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Building resilient infrastructure is at the core of sustainable development, as evidenced by the UN Sustainable Development Goal 9. In fact, the effective operation of road networks is crucial and strategic for the smooth functioning of a nation's economy. This is also fundamental from a sustainability perspective, as efficient transportation networks reduce traffic, and thus, their environmental impact. However, road networks are constantly at risk of traffic closure and/or limitations due to a plurality of natural hazards. These environmental stressors, among other factors like aging and degradation of structural materials, negatively affect the disaster resilience of both single components and the system of road networks. However, the estimation of such resilience indices requires a broad multidisciplinary vision. In this work, a framework for application to large road networks is delineated. In the proposed methodology, seismic hazard is considered, and its corresponding impacts on road networks are evaluated. The assessment encompasses not only the road network system (including squares, roads, bridges, and viaducts) but also the buildings that are located in the urban area and interact with the network. In this context, the probability that buildings will suffer seismic-induced collapse and produce partial or total obstruction of roads is considered. This scheme is designed for implementation in different geographical contexts using geo-referenced data that include information about specific risks and alternative rerouting options. The proposed methodology is expected to support the mitigation of functionality loss in road networks after disasters, contributing to both the economic and social dimensions of sustainability. To evaluate the methodology, two case studies focusing specifically on hospital-to-hospital connections were conducted in Naples and Turin, Italy. However, the proposed approach is versatile and can be extended to other critical infrastructures, such as theatres, stadiums, and educational facilities. [ABSTRACT FROM AUTHOR]

Abstract Traditional security detection methods face challenges in identifying zero-day attacks in critical infrastructures (CIs) integrated with the industrial internet of things (IIoT). These attacks exploit unknown vulnerabilities and are difficult to detect due to their connection to physical systems. The integration of legacy ICS networks with modern computing and networking technologies has significantly expanded the attack surface, making these systems more susceptible to cyber-attacks. Despite existing security measures, attackers continually find ways to breach these operating networks. Anomaly detection systems are critical in protecting these CIs from current cyber threats. This study investigates the effectiveness of unsupervised anomaly detection models in detecting operational anomalies that could lead to cyber-attacks, thereby disrupting and negatively impacting quality of life. We preprocess the data with a focus on cybersecurity and chose the SWAT dataset because it accurately represents the types of attack vectors that critical infrastructures commonly encounter. We evaluated the performance of isolation forest (IF), local outlier factor (LOF), one-class SVM (OCSVM), and Autoencoder algorithms—trained exclusively on normal data—in enhancing cybersecurity within IIoT environments. Our comprehensive analysis includes an assessment of each model’s detection capabilities. The findings highlight the VAE-LSTM model’s potential to identify cyber-attacks within seconds in a high-frequency dataset, suggesting near real-time detection capability. The final model combines the reconstruction ability of the variational autoencoder (VAE) with regularization using the Kullback–Leibler divergence, reflecting the non-Gaussian nature of industrial system data. Our model successfully detected 23 out of 26 attack scenarios in the SWAT dataset, demonstrating its effectiveness in improving the security of IIoT-based CIs.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion. [ABSTRACT FROM AUTHOR]

