Critical Assets Research Articles

Bibliometric analysis of publications on cyber risks in the services sector

Objective: To explore the progress of scientific production on cyber risks that permeate the service sector, identifying relevant researchers and institutions on this theme, measuring the impact, and identifying trends, contributions, and knowledge gaps. In addition, the study seeks to use bibliometric findings to bring academic and managerial contributions to the subject. Methodology: Bibliometric study, using the method of organization and systematization of information (Chueke & Amatucci, 2015; Guedes & Borschiver, 2015), whose structure followed the premises of the laws of Bradford, Lotka, and Zipf, using the scientific databases of the WoS - Web of Science. The bibliometric study enabled the performance of exploratory and descriptive research without the temporal cut, resulting in the identification of 115 publications (December 1995 to February 2023), which allowed measuring and presenting the characteristics and profile of the publications analyzed. Originality: The study revealed a potential for exploring the theme of Cyber Risks in the Services sector, considering the scarcity of scientific production. It also enabled the identification of emerging trends and clusters in service sector activities and the creation of a conceptual model based on the findings of the analyzed publications. Main results: The analyses revealed which sectors of the service economy are most frequently approached in publications related to the theme of cyber risks. These analyses were organized into ten areas, with the following order of relevance (frequency) of publication: Computer Science, Information Systems, Engineering, Business, Finance and Management, Telecommunications, Computer Science Theory Methods, and Computer Science Artificial Intelligence. The bibliometric findings enabled the creation of the conceptual model of Cyber Risks in Services, which proposes a cyclical and continuous improvement approach to deal with vulnerabilities, cyber threats, and consequences. This includes identifying and assessing existing vulnerabilities, implementing mitigation measures, and constantly monitoring threats and their consequences. Theoretical contributions: The conceptual model of Cyber Risks in Services can be a reference for researchers in various fields of action, considering the breadth of the services sector and the interdisciplinary nature of digital risk mitigation. Managerial contributions: Understanding of cyber risks supports the ability of the organization to respond to them, strengthening its security posture and protecting its critical assets and information from cyber threats.

Detection and Diagnostic with Random Forest Classifier (RFC) to Improve the Maintenance Management System in Steam Boiler of Power Plant

Industrial internet of things (IIoT), digital twin, and connected devices can continue to use smart equipment and improve access to data. While the data collected by sensors has been an invaluable asset to companies, the ability to understand and use this data to drive new insights. The development of Condition Monitoring (CM) technology and Computerized Maintenance Management System (CMMS) in power generation systems provides a validated set of operation and maintenance data with abundant event data. Maintenance decision-making is primarily based on equipment reliability and performance-based features for diagnosing equipment failure. The most critical asset and often reduces the reliability and availability of a Coal Fired Steam Power Plant (CFSPP) with the most frequency of disturbances is the steam boiler. As a departure from the idea of creating a digital twin, this article will focus on analyzing equipment health conditions and finding causes of failure of the tools, utilizing data for diagnostic purposes. Real-case used in this research are steam boilers, which are important assets in power plant generation. The online and Failure Mode and Effect Analysis (FMEA) module data will be combined to realize the concept of anomaly diagnosis which is driven by hybrid data. Hoping that accurate diagnosis result with the Random Forest Classifier (RFC) Algorithm can be obtained and be used to analyze the causes of failure and decrease in equipment performance resulting by a decrease of energy efficiency performance. The analytical approaches are carried out to have the goal of generating detection models and diagnostic insights of event data based on operational data and FMEA.

