Risk Ontology and Service Quality Descriptor Shared among Interdependent Critical Infrastructures

Critical infrastructures (CI) are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator GeoPN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.

Critical infrastructures are important national assets for producing or distributing continuous flows of essential goods or services. When one critical infrastructure shuts down due to an external disruption, it can be expected that other critical infrastructures that need goods or services provided by the discontinued critical infrastructure will stop shortly, exacerbating the damage caused by the external disruption. Past research has categorized critical infrastructure interdependency (CII) into four relationship types and has proposed several methods to model CII and its effects. This paper presents a knowledge discovery process for CII that can be used to extract frequent patterns of critical infrastructure failure sequences triggered by external disruptions and/or CII. The knowledge discovery process, including integration of critical infrastructure failure records and transformation into the data format needed by the data mining algorithm, is described. The analysis results of real critical infrastructure failure records in certain areas of Taipei city are addressed, followed by discussion of a mitigation approach to stopping the possible future failure sequences of CII. Disaster mitigation officials can employ the proposed approach to explore CII and to design countermeasures when a disaster hits certain areas.

Much of Critical Infrastructure (CI) interdependency research focuses on short-term cascading effects from catastrophic incidents. These relationships are most often modeled in a direct physical sense, for example, a flooded road blocking emergency vehicle response. The relationship between economic investment and degradation in CIs, however, remains an under-explored research area. Focusing on three CIs---transportation, public utilities, and communications---this paper presents a system dynamics approach to modeling degradation and infrastructure investment. Combining traditional physical and cyber approaches to infrastructure modeling with a human factors component, we present a methodology for quantifying degradation of interdependent CIs. The model shows some of the dangerous feedback cycles of degradation that can occur between infrastructures when investment and social resiliency are insufficient. The purpose of this paper is to develop a method to incorporate social systems into interdependent infrastructure research that can be built into a tool to aid policymakers in understanding and addressing investment linkages between CIs.

Recent adversarial incidents highlight the inherent vulnerabilities in multiple targeted critical infrastructures cyber-physical systems. Next-Gen critical infrastructures will continue evolving to be more distributed, inter-connected, and interdependent. Significant negative consequences could be inflected by threat actors exploiting those characteristics, requiring a more comprehensive and systematic approach. This work proposes a data-based hierarchical framework to identify, detect, and mitigate cyber and/or physical attacks through enriched dynamic situational awareness encompassing individual and interdependent critical infrastructures.

The Healthcare Critical Infrastructure (HCI) protects all sectors of the society from hazards such as terrorism, infectious disease outbreaks, and natural disasters. HCI plays a significant role in response and recovery across all other sectors in the event of a natural or manmade disaster. However, for its continuity of operations and service delivery HCI is dependent on other interdependent Critical Infrastructures (CI) such as Communications, Electric Supply, Emergency Services, Transportation Systems, and Water Supply System. During a mass casualty due to disasters such as floods, a major challenge that arises for the HCI is to respond to the crisis in a timely manner in an uncertain and variable environment. To address this issue the HCI should be disaster prepared, by fully understanding the complexities and interdependencies that exist in a hospital, emergency department or emergency response event. Modelling and simulation of a disaster scenario with these complexities would help in training and providing an opportunity for all the stakeholders to work together in a coordinated response to a disaster. The paper would present interdependencies related to HCI based on Stochastic Coloured Petri Nets (SCPN) modelling and simulation approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The entire model would be integrated with Geographic information based decision support system to visualize the dynamic behaviour of the interdependency of the Healthcare and related CI network in a geographically based environment.

This paper presents a novel approach to the critical infrastructure (CI) interdependencies analysis, based on the Dynamic Bayesian Network (DBN) formalism. Our original modeling procedure divides the DBN in three levels: an atomic events level, which models the adverse events impacting on the analyzed CIs, a propagation level, which captures CI interdependencies, and a services level, which allows to monitor the state of provided services. Three types of analyses can be performed: a reliability study, an adverse events propagation study, and a failure identification analysis. A case study provided by Israel Electric Corporation is considered, and explicative simulations are presented and discussed in detail.

The presented ongoing research project focuses on the challenges concerning the protection of critical infrastructures. Since critical infrastructures become more and more cross-linked a single breakdown can cause serious cascading effects on other infrastructures. Interdependencies of critical infrastructures are boundaryspanning and therefore public authorities, operating companies and other stakeholders (e.g. German Federal Agency for technical relief, Red Cross) as well as stakeholders of neighbouring states must have access to a solution which enables the urgently needed cooperation of all parties involved. The aim of the project is to develop an online platform to facilitate knowledge exchange among stakeholders, an online tool to analyse the interdependencies and to design a sustainable and effective knowledge management. This platform will allow registered members to find and make use of the information about the stakeholders and their competencies concerning the protection of critical infrastructures.

Proposed methodology for risk analysis of interdependent critical infrastructures to extreme weather events

All critical infrastructures (CIs) are already very complex and it is getting more complicated as different systems are integrated with each other. For this reason, implementing a mathematical model for critical infrastructure is a rarely researched topic. In this study, we investigate an optimisation model for critical infrastructure protection, rebuilding, and damage assessment. In particular, our model can be usefully applied to a case of which critical infrastructure is widely damaged by a weapon of mass destruction or by a disaster. Moreover, we introduce a novel idea regarding the implementation of interdependency as a mathematical formulation. In order to validate our model, we test our model in the water system of Seoul, South Korea. We describe computational results and insights from various experiments as well.

