Industrial Infrastructure Research Articles

A DDoS Tracking Scheme Utilizing Adaptive Beam Search with Unmanned Aerial Vehicles in Smart Grid

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.

Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic

In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems.

Advancing the analysis of water pipe failures: a probabilistic framework for identifying significant factors

The failure of water pipes in Water Distribution Networks (WDNs) is associated with environmental, economic, and social consequences. It is essential to mitigate these failures by analyzing the historical data of WDNs. The extant literature regarding water pipe failure analysis is limited by the absence of a systematic selection of significant factors influencing water pipe failure and eliminating the bias associated with the frequency distribution of the historical data. Hence, this study presents a new framework to address the existing limitations. The framework consists of two algorithms for categorical and numerical factors influencing pipe failure. The algorithms are employed to check the relevance between the pipe’s failure and frequency distributions in order to select the most significant factors. The framework is applied to Hong Kong WDN, selecting 10 out of 21 as significant factors influencing water pipe failure. The likelihood feature method and Bayes’ theorem are applied to estimate failure probability due to the pipe materials and the factors. The results indicate that galvanized iron and polyethylene pipes are the most susceptible to failure in the WDN. The proposed framework enables decision-makers in the water infrastructure industry to effectively prioritize their networks’ most significant failure factors and allocate resources accordingly.

Exploring Probability of Detection (POD) Analysis in Nondestructive Testing: A Comprehensive Review and Potential Applications in Phased Array Ultrasonic Corrosion Mapping

In nondestructive testing (NDT), ensuring defect detection, measurement accuracy, and reliability guarantees various components’ structural integrity and safety. The Probability of Detection (POD) concept has emerged as a fundamental measure of the effectiveness of an inspection technique in identifying defects. Since NDT plays a crucial role in aerospace, manufacturing, and infrastructure industries, enhancing POD has become critical. POD refers to the likelihood that a flaw or defect of a certain size will be detected using the NDT technique. The “â versus a” and the “hit/miss” methods are particularly notable among the commonly employed POD estimation methods. The POD curve is determined based on crack size measurements in the “â versus a” approach, typically used in ultrasonic testing. On the other hand, the “hit/miss” method establishes the POD curve by analysing binary outcomes, where a “hit” signifies successful detection and a “miss” denotes detection failure. This review focuses on POD in the context of NDT, specifically in phased array ultrasonic corrosion mapping (PAUCM), to uncover current uncertainty parameters and explore an innovative avenue for enhancing POD assessment by incorporating the material surface temperature as an additional parameter.

Optimizing compressive strength prediction using adversarial learning and hybrid regularization

The infrastructure industry consumes natural resources and produces construction waste, which has a detrimental impact on the environment. To mitigate these adverse effects and reduce raw material consumption, waste materials can be repurposed to achieve sustainability. However, recycled materials deteriorate the intrinsic properties of concrete. A suitable ratio of natural resources and recycled aggregates can produce the desired compressive strength. Compiling sufficient data in civil engineering laboratories to make reliable conclusions is time-consuming and costly. Therefore, this research proposes a novel approach for predicting compressive strengths using limited data. The generative adversarial network was employed to generate synthetic data. Hybrid training, utilizing either conventional loss or heuristic loss, prevents the model from overfitting by adaptively adjusting the regularization term. Random noise from a multivariate normal distribution is embedded heuristically into the training samples to capture intricate data variations. Sensitivity analysis indicated that the size of recycled coarse aggregate and water are the most significant features, aligning with their correlations. Interestingly, superplasticizer, density of recycled coarse aggregate, and water absorption ratio of recycled coarse aggregate contributed significantly to predictions despite their low correlations. The propounded method outperforms random forest, support vector regression, artificial neural network, and adaptive boosting by scoring a mean squared error of 7.97, a root mean squared error of 2.82, a mean absolute error of 2.13, and a coefficient of determination of 0.96. These results suggest that the proposed technique can effectively contribute to sustainable construction practices by accurately predicting compressive strengths.

A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

AbstractAs the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect.

