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In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented

open access article ver the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review of the methods of network detection and cyber attacks that can take place in critical infrastructure. As it is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.

The cybersecurity of critical infrastructures is an essential topic within national and international security as 16 critical infrastructure sectors touch various aspects of American society. Because the failure to provide adequate cybersecurity controls within the critical infrastructure sectors renders the country open to an attack that could have a debilitating effect on security, national public health, safety, and economic security, this matter is so vital that there is the Presidential Policy Directive (PPD) 21 Critical Infrastructure Security and Resilience advances a national policy to strengthen and maintain secure, functioning and resilient critical infrastructure. An organization identified as the Cybersecurity and Infrastructure Security Agency (CISA) at the Department of Homeland Security (DHS) has the mission to be the risk advisor for the United States (US). Other organizations, such as the National Security Agency (NSA), have approved a specific Knowledge Unit (KU) to address cybersecurity for critical infrastructures associated with doctoral-level granting programs. To address this challenge, it is necessary to identify threats better and defend against them while mitigating risks to an acceptable level. Only then can a nation build a more secure and resilient infrastructure for the future while defending against present-day bad actors as cyberwarfare, cyber espionage, and cybersecurity attacks are the modern-day threats that need to be addressed in planning, designing, implementation, and maintenance. Therefore, the researchers developed a case study reviewing threats against different sectors defined in the PPD.

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated. North Atlantic Treaty Organization; Qatar Foundation; Qatar National Research Fund Scopus

In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy., 33 pages, 11 figures

Distribution system resilience is an emerging topic of interest given an increasing number of extreme events and adverse impacts on the power grid (e.g. Hurricane Maria and Ukraine cyber-attack). The concept of resilience poses serious challenges to the power system research community given varied definitions and multivariate factors affecting resilience. The ability of nature or malicious actors to disrupt critical services is a real threat to the life of our citizens, national assets and the security of a nation. Many examples of such events have been documented over the years. Promising research in this area has been in progress focused on the quantification and in enabling resilience of the distribution system. The objective of this study is to provide a detailed overview of distribution system resilience, the classification, assessment, metrics for measuring resilience, possible methods for enabling resilience, and the associated challenges. A new multi-dimensional and multi-temporal resilience assessment framework is introduced along with a research roadmap outlining the future of resilience to help the reader conceptualise the theories and research gaps in the area of distribution system cyber-physical resilience.

This study presents a generalised critical infrastructures resilience model for extreme events with a focus on power grids. Infrastructures are modelled as three domains – physical, cyber, and human. Each domain is described with respect to the services it provides. Each domain is represented by geometric graphs for each service it provides. The resilience models use geometric graphs with each graph's nodes and edges characterised based on relevant attributes. This study also discusses various applied aspects related to resilience models including the impact of changing operating environment, human-driven processes, such as logistics, and service buffers. Due to their stated particular importance in the U.S. Presidential Policy Directive 21, particular attention is placed on the power infrastructure and its impact on the public communication infrastructures as a main critical load. This study focuses on the multi-time scale power system operation to capture cascading outages within, and subsequently to its dependent infrastructure – the public communication system (e.g. wireless or ‘cellular’ communication networks) as a main critical load. This study illustrates the merits of the proposed models in calculating resilience in extreme events and derives physical domain representation for electric and communication systems using cell tower and substation data from the USA.

This study focuses on the cyber-physical system (CPS) consisting of interdependent electrical distribution and communication networks, where the two networks are mutually dependent. A small disturbance in either of them can trigger a cascade of faults within the entire network. To investigate the failure mechanism, first, two features that affect topological resilience (TR) are defined in this study: adaptation and recovery abilities. Second, the authors model the process of cascading failures that occur in this coupled system by introducing and developing the infrastructure interdependencies simulator. The process of cascading failures is based on percolation theory, and they present a detailed analysis of cascading failure in a standard IEEE 33-bus system coupled with the 33-node communication system. This study proves that the adaptation ability of a coupled system is even lower than a single system. This is due to the interdependencies between systems, and the study of the failure mechanisms helps planers to make a better decision in the recovery process. Finally, the modified shortest path search is used to optimise the repair sequence. Their numerical results validate that the recovery ability of the coupled system is increased through the optimisation, which contributes to the TR enhancement.

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

International audience; Flooding is frequent on the Nantes ring road and disrupts users dependent on this mode of transport directly or indirectly. To deal with them, the road operator has defined preventive and curative measures based essentially on the flow of the river involved in the submergence of the roadway. The RESIIST (Resilience of Interconnected Infrastructures and Systems) research project aims to develop a methodology and tools to assess the resilience of Critical Infrastructures (within the meaning of European Directive 2008/114/EC), of which we find the ring road. In order to test them, a scenario will be used to simulate the events likely to cause flooding, characterise the disturbances caused by these events and the interactions with adjacent road infrastructures and present a decision aidingbased on the road operator's criteria. RESIIST can contribute to improving the resilience of critical infrastructures by enabling stakeholders to continuously monitor data from the context, to interpret them and deduce the disruption’s risks on infrastructure and territory and finally to benefit from proposals for solutions adapted and corresponding to their decision criteria. More modestly, this scenario could serve as a support in discussions on possible work concerning the ring road and the crisis management protocols.