Transportable Energy Storage System Planning for Mitigating Grid Vulnerability

Recently, severe wildfires occurred in some countries like Australia and U.S., and wildfires usually threaten the security of the electricity grids. Therefore, it is crucial to identify the transmission interconnections that have insufficient power transfer capacity and take preventative actions to prevent the overload problem. In this paper, we have proposed a graph theoretic-based network expansion and transportable energy storage system (TESS) planning strategy. Through graph-theoretic analysis, the critical asset and limiting critical cut-set can be identified. Two novel criteria, namely transfer margin ratio (TMR) and adequacy ratio (AR), are proposed in this paper to assess the vulnerability and the reliability of the network based on graph theory. To reduce the vulnerability and to enhance the reliability of the grid, network expansion and TESS planning are proposed as two feasible methods. Therefore, this paper presents a risk-based network expansion and TESS planning strategy, and the investment plans are assessed by TMR and AR to make a final decision. The proposed method is verified in the IEEE 30-bus and IEEE 118-bus systems. The simulation results reveal that a graph theoretic-based planning strategy saves the total cost and reduces the shed loads in contingency.

IMPLICATIONS OF SECOND-ORDER WAVE GENERATION FOR USE IN WAVESTRUCUTRE RESPONSE EXPERIMENTS

Coastal communities and critical coastal assets are therefore increasingly reliant on engineered protection from wave-induced flooding. Dynamic wave force and wave run-up are among key design parameters of such protection. Present understanding of coastal wave-structure interactions and responses was gained through large databases of experimental data as well as numerical, and field measurements. It is well known that experimental data of wave-structure interaction are contaminated by second-order error waves at sub- and super-harmonic frequencies when first-order wave generation is used. The error waves arise from disparity between linear wave-maker signals and non-linear boundary conditions at the wave generator. Herein, we conduct a novel investigation by experiment of the implications of second-order wave generation for dynamic wave force and run-up on a vertical wall, in shallower depths than previously published (kd = 0.6 - 1.1). The implications of error waves on coastal responses is quantified through comparison of first-order generated (FOG) and secondorder generated (SOG) wave group experiments.

RThreatDroid: A Ransomware Detection Approach to Secure IoT Based Healthcare Systems

The use of smartphone devices in healthcare has increased manifold due to their widespread use and ease of integration with the Internet of Things (IoT) based medical devices. In healthcare, either in-home observation or in a hospital scenario, the medical sensors use certain local communication devices to share the measured vital signs with a fog/cloud-based medical system. The large user community of Android devices has also brought some serious challenges, such as potential malicious attacks. For the past few years, ransomware attacks on healthcare have been increasing dramatically, posing several challenges. Therefore, an effective ransomware detection mechanism is needed to protect critical assets such as healthcare data, patients’ private data, etc. In this work, a novel hybrid ransomware detection method is proposed that analyzes image data, text, and application code to extract plain or encrypted threat text. Threatening text is a potential tool and could be one of the most effective features for ransomware detection. Our proposed hybrid approach utilizes both static and dynamic and uses multi-machine learning classifier models. The proposed approach also provides a family classification of ransomware. Experimental results show that the proposed approach achieves up to 94% accuracy and fewer false negatives.

Robust Anomaly-Based Insider Threat Detection Using Graph Neural Network

Misuse or malicious access to critical assets of information systems by insiders usually causes significant loss to organizations. The issue of insider threat detection for information systems has received many researchers’ attention in both security and data mining fields, and a lot of related research results were presented. However, there are still many challenges in capturing the behavior difference between malicious insiders and normal users accurately, such as lack of labeled insider threats, the subtle and adaptive nature of insider threats, complexity, heterogeneity, sparsity of the underlying data, etc. To detect insider threats with large and complex audit data, a Multi-Edge Weight Relational Graph Neural Network method (MEWRGNN) for robust anomaly detection is proposed in this paper. Unlike most existing approaches, the MEWRGNN adopts several graph neural networks to capture the contextual relationship of user behaviors over a period of time, which is a critical factor for achieving accurate anomaly identification. The MEWRGNN achieves a certain degree of interpretability through ranking the contribution of different edge-representation features. Evaluation experimental results demonstrate that the MEWRGNN can learn a model from limited sample data sets, and achieve quick and accurate insider threat detection performance. In addition, other feature ranking results allow providing security analysts with understandable insights for investigating the detected insider threats.

