Digitalisation changes operation and maintenance in railways. Emerging digital technologies facilitate implementation of enhanced eMaintenance solutions through utilisation of distributed computing and artificial intelligence. In railway, the digital technology deployment is expected to improve the railway system’s sustainability, availability, reliability, maintainability, capacity, safety, and security including cybersecurity. In digitalised railway, aspects of cybersecurity are essential in order to achieve overall system dependability. Lack of cybersecurity imposes negative impacts on the railways like reputational damage, heavy costs, service unavailability and risk to the safety of employees and passengers.

It has been observed, through open access data, that many railway organizations focus on detective measures of security threats with less emphasis on forecasting of cyber-attacks. In order to prepare in advance for cyberattacks, it is essential that Information and Communication Technology (ICT) and Operational Technology (OT) in railways need to undergo continuous updating towards security analytics approach. This approach will help the railways to produce proactive security measures to cyberattacks.

 In this work, it has been observed that there exists some standards and guidelines related to cybersecurity in railways (e.g. AS 7770- Rail Cyber Security, APTA SS-CCS-004-16, BS EN 50159:2010+A1:2020). These standards and guidelines are proprietary (i.e. either organization-specific or country-specific) and are followed by most of the railway organizations. These proprietary standards and guidelines lack in providing a holistic approach to enable interoperability, scalability, orchestration, adaptability, and agility for railway’s stakeholders. Therefore, there is a need for a generic cybersecurity framework for digitalized railways to facilitate proactive cybersecurity and threat intelligence sharing within the railways. 

The proposed framework, i.e., Cybersecurity Information Delivery Framework has been developed by integrating existing models, technologies, and standards to minimize the risks of cyber-attacks in the railways. The framework maps different layers of Open System Architecture for Condition-Based Maintenance (OSA-CBM) in the context of cybersecurity to deliver threat intelligence. The framework implements extended Cyber Kill Chain (CKC) and Industrial Control System (ICS) Kill Chain for detecting cyberattacks. The framework also incorporates proposed Railway Defender Kill Chain (RDKC) that enables proactive cybersecurity. Therefore, the proposed framework enables proactive cybersecurity and shares threat intelligence for improving cybersecurity in railways. 

The convergence of information technology (IT) and operational technology (OT) and the associated paradigm shift toward fourth industrial revolution (aka Industry 4.0) in companies has brought tremendous changes in technology vision with innovative technologies such as robotics, big data, cloud computing, online monitoring, internet of things (IoT), cyber-physical systems (CPS), cognitive computing, and artificial intelligence (AI). However, this transition towards the fourth industrial revolution has many benefits in productivity, efficiency, revenues, customer experience, and profitability, but also imposes many challenges. One of the challenges is to manage and secure large amount of data generated from internet of things (IoT) devices that provide many entry points for hackers in the form of a threat to exploit new and existing vulnerabilities within the network. This chapter investigates various cybersecurity issues and challenges in Industry 4.0 with more focus on three industrial case studies.

The convergence of information technology and operation technology and the associated paradigm shift toward Industry 4.0 in complex systems, such as railways has brought significant benefits in reliability, maintainability, operational efficiency, capacity, as well as improvements in passenger experience. However, with the adoption of information and communications technologies in railway maintenance, vulnerability to cyber threats has increased. It is essential that organizations move toward security analytics and automation to improve and prevent security breaches and to quickly identify and respond to security events. This paper provides a statistical review of cybersecurity incidents in the transportation sector with a focus on railways. It uses a web-based search for data collection in popular databases. The overall objective is to identify cybersecurity challenges in the railway sector.

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway

Purpose

The purpose of this research paper is to evaluate and estimate the cybersecurity maturity and awareness risk for workforce management in railway transportation by using Railway-Cybersecurity Capability Maturity Model (R-C2M2) and Information Security Awareness Capability Model (ISACM), respectively.

Design/methodology/approach

This research uses a case study strategy, so primary data comprise the majority of data collected. These data were collected through interviews and questionnaires. The secondary data were collected from the literature, technical reports and standards.

Findings

The results show that there is a gap in cybersecurity awareness within the workforce and there is a need to improve this gap. This paper provides some of the recommendations and literature to enhance cybersecurity workforce culture within railway organizations.

Practical implications

In this paper, the authors have demonstrated that cybersecurity awareness has positive impact on the overall dependability of the railway system.

Originality/value

This paper describes the importance of cybersecurity awareness and training in building more cyber resiliency across the operation and maintenance of railway.

