Critical Infrastructure Systems Research Articles

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems. Their loss of functionality not only hinders residential and commercial activities, but also compromises evacuation and rescue after disasters. Dealing with risks to key strategic objectives is not new to asset management, and risk management is considered one of the core elements of asset management. Risk analysis has recently focused on understanding and designing strategies for resilience, especially in the case of seismic events that present a significant hazard to highway transportation networks. Following a review of risk and resilience concepts and metrics, an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures, one that is a relevant and complementary element within a wider resilience-based framework, is proposed. The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase. Although further improvements are needed, the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.

A Critical Infrastructure Perspective and Systems Perspective on Hybrid Threats in the Black Sea Region

"The Black Sea region features high complexity and other factors, such as the lack of a regional security architecture, which, in the current security context, result in a high effectiveness for hybrid warfare approaches. This has been proven by the track record of hybrid and unconventional warfare operations in the region, targeting also civilian infrastructure. The article proposes a Critical Infrastructure perspective on Black Sea insecurity, highlighting the potentiality for hybrid warfare but also measures which can increase resilience to hybrid threats on the basis of the Critical Infrastructure Protection framework. Lastly, the article highlights contributions which Romania can make on the basis of its experience and success in implementing CIP."

The Impact of Distributed Autonomous PV Installations on Critical Infrastructure in Crisis Situations

Ensuring a functioning critical infrastructure system is crucial for many industries, including energy. Due to the transformation of the energy sector, it is necessary to take into account the impact of the induced changes on critical infrastructure. The presented paper focuses on the assessment of the effects of small dispersed photovoltaic power plants (PV), which are gaining in popularity both in households and in other buildings. Specifically, it is an impact assessment within the emergence of island operation in the selected area with the aim of restoring power to the facilities included in the critical infrastructure system as soon as possible. The specificity of the considered small photovoltaic power plants is their autonomy and impossibility of control from the superior control system. The presented case study compares possible approaches to power recovery on the one hand and the impact of different levels of dispersed PV on the other. The results provide important conclusions for the expected development of the critical infrastructure system in connection with the increase in autonomous distributed electricity sources.

ОБНАРУЖЕНИЕ ИНЦИДЕНТОВ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ НА ОСНОВЕ ТЕХНОЛОГИИ НЕЙРОННЫХ СЕТЕЙ

Objective: develop model, algorithmic and software for detecting in real time attempts to disrupt the correct functioning of critical information infrastructure systems with neural network technologies. Methods analysis of modern machine learning methods and neural network technologies, synthesis and modeling of correct behavior of programs, algorithmization of learning processes and application of neural networks, experimental studies of developed algorithms and programs on the stand. Study results: The characteristics of machine learning methods and neural network technologies used to detect software and technical impacts and information security incidents are given. The method for solving this problem based on neural networks with LSTM and FFN architectures has been developed. The description of the algorithm and fragments of the software implementation of the method in the programming languages Python3 and Go using Tensorflow and Keras libraries is given. An important advantage of the proposed approach is the possibility of adapting the neural network in the event of a change in the mode and conditions of operation of the system. The results obtained during the experiments indicate the possibility and expediency of using this approach to detect software and technical impacts on critical information infrastructure systems on a time scale close to real with a high level of reliability. Scientific novelty: consists in the application of deep learning technology based on a long-term short-term neural network LSTM, which has the ability to adapt to changing modes and conditions, to solve the problem of detecting signs of a violation of the correct functioning of nodes of information and telecommunications systems in real time.

A Formal Model and Verification for HESTIA: An Automated, Adversary-Aware Risk Assessment Process for Cyber Infrastructure

Due to the characteristics and connectivity of today’s critical infrastructure systems, cyber-attacks on these systems are currently difficult to prevent in an efficient and sustainable manner. Prevention and mitigation strategies need accurate identification and evaluation of: system vulnerabilities, potential threats and attacks, and applicable hardening measures. Furthermore, the ability to prioritize hardening measures based on accurate assessments of risk is needed. In addition, the consideration of the availability, applicability, and cost of potential mitigation strategies is also needed. To address this challenge we created HESTIA: High-level and Extensible System for Training and Infrastructure risk Assessment. In this article we present a formal model of the HESTIA system. We then also present a formal verification of the HESTIA semantic model.