In this paper, we describe the concept and ongoing work of the project defalsif-AI, which addresses the protection of critical infrastructures against disinformation and fake news. Defalsif-AI deals particularly with the protection of the main democratic processes and the public trust in democracy and its institutions against engineered social media attacks, which, for example, attempt to manipulate the electoral process. Federal ministries and media institutions require new methods and tools to evaluate the ever increasing amount of digital media in terms of identification, verification, and correction of sources. Based on these requirements, the project focuses on research on audio-visual media forensics, text analysis, and multimodal fusion with the support of artificial intelligence (AI) and machine learning methods. One main focus of this research is to make the results more comprehensible and interpretable for non-experts in the forensic/technical field. The primary project outcome is a proof of concept of a multimodal detection platform, which can operate with a variety of sources, including the surface web and social media. Additional research carried out within the project focuses on providing and generating multimodal data necessary to train and test machine learning models. Finally, an analysis and assessment concerning the law and social science are carried out as well.In diesem Beitrag beschreiben wir das Konzept und die laufenden Arbeiten des Projekts defalsif-AI, das sich mit dem Schutz kritischer Infrastrukturen vor Desinformation und Fake News beschäftigt. Insbesondere geht es bei defalsif-AI um den Schutz demokratischer Kern-Prozesse und des öffentlichen Vertrauens in die Demokratie und ihre Institutionen vor konstruierten Social-Media-Angriffen, welche beispielsweise versuchen, den Wahlprozess zu manipulieren. Die öffentliche Verwaltung, aber auch Medienhäuser, benötigen neue Methoden und Werkzeuge, um die immer größer werdenden Mengen digitaler Medien hinsichtlich der Identifizierung, Verifizierung und Korrektur von Quellen auszuwerten. Ausgehend von diesen Anforderungen konzentriert sich das Projekt auf die Forschung in den Bereichen audiovisuelle Medienforensik, Textanalyse und multimodale Fusion unter Zuhilfenahme von Methoden der künstlichen Intelligenz (KI) und des maschinellen Lernens. Ein Hauptaugenmerk dieser Forschung liegt auf der Verbesserung der Verständlichkeit und Interpretierbarkeit der Ergebnisse für Laien im forensischen/technischen Bereich. Das primäre Projektergebnis ist ein Proof-of-Concept einer multimodalen Detektionsplattform, welche mit einer Vielzahl von Quellen arbeiten kann, wie etwa dem Surface Web und sozialen Medien. Weitere Forschung innerhalb des Projekts konzentriert sich auf die Bereitstellung und Generierung multimodaler Daten, die zum Trainieren und Testen von Modellen des maschinellen Lernens erforderlich sind. Schließlich werden auch eine rechts- und sozialwissenschaftliche Analyse und Bewertung durchgeführt.

Security Information and Event Management (SIEM) systems have been widely deployed as a powerful tool to prevent, detect, and react against cyber-attacks. SIEM solutions have evolved to become comprehensive systems that provide a wide visibility to identify areas of high risks and proactively focus on mitigation strategies aiming at reducing costs and time for incident response. Currently, SIEM systems and related solutions are slowly converging with big data analytics tools. We survey the most widely used SIEMs regarding their critical functionality and provide an analysis of external factors affecting the SIEM landscape in mid and long-term. A list of potential enhancements for the next generation of SIEMs is provided as part of the review of existing solutions as well as an analysis on their benefits and usage in critical infrastructures.

Many infrastructures that shape our lifestyle depend on the GPS. Even though not used for positioning, these infrastructures need GPS to derive a precise time synchronization necessary for their operations. Examples are power distribution grids, gateways for financial transactions, and telecommunication networks. However, GPS receivers, and more in general g lobal navigation satellite system (GNSS) receivers, are vulnerable to interference. This makes GNSS-dependent infrastructures prone to time synchronization errors. This paper addresses the vulnerability of GNSS receivers to the intentional injection of counterfeit signals, an operation known as “spoofing.” The analysis is backed by a campaign of tests conducted on three receivers suitable for applications that use GNSS-based synchronization. A battery of spoofing attacks, targeting the control of the output time reference of these receivers, is purposely simulated with lab instrumentation. The effects of such attacks on the receivers’ measurements are observed and quantified; they prove that the vulnerability of GNSS receivers to malevolent radio attacks is a quantifiable reality, but such attacks “leave traces” that could trigger their detection. The purpose of this paper is to increase awareness, though not alarm, about the risk represented by time spoofing on application domains used to look at GNSS as a commodity. This paper shows that reducing this risk is possible at both receiver and system level.

