered in the problem formulation. The problem is solved by the binary particle swarm optimization algorithm initialized with the knapsack-based heuristic, and the priority indices of disrupted components for mitigation and repair are then established based on the solutions. The proposed decision model is demonstrated using a portion of the traffic-electric power system in Galveston, Texas.

Analysis of Interdependence of Arctic Critical Infrastructures as Transportation Networks

The Arctic zone of the Russian Federation (AZRF) is characterized by the specificity of the development of its interconnected critical infrastructures’ objects (ICI) - power generation and transmission systems, social infrastructure facilities, transportation (land, air, water) and communication systems, by the exceptional importance of their stable operation. The spatial isolation of the ICI objects predetermines, practically for each municipality, formation of its specific configuration, defining the range of needed capacities, corresponding set of ICI objects, etc. The main functional of Arctic ICI objects is to provide and sustain life support. Authors developed a model that permits high resolution description of ICI operation on the local level. The accuracy of the model is enough, especially for describing electrical and water supply grids and nets. Its substantial advantage is the generality of its mathematical description of the ICI of different physical nature and its capacity to account for the randomness of ICI parameters. The model permits simulation of the ICI behavior on the local level when exposed to ordinary, everyday conditions and in emergency situations (i.e., during an industrial disaster, Natural catastrophe or a terrorist attack). Several specific problems were solved using this approach for the AZRF region.

EU-CIRCLE methodological approach for assessing the resilience of the interconnected critical infrastructures of the virtual city scenario to climate change

This paper introduces a methodological approach for identifying the resilience of interconnected EU critical infrastructures to climate change. The proposed approach tries to establish a consequence-based modelling framework for assessing climate-dependent causal relationships between CI operation and response to climate impacts with an aim to minimise disruptions to service flows under diverse conditions. The proposed study provides a description of the virtual city’s forest fire and smoke reference scenario with their optional damages to the electricity transmission and distribution network.

Perceptions of cascading risk and interconnected failures in emergency planning: Implications for operational resilience and policy making

Cascading events and the disruption of interconnected critical infrastructure (CI) are a well-known challenge for emergency planning and management. Although new modelling and forecasting tools are increasing the availability of mitigation options, further work is needed to consolidate good policies and practices.This paper presents a case study on London, United Kingdom (UK). It develops an integrative research process with local stakeholders aimed at applying the theory of cascading disasters in London's response and preparedness strategies. A mixed method approach is adopted to explore the awareness of cascading risk, the perception of possible mitigation measures, and the current levels of training amongst the stakeholders of the London Resilience Partnerships. Specific items investigate the relation of cascades with cross- border crisis management, and climate change.The results propose new quantitative and qualitative evidence that highlights the divergence between risk awareness and its integration in policies and practices, discussing options for improving multi- agency coordination and organisational resilience. The conclusions define a list of actions that should be strategic priorities for policies and decision makers, such as the improvement of inter-modal coordination and the release of open access guidelines, proposing new open questions for scholars in the field.

Intelligent decision system for responsive crisis management

Disaster mitigation of severe catastrophic events depend heavily on effective decisions that are made by officials. The goal of disaster management is to make decisions that properly reallocate and redistribute the scarce resources produced by the available interconnected-critical infrastructures (CI's). This paper investigates the application of Monte Carlo (MC)-based policy estimation in reinforcement learning (RL) to mount up experience from a massive number of simulations. This method, in conjunction with an optimised set of RL parameters, will help the RL agent to explore and exploit those trajectories that lead to an optimum result in a reasonable time. It shows that a learning agent using MC estimation policy, through interactions with an environment of simulated disastrous scenarios (i2Sim-infrastrucuture interdependency simulator) is capable of making informed decisions for complex systems in a timely manner.

Intelligent decision system for responsive crisis management

Disaster mitigation of severe catastrophic events depend heavily on effective decisions that are made by officials. The goal of disaster management is to make decisions that properly reallocate and redistribute the scarce resources produced by the available interconnected-critical infrastructures (CI's). This paper investigates the application of Monte Carlo (MC)-based policy estimation in reinforcement learning (RL) to mount up experience from a massive number of simulations. This method, in conjunction with an optimised set of RL parameters, will help the RL agent to explore and exploit those trajectories that lead to an optimum result in a reasonable time. It shows that a learning agent using MC estimation policy, through interactions with an environment of simulated disastrous scenarios (i2Sim-infrastrucuture interdependency simulator) is capable of making informed decisions for complex systems in a timely manner.

A reliability & risk framework for the assessment and management of system risks in critical infrastructures with central control rooms

The submission integrates approaches and major findings generated over the course of our 15-year research project on high reliability management in California infrastructures. The resulting framework identifies issues and variables core to systemwide reliability and risks in critical infrastructures having central control rooms and operating under different standards of reliability (including safety). In this framework, issues of managing system risks follow from logically and empirically prior issues related to managing the system in real time by the infrastructure’s control room according to the systemwide reliability standards in operation. The considerable implications are drawn out for the increasingly important field of risk assessment and management in interconnected critical infrastructures.

