Critical Infrastructure Resilience Research Articles

An Integrated Infrastructure Resilience Approach, from the Geotechnical Asset to the Transport Network

The main objective of this work is to present a comprehensive and integrated methodology to enhance the resilience of transportation critical infrastructure (TCI), focusing on the interplay between geotechnical assets and the transport network. Societies are greatly dependent on transport infrastructure systems, and as the mobility of passengers and the transport of freight is continuously growing, a disruption due to natural or man-made hazards creates significant impacts and dysfunctionalities on their operation and necessitates response measures to minimize vulnerability and ensure continuous functionality and robustness through resilience. Therefore, resilience quantification allows the design of ad hoc operation action plans before, during, and after a disruption, considering the dynamics of societal, ecological, and technological (SET) environments. The current work focuses on resilience quantification methodologies for TCIs and on the influence of single geotechnical asset (i.e., slope failure) resilience capacity on the overall system (i.e., national road network) resilience. Two case studies of unexpected transport network disruptions that took place in Greece are presented, and resilience metrics and performance indicators are applied to quantify the influence of the recovery stage.

A novel approach detection for IIoT attacks via artificial intelligence

A novel approach detection for IIoT attacks via artificial intelligence

Towards decision-making support for complex socio-technical system safety assessment: A hybrid model combining FRAM and dynamic Bayesian networks

Effectively managing system safety and resilience in critical infrastructures requires addressing emergent risks and critical resonances. This study introduces a quantitative model that merges Monte Carlo Simulation (MCS) of the Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN) to assess technological, organizational, and human performance variability in complex social-technical systems. FRAM identifies system taxonomy and functional variability, while MCS pinpoints critical coupling, supplying prior probabilities for functional resonance. This information feeds into the DBN, facilitating the modeling of causal relationships and probabilistic inferences regarding risk uncertainties and system resonances. The model underwent rigorous testing and validation on a preheater cyclone system within the cement industry. This process involved the utilization of historical field data gathered from six prominent cement industries and input from thirty subject matter experts. The integrated approach deeply analyzes emerging risk indicators, unveiling interactions within organizational, human, and technical subsets, alongside performance variability. Furthermore, the model incorporates system learning parameters, aiding decision-making under uncertainty. These findings advance system safety and resilience management, offering insights for risk assessment and accident prevention across diverse scenarios in complex socio-technical systems.

Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study

The role of critical infrastructures in maintaining the functioning of the economy and society and ensuring national security, particularly their durability in delivering essential services during crises, including natural disasters such as earthquakes, is critical. This work introduces an analytical methodology to quantify potential earthquake damage to power stations and evaluate the cost-effectiveness of measures to enhance their seismic resistance. By employing fragility curves and probabilistic risk analyses, this approach provides a structured framework for the comprehensive assessment of risks and the identification of economically practical mitigation strategies. A detailed examination of strategies to protect critical power station components against seismic activity is presented, revealing that a minor investment relative to the overall project budget for earthquake-proofing measures is economically effective. This investment, representing a marginal fraction of 0.5% of the total project expenditure significantly reduces the seismic risk of power station failure by 36%. Reinforcing essential elements, including switching stations, water treatment facilities, and water tanks, is emphasized to ensure their continued operation during and after an earthquake. This research highlights the critical significance of integrating risk assessment with benefit-to-cost analysis in strategic decision-making processes, supporting the prioritization of investments in infrastructure enhancements. These enhancements promise substantial reductions of risks at minimal costs, thus protecting essential services against the impacts of natural disasters. This research contributes to state-of-the-art research in critical infrastructures resilience.

Theoretical perspectives on cybersecurity challenges in critical water infrastructure: Insights from Africa and the United States

This review paper examines the theoretical perspectives on cybersecurity challenges facing critical water infrastructure, with a focus on comparative insights from Africa and the United States. By exploring foundational theories of risk management, resilience, and deterrence, the paper delineates how these concepts are applied and adapted to address the cybersecurity needs of critical water systems within the distinct contexts of the two regions. The analysis identifies common challenges such as malware attacks, system vulnerabilities, human factors, and how regional differences influence technological infrastructure, regulatory environments, and cyber threat landscapes. Through a comparative analysis, the paper highlights lessons learned and best practices from both regions, emphasizing the importance of capacity building, comprehensive risk management, and the role of public-private partnerships. The paper concludes with a call for future research to develop adaptable theoretical models that address different regions' unique cybersecurity challenges, underlining theoretical understanding's critical role in enhancing the global resilience of critical water infrastructure against cyber threats.

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.