International audience; In this work, we propose a multi-perspective framework of analysis of critical infrastructures (CIs) with respect to supply service, topology and controllability. The framework enables identifying the role of CI elements and quantifying the consequences of scenarios of multiple failures, with respect to the different perspectives considered. To present the analysis framework, a benchmark network representative of a real gas transmission network across several countries of the European Union (EU) is considered. The information extracted from such analysis can help us to identify the critical elements and how the properties of the network are affected by failures, and to propose corresponding improvements for CIs. The findings of this paper demonstrate the interest of considering several perspectives in the analysis of CIs for providing useful information for ensuring their safe and reliable operation.

In recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people’s safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems.

Critical infrastructures are interdependent systems that are continuously evolving. Their evolution is, oftentimes, in a way that tightens, or creates new, interdependences. As urban areas continue to develop, the philosophy of officials and policy makers to meet growth in the energy demand tends to prioritize sustainable planning by deferring infrastructural upgrades and relying instead on the smart operation. Relying on information and communication technology (ICT) network is a prerequisite to achieving smart operation. With the anticipated high penetration of distributed energy resources (DERs) and electric vehicles (EVs), increased reliance on real-time monitoring and control is a must for the power grid to handle such unprecedented levels of load/generation uncertainties. This paper sheds light on how the power grid, the ICT network, and the transportation network are interrelated and interdependent. This paper also presents a benchmark problem for this type of research and a case study. Initial results of the case study show that the impact of these rising interdependences may be significant and must be fully understood and taken into consideration.

The paper at hand presents the design of a survey aiming at the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living reality was heavily disturbed and working conditions fundamentally affected. The survey is rooted in a security culture framework layered into two levels, organizational and individual, further analyzed into 10 different security dimensions consisted of 52 domains. An in-depth questionnaire building analysis is presented focusing on the aims, goals, and expected results. It concludes with the survey implementation approach while underlining the framework's first application and its revealing insights during a global crisis., 18 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:2012.13718

Most real-world networks, from the World-Wide-Web to biological systems, are known to have common structural properties. A remarkable point is fractality, which suggests the self-similarity across scales of the network structure of these complex systems. Managing the computational complexity for detecting the self-similarity of big-sized systems represents a crucial problem. In this paper, a novel algorithm for revealing the fractality, that exploits the community structure principle, is proposed and then applied to several water distribution systems (WDSs) of different size, unveiling a self-similar feature of their layouts. A scaling-law relationship, linking the number of clusters necessary for covering the network and their average size is defined, the exponent of which represents the fractal dimension. The self-similarity is then investigated as a proxy of recurrent and specific response to multiple random pipe failures – like during natural disasters – pointing out a specific global vulnerability for each WDS. A novel vulnerability index, called Cut-Vulnerability is introduced as the ratio between the fractal dimension and the average node degree, and its relationships with the number of randomly removed pipes necessary to disconnect the network and with some topological metrics are investigated. The analysis shows the effectiveness of the novel index in describing the global vulnerability of WDSs.

In recent years, the power system studies changed dramatically, shifting their focus from power system expansion planning as well as operation quality enhancement to other issues such as the integration of large amounts of renewable resources, the definition of new roles for the Transmission System Operator and Distribution System Operator since more and more generation is moving on the distribution side, resiliency of grids and economic sustainability of the on-going revolution in the energy field. Most energy infrastructures in the world were developed in the second half of the 20th century. The main question in energy system planning and development is whether these old systems can meet future growing needs for different types of energy carriers or not. Along with composite energy transfer systems, many of the installed equipment and tools are getting close to their useful lifetime or their relevant operational limitations. In addition, challenges such as the continuous growth of energy demand, still a strong dependence on fossil fuels, the need for power system reinforcement and the deep penetration of clean and sustainable energy resources raise the need for a new vision of energy systems along with some basic changes in existing systems. Adopting the distributed generation units as well as gas-fired power generation technologies such as combined heat and power units or combined cooling, heat and power units, gas furnaces, gas converters, etc., in an energy hub has motivated applied research to investigate how to integrate different infrastructures such as electricity, natural gas and heat. The resulting system, called multienergy carriers, hybrid systems or combined energy systems, requires the application of new and integrated modelling as well as operation tools. Possible scenarios for the future of the energy systems have been developed to overcome limitations of current structure due to a stronger interaction among different infrastructures (for example, gas and electricity). Many studies were oriented to guarantee standard levels of voltage and frequency in electrical systems or standard gas pressure in pipes in presence of increasing gas and electrical loads. Although these studies provided important details for the systems under investigation, they often propose solutions in line with existing reality and limit the analysis in finding an optimisation of the existing system without considering the possibilities deriving from different architectures. Modern energy systems, nowadays, must consider the coexistence of multiple carriers of energy, and, in the future, the concurrent planning and operational optimisation of them can provide an effective approach for improving economical performances, increasing sustainability and ensuring a secure and resilient operation. This chapter will present some modelling and optimisation issues of strongly interacting energy systems and explore the possibility offered by multicarrier energy systems to improve the overall efficiency of coupled systems.

