Interdependency modeling and analysis of critical infrastructures based on Dynamic Bayesian Networks

This paper presents a novel approach to the CI interdependencies analysis, based on the DBN formalism. An original modeling procedure is illustrated, which divides the DBN in three distinct levels: an atomic events level, a propagation level, and a services level. The first level models the adverse events that may impact on the analyzed CIs, the second one properly captures interdependencies among CIs' services and devices, and the last one allows to monitor the state of provided services. The resulting DBN permits to perform three kinds of analysis: a reliability study, which allows to find structural weaknesses of interconnected CIs, an adverse events propagation study, which highlights the role interdependency plays in the propagation of adverse events, and a failure prediction analysis, that can serve as an useful guide to the fault localization process (failures may have many different explanations due to interdependency). A specific case study provided by Israel Electric Corporation is considered, and explicative simulations are presented and discussed in detail.

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.

Although various aspects of interdependencies among Power, Water, and Gas (PWG) infrastructures have been studied in the last couple of decades, there still exists void of modeling techniques capable of revealing the interdependencies among real-world infrastructures. This paper presents review of the state-of-the-art knowledge on interdependent critical infrastructure (ICI) modeling techniques along with delineation and discussion of interdependencies at transmission and distribution levels among PWG networks. The present study will also outline the suitability of various approaches to work with real-time infrastructure monitoring data that is becoming abundantly available in the recent years. The paper also highlights some directions on future research in interdependency modeling as well as suggestions on improving real-time interdependent models in operational and physical aspects.

Efforts have been significantly invested in the last years on analysis of interdependencies in critical infrastructures, including Electric Power Systems (EPS). Among other studies, a modeling and analysis framework has been proposed, able to account for the impact of interdependencies in EPS organized as a set of interconnected regions, both from the point of view of the electric power grid and of the cyber control infrastructure. Indicators representative of the quality of service (QoS) perceived by users have been defined and assessed on reference test cases under homogeneous conditions of the network parameters and of the cost associated to power losses and to power generation. This paper advances such previous studies by extending the developed framework considering loads of different criticality (from the point of view of consequences of power loss) and different failure rates for the power lines. The proposed extensions address relevant aspects of non-homogeneity of real EPS infrastructures, which need to be taken into account in assessment studies to get accurate results, to be exploited as useful guidelines to appropriate configuration and reconfiguration policies.

This paper presents first an ontology of risk for interdependent and heterogeneous Critical Infrastructures (CIs). It defines a data structure called Service Quality Descriptor (SQD) specifying the degradation of QoS over time, which should be shared between interconnected CI. SQD are shared in real time and contain a precise prediction of the future quality of service, so that this sharing can be useful to avoid failures, identify interdependencies, or accelerate and coordinate power failure recoveries and service restoration. Finally, the paper proposes a simplified method to determine the SQD of a delivery service as a linear function of the SQD of supporting services and parameters depending on the service delivery infrastructure. The approach has been defined within the European FP-7 project MICIE, in line with the EU initiative to establish a Critical Infrastructure Warning Information Network (CIWIN).KeywordsRisk analysisSecurity Quality DescriptorQuality of ServiceRisk ontologyInterdependency riskCritical infrastructure ProtectionRisk Level

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.

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

Modern society's physical health depends vitally upon a number of real, interdependent, critical infrastructure networks that deliver power, petroleum, natural gas, water, and communications. Its economic health depends on a number of other infrastructure networks, some virtual and some real, that link residences, industries, commercial sectors, and transportation sectors. The continued prosperity and national security of the US depends on our ability to understand the vulnerabilities of and analyze the performance of both the individual infrastructures and the entire interconnected system of infrastructures. Only then can we respond to potential disruptions in a timely and effective manner. Collaborative efforts among Sandia, other government agencies, private industry, and academia have resulted in realistic models for many of the individual component infrastructures. In this paper, we propose an innovative modeling and analysis framework to study the entire system of physical and economic infrastructures. That framework uses the existing individual models together with system dynamics, functional models, and nonlinear optimization algorithms. We describe this framework and demonstrate its potential use to analyze, and propose a response for, a hypothetical disruption.

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.

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry

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.