Industry 5.0 promises to revolutionize the industry by focusing on human-centric, sustainability, and resilience empowered by emerging technologies such as Artificial Intelligence (AI) and digitalization. This paradigm shift is expected to bring significant advancements in sustainability, resilience, productivity, effectiveness, efficiency, customization, reliability, safety, security, maintainability etc. However, this shift of the industrial paradigm introduces substantial cybersecurity challenges due to the increased attack surface and data sensitivity. Therefore, the objective of this paper is to conduct a thorough literature review of the recent research on cybersecurity in Industry 5.0, highlighting emerging trends, gaps, and potential solutions. To conduct this research, the authors have applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate cybersecurity solutions in Industry 5.0. The findings reveal that conceptual research dominates, with AI, Blockchain, and Internet of Things (IoT) most prevalent but highlights a gap in linking cybersecurity to resilience and sustainability. Furthermore, the paper aims to present trends in cybersecurity research with more relevant results from 2022 to 2024. It conducts a thorough review of the literature, highlighting the evolving landscape of cybersecurity applications in Industry 5.0.

Cloud Computing is an emerging research area. It can be utilised for acquiring an effective and efficient information logistics. This paper uses cloud-based technology for the establishment of information logistics for railway system which requires information based on data from different data sources (e.g. railway maintenance, railway operation, and railway business data). In order to improve the performance of the maintenance process relevant data from various sources need to be acquired, filtered, fused, integrated, and analysed for decision making and to facilitate for example, diagnostics and prognostics decisions. The process of maintenance information provisioning is highly dependent on an appropriate information logistics. In this paper the authors have tried to study the improvement in the maintenance performance of railway operations in Sweden.

Radio Frequency Identification (RFID) helps automatic identification of objects using radio waves. This is not a new technology instead decades old and has been used during the World War II, when it was used by allied ground forces to track German bombers. It is a technology for wireless communication between a reader and a transponder/tag. This technology permits the transfer of data to the most diverse objects without the need for physical contact and uses intelligent barcodes to track items and have been successfully applied in military, security, healthcare, real time location tracking, vehicle identification and other areas. This paper is based on applications of radio frequency identification technology with eMaintenance cloud for railway system to analyze and visualize data of trains for the cost effective maintenance planning. Further, cloud computing is an emerging research area that can be utilised for acquiring an effective and efficient information logistics. Specifically, the widespread use of RFID will enable wagons to be tracked leading to better resource utilization, lower freight costs, and better maintenance. Therefore, it helps to provide greater control of the train carriages, making it easier to plan resources. However, RFID is a powerful tool that can help to improve industry proficiency, implementing this technology is not easy. Furthermore, operating RFID systems can be a challenging process. Thus, this paper is based on the application of RFID in the context of railway operation.

With the advancements in and widespread adoption of information and communication technologies in infrastructures, cyber-attacks are becoming more frequent and more severe. Advanced cybersecurity threats with automated capabilities are increasing in such sectors as finance, health, grid, retail, government, telecommunications, transportation, etc. Cyber-attacks are also increasing in railways with an impact on railway stakeholders, e.g. threat to the safety of employees, passengers, or the public in general; loss of sensitive railway information; reputational damage; monetary loss; erroneous decisions; loss of dependability, etc. There is a need to move towards advanced security analytics and automation to identify, respond to, and prevent such security breaches. The objective of this research is to reduce cyber risks and vulnerabilities and to improve the cybersecurity capabilities of railways by evaluating their cybersecurity maturity levels and making recommendations for improvements. After assessing various cybersecurity maturity models, the Cybersecurity Capability Maturity Model (C2M2) was selected to assess the cybersecurity capabilities of railway organizations. The contributions of this research are as follows. First, a new maturity level MIL4 (Maturity Indicator Level 4) is introduced in the C2M2 model. Second, the C2M2 model is adapted by adding advanced security analytics and threat intelligence to develop the Railway-Cybersecurity Capability Maturity Model (R-C2M2). The cybersecurity maturity of three railway organizations is evaluated using this model. Third, recommendations and available standards & guidelines are provided to the three railway organizations to improve maturity levels within different domains. In addition, they are given an action plan to implement the recommendations in a streamlined way. The application of this model will allow railway organizations to improve their capability to reduce the impacts of cyber-attacks and eradicate vulnerabilities. The approach can also be extended to other infrastructures with necessary adaptations.