Modelling and analysis of cascading failures in interoperable critical infrastructure systems

Studying the behaviour and structure of the critical infrastructure systems (CISs) and the interdependency among them is of special significance, and if it is not determined properly, in addition to failure and cascading failure, there will be a national catastrophe. One of the reasons for not using the proper technique for critical infrastructure modelling is not to specify the entire sample space of the system and express all its limitations so that in addition to displaying accurate infrastructure behaviour, cascading failures can be prevented to an acceptable extent. Therefore, there is a need for a CIS formal specification modelling to develop different instances of a CIS, and selecting the best and the failure-free instance of it. In this paper, we provide a formal declarative specification of CIS that supports detection and prevention of failure and cascading failure automatically in using Alloy. Since failures and cascading failures occur in dynamic and time-dependent environments, the proposed model considered dynamic aspects of CISs. The results show that using Alloy Analyser, all the models made and the current failures in infrastructure are determined using counter-examples and the best instance in terms of minimum and even without failure can be selected.

Toward Autonomous Physical Security Defenses Using Machine Learning

The sheer increase in interconnected devices, reaching 50 B in 2025, makes it easier for adversaries to have direct access to the target system and perform physical attacks. This risk is exacerbated by the proliferation of Internet-of-Battlefield Things (IoBT) and increased reliance on the use of embedded devices in critical infrastructure and industrial control systems. Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). The system also has a layered response mechanism and recovery scheme, which reduces false alarms and prolongs the operational time. An experimental platform was constructed and used for data collection and machine learning model training. This study also explored the impact of adversarial learning attacks on the proposed system and subsequently developed a countermeasure. The final prototype was capable of recognizing two types of normal operating conditions (sheltered and exposed environments) and four types of physical attacks. It also has adaptive response and recovery mechanisms.

AnomalyAdapters: Parameter-Efficient Multi-Anomaly Task Detection

The emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers’ variant to encode a log sequences and utilizes adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.

Predicting Resilience of Interdependent Urban Infrastructure Systems

Climate change is increasing the frequency and the intensity of weather events, leading to large-scale disruptions to critical infrastructure systems. The high level of interdependence among these systems further aggravates the extent of disruptions. To mitigate these impacts, models and methods are needed to support rapid decision-making for optimal resource allocation in the aftermath of a disruption and to substantiate investment decisions for the structural reconfiguration of these systems. In this paper, we leverage infrastructure simulation models and Machine Learning (ML) algorithms to develop resilience prediction models. First, we employ an interdependent infrastructure simulation model to generate infrastructure disruption and recovery scenarios and compute the resilience value for each scenario. The infrastructure-, disruption-, and recovery-related attributes are recorded for each scenario and ML algorithms are employed on the synthetic dataset to develop accurate resilience prediction models. The results of the prediction models are analyzed and possible design strategies suggested based on the resilience enhancement attributes. The proposed methodology can support infrastructure agencies in the resource-allocation process for pre- and post-disaster interventions.

BUILDING SECURE INFORMATIONAL COMMUNICATION BETWEEN AUTOMATED CRITICAL INFRASTRUCTURE SYSTEMS THROUGH AN OPEN INTERNET NETWORK

The article provides the basic principles of building a secure network interaction of automated critical infrastructure systems (ACSIs) in a secure execution and solving the problem of safe communication of local networks through the open Internet network via Intranet technology.

Risk-Informed Decision-Making for Predisaster Risk Mitigation Planning of Interdependent Infrastructure Systems: Case Study of Jamaica

Hazards cause huge social disruptions and economic losses each year. Community disaster resilience relies on the capacity of critical infrastructure systems to maintain acceptable levels of functionality during a disruptive event and to recover promptly after the event. A proper predisaster risk mitigation planning framework can aid communities by working in advance to understand the preexisting local needs, identify the leaders, form partnerships, establish resources, and seize opportunities to increase community disaster resilience. Although numerous theoretical studies exist on developing models to support infrastructure predisaster risk mitigation, these models have not been properly incorporated into the decision-making process in existing studies. This lack of incorporation impedes the transfer of theoretical knowledge to practical policymaking. High-level predisaster infrastructure risk mitigation decisions for multiple infrastructure systems are especially in need of such an illustration due to the complexity introduced by the scale. This paper defines the interdependent infrastructure risk mitigation (IIRM) problem in a risk-informed decision-making framework and illustrates the entire decision-making process using a case study on predisaster risk mitigation planning of the interdependent critical infrastructure systems in Jamaica. This region is vulnerable to natural hazards but has received little attention in the literature.