The rapid advancement of communication technologies underscores the urgent need for robust and adaptable emergency communication systems (ECSs), particularly crucial during crises and natural disasters. Although network-based ECSs have been extensively studied, integrating open-source technologies, such as software-defined wide area networks (SD-WAN) with private long-term evolution (LTE) base stations, is a relatively unexplored domain. This study endeavors to fill this gap by introducing an experimental ECS platform that utilizes a hybrid network, incorporating a VoIP network to enhance open-source and on-premises communications in targeted areas. Our hypothesis posits that a hybrid network architecture, combining SD-WAN and private LTE, can substantially improve the reliability and efficiency of ECSs. Our findings, supported by the open-source OMNeT++ simulator, illuminate the enhanced communication reliability of the network. Moreover, the proposed platform, characterized by autonomous wireless 4G/LTE base stations and an Asterisk VoIP server, demonstrates improved quality of service (QoS) and quality of experience (QoE), with minimal data loss. This research not only has immediate practical applications but also bears significant implications for the development of cost-effective, open-source communication networks, optimized for emergencies, critical infrastructure, and remote areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A multi-hazard analysis (seismic, landslide, flood) is conducted to verify the impact on the road network. The ENEA CIPCast platform is an innovative Decision Support System (DSS) that is used to implement the analyses using GIS. Using analytical and geoprocessing tools, the hazards were assessed and mapped. The overlapping of different geospatial layers allowed the implementation of a specific hazard map for the road network. Multi-hazard values were obtained using an appropriate matrix of single values, which were classified, and then summarized into four classes of values. The analyses were conducted at the regional (Campania region), provincial (Metropolitan City of Naples), and local scales (island of Ischia and municipality of Casamicciola Terme). In particular, the landslide event that struck Ischia island on 26 November 2022 and the municipality of Casamicciola Terme was considered as a case study to determine the impact on the road network, infrastructures, buildings, and jeopardizing inter-municipal connections. The results are mainly visualized through map processing and statistical summaries of the data. The management of the landslide debris, which can contain a multitude of fractions (waste, biomass and vegetation, sludge, soil, and rocks transported downstream by water), was also explored. This is a frontier issue for which international manuals and guidelines, as well as national and emergency acts, have been examined. A specific protocol for the sustainable management of the debris generated by floods and landslides is needed, and discussed in the present paper, to overcome emergencies after catastrophic events. [ABSTRACT FROM AUTHOR]

In today's world, where globalization has led to the emergence of various state and non-state actors, including sub-national ones, with differing interests and motivations that often conflict with one another, the provision of essential services such as transportation, electricity, drinking water, medical and digital services, and others is of critical importance to contemporary society. About half a decade ago, when the critical infrastructures that provide these services were built, threats to them, such as terrorism, organized crime, asymmetrical or hybrid ones, were almost non-existent and not as much of a concern to the critical entities responsible for protecting them. The present research is intended to carry out an analysis of two threats to critical infrastructures - terrorism and organized crime, with the aim of identifying their typology, the effects they produce, the method of operation employed by the non-state actors that conduct such activities. The article's findings have demonstrated that ideological, political, and religious motivations of terrorist organizations, as well as the financial motivations of organized crime hold special significance as they yield effects in the modus operandi of these entities and also shape the response of law enforcement authorities. [ABSTRACT FROM AUTHOR]

In today's world, where globalization has led to the emergence of various state and non-state actors, including sub-national ones, with differing interests and motivations that often conflict with one another, the provision of essential services such as transportation, electricity, drinking water, medical and digital services, and others is of critical importance to contemporary society. About half a decade ago, when the critical infrastructures that provide these services were built, threats to them, such as terrorism, organized crime, asymmetrical or hybrid ones, were almost non-existent and not as much of a concern to the critical entities responsible for protecting them. The present research is intended to carry out an analysis of two threats to critical infrastructures - terrorism and organized crime, with the aim of identifying their typology, the effects they produce, the method of operation employed by the non-state actors that conduct such activities. The article's findings have demonstrated that ideological, political, and religious motivations of terrorist organizations, as well as the financial motivations of organized crime hold special significance as they yield effects in the modus operandi of these entities and also shape the response of law enforcement authorities. [ABSTRACT FROM AUTHOR]

Abstract A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems' vulnerabilities, attack structures, motives, and targets for assisting CIs' designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community. [ABSTRACT FROM AUTHOR]

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems. [ABSTRACT FROM AUTHOR]

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender's side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times. [ABSTRACT FROM AUTHOR]