Near real-time monitoring and control of critical infrastructure is essential for the operation and management of cities in a world that is, today, more complex and interconnected than ever. Such an infrastructure can be represented as complex networks an some of their related indices and statistics, many of them based on the shortest paths, play a pivotal role in the decision making for public services such as internet, energy or water. Particularly, the literature has shown that shortest paths are key for resilience and criticality assessment in a water distribution systems (WDS). This paper proposes a procedure to speed-up the computation of shortest paths in a WDS, as it can straightforwardly benefit any critical infrastructure. The proposal is based on a reduced dimension of a complex network representing any critical infrastructure. Despite the consequent decrease in the number of all possible paths in the network, the main advantage and novelty of this proposal is to continue finding the exact solution for the shortest paths. Experimental results show that the procedure brings a computational-time reduction consistently over 50% and up to 90% in some cases. In addition, the paper reveals how the use of shortest paths benefits WDS operation and management, as well as playing a key role in near real-time contamination detection and leakage control.

Cyber-Physical Systems (CPS) use computational resources to control physical process and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical process. In this paper, we present metrics to quantify the resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on switched linear systems and actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennesse Eastman control challenge problem., 11 pages, 2 figures, 6 tables

Resilient critical airport infrastructures affected by a disaster need to sustain minimal functionality and quickly resume full operation, while at the same time coping with the increased operational demands imposed by the unfolding disaster response. In this paper, we develop a resilience framework and model-driven approach that focuses on the ability of the infrastructure to rapidly adapt to a new steady state under these conditions. This requires both the extension of capacity as well as the adaptation of key processes. Through discrete event simulations, we study the implications of different policies to improve airport resilience under different disaster impact scenarios for a stylized case. Our results show that although decision-makers may be tempted to focus on short-term measures that can be implemented immediately, resilience is improved most by a combination of rapid process changes and longer-term measures that structurally increase airport capacity.

Industrial Control Systems (ICSs) are widely used in critical infrastructures to support the essential services of society. Therefore, their protection against terrorist activities, natural disasters, and cyber threats is critical. Diverse cyber attack detection systems have been proposed over the years, in which each proposal has applied different steps and methods. However, there is a significant gap in the literature regarding methodologies to detect cyber attacks in ICS scenarios. The lack of such methodologies prevents researchers from being able to accurately compare proposals and results. In this work, we present a Methodology for Anomaly Detection in Industrial Control Systems (MADICS) to detect cyber attacks in ICS scenarios, which is intended to provide a guideline for future works in the field. MADICS is based on a semi-supervised anomaly detection paradigm and makes use of deep learning algorithms to model ICS behaviors. It consists of five main steps, focused on pre-processing the dataset to be used with the machine learning and deep learning algorithms; performing feature filtering to remove those features that do not meet the requirements; feature extraction processes to obtain higher order features; selecting, fine-tuning, and training the most appropriate model; and validating the model performance. In order to validate MADICS, we used the popular Secure Water Treatment (SWaT) dataset, which was collected from a fully operational water treatment plant. The experiments demonstrate that, using MADICS, we can achieve a state-of-the-art precision of 0.984 (as well as a recall of 0.750 and F1-score of 0.851), which is above the average of other works, proving that the proposed methodology is suitable for use in real ICS scenarios.

International audience; Cyber-physical systems (CPS) integrate computation and networking resources to control a physical process. The adoption of new communication capabilities comes at the cost of introducing new securiy threats that need to be handled properly. Threats must be addressed at cyber and physical domains at the same time in order to detect and automatically mitigate the threats. In this paper, we elaborate an approach to attenuate cyber-physical attacks driven by reflective programmable networking actions, in order to take control of adversarial actions against cyber-physical systems. The approach builds upon the concept of programmable reflection and programmable networking. We validate the approach using experimental work.

A disruption in one critical infrastructure can quickly lead to cascading effects in several other ones. Much research has been done to analyze dependencies between different critical infrastructures, but little is known about how to mitigate escalation and cascading effects across several critical infrastructures, i.e. how to develop collective critical infrastructure resilience. This research presents the results of 15 simulation-games where groups of 6 to 8 field experts from different sectors were challenged to collaboratively manage a disruption in the payment system that quickly affected food distribution, fuel distribution, transport, health care et cetera. Teams discussed possible strategies, which next were implemented in a computer simulation. Teams could influence the sequence of events on 4 decision points during a 10 day scenario, and play the same scenario several times to test alternative solutions. Each simulation-game session lasted a full day. Data analysis involved the recorded team discussions as well as computer simulation logs of the implemented decisions and their impacts. The results show how escalation and the severity of cascading effects largely depends on the quality of the early crisis response and not so much on the initial disruption. Also, it is shown how cross sectorial collaboration is required. Responses where groups focus too much on cascading effects in one area lead too poor overall performance for society at large. Groups tend to overbalance their mitigating strategies initially, until they arrive at a more balanced strategy that covers challenges in several different critical infrastructures from an integral perspective. CCRAAAFFFTING

