Critical Infrastructure Sectors Research Articles

SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study

The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.

Risk and Advantages of Federated Learning for Health Care Data Collaboration

AbstractThis paper explores the problem of data collaboration in health care, which is the one of the critical infrastructure sectors designated by the Department of Home Security. Limitations to d...

Water infrastructure and economic security of regional socio-economic systems: evidence from Ukraine

The correlation between the development of a regional economy and the degree of infrastructural development of a country or its territory is a common practice. Considering this, the paper is aimed at studying the impact of water infrastructure on the economic security of regional socio-economic systems in order to develop a water security system for them. A variety of threats against key waterworks can seriously undermine the national economy and water security of regions and cities. Thus, the study analyzes the process of transformation of water infrastructure in the context of ensuring the economic security of regional socio-economic systems. It offers the definition of such categories as “water infrastructure” and “water security of a regional socio-economic system” to formulate a relevant economic policy in Ukraine based on world experience. The study proves the need to attribute water infrastructure to the critical infrastructure sectors of Ukraine and its regions, based on the best world practices in managing water resources and protecting the water infrastructure itself from technogenic, physical, and cyber-physical threats. According to the study results, conceptual bases for ensuring the economic security of regional socio-economic systems are developed, which differ in view of the water security system of the regional socio-economic system, giving priority to the engineering and technical infrastructure of a region.

High Reliability Organization (HRO) systematic literature review: Discovery of culture as a foundational hallmark

AbstractThe term “high reliability organization” (HRO) evolved from research countering the claim by Perrow after the 1979 Three Mile Island nuclear plant incident that “accidents happen,” implying that failures were inevitable. Soon after the publication of Perrow's book researchers began questioning his assertion, by observing that some organizations exhibited significantly higher rates of reliability. In 2001, Weick and Sutcliffe converged on five principles, or hallmarks, which became the de facto characteristics most often cited since. Since 2001, HRO theory spread to non‐high‐risk industries and the amount of published literature has more than doubled, including several literature reviews. This led to the questions, “how has HRO theory evolved since the seminal research, how far has it spread from high risk industries, and what is the focus of HRO research?” Seeking to capture the breadth of spread of HRO as well as the dominant areas of research and practice, a comprehensive literature review was performed to capture the most current state of theory and practice. The results show HRO theory within the published literature has not significantly evolved past the original characteristics and hallmarks. Though the theoretical implications have not changed much the practical implications have grown and spread throughout the critical infrastructure sectors. In conclusion, the research team believes that a culture of reliability is the sixth hallmark that works synergistically with certain foundational organizational characteristics and allows mindfulness, which is the distinctive hallmark of HROs, to thrive and grow. This is the fundamental difference between “reliable” and “highly reliable” operations and differentiates HROs from well‐run organizations that are not HROs.

Game-Theoretic Resource Allocation for Fog-Based Industrial Internet of Things Environment

The significant volume, variety, and velocity of data received from the many Industrial Internet of Things (IIoT) devices and other systems in a cloud-based or fog-based environment can complicate an organization’s effort in ensuring high quality of experience for data users (DUs). For example, how do we efficiently and fairly allocate resources among cloud centers (CCs), fog service providers (FSPs), and DUs? This is particularly crucial for the IIoT environment, such as those in critical infrastructure sectors, such as energy and dams. Therefore, in this article, we propose an optimal resource allocation scheme for a fog-based IIoT environment. Specifically, we introduce fog nodes (or FSPs) that compete with each other to provide services for the DUs using resources from the CC. To maximize resource utilization, we model the resource allocation problem as a double-stage Stackelberg game and propose three algorithms to achieve Nash equilibrium and Stackelberg equilibrium. Then, we evaluate the performance of our proposed scheme with and without having FSPs, as well as with another competing scheme. The findings demonstrate the importance of fog computing in resource allocation, and the performance of our scheme outperforms that of the other scheme.