The research analyzes the national and international cybersecurity policies of the United States, Mexico, and Canada. It starts from the hypothesis that the active foreign policy profile with regional and international impact, and the individual historical trajectory of more than two decades of the United States and Canada, explain the level of consolidation in the development of their cybersecurity policy. An eclectic review of international relations theories such as realism, liberalism, constructivism, and the theory of war was conducted to identify theoretical elements for the analysis of the international and national policies of the three countries. It is concluded that both the United States, considered a global power in cybersecurity, and Canada, a regional power, have an active foreign policy in the field, with a trajectory of almost two decades. Meanwhile, Mexico has a significant lag in the field that is not related to an effective national and international cybersecurity policy. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) have been increasingly attacked by hackers. CPSs are especially vulnerable to attackers that have full knowledge of the system's configuration. Therefore, novel anomaly detection algorithms in the presence of a knowledgeable adversary need to be developed. However, this research is still in its infancy, due to limited attack data availability and test beds. By proposing a holistic attack modeling framework, we aim to show the vulnerability of existing detection algorithms and provide a basis for novel sensor-based cyber-attack detection. Stealthy Attack GEneration (SAGE) for CPSs serves as a tool for cyber-risk assessment of existing systems and detection algorithms for practitioners and researchers alike. Stealthy attacks are characterized by malicious injections into the CPS through input, output, or both, which produce bounded changes in the detection residue. By using the SAGE framework, we generate stealthy attacks to achieve three objectives: (i) Maximize damage, (ii) Avoid detection, and (iii) Minimize the attack cost. Additionally, an attacker needs to adhere to the physical principles in a CPS (objective (iv)). The goal of SAGE is to model worst-case attacks, where we assume limited information asymmetries between attackers and defenders (e.g., insider knowledge of the attacker). Those worst-case attacks are the hardest to detect, but common in practice and allow understanding of the maximum conceivable damage. We propose an efficient solution procedure for the novel SAGE optimization problem. The SAGE framework is illustrated in three case studies. Those case studies serve as modeling guidelines for the development of novel attack detection algorithms and comprehensive cyber-physical risk assessment of CPSs. The results show that SAGE attacks can cause severe damage to a CPS, while only changing the input control signals minimally. This avoids detection and keeps the cost of an attack low. This highlights the need for more advanced detection algorithms and novel research in cyber-physical security. [ABSTRACT FROM AUTHOR]

This paper highlights the crucial role of wireless sensor networks (WSNs) in the surveillance and administration of critical infrastructures (CIs), contributing to their reliability, security, and operational efficiency. It starts by detailing the international significance and structural aspects of these infrastructures, mentions the market tension in recent years in the gradual development of wireless networks for industrial applications, and proceeds to categorize WSNs and examine the protocols and standards of WSNs in demanding environments like critical infrastructures, drawing on the recent literature. This review concentrates on the protocols and standards utilized in WSNs for critical infrastructures, and it concludes by identifying a notable gap in the literature concerning quality standards for equipment used in such infrastructures. [ABSTRACT FROM AUTHOR]

The frequency and severity of natural or human-induced disaster events, such as floods, earthquakes, hurricanes, fires, pandemics, hazardous material spills, groundwater contamination, structural failures, explosions, etc., as well as their impacts, have greatly increased in recent decades due to population growth and extensive urbanization, among other factors. The World Bank estimates that the total cost of cities' and communities' vulnerability to these types of disasters could reach more than USD 300 billion per year by 2030. However, it has been argued that investment to improve the quality and resilience of engineered physical assets that are the backbone of modern societies, such as critical infrastructure, industrial facilities, and buildings, could significantly contribute to more sustainable and prosperous societies. Engineered assets are key to the delivery of essential services, such as transport, food, water, electricity supply, health and safety, etc. Some of these physical assets are integrated into asset systems and national or regional networks, with life cycles of several decades or even centuries. It is, therefore, of great importance that strategies and life cycle decisions, such as those related to short- and long-term capital investment planning, maintenance strategies, operational plans, and asset disposal, lead to the maximization of the value derived from these assets. Moreover, it is essential that the achievement of these goals is sustainable over time. Organizations dealing with engineering assets, both public and private, must, therefore, integrate sustainability and resilience concerns into everyday operations, using budgets that are often restricted, while also meeting demanding performance requirements in risky and uncertain environments. This Special Issue collates a selection of papers reporting the latest research and case studies regarding the trends and emerging strategies used to address these challenges, with contributions discussing how asset management principles and techniques can help to push the boundaries of sophistication and innovation to improve the life cycle management of engineered assets to ensure more sustainable and resilient cities and societies. [ABSTRACT FROM AUTHOR]

E-Learning has transformed the delivery of education and training in diverse fields, such as air surveillance officer education and radar system maintenance. This article investigates the crucial function of Information Resource Management (IRM) in elevating the efficiency of e-Learning courses in these domains. Focusing on enhancing the distribution, usage, and protection of information resources, this article highlights the progressive potential of e-Learning and IRM in updating training and maintenance processes. The herein analysis and case studies exhibit how institutions can employ e-Learning to provide air surveillance officers with vital knowledge and abilities whilst enhancing the upkeep of radar systems. In the rapidly evolving technological landscape, coupled with the need for greater efficiency, this article try to offers valuable insights into the synergy between e-Learning and IRM in addressing the demands of contemporary air surveillance and radar system maintenance. [ABSTRACT FROM AUTHOR]