Modern society is highly dependent on its critical infrastructures. These infrastructures usually suffer intentional attacks, which is a serious threat to social wellbeing, making the protection of them to be a great challenge for the security agencies. Many game models have been proposed to tackle this problem. However, little of them consider the interrelationship of different targets within the infrastructures. In this paper, the protection of critical infrastructures against a malicious attacker is modeled as a simultaneous game, where the payoffs of the players are evaluated on the basis of the topology structure of the infrastructure system. An efficient algorithm is adopted to obtain the Nash equilibrium solution. The experimental results reveal that in the equilibriums, the defender distributes higher probabilities on protecting the targets who are more important, for example, those with large degrees or betweenness, while the attacker prefers attacking targets with medium degrees or betweenness. The effectiveness of the game-theoretic solution is validated, and the experimental results in a real-world network provide us a clear insight on which targets to protect.

The present article focuses on the research and development planning for innovative railway systems. Within such a general framework, the specific objectives of the research have been defined within the framework of a large Intelligent Railway System project in Hungary. Our theoretical research work at the university is combined with practical experience gained at the Hungarian State Railways. In the course of this research work, the development of an intelligent railway system has been investigated by leveraging on the fruitful cooperation between academic and industrial partners, in order to promote the application and integration possibilities of the development results, as well as the introduction of innovative components in the railway system. In such a context, this article discusses the research plan, preliminary and long-term expected results, sharing objectives and experiences with the aim of providing novel views in an extremely current and challenging field of research.

The construction of large infrastructures (e.g., railways, gas pipelines or power grids) is increasingly facing widespread and violent opposition of radical environmentalist and ideological groups. Therefore, it is necessary to consider also the risk related to violent opposition actions when selecting construction sites. However, the classical paradigm of risk considering probability and impact hardly applies in this context, especially because of the difficulty of assessing probabilities, due to the lack of historical and/or reliable data. This paper develops a novel framework to support the selection of construction sites for infrastructure development, considering the risk of attacks by radical or ideologically motivated groups. The main originality is that the risk of attacks is evaluated considering the attractiveness of the different locations for the attackers, based on several criteria. Specifically, a novel multi-criteria decision model is introduced, which involves fourteen criteria related to the effort required to the opponents for performing the attack and to the expected outcomes of the attack in terms of potential damage to the targeted infrastructures and visibility of the action; moreover, the lifespan of the construction site and its influence on the possibility of an attack are taken into account. The criteria have been selected by a pool of forty-four Subject Matter Experts (SMEs), i.e., security managers and experts from the academia, industry and law enforcement. With such criteria, it is possible to construct a holistic index of attractiveness and quantify it by the Sparse Analytic Hierarchy Process (SAHP), based on the evaluations of the criteria provided by the pool of SMEs. The proposed methodology is applied to support the localization of the construction sites for the cross-border section of the Turin–Lyon High Speed and High Capacity Railway, including a new base-tunnel. Indeed, such an 8.6 billion Euro project has experienced strong opposition in Italy and, hence, security has become one of the main issues to be considered. The proposed model constitutes a decision aid tool to support the selection of construction plans, considering attractiveness and impact of attack.

Petri nets are used in this paper to estimate the indirect consequences of accidents in a railway network, which belongs to the class of the so-called transportation Critical Infrastructures (CIs), that is, those assets consisting of systems, resources and/or processes whose total or partial destruction, or even temporarily unavailability, has the effect of significantly weakening the functioning of the system. In the proposed methodology, a timed Petri ne

A hybrid Petri net formalism that allows deterministic, and fluid transitions is extended by generally distributed transitions that moves discrete tokens. Models in this formalism can be analyzed with Parametric Reachability Analysis, by computing all reachable locations, and by separating the deterministic and the stochastic evolution of the system. Several performance metrics, such as the distribution of fluid over time, can be derived by deconditioning according to arbitrary continuous probability distributions. This efficient concept allows for the analysis of an arbitrary number of fluid places, as opposed to classical stochastic hybrid Petri net approaches. Moreover, validation of our results against a FSPN tool shows that parametric reachability analysis provides more accurate results. A case study motivates and shows the potential of our approach.

Boosting critical infrastructures’ (CIs) preparedness to threats, including natural disasters and manmade attacks, is a global imperative. The intrinsic dependencies and interdependencies between CIs hinder their resiliency. Moreover, the evolution of CIs is, in many cases, en routè to tighten those interdependencies. The goal of this paper is to uncover and analyze the rising interdependency between the electric power grid, information and communication technology (ICT) networks, and transportation systems that are heavily reliant on electric-power drivetrains, collectively referred to hereafter as electro-mobility (e-mobility). E-mobility includes electric vehicles (EVs) and electric railway systems. A new influence graph-based model is introduced, as a promising approach to model operational interdependencies between CIs. Each of the links of the influence graph represents the probability of failure of the sink node following a failure of the source node. A futuristic scenario has been analyzed assuming increased dependency of the power grid on ICT for monitoring and control, and high penetration levels of EVs and distributed energy resources (DERs) in an urban region. Inspecting the influence graph shows that the impact of interdependency between the power grid, the ICT network, and the transportation network, for the case study analyzed in this paper, does not lead to failures during normal operation with proper design; however, it is severe during emergency conditions since it leads to failure propagation among the three CIs. This paper sets the stage for more research on this topic, and calls for more attention to interdependency analysis.