Coupled Continuous-Time Markov Chain–Bayesian Network Model for Dam Failure Risk Prediction

Hydropower dams are critical infrastructure systems characterized by their complex, dynamic, and stochastic behaviors. The frequent variation in the hydrological and meteorological variables poses a higher probability of dam failure, highlighting the need to improve pertinent risk assessment approaches to predict failure risks, considering the uncertain states of such variables. Bayesian networks (BN) analysis has been a key risk assessment tool for decades; however, BN’s static acyclic nature is a recognized drawback. In this paper, a continuous-time Markov chain (CTMC) is coupled with a BN model to enable the dynamic assessment of dam failure risk. In this respect, BN is used to represent the interrelation among the system variables and simulate the propagation of uncertainties throughout the system, whereas the CTMC is adopted to describe the continuous transition of the system variables over their respective states. To demonstrate its applicability, the developed coupled BN-CTMC model was employed to predict the probability of failure of the Daisy Lake Dam in the province of British Columbia, Canada, under the uncertainty of reservoir water level, inflow, and wind speed states. The developed BN-CTMC modeling approach can aid in the development of reliable dam operation schemes and risk mitigation strategies through (1) adequately representing the propagation of the hydrological (e.g., inflow and reservoir water level) and meteorological (e.g., wind speed) variable uncertainties through dam system dynamical processes; (2) effectively quantifying dam failure risk under different operational conditions and failure scenarios; (3) accurately specifying the critical periods of dam system operational safety; and (4) providing in-depth understanding of the relationships between the dam system’s failure and associated variables over time.

Flood risk assessment and application of risk curves for design of mitigation strategies

A transportation network is a critical infrastructure system that forms the backbone for economy, serves the everyday life, and is critical to national safety. Holistic design of such a system is required to ensure smooth operation under both operational and emergency conditions. However, with ever-changing climate, transportation systems are exposed to significant weather-related hazards. For example, flood events are proven to be a dominant hazard in the U.S. due to their frequency and intensity. While flood events affect state agencies by requiring direct tax-dollar investments to repair damages, they also adversely influence communities by producing substantial indirect losses. This motivates state agencies, asset owners, and planners to developcost-effective mitigation strategies. However, the uncertainty of flooding and the interdependency between assets (such as roads and bridges) within the transportation system and traffic users on one hand and limited budgetary resources on the other hand, challenge the design of a cost-effective risk mitigation strategy. This is exacerbated by the fact that the estimation of indirect losses associated with closures resulting from damaged assets is difficult to assess. To address such gaps, this paper develops an integrated risk assessment method that synthesizes various inputs, including hazards, geographic features, spatial distribution of assets, and traffic, to simulate the flood-induced risk to a real-life transportation system. This framework is capable of estimating actual physical infrastructure damages as well as quantitatively evaluating the indirect losses of traffic users such as traffic delays and opportunity costs closely associated with flood risk. The developed flood risk curves can be used by decision-makers to implement proper pre-event mitigation or post-event response plans.

What Does More Look Like?