Building resilience of critical infrastructure in the light of asymmetric threats and terrorism. Legislative trends in the Polish implementation of the CER Directive with particular reference to aspects of standardisation and certification of organisational and technical solutions

Building resilience of critical infrastructure in the light of asymmetric threats and terrorism. Legislative trends in the Polish implementation of the CER Directive with particular reference to aspects of standardisation and certification of organisational and technical solutions

Evolving Access Control Paradigms: A Comprehensive Multi-Dimensional Analysis of Security Risks and System Assurance in Cyber Engineering

This study evaluates the effectiveness of traditional access control paradigms—Role-Based Access Control (RBAC), Policy-Based Access Control (PBAC), and Attribute-Based Access Control (ABAC)—against ransomware threats in critical infrastructures and examines the potential benefits of integrating machine learning (ML) and artificial intelligence (AI) technologies. Utilizing a quantitative research design, the investigation collected data from 383 cybersecurity professionals across various sectors through a systematically structured questionnaire. The questionnaire, which demonstrated excellent internal consistency with a reliability score of 0.81, featured Likert scale questions aimed at assessing perceptions and experiences concerning the efficacy of different access control models in combating ransomware. Employing multiple regression analysis, the study explored the relationship between access control paradigms and their capability to mitigate ransomware risks, while also considering the impact of cybersecurity awareness among employees. The findings indicate that traditional access control methods are less effective against the dynamic nature of ransomware attacks, primarily due to their static configurations. In contrast, the integration of ML and AI into access control systems significantly enhances their adaptability and effectiveness in detecting and preventing ransomware incidents. Additionally, the study highlights the crucial role of cybersecurity awareness and training among employees in fortifying critical infrastructures against cyber threats. The adoption of a layered security strategy, incorporating advanced technological solutions and comprehensive cybersecurity practices, was found to markedly improve the resilience of critical infrastructures against ransomware attacks. Based on these insights, the study recommends the embrace of ML and AI technologies in access control systems, the prioritization of cybersecurity training for all organizational members, and the implementation of a multifaceted security approach to better defend against the evolving threat of ransomware. These strategies are essential for safeguarding the continuity and reliability of essential services in an increasingly digital and interconnected world.

REZILIENȚA INFRASTRUCTURILOR CRITICE ÎN CONTEXTUL INSECURITĂȚII CIBERNETICE PROVOCATE DE AMENINȚĂRILE DE TIP HIBRID*

Critical infrastructure resilience in the context of cyber insecurity caused by hybrid threats Abstract: Nowadays, as state and non-state actors have developed new hybrid methods to promote their interests, the concept of critical infrastructure protection has gained new value from a resilience perspective, and today, most security incidents occur in cyberspace. This article examines the interconnection between the concepts of “hybrid warfare - critical infrastructures - cyberattacks”. Therefore, we begin with the assumption that cyberspace is the most susceptible environment to vulnerabilities and potential attacks, subsequently impacting vital services for populations numbering in the millions. Accordingly, our research endeavors to address the following question: Within the realm of hybrid threats, can minor vulnerabilities in cyberspace generate strategic-level effects on critical infrastructures? To accomplish this, we have used the analytical tool Connected Papers to identify scientific papers that explore the correlation between “hybrid war - critical infrastructures - cyber-attacks”. Subsequently, we expanded our analysis to encompass articles focused on studying either two or even a single concept among those mentioned. The results of our analysis demonstrate that vulnerabilities exploited within a Wi-Fi network or an industrial control panel of a critical infrastructure in cyberspace can have far-reaching consequences that may impact the vital services required by the population, resulting in a temporary disruption. Such an attack, originating from minimal efforts in terms of time, personnel, and financial resources, can lead to substantial property damage.

Unraveling the dynamics of the cyber threat landscape: Major shifts examined through the recent societal events

The continuous expansion of cyberspace offers numerous advantages but also broadens the cybersecurity attack surface, thereby affecting the resilience of critical infrastructure and societal systems. Given the complexity of factors affecting cybersecurity, a data-centric approach is needed, which involves leveraging existing datasets on cyber incidents and utilizing advanced data analytics techniques for enhanced preparedness against evolving cyber threats. In this study, we optimize past cyberattack data for statistical analysis and introduce a methodology for detecting significant shifts in cyberattack patterns through intervention analysis. By applying this approach, we correlate major shifts in the cyber threat landscape over the past six years with critical societal events like COVID-19 and the Russia–Ukraine War, offering insights meaningful for both society and policy formulation.