Evaluation of the Pavement Distress and its Impact on the Sustainability of the Traffic Operation for Selected Roads in Al-Diwaniyah City

Roads and transportation infrastructure are critical assets for supporting political stability as well as economic and sustainable expansion in developing countries. Yet, pavement maintenance backlogs and the high capital expenditures of road rehabilitation need the adoption of pavement evaluation methods to ensure the best value for the investment. road maintenance is considered one of the main factors for preserving the road structurally and functionally, and before carrying out maintenance, pavement defects must be identified by evaluating the condition of the pavement to perform the appropriate maintenance. In this study, the Pavement Condition Index (PCI) method was relied upon to evaluate roads in Al-Diwaniyah City. Where several data were collected for the study, including the number of crashes for each region, the number of lanes, street width, the presence of a median, and the pavement condition evaluated for the regions with the highest crash rate, which are Al-Askari District with 707 crashes, Al-Urouba District with 379 crashes, Al-Wahda District with 271 crashes, Al-Jamhouri District with 235 crashes, and Al-Jazaer District with 212 crashes. This study is considered the first study to evaluate roads in Al-Diwaniyah city. The results of the study showed that the condition of the roads ranged from poor to satisfactory and there is no road reach to serious and failed. The results showed that the worst road is Al-Askari District, which lacks a lot of maintenance, at a rate of PCI 52.1. It is followed by Al-Wahda District with a PCI rate of 57.6, Al-Jamhouri District with a PCI rate of 67.2, Al-Urouba District with a PCI rate of 70.7, and Al-Jazaer District with a PCI rate of 73.7. The deterioration of the pavement surface condition affects the rash rate, and it is more dangerous when it is correlated to other road factors. Additionally, it was noted that the most prominent defects in the pavement surface of the five evaluated roads are: potholes, rutting, longitudinal and transverse cracks, corrugation, patching, raveling, depressions, and alligator cracks. The study recommended conducting more research on evaluating more roads in Al-Diwaniyah City on an annual basis and conducting periodic maintenance in order to improve the status of this city, which suffers from neglect in all respects, measures must be taken to enhance the environment and economy, sustain the streets, reduce maintenance costs, ensure traffic safety, and protect the lives of citizens.

Fragility analysis of slopes exposed to seismic hazard employing surrogate modeling techniques

AbstractNatural disasters may lead to failure of critical assets part of railway systems. Slope failure often results in major economic consequences. Due to the economic importance that railways systems have, it is pertinent to ensure good performance and long‐term. The objective of the present research is to propose tools that may allow obtaining the failure probability of soil slopes under a seismic event by employing surrogate models to ease the computational cost of considering the uncertainties associated with geotechnical and geometrical properties of a case study located in Portugal. The presented methodology and derived fragility curves, using peak ground acceleration (PGA) as the intensity measure (IM), can be used to assess slope performance under a seismic event for different safety levels, thus providing useful information for prioritizing assets and taking preventive actions to maintain the desired performance of the railway system.

Towards standardized guidelines in digital data acquisition for monitoring and maintenance of European transport infrastructures

AbstractThe digital transformation of the architecture, engineering, and construction sector is associated with an enormous production of data. The appearance of new technologies is leading the way for more sustainable and interconnected infrastructures. Therefore, data analytics is crucial to know the current state of infrastructure and will help towards the efficient management of assets. The main issue is how to collect and exploit meaningful data throughout the lifecycle of infrastructure assets to further allow human experts and computers to make accurate diagnostics, predictions, and decisions. This contribution presents the insights gained in the course of IM‐SAFE project, showing the strategy followed to create guidelines for data collection to monitor and maintain critical assets in the infrastructure network (bridges and tunnels). It tackled a variety of surveying technologies, from destructive and non‐destructive, to chemical methods, among others. The results obtained will support the European Committee for Standardization (CEN) in the standards for monitoring and maintenance of bridges and tunnels, accelerating the adoption of new standards by public authorities and stakeholders in transport infrastructure.

"Asking for help is a strength"-how to promote undergraduate medical students' teamwork through simulation training and interprofessional faculty.