The term eMaintenance emerged in the early 2000s and has become a popular topic in maintenance related literature because of ongoing technological improvements. This paper uses a recent approach, i.e. cloud-based technology, to provide an eMaintenance solution for online time data analysis to make effective and efficient railway maintenance decisions. Due to increased traffic, the Swedish railway sector needs to optimise maintenance, using predictive maintenance to a much higher degree so that unplanned breakdowns and downtime are drastically reduced. The paper shows how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved condition monitoring, better maintenance and increased uptime. In the proposed solution, data are acquired from railway measurement stations and sent to the eMaintenance cloud, where they are filtered, fused, integrated and analysed to assist maintenance decisions. The paper provides a concept for a web-based eMaintenance solution to assist railway maintenance stakeholders make fact-based decisions and develop more efficient and economically sound maintenance policies.

This paper explores the potential of Metaverse technology in industrial Asset Management (AM). By integrating AI and digital technologies, the Metaverse can enhance Human-System-Interaction (HSI) and optimise AM processes. However, implementing a Metaverse in industrial contexts faces challenges, particularly in visualising physical and virtual assets. This paper conducts a systematic review to address these challenges and identify potential solutions. The findings reveal that while the necessary technologies are available, their widespread adoption in industrial AM is limited. The paper presents a comprehensive overview of research themes related to Metaverse applications in industrial contexts, highlighting the evolving landscape and potential benefits. Ultimately, this research aims to contribute to the advancement of Metaverse technology in industrial AM by providing insights into its development, implementation, and challenges along with an Industrial Metaverse Framework. An example of applying the Metaverse concept in the railway sector has been presented and validated using railway digital assets available within the eMaintenance LAB. The practical implications of this work are expected to result in increased efficiency and effectiveness in the operation and maintenance procedures across various industrial sectors.

The ongoing digitalisation of industrial systems is bringing new challenges in managing, monitoring, and predicting the overall reliability performance. The overall reliability of a cyber-physical system, such as railways, is highly influenced by the level of resilience in its inherent digital items. The objective of this paper is to propose a systematic approach, based on an enhanced Cyber Kill Chain model, to improve the overall system resilience through monitoring and prediction. The proposed cybersecurity approach can be used to assess the future cyberattack penetration probabilities based on the present security controls. With the advancement in cybersecurity defensive controls, cyberattacks have continued to evolve through the exploitation of vulnerabilities within the cyber-physical systems. Assuming the possibility of a cyberattack it is necessary to select appropriate security controls so that this attack can be predicted, prevented, or detected before any catastrophic consequences to retain the resilience of the system. Insufficient cybersecurity in the context of cyber-physical systems, such as railways, might have a fatal effect on the whole system availability performance and sometimes may lead to safety risks. However, to improve the overall resilience of a cyber-physical system there is a need of a systematic approach to continuously monitor, predict, and manage the health of the system’s digital items with respect to security. Furthermore, the paper will provide a case-study description in railway sector, which has been used for the verification of the proposed approach.

Digitalisation is changing the railway globally. One of the major concerns in digital transformation of the railway is the increased exposure to cyberattacks. The railway is vulnerable to these cyberattacks because the number of digital items and number of interfaces between digital and physical components in these systems keep growing. Increased number of digital items and interfaces require new methodologies, frameworks, models, concepts, and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as adoption and convergence of Information Technology (IT) and Operational Technology (OT) technology within the railway. This convergence has brought significant benefits in reliability, operational efficiency, capacity as well as improvements in passenger experience but also increases the vulnerability towards cyberattacks from individuals, organizations, and governments. This paper proposes a methodology on how to deals with OT security in the railway signalling using failure mode, effects and criticality analysis (FMECA) and ISA/IEC 62443 security risk assessment methodologies.