The prominence of cyber security continues to increase on a daily basis. Following the cyber-attacks in recent years, governments have implemented a range of regulations. The advancement of technology and digitalization has led to the creation of new vulnerabilities that cyber attackers can exploit. The digitalization of facilities such as energy distribution networks and water infrastructures has enhanced their efficiency, thereby benefiting states and society. The modern sensors, controllers, and networks of these new generation facilities have made them susceptible to cyber attackers. While all forms of cyber-attacks are detrimental, targeting critical cyber-physical systems presents a heightened level of peril. These assaults have the potential to disrupt the social structure and pose a threat to human lives. Various techniques are employed to guarantee the security of these facilities, which is of utmost importance. This study examined the applications of machine learning and deep learning methods, which are sub-branches of artificial intelligence that have recently undergone a period of significant advancement. Intrusion detection systems are being created for the networks that facilitate communication among the hardware components of the cyber-physical system. Another potential application area involves the development of models capable of detecting anomalies and attacks in the data generated by sensors and controllers. Cyber physical systems exhibit a wide range of diversity. Due to the wide range of variations, it is necessary to utilize specific datasets for training the model. Generating a dataset through attacks on a functional cyber-physical system is unattainable. The study also analyzed the solutions to this problem. Based on the analyzed studies, it has been observed that the utilization of artificial intelligence enhances the security of cyber physical systems.

Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are collectively binding. When these technologies become part of critical infrastructures, such as energy networks, citizens are affected by these decisions whether they like it or not, and they usually do not have much say in them. The democratic challenge for those working on AI technologies with collectively binding effects is both to develop and deploy technologies in such a way that the democratic legitimacy of the relevant decisions is safeguarded. In this paper, we develop a conceptual framework to help policymakers, project managers, innovators, and technologists to assess and develop approaches to democratize AI. This framework embraces a broad sociotechnical perspective that highlights the interactions between technology and the complexities and contingencies of the context in which these technologies are embedded. We start from the problem-based and practice-oriented approach to democracy theory as developed by political theorist Mark Warren. We build on this approach to describe practices that can enhance or challenge democracy in political systems and extend it to integrate a sociotechnical perspective and make the role of technology explicit. We then examine how AI technologies can play a role in these practices to improve or inhibit the democratic nature of political systems. We focus in particular on AI-supported political systems in the energy domain. [ABSTRACT FROM AUTHOR]

The paper proposes an approach for anticipating and dealing with future extreme threats (x-threats or XTs, e.g., due to climate change) and threats challenging the resilience of the affected geographical areas and their infrastructures. The approach relies on a new composite indicator (CIRIX—Critical Infrastructure Resilience Index for X-threats) that combines indicators characterizing a particular XT and indicators characterizing the resilience of a given infrastructure to that XT. CIRIX, therefore, provides a threat- and infrastructure-specific measure of resilience for a given XT. We quantify the extremeness of XTs using a sub-hierarchy of indicators, including not only increased intensity but also increased complexity, increased impacts, and other characteristics. To illustrate the use of CIRIX for stress testing, we construct a synthetic but plausible XT scenario based on a historical event, the 2005 flooding in the Swiss city of Lucerne. We analyze it using CIRIX to investigate the resilience of the area's most critical infrastructures. The threat-and-resilience assessments were performed using the ResilienceTool. The approach allows to (a) enhance preparedness for XTs, (b) perform "what-if" analyses (e.g., by varying values of threat or resilience indicators), (c) optimize resilience-related decisions, (d) stress test the resilience of critical infrastructures, and (e) potentially use the analysis results for insurance purposes (e.g., using CIRIX for parametric insurance). [ABSTRACT FROM AUTHOR]