Can world heritage sites boost sales in non‐tourism firms? The role of visibility

AbstractWorld Heritage sites can positively impact local manufacturing industries. Using two‐way fixed effects estimation, we find that manufacturing firms in cities with World Heritage sites see a 9% increase in operating revenue. This finding is robust to matching methods and a control function method. The impact is especially strong for businesses with low online visibility, highlighting that World Heritage sites enhance visibility toward lesser‐known enterprises. The study also shows that the positive impact on sales is greater in cities with better support for economic development, consistent with the notion that firms need good industrial infrastructure and financial resources to produce high‐quality goods.

Estimating the vulnerability of industrial network infrastructure in Central and Eastern Europe

Industrial infrastructure has suffered an unprecedented number of attacks in Central and Eastern Europe (CEE). This situation can be attributed to many geopolitical factors, including hybrid military conflicts and criminal activity. Industrial networks belonging to the countries that were once under Soviet influence suffer from an elevated risk of cyberattacks. The goal of this work is to propose an easy way to estimate the vulnerabilities of industrial networks to cyber threats on a national level. Since analysis of the industrial vulnerability landscape is difficult, this study proposes an assessment based on the popularity of vulnerable technologies—VPc. This metric is composed of search volume data on keywords related to industrial network technologies and reported security vulnerabilities associated with these words. Data on 116 keywords was analysed and a country-specific VPc index was calculated for twenty states in CEE. The analysis of the popularity of industrial technologies and vendors in CEE reveals interesting information about the industrial security and vulnerability landscape. The results show that some countries (e.g. Estonia) have more resilient industrial infrastructure than others (e.g. Belarus). The results presented in this study are not in conflict with other data and estimation attempts, including the National Cyber Security Index (NCSI). As new vulnerabilities are noted every day, the industrial security landscape changes rapidly. Therefore, a new easy-to-use metric (VPc) can be successfully used for general estimations. This work shows that the VPc score agrees with other estimates and analyses, but as with any other general estimation tool, it must be used with caution.

Versatile, modular, and customizable magnetic solid-droplet systems

Magnetic miniature robotic systems have attracted broad research interest because of their precise maneuverability in confined spaces and adaptability to diverse environments, holding significant promise for applications in both industrial infrastructures and biomedical fields. However, the predominant construction methodology involves the preprogramming of magnetic components into the system's structure. While this approach allows for intricate shape transformations, it exhibits limited flexibility in terms of reconfiguration and presents challenges when adapting to diverse materials, combining, and decoupling multiple functionalities. Here, we propose a construction strategy that facilitates the on-demand assembly of magnetic components, integrating ferrofluid droplets with the system's structural body. This approach enables the creation of complex solid-droplet robotic systems across a spectrum of length scales, ranging from 0.8 mm to 1.5 cm. It offers a diverse selection of materials and structural configurations, akin to assembling components like building blocks, thus allowing for the seamless integration of various functionalities. Moreover, it incorporates decoupling mechanisms to enable selective control over multiple functions, leveraging the fluidity, fission/fusion, and magneto-responsiveness properties inherent in the ferrofluid. Various solid-droplet systems have validated the feasibility of this strategy. This study advances the complexity and functionality achievable in small-scale magnetic robots, augmenting their potential for future biomedical and other applications.

Stakeholder-Induced Disputes and Resolution Strategies in the Infrastructure Industry

Stakeholder-Induced Disputes and Resolution Strategies in the Infrastructure Industry

Integrating building information modelling (BIM) and extended reality (XR) in the transportation infrastructure industry

Although building information modelling (BIM) has been widely used in the building industry, its usage in infrastructure projects such as bridges has been very challenging. Extended Reality (XR) that simulates a construction project is still considered a new technology in the Architecture, Engineering, and Construction (AEC) industry. This paper investigates the viability of integrating both BIM and XR technologies into transportation infrastructure projects. A fully integrated workflow for introducing different XR, Augmented Reality (AR), and Virtual Reality (VR), and BIM technologies using different software for a case study of El-Merghani bridge, a reinforced concrete girder type bridge, was developed. The models for the bridge included GIS integration and geometric road design according to AASHTO, documentation, shop drawings and quantification were developed for the bridge. A hypothetical time schedule was generated in Oracle Primavera P6 elevating the BIM model to 4-D and developing VR and AR virtual experiences with the capability to investigate, visualize, and present the model in a virtual environment, at a high level of immersion. Additionally, the associated risk reduction for the considered XR technology was assessed using Monte Carlo simulation. The workflow and the detailed 3D models developed for the bridge along with the highly immersive VR and AR experiences have provided an interactive platform for engineers and different stakeholders to monitor the project during the design and construction phases. The risk analysis showed that significant cost savings can be achieved with the utilization of VR and AR technology in bridges construction.