Digitalisation is transforming the railway globally. One of the major considerations in digital transformation of any industry including the railway is the increased exposure to cyberattacks. The railway industry is vulnerable to these attacks because since the number of digital items and also number of interfaces between digital and physical components in the railway systems keep increasing. Increased number of items and interfaces require new frameworks, concepts and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as lack of proactiveness, lack of holistic perspective and obsolescence of safety systems exposed to current and future cyber threats landscape. To this date, there are several works carried out in the literature that studied the cybersecurity aspects and its application on railway infrastructure. However, to develop and implement an appropriate roadmap to cybersecurity in railways, there is a need of describing emerging challenges, and approaches to deal with these challenges and the possibilities and benefits of these.Hence, the objective of this paper is to provide a systematic review and outline cybersecurity emerging trends and approaches, and also to identify possible solutions by querying literature, academic and industrial, for future directions. The authors of this paper conducted separate searches through four popular databases, that is, Google Scholar, Scopus, Web of Science and IEEE explore. For the screening process, authors have used keywords with Boolean operators and database filters and identified 90 articles most relevant to the study domain. The analysis of 90 articles shows that majority of the cybersecurity studies lies within the railways are conceptual and lags in application of Artificial Intelligence (AI) based security. Like other industries, it is very important that railways should also follow latest security technologies, trends and train their workforce for cyber hygiene since railways are already in digitalization transition mode.

Adoption of information and communication technologies (ICT) in railway has improved the reliability, maintainability, operational efficiency, capacity as well as the comfort of passengers. This adoption introduces new vulnerabilities and entry points for hackers to launch attacks. Advanced cybersecurity threats with automated capabilities are increasing in such sectors as finance, health, grid, retail, government, telecommunications, transportation, etc. These cyber threats are also increasing in railways and, therefore, it needs for cybersecurity measures to predict, detect and respond these threats. The cyber kill chain (CKC) model is a widely used model to detect cyber-attacks and it consists of seven stages/chains; breaking the chain at an early stage will help the defender stop the adversary’s malicious actions. Due to lack of real cybersecurity data, this research simulates cyber-attacks to calculate the attack penetration probabilities at each stage of the cyber kill chain model. The objective of this research is to predict cyber-attack penetrations by implementing various security controls using modeling and simulation. This research is an extension of developed railway defender kill chain which provides security controls at each stage of CKC for railway organizations to minimize the risk of cyber threats.

The railway is one of the most important infrastructures and its security is as important as other critical infrastructures. Due to the increase in cyber-attacks, there is an increasing trend in the field of cybersecurity. The history of cyber incidents suggested that the railway needs immediate security measures or defensive controls for forthcoming advanced persistent threats (APT). Cyber Kill Chain (CKC) is one of the most widely used models for the identification, detection, and prevention of advanced persistent threats. CKC model was introduced by Lockheed Martin that consists of seven stages as Reconnaissance, Weaponize, Delivery, Exploitation, Installation, Command & Control, and Act on Objective. Breaking the chain as early as possible in the CKC model will help the defender to stop adversary’s malicious actions. As the railway is adapting digital technologies and, therefore, there is a risk that adversary can penetrate into the system following the steps of CKC. The objective of this research is to reduce the risk of cyber-attacks by proposing Railway Defender Kill Chain (RDKC) that provides security controls at each phase of Cyber Kill Chain to predict, prevent, detect and respond to cyber threats.

Most organizations focus on intrusion prevention technologies, with lessemphasis on prediction and detection. This research looks at prediction anddetection in the railway industry. It uses an extended cyber kill chain (CKC)model and an industrial control system (ICS) cyber kill chain for detectionand proposes predictive technologies that will help railway organizationspredict and recover from cyber-attacks. The extended CKC model consistsof both internal and external cyber kill chain; breaking the chain at anearly stage will help the defender stop the adversary’s malicious actions.This research incorporates an OSA (open system architecture) for railwayswith the railway cybersecurity OSA-CBM (open system architecture forcondition-based maintenance) architecture. The railway cybersecurity OSA-CBM architecture consists of eight layers; cybersecurity information movesfrom the initial level of data acquisition to data processing, data analysis, inci-dent detection, incident assessment, incident prognostics, decision support,and visualization.The main objective of the research is to predict, prevent, detect, andrespond to cyber-attacks early in the CKC by using defensive controls calledthe Railway Defender Kill Chain (RDKC).The contributions of the research are as follows. First, it adapts and mod-ifies the railway cybersecurity OSA-CBM architecture for railways. Second,it adapts the cyber kill chain model for the railway. Third, it introduces theRailway Defender Kill Chain. Fourth, it presents examples of cyber-attackscenarios in the railway system.

Big Data Analytics (BDA) is becoming a research focus in transportation systems, which can be seen from many projects within the world. By using sensor and Internet of Things (IoT) technology in transportation system, huge amount of data is been generated from different sources. This data can be integrated, analyzed and visualized for efficient and effective decision-making for maintaining transportation systems. The key challenges that exist in managing Big Data are the designing of the systems, which would be able to handle huge amount of data efficiently and effectively and to filter the most significant information from all the collected data. This chapter will draw attention towards the present scenario and future projections of big data in transportation systems. It also presents big data tools and techniques and then presents one brief case study of BDA in each type of transportation system. In this chapter, a broad overview of Big Data definitions, its history, present, and future prospects are briefed. Several tools and technologies especially for transportation are pointed out for maintaining transportation systems. At the end of the chapter, a definitive case studies on each transportation area is demonstrated.