The Need for Cyber Resilient Enterprise Distributed Ledger Risk Management Framework

Critical infrastructure sectors are increasingly adopting enterprise Distributed Ledgers (DL) to host long-term assets, systems, and information that is considered vital to an organization’s ability to operate without clear or public plans and strategies to migrate safely and timely to Post Quantum Cryptography (PQC). A quantum computer (QC) compromised DL would allow, eavesdropping, unauthorized client authentication, signed malware, cloak-in encrypted session, a man-in-the-middle attack (MITM), forged documents and emails. These attacks can lead to disruption of service, damage of reputation and trust, injury to human life, and the loss of intellectual property, assets, regulated data, and global economic security. In 2018, Gartner revealed that a QC is a digital disruption that organizations may not be ready and prepared, and CIOs may not see coming. On September 18, 2019, IBM announced the largest universal QC available for commercial use would be available in October 2019. On October 23, 2019, Google officially announced “Quantum Supremacy,” “by performing a calculation in 200 seconds that would take a classical supercomputer approximately 10,000 years.” DL Cyber Resilience requires “reasonable” measures, policies, procedures, strategies, and risk management before large-scale deployment. Cyber Resilience implementations must be a critical component during the design and building phase, or during the initialization phase. The most significant existing attack vectors for enterprise DLs is the Public Key Infrastructure (PKI), which is fundamental in securing the Internet and enterprise DLs and is a core component of authentication, data confidentiality, and data and system integrity [1] [2]. Effectively implementing and managing a quantum-resistant PKI solution requires adherence to PKI standards, industry requirements, potential government mandates, certificate management policies, training personnel, and data recovery policies that currently do not exist. This research discusses security risks in enterprise DL PKI, areas that can be compromised, and provides an idea of what should be in a PKI DL Risk Management Framework plan.

A GIS-based framework for high-level climate change risk assessment of critical infrastructure

The IPCC states that climate change unequivocally impacts on various aspects of the natural and built environment, including our vital critical infrastructure systems (transport, energy, water/wastewater and communications). It is thus essential for countries to gain an understanding of critical infrastructure vulnerability to current and future climate-related threats, in order to develop effective climate adaptation strategies. The first requisite step towards implementing these strategies, before any detailed analysis can commence, is high-level vulnerability or risk assessments. The work in this paper is concerned with such high-level assessments, however the framework presented is GIS-based, facilitating modelling of geographical variability in both climate and asset vulnerability within a country. This permits the identification of potential climate change risk hotspots across a range of critical infrastructure sectors. The framework involves a number of distinct steps. Sectoral information matrices are developed to highlight the key relationships between the infrastructure and climate threats. This information is complemented with sectoral maps showing, on an asset-level, the potential geospatial impacts of climate change, facilitating initial quantification of the vulnerable portions of the infrastructure systems. Finally, the approach allows for development of multi-sectoral semi-quantitative risk ranking maps that account for the geographical proximities of various assets from different critical infrastructure sectors which are vulnerable to a specific climate threat. The framework is presented in the paper and applied as a case study in the context of Irish critical infrastructure. The case-study identified for instance, potentially substantial increases in fluvial flooding risk for Irish critical infrastructure, while the multi-sectoral risk ranking maps highlighted a number of Ireland’s urban and rural areas as climate change risk hotspots. These high-level insights are likely to be useful in informing more detailed assessment, and initiating important conversations relating to a region’s critical infrastructure cross-sectoral risk.

On the Use of Differential Correction Clustering for Facing Spoofing Attacks to GNSS Augmentation Networks