Modeling the resilience of interdependent critical infrastructure (ICI) requires a careful assessment of interdependencies as these systems are becoming increasingly interconnected. The interdependent connections across ICIs are often subject to uncertainty due to the lack of relevant data. Yet, this uncertainty has not been properly characterized. This paper develops an approach to model the resilience of ICIs founded in probabilistic graphical models. The uncertainty of interdependency links between ICIs is modeled using stochastic block models (SBMs). Specifically, the approach estimates the probability of links between individual systems considered as blocks in the SBM. The proposed model employs several attributes as predictors. Two recovery strategies based on static and dynamic component importance ranking are developed and compared. The proposed approach is illustrated with a case study of the interdependent water and power networks in Shelby County, TN. Results show that the probability of interdependency links varies depending on the predictors considered in the estimation. Accounting for the uncertainty in interdependency links allows for a dynamic recovery process. A recovery strategy based on dynamically updated component importance ranking accelerates recovery, thereby improving the resilience of ICIs.

With the development of socio-economic, interdependencies between critical infrastructures become much closer, that resulting in the fragility of the system. This paper proposes a model of interdependent critical infrastructures network in consideration of the hierarchy. Physical interdependence among critical infrastructures is taken into consideration. Even more specifically, the interdependent network is constructed by analyzing the energy, water supply, telecommunication and transportation, together with the consideration of the hierarchy structure. Moreover, within the interdependent network, the determining method of edges weight is developed on the basis of the supply capacity and quantity of critical infrastructures system elements, which helps to describe the regional characteristics. Finally, the interdependent network with the hierarchy is constructed by taking energy, water supply, telecommunication and transportation of a city in China as the objects. And the analysis on the structural characteristics of the network shows that energy nodes have a greater influence, which suggests that the energy related critical infrastructures need more attention.

The chapter engages in exploring possibilities of innovation and use of future technology arising from multistakeholder collaboration in essential societal functions such as public services, critical infrastructure systems and public policy arena. Multistakeholder collaboration is the key to adaptation and resilience amidst the fast-changing twenty-first century world around us. Owing to increased interconnectedness and interdependencies of complex socio-technical systems, critical infrastructures and essential services, our socio-technical relationships have also grown more complex. On one hand, the tightly coupled systems, services and infrastructures are highly optimized while on the other, because of their high optimization and tightly coupled nature, these systems are often more prone to catastrophic disruptions and crises. So, what is the way forward to effectively increase adaptation and resilience? The chapter engages in the discussion of boundary spanning activities such as the essential public services where multistakeholder collaboration among various actors, i.e., public, private, academia and the industry is inevitable due to cross-cutting issues, demands and challenges. With examples of successful cases and evidence from scientific literature, the chapter makes a convincing case in favour of multistakeholder collaboration as a powerful tool to raise societal resilience.KeywordsMultistakeholder collaborationInnovationSocio-technical systemsCrisis managementDigitalization

The notion that our nation's critical infrastructures are highly interconnected and mutually dependent in complex ways, both physically and through a host of information and communications technologies (so-called cyberbased systems), is more than an abstract, theoretical concept. As shown by the 1998 failure of the Galaxy 4 telecommunications satellite, the prolonged power crisis in California, and many other recent infrastructure disruptions, what happens to one infrastructure can directly and indirectly affect other infrastructures, impact large geographic regions and send ripples throughout the national a global economy. This article presents a conceptual framework for addressing infrastructure interdependencies that could serve as the basis for further understanding and scholarship in this important area. We use this framework to explore the challenges and complexities of interdependency. We set the stage for this discussion by explicitly defining the terms infrastructure, infrastructure dependencies, and infrastructure interdependencies and introducing the fundamental concept of infrastructures as complex adaptive systems. We then focus on the interrelated factors and system conditions that collectively define the six dimensions. Finally, we discuss some of the research challenges involved in developing, applying, and validating modeling and simulation methodologies and tools for infrastructure interdependency analysis.

The ability to assess and therefore react to risk exposure in critical infrastructures and supply chains environments greatly contributes to reaching suitable protection levels and response mechanisms. Due to the unavoidable interdependencies among those infrastructures, that allow disruptions to spread from one to another and likely cause a great impact on society’s welfare state, risk management might be seen as a common and shared concern. The Comprehensive Risk Management approach tries to face this process by gathering information from a broad range of disciplines (physical and logical security, safety, environmental threats, etc.) while taking into account interdependencies of critical infrastructures and supply chains at different layers, going from critical infrastructure operators point of view, to sectoral, national and finally supranational levels. Besides, risk assessment and management processes rely on accurate and timely information to assist decision making, but this information (security holes, attacks or even disruptions suffered by an infrastructure or supply chain)—due to its sensitiveness—does not easily flow between involved or interested parties. This paper provides an analysis of this situation and suggest future fields of action, supported by conclusions drawn from the FOCUS project.

Critical infrastructures provide essential services which enable our society to function. Disruptions in infrastructures can have widespread effects, not only for the originating infrastructure but also, through mutual dependencies, for other infrastructures. Identifying vulnerabilities inherent in these system-of-systems is thus highly critical for the proactive management and avoidance of future crises. A modelling approach for interdependent technical infrastructures is proposed and three perspectives for the analysis of vulnerabilities are introduced, addressing the complexities associated with comprehensively analysing technical interdependent infrastructures. An empirical analysis of the railway system in southern Sweden is conducted, a system consisting of seven interdependent supporting systems. It is concluded that the proposed modelling approach and the three perspectives of vulnerability analysis give valuable insights for the proactive risk management of technical infrastructures.