Resilience Analysis Framework for Interconnected Critical Infrastructures

Abstract To investigate the resilience of interconnected critical infrastructures (CIs), a framework combining dynamic modeling and resilience analysis is proposed. Resilience is defined in this work as the capacity of a system to absorb the impacts of perturbations and recover quickly from disruptive states. It is seen as a property of the system, which depends on a number of design, operation, and control parameters. Within this framework, we introduce the concept of resilience regions in the parameters space: as long as the parameters values remain inside these regions during operation, the system visits only recoverable states or, in other words, it maintains nominal operation or recovers quickly to it. Based on this concept, we perform a resilience analysis of two interconnected critical infrastructures, a gas network and an electric power system. The analysis is performed by numerical calculation of the resilience conditions in terms of design, operation, and control parameters values for given failure scenarios. To render computationally feasible analysis, we resort to an abstract representation of the system dynamics by a linear model of switching dynamics. Although the high-level modeling adopted may suffer from predictive accuracy, the proposed framework can still provide valuable insights in the analysis of system resilience and its dependence on the design, operation, and control parameters under different failure scenarios, which can be valuable to inform the decision making process of CIs operators and other stakeholders.

Using interdependency matrices to mitigate targeted attacks on interdependent networks: A case study involving a power grid and backbone telecommunications networks

Analysis of the interdependencies between interconnected critical infrastructures can help enhance the robustness of the individual infrastructures as well as the overall interconnected infrastructures. One of the most studied interdependent critical infrastructure network scenarios is a power grid connected to a backbone telecommunications network. In this interdependent infrastructure scenario, the robustness of the entire system is usually analyzed in the context of cascading failure models in the power grid. However, this paper focuses on targeted attacks, where an attack on a telecommunications network node directly affects a connected power grid node, and vice versa. Cascading failures are outside the scope of this paper because the objective is to enhance the robustness of the interconnections between the infrastructures. In order to mitigate the impacts of targeted attacks on the interdependent infrastructures, three interdependency matrices for connecting the infrastructures are specified and analyzed. The analysis identifies the interdependency matrix that best reduces the impacts of targeted attacks and the propagation of failures between the infrastructures. Additionally, the impacts of interconnecting a power grid to different telecommunications networks, each with different susceptibilities to targeted attacks, is evaluated.

A collaborative cyber incident management system for European interconnected critical infrastructures

Today's Industrial Control Systems (ICSs) operating in critical infrastructures (CIs) are becoming increasingly complex; moreover, they are extensively interconnected with corporate information systems for cost-efficient monitoring, management and maintenance. This exposes ICSs to modern advanced cyber threats. Existing security solutions try to prevent, detect, and react to cyber threats by employing security measures that typically do not cross the organization's boundaries. However, novel targeted multi-stage attacks such as Advanced Persistent Threats (APTs) take advantage of the interdependency between organizations. By exploiting vulnerabilities of various systems, APT campaigns intrude several organizations using them as stepping stones to reach the target infrastructure. A coordinated effort to timely reveal such attacks, and promptly deploy mitigation measures is therefore required. Organizations need to cooperatively exchange security-relevant information to obtain a broader knowledge on the current cyber threat landscape and subsequently obtain new insight into their infrastructures and timely react if necessary. Cyber security operation centers (SOCs), as proposed by the European NIS directive, are being established worldwide to achieve this goal. CI providers are asked to report to the responsible SOCs about security issues revealed in their networks. National SOCs correlate all the gathered data, analyze it and eventually provide support and mitigation strategies to the affiliated organizations. Although many of these tasks can be automated, human involvement is still necessary to enable SOCs to adequately take decisions on occurring incidents and quickly implement counteractions. In this paper we present a collaborative approach to cyber incident information management for gaining situational awareness on interconnected European CIs. We provide a scenario and an illustrative use-case for our approach; we propose a system architecture for a National SOC, defining the functional components and interfaces it comprises. We further describe the functionalities provided by the different system components to support SOC operators in performing incident management tasks.

Risk assessment and management for interconnected critical infrastructure systems at the site and regional levels in California's Sacramento-San Joaquin Delta

This article summarises research-in-progress for improved risk assessment and management (RAM) of critical infrastructures that interconnect across California's Sacramento-San Joaquin Delta. The need for improved RAM is patent in the Delta as elsewhere: a 'patch and pray' stalemate has developed which focuses on short-term reactive marginal maintenance and emergency response and recovery systems, all pushing infrastructures - and their engineers, designers and operators - increasingly to their performance edges and beyond. The research focuses on water supply, transportation, energy and flood protection systems, all of which are embedded in a dynamic ecosystem and showing clear signs of deterioration. Provisional findings of research activities are discussed. This article addresses critical infrastructure modelling uncertainties and ways to better understand, reduce or otherwise accommodate human/organisational and informational uncertainties in any RAM focused at the interconnected critical infrastructure system level.

Evaluating the effects of cascading failures in a network of critical infrastructures

Networks of interconnected critical infrastructures are the supporting mechanisms of every industrialised nation. Mutually reliant on each other, their service provisions cross borders. This reliance is also a great weakness. The level of dependence each infrastructure has on another means that a failure has the potential to cascade, resulting in devastating impact on the economy, e-government, defence and society as a whole. Predicting the effects of a cascading failure is a challenge. In this paper, an approach for identifying the effects of a cascading failure is portrayed. A simulation depicting a virtual city is presented, in order to assess the spread of faults originating from a telecommunications infrastructure. Subtle behaviour changes have the potential to spread, with both significant and minor impacts. These variations can be mitigated for using data classification techniques to assess behaviour changes, with an overall accuracy of 85.61% using the TreeC classifier.