Nowadays, Global Navigation Satellite Systems are the main source for high accuracy positioning and timing. For this reason, they are essential both for everyday activities and services, and for the industrial and critical infrastructure sectors. Moreover, the spread of increasingly autonomous vehicles results in strict accuracy and integrity requirements. This leads to the need for additional infrastructure to send corrections to the end users and mitigate the measurement errors, the Augmentation Networks. However, due to the increasing exploitation of localization functionalities, the Augmentation Networks could become a primary target for attackers resulting in a high financial and safety cost. Among the possible attacks, spoofing, that is the generation of a fake satellite signal which is seen as genuine by the receiver, is one of the most powerful and tricky. In this contribution, a detection and mitigation strategy for Augmentation Network spoofing attacks is proposed. We introduce two attack models and present a technique based on K-means clustering to counteract them. More in details, our approach is based on the computation of the number of clusters formed by the Augmentation Network corrections. Starting from the hypothesis that under nominal conditions only one cluster is present, the effects of the attacks on the clustering procedure are analyzed, and several attack simulations are performed to evaluate the algorithm performances. The proposed method has been compared both to an Augmentation Network attack detection technique, and to a receiver-level spoofing mitigation approach, showing comparable or better performances. Moreover, to the best of our knowledge, this is the first work addressing mitigation for spoofing attacks which target an Augmentation Network.

IEEE Access Special Section Editorial: Security and Trusted Computing for Industrial Internet of Things: Research Challenges and Opportunities

Industrial IoT (IIoT) interconnects critical devices and sensors in critical infrastructure sectors with existing Internet of Things (IoT) devices and applications. Generally, IIoT deployment allows organizations and users to gain invaluable insights into industrial processes and achieve high-productivity gains while reducing cost. Their role will be increasingly important as we move toward Industry 5.0. Hence, it is also crucial to understand and address any security and privacy risks that may arise, including those discussed in the articles accepted in this Special Section.

DDoS Attacks on 9-1-1 Emergency Services

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this article we show how attackers can launch several types of DDoS attacks from mobile phone botnets. In one of the attacks, which we demonstrate, the attacker has the botnet randomize all cellular identifiers while issuing emergency calls repeatedly. Since there exists legitimate unidentified emergency calls, and since the FCC requires such calls to be forwarded, the network and the emergency call centers cannot block these calls (technically and legally). To understand and verify the threat of DDoS attacks on 911, we explore the 911 infrastructure and implement different forms of the attack on a small cellular network. Finally, to quantify the threat, we simulate and analyze DDoS attacks on a model of current 911 infrastructure in the US. We found that with less than 6K bots (or $100K hardware), attackers can block emergency services in an entire state for days. We believe that this article will assist the respective organizations in preventing possible 911-DDoS attacks in the future.

BoSMoS: A Blockchain-Based Status Monitoring System for Defending Against Unauthorized Software Updating in Industrial Internet of Things

The role of the Industrial Internet of Things (IIoT) in critical infrastructure sectors, such as power, chemistry, and manufacturing, will be increasingly important as we move toward Industry 5.0. For example, IIoT devices are deployed in factories to help the manufacturing companies (e.g., automotive) gain in-depth insight into the various states of production, and thus improving production efficiency and achieving cost reductions. However, malicious code may compromise IIoT devices if either the devices are exposed to outside or unexposed inner devices are updated unauthentically. Due to their limited resources and features, it is challenging to implement strong security solutions for such embedded devices. In this article, we propose a blockchain-based software status monitoring system, called BoSMoS. The system is designed to monitor the software status of IIoT devices to detect and respond to identified malicious behaviors (e.g., intrusions). BoSMoS takes a snapshot of the statue of monitored software and monitors its file system calls. In order to ensure the software integrity information, we use blockchain as the distributed ledger to store a snapshot of software status. The blockchain network of BoSMoS can employ different consensus algorithms. We also evaluate the performance of BoSMoS, in terms of exception response delay, resistance performance to various intrusions, and scalability. The experimental results justify that BoSMoS is practical and sound. In addition, the evaluation of scalability and security demonstrates that the system can carry deployment of large-scale IIoT devices and can guarantee authenticated software updating, as well as detect unauthorized software status.