The ability to team up and safely work in any kind of healthcare team is a critical asset and should be taught early on in medical education. Medical students should be given the chance to "walk the talk" of teamwork by training and reflecting in teams. Our goal was to design, implement and evaluate the feasibility of a simulation-based teamwork training (TeamSIM) for undergraduate medical students that puts generic teamwork skills centerstage. We designed TeamSIM to include 12 learning objectives. For this pre-post, mixed-methods feasibility study, third-year medical students, organized in teams of 11-12 students, participated and observed each other in eight simulations of different clinical situation with varying degrees of complexity (e.g., deteriorating patient in ward; trauma; resuscitation). Guided by an interprofessional clinical faculty with simulation-based instructor training, student teams reflected on their shared experience in structured team debriefings. Using published instruments, we measured (a) students' reactions to TeamSIM and their perceptions of psychological safety via self-report, (b) their ongoing reflections via experience sampling, and (c) their teamwork skills via behavior observation. Ninety four students participated. They reported positive reactions to TeamSIM (M = 5.23, SD = 0.5). Their mean initial reported level of psychological safety was M = 3.8 (SD = 0.4) which rose to M = 4.3 (SD = 0.5) toward the end of the course [T(21) = -2.8, 95% CI -0.78 to-0.12, p = 0.011 (two-tailed)]. We obtained n = 314 headline reflections from the students and n = 95 from the faculty. For the students, the most frequent theme assigned to their headlines involved the concepts taught in the course such as "10 s for 10 min." For the faculty, the most frequent theme assigned to their headlines were reflections on how their simulation session worked for the students. The faculty rated students' teamwork skills higher after the last compared to the first debriefing. Undergraduate medical students can learn crucial teamwork skills in simulations supported by an experienced faculty and with a high degree of psychological safety. Both students and faculty appreciate the learning possibilities of simulation. At the same time, this learning can be challenging, intense and overwhelming. It takes a team to teach teamwork.

Subsea Power Cable Health Management Using Machine Learning Analysis of Low-Frequency Wide-Band Sonar Data

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables.

A generic framework for qualifications of digital twins in maintenance

Digital twins have emerged as a promising technology for maintenance applications, enabling organizations to simulate and monitor physical assets to improve their performance. In Operation and Maintenance (O&M), digital twin facilitates the diagnosis and prognosis of critical assets, forming the basis for smart maintenance planning and reducing downtime. However, there is a lack of standardized approaches for the qualifications of digital twins in maintenance, leading to low trustworthiness and limiting its application. This paper proposes a novel framework for the qualifications of digital twins in maintenance based on five pillars, namely fidelity, smartness, timeliness, integration, and standard compliance. We demonstrate the effectiveness of the framework through two case studies, showing how it can be implemented on digital twins for preventive maintenance and condition-based maintenance. Our proposed framework can help organizations across different industrial domains develop and implement digital twins in maintenance more effectively and efficiently, leading to significant benefits in terms of cost reduction, performance improvement, and sustainability.

A Novel Security Framework to Mitigate and Avoid Unexpected Security Threats in Saudi Arabia

Many organizations around the world suffer large losses due to unexpected risks which can have a profound impact on their survival. This paper presents a novel security framework to address the security needs of Saudi organizations. There are four stages in the security framework: risk assessment and management, security intelligence and analytics, security policies and procedures, and security monitoring. A comprehensive security solution was provided by combining common security frameworks, e.g. ISO/IEC 27001:2013, NIST Cybersecurity Framework, and COBIT. The developed framework was designed to help Saudi organizations identify, assess, and control risks and respond to unexpected events in a timely and effective manner. It is expected to help organizations develop and implement effective security measures to protect their critical assets and operations from security threats. The proposed framework is comprehensive and can cover most organizations' requirements.