Purpose – The purpose of this paper is to demonstrate how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved condition monitoring, better maintenance and increased uptime. This eMaintenance solution is based on the on-line data acquisition, integration and analysis leading to effective maintenance decision making.Design/methodology/approach – In the proposed methodology, data are acquired from railway measurement stations to the eMaintenance cloud, where they are filtered, fused, integrated and analyzed to assist maintenance decisions. Extensive consultation with stakeholders has resulted in the analysis of railway data.Findings – The paper provides a concept for a web-based eMaintenance solution for railway maintenance stakeholders for making fact-based decisions and develops more efficient and economically sound maintenance policies. Train wheels reaching their maintenance and safety limits are visualised in grids and graphs to assist stakeholders in making the appropriate maintenance decisions.Practical implications – In this paper the authors have demonstrated that the wheel profile and force data can be remotely collected through cloud utilization. The information generated can be used for maintenance decision making. Similarly, other measurable data can also be utilized for maintenance decision making.Originality/value – This paper describes the importance of eMaintenance solution through online data analysis to make effective and efficient railway maintenance decisions, as a case study.

Railway will continue to adapt new digital solutions which are necessary and vulnerable to cyber threats. The history of cyber-attacks on critical infrastructures including railway suggests that there is a need for cybersecurity awareness. Both for employees and the general public. The very first step in cyber hygiene is cybersecurity training and awareness for the workforce. A well-educated workforce plays a vital role in building more cyber resiliency across the organization's operation and maintenance. The objective of this research is to evaluate the cybersecurity maturity level for workforce management in three railway organizations. The results show that there is a cybersecurity workforce gap and there is a need to eliminate this gap by enhancing cybersecurity workforce culture. Henceforth, this gap can be improved by developing cybersecurity culture, including cybersecurity training and awareness and by following recommendations provided in this paper.

Recently, due to the advancements in the ICT (Information and Communication Technology), there has been lot of emphasis on digitization of the existing and newly developed infrastructure. In transportation infrastructure, in general, 80% of the assets are already in place and there has been tremendous push to move to the digital era. For efficient and effective design, construction, operation and maintenance of the infrastructure, due to this digitization, there is increasing research trend in data-driven decision-making algorithms that are proved to be effective because of several advantages. Since railway is the backbone of the society, the data-driven approaches will ensure the continuous operation, efficient maintenance, planning and potential future investments. The breach and leak of this potential data to the wrong hands might result in havoc, risk, trust, hazards and serious consequences. Hence, the main purpose of this paper is to stress the potential challenges, consequences, threats, vulnerabilities and risk management of data security in the railway infrastructure in context of eMaintenance. In addition, this paper also identifies the research methods to obtain and secure this data for potential possible research.

The health of mobile mining machinery is critical to the achieve effectiveness and efficiency in mining production. However, the performance of mobile mining machinery, such as dumpers, is influenced by factors such as the operational environment, machine reliability, maintenance regime, human factor, etc., that lead to the downtime of dumpers. These downtimes have significant consequences on the overall equipment effectiveness (OEE) and lead to decreased capacity, increased maintenance costs and reduces availability. The enablement of prognostics and health management (PHM) can contribute to improve the OEE in mining production.

Conventionally, the existing solutions focus mostly on the reliability and maintainability analysis of dumpers using failure data, maintenance data, operation data etc. Though several existing methods utilize condition monitoring techniques, there is less focus on monitoring the engine vibration and impact the health of the driver. In addition, the existing solutions are not real-time, scalable, or offline-based. Hence, the objective of this paper is to develop a concept for the enablement of PHM for the engine and driver comfort of dumpers. Furthermore, a cloud-based solution for condition monitoring of dumpers has been designed and developed. The solution can be used to assess the engine vibrations and seat vibrations and to estimate the remaining useful life (RUL) of the selected features using standards. The cloud-based architecture is implemented on the AI Factory platform that enable PHM for the improvement of OEE. This platform also facilitates the enablement of a digital twin for components and systems within dumpers or other mobile mining machinery.