The article studies cybersecurity projects from the perspective of social systems theory. Measures taken to increase cybersecurity are aimed at the robustness and resilience of specific systemic entities in society. We argue that this does not necessarily mean that systems in which these entities are embedded become more robust or resilient. The article investigates the objectives of 20 projects funded by different public organizations to increase cybersecurity of critical infrastructures. The findings show that robustness and resilience are addressed by these projects on different layers of systems in society, without a clear picture of how they are supposed to fit together in a wider effort to protect society against disruption. The detailed analysis of the different facets of robustness and resilience addressed in cybersecurity projects paves the way for a better understanding of this problem. The research shows how organizations may avoid the negative consequences of measures to secure specific systemic entities in society in the case of critical infrastructures, other application scenarios, and digital innovation in general. [ABSTRACT FROM AUTHOR]

Abstract Cyber‐Physical Systems (CPSs) are becoming more automated and aimed to be as efficient as possible by enabling integration between their operations and Information Technology (IT) resources. In combination with production automation, these systems need to identify their assets and the correlation between them; any potential threats or failures alert the relevant user/department and suggest the appropriate remediation plan. Moreover, identifying critical assets in these systems is essential. With numerous research and technologies available, assessing IT assets nowadays can be straightforward to implement. However, there is one significant issue of evaluating operational technology critical assets since they have different characteristics, and traditional solutions cannot work efficiently. This study presents the necessary background to attain the appropriate approach for monitoring critical assets in CPSs' Situational Awareness (SA). Additionally, the study presents a broad survey supported by an in‐depth review of previous works in three important aspects. First, it reviews the applicability of possible techniques, tools and solutions that can be used to collect detailed information from such systems. Secondly, it covers studies that were implemented to evaluate the criticality of assets in CPSs, demonstrates requirements for critical asset identification, explores different risks and failure techniques utilised in these systems and delves into approaches to evaluate such methods in energy systems. Finally, this paper highlights and analyses SA gaps based on existing solutions, provides future directions and discusses open research issues.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.

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.

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.

A multi-hazard analysis (seismic, landslide, flood) is conducted to verify the impact on the road network. The ENEA CIPCast platform is an innovative Decision Support System (DSS) that is used to implement the analyses using GIS. Using analytical and geoprocessing tools, the hazards were assessed and mapped. The overlapping of different geospatial layers allowed the implementation of a specific hazard map for the road network. Multi-hazard values were obtained using an appropriate matrix of single values, which were classified, and then summarized into four classes of values. The analyses were conducted at the regional (Campania region), provincial (Metropolitan City of Naples), and local scales (island of Ischia and municipality of Casamicciola Terme). In particular, the landslide event that struck Ischia island on 26 November 2022 and the municipality of Casamicciola Terme was considered as a case study to determine the impact on the road network, infrastructures, buildings, and jeopardizing inter-municipal connections. The results are mainly visualized through map processing and statistical summaries of the data. The management of the landslide debris, which can contain a multitude of fractions (waste, biomass and vegetation, sludge, soil, and rocks transported downstream by water), was also explored. This is a frontier issue for which international manuals and guidelines, as well as national and emergency acts, have been examined. A specific protocol for the sustainable management of the debris generated by floods and landslides is needed, and discussed in the present paper, to overcome emergencies after catastrophic events.

Emergency Communication Systems (ECS) are network-based systems that may enable people to exchange information during crises and physical disasters when basic communication options have collapsed. They may be used to restore communication in off-grid areas or even when normal telecommunication networks have failed. These systems may use technologies such as Low-Power Wide-Area(LPWAN) and Software-Defined Wide Area Networks (SD-WAN), which can be specialized as software applications and Internet of Things (IoT) platforms. In this article, we present a comprehensive discussion of the existing ECS use cases and current research directions regarding the use of unconventional and hybrid methods for establishing communication between a specific site and the outside world. The ECS system proposed and simulated in this article consists of an autonomous wireless 4G/LTE base station and a LoRa network utilizing a hybrid IoT communication platform combining LPWAN and SD-WAN technologies. The LoRa-based wireless network was simulated using Network Simulator 3 (NS3), referring basically to firm and sufficient data transfer between an appropriate gateway and LP-WAN sensor nodes to provide trustworthy communications. The proposed scheme provided efficient data transfer posing low data losses by optimizing the installation of the gateway within the premises, while the SD-WAN scheme that was simulated using the MATLAB simulator and LTE Toolbox in conjunction with an ADALM PLUTO SDR device proved to be an outstanding alternative communication solution as well. Its performance was measured after recombining all received data blocks, leading to a beneficial proposal to researchers and practitioners regarding the benefits of using an on-premises IoT communication platform.