Investigation of Dynamic Characteristics of Vibrating Components and Structure Through Vibration Measurement and Operational Modal Analysis

Smooth plant production is utmost expected by plant owners since any unplanned shutdown is very much undesirable as it may incur loss of company’s profits and other repercussions. Critical operational assets such as rotating machineries and their components, e.g., motor, pump, compressor, and turbines are crucial to be monitored in ensuring continuous operation of the plant. Therefore, vibration being one of the elements in predictive maintenance is much needed. This paper addressesthe monitoring and findings for predictive maintenance on high vibration of a make-up gas compressor in one of the oil & gas and petrochemical plant in Malaysia. The conventional vibration measurement using vibration analyzer was utilized to identify the severity of the machine vibrations through the overall vibration value obtained on the motor and compressor. A specialized vibration measurement technique, namely, Operational Modal Analysis (OMA) was further deployed to verify the presence of resonance on the equipment by obtaining its dynamic characteristics, i.e., natural frequency and mode shape. The findings showed clear indication of resonance occurrence as similar frequency (112 Hz) was identified from both vibration measurement and operational modal analysis.

Database security at cache layer

As database security is the main concern, we are going to place an encryption module at the cache layer. All the encryption and decryption will be done at the cache layer before storing that data to the database, which will provide more security to the database Higher level security should be provided to the database so here we are placing an idea of Elliptical Curve Cryptography. A database is a collection of data that helps us to collect, retrieve, organize and manage the data in an efficient and effective manner. Databases are critical assets. They store client details, financial information, personal files, company secrets and other data necessary for business. Today database security is getting greater importance than the past which is the amount of data is stored in a corporate database is increasing. People are depending more on the corporate data for making a decision, management of customer service and supply chain management etc. Data stored in databases is usually very vulnerable data so it becomes very important and crucial to maintain this data securely. There are many front end applications that fetch data from secondary storage to main memory for processing and from main memory, data is stored in cache for temporary processing. Any loss or unavailability of data may seriously affect its performance. The database security should provide protected access to the contents of a database and should preserve the integrity, availability, consistency, and quality of the data. In this paper, I present a model where data is protected even on cache layer.

A metaheuristic-driven physical asset risk management framework for manufacturing system considering continuity measures

To ensure the uninterrupted operation of physical assets, the effective implementation of risk mitigation plans (RMPs) and business continuity plans (BCPs) for critical assets is required. However, manufacturing decision-makers face the challenge of allocating limited resources between RMPs and BCPs while prioritizing sustained success. To address this challenge, a novel approach proposes integrating a metaheuristic algorithm into a risk management framework that considers interconnected risks. The framework utilizes a Bayesian Network (BN) to model interdependencies among critical physical asset risks. It determines the optimal combination of BCPs and RMPs, considering continuity measures and resource limitations. A physical asset risk assessment process evaluates operational and disruption risks based on expected loss and probabilities within the risk network. Given the complexity, the proposed framework incorporates hybrid multi-objective metaheuristic algorithms (e.g., Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Particle Swarm Optimization (MOPSO), and Pareto Envelope-based Selection Algorithm II (PESA-II)) combined with a reinforcement learning approach. A case study of a manufacturing company demonstrates the framework’s applicability and discusses the results. The findings show a significant reduction in the expected loss within the risk network and migration of most physical asset risks from high-risk to acceptable areas. Additionally, a sensitivity analysis examines the available budget, aiding decision-makers in determining the necessary compromise between asset availability and network risk level. In conclusion, the proposed framework empowers decision-makers to allocate resources effectively, prioritize RMPs and BCPs, and ensure continuity for critical physical assets, thereby fostering sustained success in manufacturing systems.

Finite element modelling of lattice angle steel structures exposed to wildland fires