The critical infrastructure resilience depends on several factors that go beyond the physical reliability and capacity to repair the system after a disruption. The overall critical infrastructure resilience includes aspects related to the social and economic backbone governing its capacity to deliver its service. This contribution presents a theoretical toolkit to calculate the overall resilience of critical infrastructures developed within the European project LIQUEFACT for earthquakeinduced soil liquefaction disasters. The toolkit combine several aspects organized in three dimensions: organizational and management, the physical or technical system and operational capacity to deliver the service. The toolkit clearly defines also resilience aspects, such as preparedness, absorption, recovery and adaptation. For each dimension and aspect of the resilience several indicators are developed. A critical and technical explanation of each indicator is here proposed, as well a systematic methodology to combine them in the resilience toolkit. The novelty of this study is the systematic analysis of dimensions, aspects and indicators that made the proposed resilience toolkit original. The study is concluded with analyses of feasibility of the toolkit to natural disasters and applicability to localized disasters, such as earthquake-induced soil liquefaction events. Finally, the key factors of toolkit influencing a built asset model of critical infrastructures are identified.

International audience; This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.

A practical procedure of risk and resilience assessment, with cascading failures occurring in the fuel and energy economy, is proposed. It is based on the threshold values and multi-energy methods. The probability analysis of the equipment damage is carried out using vulnerability models for various equipment classes and in the process the probit-functional analysis is applied. Resilience indices-criteria are used for assessing the resilience of plants as complex mechanical systems. The procedure proposed enables calculating threshold values of cascading failure; safe distances for equipments; probability assessment of failure propagation. It also adds to identifying the facilities triggering cascading effects; probability of the equipment damage; to assessing the amount of damage and the damage loss rate. The strategy (procedure) allows ranking the equipment as related to the risk of domino effects' occurrence; analyzing consequences for each cascade direction and the possibility of its propagation on to the following level; evaluating the resilience state of the plant as a technical system. The damage amount and damage loss rate are evaluated comparing the load received and predetermined threshold values of cascading failure for various equipment classes. The strategy (procedure) developed will allow judging about safety of one or other type of a tank farm containing hydrocarbons; the results obtained in the process and the actual data converge. This strategy (procedure) is strongly recommended to be applied by experts in the field of industrial safety in developing industrial safety manifests, safety justification as well as in developing safety registration certificates of critical infrastructures. © 2019 Published under licence by IOP Publishing Ltd.

Methodology for assessing human error probability under emergency conditions, taking into account internal and external factors, the degree of uncertainty, which can influence the reliability of a "man-machine" system, is presented. A model based on the HRA (Human reliability analysis) methodology is used. Probability of a human error is determined, which combines the nominal error and the influence of environmental conditions. The error is compared to the rated (design) permissible critical value dependent on the system state. It enables steadily monitoring the technical state of the system. In case of an unsatisfactory state, correcting actions are performed and probability for a human error is again assessed as well as the extent of the system improvement is tested. The methodology uses reliability criteria taking into account the degree of human involvement into the process of controlling critical infrastructures. The methodology includes efficiency analysis and assessment of the safeguarding system with regard to the human factor. Currently, there is no special documentation setting the procedure of human behavior under emergency conditions by means of quantitative methods. The methodology can serve the purpose of developing a practical manual for people having to make decisions; it can be used in training the personnel for acting in emergency situations. It is useful for assessing the influence of human factor on operation of critical infrastructures used for various purposes. © 2019 Published under licence by IOP Publishing Ltd.

The article is devoted to the issues of waterproofing of expansion and technological joints. The necessity of using reliable waterproofing methods during construction is identified and justified. Particular attention is drawn to the shortcomings of existing methods, namely bentonite and hydrophilic waterproofing cords related to installation problems and other technical issues. System installation waterstop for technological joint mating base plate-wall and the repair scheme of the one with using the polyurethane resin are described. Comparison is given of the cost of repairing the technological joint and the installation of a waterstop in new construction. The influence of the construction quality on the further cost operation of the building is revealed. © 2019 Published under licence by IOP Publishing Ltd.