Analysing Coastal Flood Risk: Assessing the Impact on Critical Water Infrastructures

There is a need to enhance the resilience of critical infrastructures. The effects of climate change have already led to increased flooding of critical water infrastructure. Furthermore, predictions indicate that more infrastructures will be affected in the future. Currently, methods for accurately quantifying the economic losses resulting from sea level rise and extreme weather events are lacking. This article proposes a risk assessment methodology for coastal flooding in the Galicia region of Spain. The method estimates the losses incurred by water infrastructure and indirect industry losses due to the interruption of water infrastructure services. The results indicate that losses currently exceed 90 million euros due to extreme weather events. In the most pessimistic scenario, these losses increase to 127 million euros in 2050 and 451 million euros in 2100. In both cases, indirect industry losses account for more than 80% of the total losses.

Agent-based modelling of high-speed railway interdependent critical infrastructures facing physical and cyber threats

Globally, high-speed rail systems serve nearly 2 billion passenger-km daily. By virtue, they are a critical infrastructure like telecommunication and power networks. Accordingly, they become a catalyst for societal and economic growth stemming from the mobility business. The highspeed rail operations are very complex and interdependent, owing to the escalated demands for long-distance interconnected transportation. In recent years, there have been unreasonable delays for passengers as a new norm due to unfortunate train cancellations and relaxation of mobility performance requirements. Therefore, accurate measurements, monitoring and prediction of disruptive impacts and service performance metrices are indispensable. Within the scope of high-speed rail services, this paper examines how agent-based and multi-agent-based models are utilized to address such the challenges. Our findings reveal that the current use of agents or multi-agent models has some limitations for practical applications. Previous studies showed that mathematical methods to assess the resilience of critical infrastructures, railway scheduling, and vehicle dispatching can yield more satisfactory outcomes, although the approaches can be relatively time-consuming. In contrast, agent-based and multi-agent-based models can shorten processing time and uncover disruptive events more promptly. The paper thus showcases several emerging concepts, including i) the utilization of big data for crisis management, ii) interconnectivity analysis of high-speed rail infrastructures, and iii) enhancement of transport resilience. In addition, our findings identify the most influential agents and their possible applications to enhance systems resilience of highspeed rail networks when dealing with unforeseen physical and cyber threats.

Discussing trust and resilience: The need for a healthy dose of distrust

AbstractHow are trust and reslience related? There is a lack of conceptual discussions. This paper analyzes trust and resilience with respect to the system of critical infrastructures and engineers working on critical infrastructure resilience. The results are three findings based on the assumption that resilience and trust are mechanisms to cope with complexity and uncertainty inherent to modern societies and critical infrastructures. First, they are no functional alternatives. Trust is an everyday process where favorable expectations help to act “as if” uncertainty was successfully resolved. Resilience helps to cope in “what if” cases when disaster strikes. Second, resembling the risk perception paradox, too much trust in critical infrastructures might lead to complacency, decreasing individual coping capacities and subsequently resilience. People need a healthy dose of distrust—distrust being a functional alternative to trust and not its opposite—to be aware that a disruption might happen and prepare for it. Third, notwithstanding the difficulties of uncertainty communication, engineers need to communicate limits to knowledge resulting from complexity transparently. This might decrease trust in specific solutions, but could sustain overall system trust, because it helps to adjust favorable expectations.

Resilient critical infrastructures: An innovative methodological perspective for critical infrastructure (CI) integrated assessment models by inducing digital technologies during multi-hazard incidents

Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today's densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.

DomiRank Centrality reveals structural fragility of complex networks via node dominance

Determining the key elements of interconnected infrastructure and complex systems is paramount to ensure system functionality and integrity. This work quantifies the dominance of the networks’ nodes in their respective neighborhoods, introducing a centrality metric, DomiRank, that integrates local and global topological information via a tunable parameter. We present an analytical formula and an efficient parallelizable algorithm for DomiRank centrality, making it applicable to massive networks. From the networks’ structure and function perspective, nodes with high values of DomiRank highlight fragile neighborhoods whose integrity and functionality are highly dependent on those dominant nodes. Underscoring this relation between dominance and fragility, we show that DomiRank systematically outperforms other centrality metrics in generating targeted attacks that effectively compromise network structure and disrupt its functionality for synthetic and real-world topologies. Moreover, we show that DomiRank-based attacks inflict more enduring damage in the network, hindering its ability to rebound and, thus, impairing system resilience. DomiRank centrality capitalizes on the competition mechanism embedded in its definition to expose the fragility of networks, paving the way to design strategies to mitigate vulnerability and enhance the resilience of critical infrastructures.