Essential civil infrastructure such as transmission line supporting towers and communication base towers that are traditionally formed from lattice angle steel structures are critical global assets, since their development promotes the establishment of electric power supply systems and modern telecommunication networks. Because this infrastructure is commonly located in rural and urban wildland areas, such towers may be prone to wildland fire attack, particularly in the current climate of severe heatwaves and droughts. Accordingly, a comprehensive study of the behaviour and response of lattice angle steel structures under fire loading is needed to assist in structural design to resist fire loading. In this paper, a finite element (FE) model of lattice angle steel structures is constructed using ABAQUS software. Several aspects that include material and geometric non-linearities, load eccentricities on component members, bolted joint slippage, and the use of the explicit dynamic analysis technique for a quasi-static loading procedure, are considered and applied in the FE model. The accuracy and reliability of the FE representation are validated by comparing its predictions with relevant existing experimental results. By incorporating the codified temperature-dependent material properties of structural steel as well as a calibrated load-slip relationship for bolted connections at elevated temperatures, the FE model is extended for performing structural fire analysis. The variations of the steelwork temperatures in wildland fires can be calculated based on representative temperature-time wildland fire curves and is adopted in the fire analysis. By invoking the FE model, an example of lattice angle steel structures is contrived to elucidate the fundamental behaviour of these structures in wildland fires, through both steady and transient state fire analyses. Parametric studies are also undertaken to evaluate the effects of the changes in the wind load direction, bolt pretension and the protection of critical panel sections on the behaviour of lattice structures when exposed to wildland fires.

Analyzing the Highway Failure & their Maintenance a Case Study

Highways provide facilitating transportation with connecting regions, and developed economic growth. The failure of highway decreases the efficiency of road. These critical infrastructure assets are crucial for ensuring their higher functionality, comfort, and life span. This abstract provides a comprehensive overview of the analysis of cause of failure and to understand the role of highways and the significance of effective maintenance practices. A good study starts by the examining the importance of highways as key elements of transportation networks, supporting the movement of people, goods, and service facilities. It highlights their contribution to economic development, regional connectivity, and accessibility. Furthermore, the paper investigates the various factors that influence the performance and lifespan of highways, including traffic density, environmental factors, and design importance. The abstract further examines the challenges and considerations associated with highway maintenance, such as funding constraints, resource allocation, and the utilization of advanced technologies. It explores the importance of data-driven decision-making processes, asset management systems, and the adoption of innovative approaches like predictive maintenance to optimize maintenance practices. By analyzing case studies and existing literature, this study identifies best practices and lessons learned from successful highway maintenance initiatives around the world. It emphasizes the importance of proactive and well- planned maintenance programs, collaboration between stakeholders, and the integration of sustainable practices to ensure the longevity and cost-effectiveness of highways. In conclusion, this abstract presents a comprehensive analysis of the role of highway failure and their maintenance. It highlights the significance of maintaining these critical transportation assets to support economic growth factor, regional connectivity, and overall societal well-being. The findings provide valuable insights for policymakers, transportation agencies, and infrastructure professionals involved in managing and maintaining highway networks. Key Words: Safety, life span, analysis, significance, performance, lifespan, traffic volume, environmental conditions, design characteristics, routine maintenance, major maintenance interventions

Employing robotics for inspection operations

Employing robotics for inspection operations Kostas Alexis of the Autonomous Robots Lab, Department of Engineering Cybernetics at the Norwegian University of Science and Technology, explores the use of autonomous robotics for inspection operations. Inspection operations maintain a crucial role within our societies. Industrial inspection is essential to ensure the safety of critical assets such as underground mines, maritime structures, power plants, and aerospace facilities. Notably, the costs associated with such inspection operations can be immense, while the underlying activities often expose humans to various risks and hazards.

Data-Driven Asset Health Index – an application to evaluate Quay Cranes in container ports

ABSTRACT Ports worldwide seek for more efficient and sustainable operations, relying on critical and valuable assets as their cranes. Internet of Things (IoT) technologies enable a better understanding of critical assets’ behaviour and lifecycle based on data, and development of asset management decision support tools. The Asset Health Index (AHI) is an asset condition score, designed to characterise the state of complex assets. This paper describes the process of developing a data-driven AHI. IoT sensors and Programmable Logic Controllers (PLC) in real operation time, together with information systems, and other more traditional sources of data are some of the data sources for the AHI. The index captures and quantifies dimensions affecting the asset lifecycle, allowing the comparison of the condition of assets and prioritise maintenance investments. The case study is conducted in Quay Cranes of the Port of Felixstowe (UK) as the object of this study and proof of concept.