The paper considers the problem of constructing a full group of failure scenarios for physical infrastructures when subjected to cyber attacks (CAs). Physical infrastructures actually are systems of systems, or network of networks [1]. The main idea of the research rests on the assumption, that in order to damage any physical infrastructure by a cyber attack, it has to be able to produce a powerful enough physical impact on the most vulnerable part(s) of the infrastructure. Only civil engineering and industrial structures and installations connected to Internet and World Wide Web are considered. Hence, all infrastructures discussed below have to be elements of the Enterprise IoT or IoT, namely: electrical grids, oil, gas and product pipeline systems, water supply and disposal (waste) systems, rail networks, air traffic control and telecommunications (finance, commerce, business) networks, etc. The paper discusses how to construct a full group of scenarios of physical impacts on an infrastructure and how to calculate reliability, resilience and safety of infrastructures exposed to CAs. This paper should calm down the legitimate concerns of lay people about disclosing vulnerabilities of critical infrastructures, because it raises the awareness and offers infinitely much more to the armor/shield than to the canon/spear. © 2019 Published under licence by IOP Publishing Ltd.

The paper describes the basics of a new convergent science-Infranetics-based on the MAICS-convergent technology (Digital Stochastic Mechanics, Artificial Intelligence, Information Theory, Cognitive and Social Sciences). The name Infranetics comes from Infrastructures + Cybernetics. Infranetics is being constructed for solving the central problem of safe innovative development of a region/territory/municipality by creating a methodology of harmonized regulation of regional risk. This methodology is based on optimal management of systems of interdependent critical infrastructures (ICI) that play crucial role in the sustainable development of a modern society of risk. To this end following four aggregate optimization criteria are proposed: 1) regional average life expectance at birth (RALE); 2) regional (municipal) life quality index (RLQI); 3) resilience of regional systems of interdependent smart infrastructures (RICI) and 4) spatial entropy of creation and destruction of a regional system of ICI. Infranetics is a useful tool of managing and governance for regional decision makers (DMs), as they can track how their decisions influence the RALE, quality of life and the wellbeing of their citizens/constituents. The paper demonstrates how Infranetics is used to solve real life problems related to regional resilience and governance. © 2019 Published under licence by IOP Publishing Ltd.

The Urban guided transport management system (UGTMS) as subway, transports from several hundreds of thousands to millions of passengers per day. Size and irreplaceability of subway transport capacity include the subway transport to the critical infrastructures of cities, regions or countries. Modern transport critical infrastructures contain in addition to physical and social parts also the cyber control systems and they are marked as cyber-physical systems (CPS). The CPSs are characterized by safety-critical nature, complexity, connectivity, and open technology. The CPS complexity, openness and dynamics form a large attack surface that may lead to failures and irreparable damage. Multiple Independent Levels of Security (MILS) can meet the high system security requirements. The MILS is a high-assurance security architecture based on the concepts of separation and controlled information flow. The article discusses the possibilities of using the MILS platform in the data communication subsystem, which connects the individual UGTMS subsystems (Wayside subsystem, On-board subsystem and operation control subsystem). Therefore, the communication system should guarantee transmission parameters and do not affect security level of the respective subsystems.

This paper reviews performance of combined pile-raft foundations (CPRF). The goal of the study is obtaining the ability to design solid, reliable and cost-effective foundations allowing for minimum settlements. The task is to develop methods for calculating CPRF for optimum choice of the foundation raft size, dimensions and number of piles. To achieve these results, an analytical method was used, curves of the ratio of CPRF settlement to raft foundation settlement vs. total load N on the piles were plotted. The scientific significance of the study is in the method of engineering calculation of CPRF, which is better correlated to the experimental data and allows optimal distribution of loads between the raft and piles, ensuring reliability and operational integrity of buildings, reduction of settlements and the number of piles, which would lead to cost savings and reduced consumption of materials for foundations. A new coefficient showing the residual stress has been introduced, and a method for its calculation has been proposed. A new equation for determination of the share of total load N on the piles has been obtained. This equation is more general in comparison with the previously generated equations. It allows excluding the assumption that the stresses arising under the raft completely dissipate at the level of the pile end. Application of the developed methods will increase reliability and reduce settlements and the number of piles, which ultimately will lead to cost savings and reduced consumption of materials for foundations. © 2019 Published under licence by IOP Publishing Ltd.

Industrial Control Systems are an essential part of our daily lives and can be found in industries such as oil, utilities, and manufacturing. Rapid growth in technology has introduced industrial components with network capabilities that allow them to communicate with traditional computer networks, thus increasing their exposure to cyber-attacks. Current research on Industrial Control Systems suffer from lack of technical information as these systems are part of critical infrastructures. To overcome this, researchers have employed different types of testbeds to develop their mechanisms of cyber-attack detection and prevention. This manuscript describes, implements, and evaluates physical, hybrid, and virtual application of a clean water supply system developed for cybersecurity research. The results show that physical testbeds allow an understanding of the behaviour and dynamics of control components like sensors and actuators, which might be affected by external influences such as noise, vibration, temperature, and non-ideal device behaviour. Although, hybrid testbeds reduce the cost of implementation, they ignore the physical dynamics of the system as explained above. Virtual testbeds are the cheapest option in comparison with physical and hybrid testbeds; however, they provide a limited view of the control system operation that could have negative consequences when developing a detection/prevention system.