Current cybersecurity governance in the water sector is based on a matrix of mandatory and voluntary standards, guidance, and associated legal principles. To date, market and regulatory signals have resulted in an inconsistent application of cybersecurity fundamentals. Technologies that operate critical infrastructure often were developed and installed before the reality of cyber threats created a demand on initial system design. Today, we know the threat posed by cyber bad actors is real and growing. Because the sector collectively is faced with a complex set of information systems and hardware that must operate 24–7 to ensure public health and safety, there is an opportunity and a need to improve the water sector's cybersecurity posture, which will require reconsideration of the sector's current approach to oversight and accountability. Given the number of high-profile cyber incidents that have affected multiple sectors in the past year, Congress has taken action to create mandatory cyber-incident reporting requirements for critical infrastructure systems—this includes water utilities. The Department of Homeland Security will develop the reporting requirements, including how information will be shared with a sector-specific risk management agency; this is the US Environmental Protection Agency (USEPA) for water systems. These same events have led to multiple directives from the White House for enhancing cybersecurity within the US government and the nation's critical infrastructure systems. As part of a general call for implementing best practices, the president and National Security Council mentioned water specifically as one of four sectors in need of focused attention on securing industrial controls systems. The congressional chartered bipartisan Cyberspace Solarium Commission (Solarium) has also focused on the water sector and will be issuing recommendations for improving cybersecurity this fall. It is anticipated that the Solarium report will identify options that Congress may take to stimulate the implementation of cybersecurity best practices, including requiring greater support from USEPA, building capacity, and providing directed funding for implementation of cybersecurity. There is intense federal attention on what various sectors are doing to manage the risk from cyber threats. This raises questions on what more should be done to ensure that water systems are taking appropriate actions to implement cybersecurity controls. We are therefore at a rare inflection point when it comes to informing a new oversight structure for cybersecurity in the water sector. One option is maintaining the voluntary use of existing guidance and standards. That was the approach taken by the pipeline sector until the Colonial Pipeline incident led to rapid implementation of highly prescriptive mandates that are not risk based. Another approach includes USEPA's planned action to incorporate cybersecurity into sanitary surveys. Conceptually, we can agree that cybersecurity is important to operational integrity; however, this state-run program does not have the resources nor cybersecurity expertise to implement appropriately. In April, AWWA's Water Utility Council commissioned a report to examine an approach that puts the water sector in a lead role, following the model used successfully in the electric sector. In particular, the report analyzes the creation of a sector-led process in close partnership with USEPA. This co-regulatory approach would be centered on a new entity, the Water Risk and Resilience Organization (WRRO), that would develop enforceable cyber standards and provide third-party audits. The diversity in utility size would require a tiered level of engagement, most likely starting with the largest drinking water and wastewater systems. Federal oversight would come from USEPA, with support from other federal agencies in the form of reviewing and approving standards developed by the WRRO. This approach provides a process that is capable of quickly adapting to the needs of the sector while ensuring that utility-based expertise guides standard development. In this case, the water sector is the driver rather a passenger. The only remaining question is if the sector is willing to take that responsibility and keep a hand on the wheel of governance or let go and hope for the best. Kevin M. Morley is manager of federal relations at the AWWA Government Affairs Office in Washington, D.C. He can be reached at kmorley@awwa.org. Paul N. Stockton is president of Paul N. Stockton LLC in Sante Fe, N.M.

Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Cascading failure modeling in interdependent critical infrastructure systems (CISs) is an important but challenging work that lays the foundation for disaster impact assessment and recovery planning. However, despite the increasing volume of studies that examine cascading failure across interdependent CISs, the majority of modeling approaches proposed in prior studies do not capture the complex heterogeneous nature and behavior of the CISs under disaster, limiting the ability of these approaches to detailly model the cascading failure process. To address the above limitation, this study proposes a high level architecture (HLA)-based co-simulation approach to integrate heterogeneous domain-specific CIS models and model the cross-domain interdependencies and failure propagation processes. Moreover, the approach provides a novel solution for modeling the dynamic impact of disaster on CISs and analyze its impact on the cascading failure of interdependent CISs. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed approach. The results showed that the model developed using the proposed approach could provide granular data of the state and heterogeneous behaviors of the systems, capture the dynamic evolution of disaster impact on system components, and reveal the paths and mechanisms of failure propagation within and across the systems. The proposed modeling approach can overcome the drawbacks in current interdependent CISs cascading failure models, and provide a foundation for resilience assessment of CISs.