Cybersecurity Improvement Design in Critical Infrastructure PT XYZ Case Study

Prevention of cyber attacks is an important factor that organizations need to do to support maximum business process continuity. One of the important factors needed is the protection of data and information assets by the critical infrastructure owned. This paper discusses increasing the capacity of critical infrastructure at XYZ organization. The NIST CSF framework is a benchmark assessment tool for critical infrastructure capacity at PT XYZ. The results obtained from the assessment are used to make recommendations for controls related to critical infrastructure.  The risk assessment found 107 risk scenarios that led to 11 recommendations for critical infrastructure improvement areas. Prioritization of the resulting recommendations is expected to increase the resilience and capacity of critical infrastructure at PT XYZ in the face of current and future cyber threats.

Anomaly Detection with Gradient Boosting Regressor on HVAC Systems

HVAC systems are important in buildings due to their significant energy consumption, impact on indoor air quality, and role in occupant comfort. Optimizing the operation and control of these systems is crucial for improving energy efficiency and reducing costs. Anomaly detection in HVAC systems aims to optimize energy consumption, improve thermal comfort and indoor air quality, detect and isolate sensor faults, and, more importantly, detect cyber-attacks. By analyzing system data for unusual patterns or unauthorized access attempts, anomaly detection can play a vital role in safeguarding HVAC systems against cyber threats. Detecting and isolating potential cyber-attacks can prevent disruptions in building operations, protect sensitive data, and ensure the continued functionality of HVAC systems securely and reliably. In this study, Gradient Boosting Regressor is used to improve the anomaly detection capabilities of HVAC systems. Traditional anomaly detection methods often struggle to adapt to the dynamic nature of HVAC systems and may generate false alarms or miss critical issues. To address these challenges, we propose the application of Gradient Boosting Regressor, a powerful machine learning technique, to enhance anomaly detection accuracy and reliability. We evaluate the model's performance using real-world HVAC data, comparing it with existing anomaly detection methods. The results demonstrate significant improvements in the system's ability to identify anomalies accurately while minimizing false alarms. This research advances HVAC system security by providing a more robust and adaptive anomaly detection solution. Integrating Gradient Boosting Regressor into the cybersecurity framework of HVAC systems offers improved protection against cyber threats, thereby enhancing the resilience and reliability of critical infrastructures.

Indicator-based assessment of extreme threats (XTs) and their impacts on the resilience of geographical areas and critical infrastructures

Indicator-based assessment of extreme threats (XTs) and their impacts on the resilience of geographical areas and critical infrastructures

Modeling and assessing seismic resilience leveraging systems dynamic approach: A case study of society 5.0

Society 5.0 is a transformation strategy centered on integrating digital technologies unveiled by the Japanese government to create a human-centric society for economic development and mitigate sustainability issues. Since societies are constantly exposed to various natural disasters like earthquakes, many precautions must be taken both before and after a disaster to minimize the damage. Seismic resilience is one of the practical assessments that may be taken in this regard. Quantifying the functionality of seismic resilience requires a systematic examination of the relevant components and their functional impact. We present a framework based on fragility, consequence and recoverability stages for evaluating the impact of a component on its functionality for earthquake events. Within this study, we introduce a novel set of indicators, which are derived from the key variables impacted by earthquakes, including hospitals, grids, and infrastructures. To that end, we have developed a system dynamics (SD) model to assess earthquake resilience in the context of Society 5.0, considering three earthquake magnitudes (7, 8, and 9 Mw) to simulate societal seismic resilience. We also perform sensitivity analysis to validate the outcomes of the policy simulations. Our findings affirm that by scrutinizing the seismic resilience of critical infrastructure and proposing relevant policies, it is possible to minimize disaster-related damage. This represents a pragmatic step forward in the field of disaster risk management.

A Systematic Review of Integrated Frameworks for Resilience and Sustainability Assessments for Critical Infrastructures

There is a growing tendency to assess resilience and sustainability of critical infrastructures (CI), given the significant increment in high-impact natural hazard events affecting socio-economic welfare. Historically, these assessments have been conducted separately due to the independent evolution of each concept. However, recent contributions tend to integrate them. This paper provides a state-of-the-art review of integrated assessments for resilience and sustainability in CI, examining concepts, indicators, frameworks, and methodologies. Additionally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was performed to gain further insights into the prospects of integrated assessments. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, eligibility criteria were established, leading to the selection of twelve studies. These works were compared based on five dimensions (economic, environmental, social, technical, and governance) to highlight the differences in the indicators used. While all studies considered the social, environmental, and economic dimensions, some did not further analyze sufficient indicators to evaluate environmental and social effects, with governance often neglected. This study emphasizes the relevance of establishing common metrics for a convergent frame for the resilience and sustainability assessment. The findings presented suggest that integrated assessments lead to a more strategic use of resources toward more resilient CI.