Critical Infrastructure - Perspectives on the Role of Government in Cybersecurity

Since the end of the cold war era (1945 - 1989), the United States government (USG) has been gradually reduced, and in some cases eliminated direct support for the development of critical infrastructure. The reduction of public investment, and the subsequent transfer of critical infrastructure from public to private ownership, has significantly increased the risk not only to critical infrastructure, but to the entire nation. The use of non-isolated, public communication and information technology, without any contingency or emergency backups is a national disaster waiting to happen. The USG must vigorously support - both financially and through active management, policy and regulation - the creation of isolated, secure, and resilient communications and information technology infrastructures, that provide non-public redundancy, thereby ensuring that public communication and information technology failures, as a result of man-made attacks or natural events, do not result in catastrophic outcomes to not only other critical infrastructure sectors, but to the nation at large. This paper surveys historical and contemporary government roles in the development of critical infrastructure, particularly, communication and information technologies, and provides recommendations for how the US government can significantly improve the security and resilience of its current critical infrastructure systems.

МЕТОДОЛОГИЯ ПОСТРОЕНИЯ ПОЗНАВАТЕЛЬНОЙ МОДЕЛИ БЕЗОПАСНОСТИ КРИТИЧЕСКОЙ ИНФРАСТРУКТУРЫ

The paper presents the relationship between the constructed structural and linguistic schemes: priorities of cybersecurity in Ukraine and strategic goals, criteria for classifying facilities as critical infrastructure, critical infrastructure sectors, th

A quantitative assessment of institutions of higher education disaster preparedness and resilience.

Assess levels of disaster preparedness in institutions of higher education (IHEs) in the United States. An anonymous, 57-question survey targeted individuals responsible for emergency management at IHEs across the US descriptive statistics and bivariate chi-square analysis were reported. Using the established threshold score of the initial Cities Readiness Initiative from the CDC, an individual respondent's composite score of 70 percent or higher across 23 specific questions within the 57-question survey was labeled as "prepared." Chi-square analysis identified variables associated with lower preparedness levels at IHEs not achieving the minimum 70 percent score. Having a campus law enforcement officer serve the additional role of emergency manager had a negative association with being prepared [χ2 (1) = 10.18, p < 0.001]. Having emergency management as a separate university function from campus law enforcement had a positive relationship with being prepared [χ2 (1) = 18.55, p < 0.001]. Staffing the emergency management function with a professional having less than 3 years of emergency management experience had a negative association with being prepared. Our results indicate that minimizing the mission of emergency management by simply tasking a campus law enforcement officer with the extra responsibility of emergency management or entertaining less professionally qualified personnel to lead emergency management's complex mission can lead to disastrous results. Not only is preparedness impacted, but also resilience when facing disaster situations. Our nation continues to strive to become more resilient when facing such adverse events, as formally embraced and emphasized in the 2017 National Security Strategy. Research continues to offer best practices and unfortunately continues to highlight gaps. While the higher education community is not one of the 16 federal critical infrastructure sectors, identified gaps such as those presented in our findings as well as those published by the National Academies of Sciences are cause for alarm. Not only are higher education campuses generating invaluable contributions to society in general, bio-innovation, public health, and medicine, to name a few, they are a core stakeholder in resilience research and implementation. Yet, research continues to indicate preparedness and therefore resilience gaps in this sector. The authors propose implications for practice, policy, and research to assist IHEs in achieving a more comprehensive, sustainable level of resilience.

Comprehending the IoT cyber threat landscape: A data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns

The resource-constrained and heterogeneous nature of Internet-of-Things (IoT) devices coupled with the placement of such devices in publicly accessible venues complicate efforts to secure these devices and the networks they are connected to. The Internet-wide deployment of IoT devices also makes it challenging to operate security solutions at strategic locations within the network or to identify orchestrated activities from seemingly independent malicious events from such devices. Therefore, in this paper, we initially seek to determine the magnitude of IoT exploitations by examining more than 1 TB of passive measurement data collected from a/8 network telescope and by correlating it with 400 GB of information from the Shodan service. In the second phase of the study, we conduct in-depth discussions with Internet Service Providers (ISPs) and backbone network operators, as well as leverage geolocation databases to not only attribute such exploitations to their hosting environment (ISPs, countries, etc.) but also to classify such inferred IoT devices based on their hosting sector type (financial, education, manufacturing, etc.) and most abused IoT manufacturers. In the third phase, we automate the task of alerting realms that are determined to be hosting exploited IoT devices. Additionally, to address the problem of inferring orchestrated IoT campaigns by solely observing their activities targeting the network telescope, we further introduce a theoretically sound technique based on L1-norm PCA, and validate the utility of the proposed data dimensionality reduction technique against the conventional L2-norm PCA. Specifically, we identify “in the wild” IoT coordinated probing campaigns that are targeting generic ports and campaigns specifically searching for open resolvers (for amplification purposes). The results reveal more than 120,000 Internet-scale exploited IoT devices, some of which are operating in critical infrastructure sectors such as health and manufacturing. We also infer 140 large-scale IoT-centric probing campaigns; a sample of which includes a worldwide distributed campaign where close to 40% of its population includes video surveillance cameras from a single manufacturer, and another very large inferred coordinated campaign consisting of more than 50,000 IoT devices. The reported findings highlight the insecurity of the IoT paradigm at large and thus demonstrate the importance of understanding such evolving threat landscape.

Assessing Cyberbiosecurity Vulnerabilities and Infrastructure Resilience.

The convergence of advances in biotechnology with laboratory automation, access to data, and computational biology has democratized biotechnology and accelerated the development of new therapeutics. However, increased access to biotechnology in the digital age has also introduced additional security concerns and ultimately, spawned the new discipline of cyberbiosecurity, which encompasses cybersecurity, cyber-physical security, and biosecurity considerations. With the emergence of this new discipline comes the need for a logical, repeatable, and shared approach for evaluating facility and system vulnerabilities to cyberbiosecurity threats. In this paper, we outline the foundation of an assessment framework for cyberbiosecurity, accounting for both security and resilience factors in the physical and cyber domains. This is a unique problem set, but despite the complexity of the cyberbiosecurity field in terms of operations and governance, previous experience developing and implementing physical and cyber assessments applicable to a wide spectrum of critical infrastructure sectors provides a validated point of departure for a cyberbiosecurity assessment framework. This approach proposes to integrate existing capabilities and proven methodologies from the infrastructure assessment realm (e.g., decision science, physical security, infrastructure resilience, cybersecurity) with new expertise and requirements in the cyberbiosecurity space (e.g., biotechnology, biomanufacturing, genomics) in order to forge a flexible and defensible approach to identifying and mitigating vulnerabilities. Determining where vulnerabilities reside within cyberbiosecurity business processes can help public and private sector partners create an assessment framework to identify mitigation options for consideration that are both economically and practically viable and ultimately, allow them to manage risk more effectively.

A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise

Cyber threat attribution identifies the source of a malicious cyber activity, which in turn informs cyber security mitigation responses and strategies. Such responses and strategies are crucial for deterring future attacks, particularly in the financial and critical infrastructure sectors. However, existing approaches generally rely on manual analysis of attack indicators obtained through approaches such as trace-back, firewalls, intrusion detection and honeypot deployments. These attack indicators, also known as low-level Indicators of Compromise (IOCs), are rarely re-used and can be easily modified and disguised resulting in a deceptive and biased cyber threat attribution. Cyber attackers, particularly financially-motivated actors, can use common high-level attack patterns that evolve less frequently as compared to the low-level IOCs. To attribute cyber threats effectively, it is necessary to identify them based on the high-level adversary’s attack patterns (e.g. tactics, techniques and procedures - TTPs, software tools and malware) employed in different phases of the cyber kill chain. Identification of high-level attack patterns is time-consuming, requiring forensic investigation of the victim network(s) and other resources. In the rare case that attack patterns are reported in cyber threat intelligence (CTI) reports, the format is textual and unstructured typically taking the form of lengthy incident reports prepared for human consumption (e.g. prepared for C-level and senior management executives), which cannot be directly interpreted by machines. Thus, in this paper we propose a framework to automate cyber threat attribution. Specifically, we profile cyber threat actors (CTAs) based on their attack patterns extracted from CTI reports, using the distributional semantics technique of Natural Language Processing. Using these profiles, we train and test five machine learning classifiers on 327 CTI reports collected from publicly available incident reports that cover events from May 2012 to February 2018. It is observed that the CTA profiles obtained attribute cyber threats with a high precision (i.e. 83% as compared to other publicly available CTA profiles, where the precision is 33%). The Deep Learning Neural Network (DLNN) based classifier also attributes cyber threats with a higher accuracy (i.e. 94% as compared to other classifiers).