Truck Impact on Buried Water Pipes in Interdependent Water and Road Infrastructures

In the development of sustainable and resilient infrastructures to adapt to the rapidly changing natural and social environment, the complexity of the dependencies and interdependencies within critical infrastructure systems need to be fully understood, as they affect various components of risk and lead to cascading failures. Water and road infrastructures are highly co-located but often managed and maintained separately. One important aspect of their interdependence is the impact of vehicle loading on a road on underlying water pipes. The existing studies lack a comprehensive evaluation of this subject and do not consider possible critical failure scenarios. This study constructed finite element models to analyze the responses of buried water pipes to vehicle loads under an array of scenarios, including various loads, pipe materials, pipe dimensions, and possible extreme conditions, such as corrosion in pipes and a sinkhole under the pipe. The results showed negligible impact of heavy trucks on buried water pipes. The pipe deflection under a maximum allowable truck load in the worst condition was still within the allowable range specified in standards such as those from the American Water Works Association. This implies that the impact of heavy vehicles on water pipes may not need to be considered in the context of the interdependency between water and road infrastructures, which leads to a more unidirectional dependency between these two infrastructures.

Artificial Intelligence in Critical Infrastructure Systems

Seven expert panelists discuss the use of artificial intelligence in critical infrastructure systems and how it can be used and misused. They also address issues of public confidence in such systems and many more important questions.

Restoration versus transformative adaptation of community drinking water systems after Hurricanes Irma and Maria in Puerto Rico.

Hurricane Irma then Maria hit Puerto Rico in September 2017, exposing the heightened vulnerability of the island's Critical Infrastructure Systems and Processes (CRISPs) and putting the resilience of some of the most impoverished communities to the test. Being one of these CRISPs, the island's centralized drinking water system operated by the Puerto Rico Aqueduct and Sewer Authority suffered heavy damage leaving over 200,000 people off-grid for months. Decentralized community aqueducts were also affected. However, most were able to sustain operations, with only 15 percent incapacitated during the first few weeks after Maria. Of the 205 community aqueducts serving low-income communities in the island's central mountainous areas, only 35 failed. This article explores how and why these systems failed and what actions the communities should take to recover in a relatively short time in comparison to the centralized system. It defines the factors that account for the differences, the systems' capacity to meet water quality requirements, and potentially transformative adaptations generated to face future disturbances. We were interested in understanding (a) how system capacity was affected by the restoration process, (b) if adaptation resulted in significant operational changes, and (c) community member engagement. Finally, we explored governance transformations that increased stakeholder's participation, including community aqueducts representatives in decision-making and policy-making. Data collection included interviews with water system managers, government, and nongovernmental organization (NGO) representatives who regulate these systems or assisted communities in recovering their systems. We also surveyed water systems that had operational problems within the first 3 weeks. The data revealed a diversity of actions along the disaster cycle through which communities prepared for, restored, recovered, and cocreated transformative adaptations to their systems. Findings reflect that despite economic deficiencies and lack of emergency plans, many communities were able to improvise and restore their water systems soon after the disaster. As part of their postdisaster organization, communities increased their collaborative networks with governmental and NGOs to cocreate improvement projects to enhance resiliency. Adaptations included (1) increased community autonomy, (2) system redundancy, and (3) improved capacity to participate in government discussion forums related to their systems.

A Functional All‐Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President's Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all‐hazards perspective remains a difficult problem. In this paper we introduce the All‐Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross‐sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

SYSTEMIC APPROACH FOR CRITICAL INFRASTRUCTURES, HIDDEN VULNERABILITIES OF INTERDEPENDENCIES

Abstract: The growing scientific interest in the field of critical infrastructure protection has been determined by society's growing dependence on the essential services provided by these infrastructures. Critical infrastructures do not work isolated, they establish complex and dynamic networks of interdependencies. A critical infrastructure is itself a system with its own components and operating principles, a system connected to a system of systems based on multiple determinations. Critical infrastructure systems are built based on interdependent relationships between infrastructures, manifesting itself as an open system that relates to the macro-social system. Critical infrastructure systems behave dynamically and react to external stimuli represented by threats. Interdependencies between critical infrastructures generate new vulnerabilities and threats, based on the spread of effects and consequences. A comprehensive approach for critical infrastructure risks requires a systemic approach, taking into account that the interconnection of critical infrastructure objectives also involves the interconnection of threats and vulnerabilities.