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens&rsquo, quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

The adequate functioning of critical infrastructures is crucial for sustaining the development of today’s societies and economies. It is a priority, therefore, to foster our understanding of such systems and ability to assess interdependencies, vulnerabilities, and resilience. Starting from the DMCI (Dynamic functional Modelling of vulnerability and interoperability of Critical Infrastructure systems) framework proposed in [1], in this paper we present an evolved formalism (DMCI-e). This introduces novel modeling features and enhances applicability while keeping the original focus on a dynamic and network-centric characterization of disservice. A key objective is to respond to the need, expressed by policy-makers and critical infrastructure regulators, for sector-agnostic and multi-granular infrastructure models for the estimation of service supply capabilities and response during and after disruptive events.

The deep integration between the cyber and physical domains in complex systems make very challenging the security evaluation process, as security itself is more of a concept (i.e., a subjective property) than a quantifiable characteristic. Traditional security assessing mostly relies on the personal skills of security experts, often based on best practices and personal experience. The present work is aimed at defining a security metric allowing evaluators to assess the security level of complex cyber-physical systems (CPSs), as critical infrastructures, in a holistic, consistent and repeatable way. To achieve this result, the mathematical framework provided by the open source security testing methodology manual (OSSTMM) is used as the backbone of the new security metric, since it allows to provide security indicators capturing, in a non-biased way, the security level of a system. Several concepts, as component lifecycle, vulnerability criticality and damage potential - effort ratio are embedded in the new security metric framework, developed in the scope of the H2020 project ATENA.

The operations of many critical infrastructures (e.g., airports) heavily depend on proper functioning of the radio communication network supporting operations. As a result, such a communication network is indeed a mission-critical communication network that needs adequate protection from external electromagnetic interferences. This is usually done through radiogoniometers. Basically, by using at least three suitably deployed radiogoniometers and a gateway gathering information from them, sources of electromagnetic emissions that are not supposed to be present in the monitored area can be localised. Typically, relay nodes are used to connect radiogoniometers to the gateway. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance.In this paper address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance).We show that the above problem can be formulated as a MILP as well as a PB optimisation problem and present experimental results comparing the two approaches on realistic scenarios.

Industrial and Automation Control systems traditionally achieved security thanks to the use of proprietary protocols and isolation from the telecommunication networks. Nowadays, the advent of the Industrial Internet of Things poses new security challenges. In this paper, we first highlight the main security challenges that advocate for new risk assessment and security strategies. To this end, we propose a security framework and advanced tools to properly manage vulnerabilities, and to timely react to the threats. The proposed architecture fills the gap between computer science and control theoretic approaches. The physical layers connected to Industrial Control Systems are prone to disrupt when facing cyber-attacks. Considering the modules of the proposed architecture, we focus on the development of a practical framework to compare information about physical faults and cyber-attacks. This strategy is implemented in the ATENA architecture that has been designed as an innovative solution for the protection of critical assets.

International audience; With recent advances in systems-of-systems, reliability analysis becomes a very challenging research topic. One of the most pressing issues is to figure out a plan to handle resilience. On this basis, we propose, in this paper, a structural deterministic approach to quantitatively measure systems resilience. This approach is based on a three-step method. First, evaluate the functional dependencies between groups by considering a system-of-systems as a large-scale interconnected network of systems distributed into interdependent groups. This leads us to better understand the overall connections and process continuity. Next, analyse how much the global architecture of the system-of-systems depends on every group. Last, estimate its structural resilience by measuring the impact of each system's failure on the other systems forming the global system and building the process. Two case studies are provided to experiment our approach. The results are cross-compared and evaluated.

We suggest an approach to assessing critical infrastructure resilience (CIR) as a step towards informed resource allocation and operation when planning to cope with CI disruptions in the context of emergency management or multi stakeholder planning. The approach is capabilities-based, where a capability is defined as a combination of assets, resources and routines specifically arranged to accomplish a critical task and assure a key objective. The capabilities (intra- and inter-institutional) are grouped into clusters according to the resilience phase (preventive, absorptive, adaptive and restorative) where they are invoked; and according to the system type (technical, operational, social and economic) which they belong to. An overall resilience capability building cycle completes the framework, enabling a systematic implementation of relevant capabilities and making gap analysis with regard to resilience deficits. A simplified test case exemplifying the use of the framework in the context of a regional public-private collaboration for CIR is provided.

The cloud computing has recently emerged as compelling paradigm for managing and delivery services over the internet. However, users as well as critical infrastructure operators, have legitimate concerns about the confidential- ity, integrity and availability, in short the dependability, of applications and their data hosted on a third-party cloud. The dependability is become a commercial imperative for cloud providers, especially to support cloud computing for critical infrastructures. In this paper the SecureCloud project, its approach and goals are presented. SecureCloud aims to remove technical impediments to dependable cloud computing, encouraging and enabling a greater uptake of cost-effective, environment-friendly, and innovative cloud solutions, in par- ticular, for critical infrastructure applications.