The Evaluation System to Ensure the Transport of Emergency Supplies of Fuel to the Hospitals

Nowadays we can see an increase of emergency and crisis. One of these may be a power outage. However, this can also be a cascade effect because of the windshield. Hospitals are a critical infrastructure sector where it is essential to ensure a constant supply of electricity. At the time of the power outage aggregates are used for which it is necessary to provide the supply of fuel. Each hospital has different stocks of fuel and therefore needs emergency supplies of this raw material. For this purpose, an assessment system is proposed for hospitals, but also for regions, which would determine the current state of fuel availability in hospitals in the area. The aim of this paper is to introduce an assessment system for providing emergency supplies of fuel deliveries to hospitals in the event of a power outage. The paper will address hospital capacities regarding fuel stocks in the event of an outage. In the next part, the analysis of selected hospitals and their subsequent comparison will be carried out. Furthermore, the area of emergency fuel supplies for hospitals will be addressed. At the end of the paper, an assessment system will be designed to ensure the delivery of emergency fuel supplies.

Cryptographic Accumulator-Based Scheme for Critical Data Integrity Verification in Cloud Storage

Public cloud storage is a fundamental cloud computing service. Currently, most owners of large data outsource their data to cloud storage services—even high-profile owners such as governments. However, public cloud storage services are not optimal for ensuring the possession and integrity of the outsourced data, a situation that has given rise to many proposed provable data possession check schemes (PDP). A PDP scheme allows data owners to efficiently, periodically, and securely verify that a cloud storage provider possesses the outsourced data. Most of the currently available provable data possession check schemes make selective (i.e., probabilistic) checks using random data blocks to verify data integrity rather than checking the entire dataset. Therefore, these schemes are considered inadequate by critical infrastructure sectors that involve highly sensitive data (critical data). In this paper, a new and efficient deterministic data integrity check scheme called cryptographic-accumulator provable data possession (CAPDP) is proposed. The CAPDP surpasses the common limitations exhibited by other currently proposed schemes. The underlying scheme of the CAPDP is based on a modified RSA-based cryptographic accumulator that has the following advantages: 1) it allows the data owner to perform an unlimited number of data integrity checks; 2) it supports data dynamics; 3) it is efficient in terms of communication, computation and storage costs for both the data owner and the cloud storage provider; 4) the verification operation in the proposed scheme is independent of the number of blocks being verified; 5) it minimizes the burden and cost of the verification process on the data owner’s side, enabling verification to be performed even on low-power devices; and 6) it prevents tag forgery, data deletion, replacement, and data leakage attacks and detects replay attacks. Moreover, the prototype implementation and experimental results show that the scheme is applicable in real-life applications.

Objectification of Criteria for a Critical Infrastructure Elements in the Rail Transport Sub-sector

Abstract Transport has become a daily need of mankind, people go home, or to the family, to the friends, and commodities from destination to destination. Humankind as such is dependent on all modes of a transport. Transport is a part of the Critical Infrastructure sectors and has the following subsectors: road transport, air transport, water transport and rail transport. The issue of objectified identification of infrastructure objects as elements of a critical infrastructure in rail transport is a little addressed in the Slovak Republic. When exploring identification issues, it is necessary to analyze foreign sources and propose solutions as important railway infrastructure objects to be among the potential elements of critical infrastructure in the transport sector. These suggestions will be tested and my own solution will consist of multiple views that will be more closely described in the work.