CONVERSATIONS WITH MAHATHIR MOHAMAD (GIANTS OF ASIA SERIES) DR M: OPERATION MALAYSIA BY TOM PLATE, MARSHALL CAVENDISH, 2012. ISBN: 9789814276634

Conversations with Mahathir Mohamad: Doctor M: Operation Malaysia by Tom Plate tells the experience of Mahathir Mohamad as a Malaysian Prime Minister with a superior legacy of his own, especially during his reign of power between 1981 to 2003. The book contains Plate’s detailed retelling of his experience interviewing Mahathir, besides reviewing the answers Mahathir provided on the issues surrounding his leadership. The combination of these two elements is the background that underlies the book’s narration. The contents are constructed by emphasising Plate’s critical perspectives as a former journalist from the West and a current professor at the Loyola Marymount University, Los Angeles, towards third-world country leaders who are often associated with autocratic and dictatorial leadership and the answers from the main character himself, Mahathir, as a third world country leader who considered himself as purporting accustomed democratic style which was well-tailored to be implemented to Malaysians and the country due to its historical background and at that time – timely needed. Through this book, Mahathir has attested that he was a leader who had established the nation’s industrial infrastructure and brought its name to the attention of the world. However, along the line, as he was too focused and had driven solely by the ambition, he has single-handedly tightened and degraded the country’s institutions with somewhat – oppressive with authoritarian manner. Beyond recall, the book presents readers with fresh insights and viewpoints regarding Mahathir’s leadership, which is often fraught with controversies. Keywords: Mahathir Mohamad, prime minister, modernisation, leadership, autocratic. Cite as: Razali, N. A., & Sabtu, M. H. (2024). Conversations with Mahathir Mohamad (Giants of Asia series) Dr M: Operation Malaysia by Tom Plate. (Book review). Journal of Nusantara Studies, 9(2), 34-39. http://dx.doi.org/10.24200/jonus.vol9iss2pp34-39

Cybersecurity Integration in Enterprise Architecture for IoT Infrastructure in Steel Manufacturing

As a result of the widespread adoption of Internet of Things technology in the steel manufacturing industry, there is an urgent requirement for the implementation of robust cybersecurity measures. The proliferation of IoT devices has caused a data explosion, which in turn has increased the risk of cyberattacks. The purpose of this research is to develop an enterprise architecture model that is capable of effectively managing cybersecurity risks on Internet of Things infrastructure in the steel manufacturing industry. This is a response to the urgent challenge that has been presented. The methodology utilized in this study is a rigorous qualitative approach, which involves the collection and analysis of data through interviews and literature reviews related to the topic. Following an in-depth analysis of the findings of the research, several important goals have been established. These goals include the identification of potential dangers, the reduction of potential risks, and the effective implementation of security controls. Within the context of the steel manufacturing industry, this research makes a significant contribution to the improvement of cybersecurity in Internet of Things infrastructure. In addition to identifying potential dangers and mitigating risks, the architecture model that has been proposed is about more than that. It offers a comprehensive and well-coordinated safety strategy, which guarantees a strong defense against cyber threats.

A 3-DOF caudal fin for precise maneuvering of thunniform-inspired unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) will see increased use in scientific research, military operations and maintenance of industrial infrastructure. Many of these applications require that the vehicle possess a long range while retaining precise maneuvering or station-keeping capabilities. Both current UUVs and biological swimmers, often considered the basis for the next generation of UUVs, face a trade-off between the two characteristics. Here, we introduce a novel hybrid propeller concept that enables thunniform-inspired vehicles, which imitate nature’s most efficient swimmers, to also achieve high maneuverability. The propeller can produce enhanced three-dimensional kinematics of the caudal fin. An optimization procedure based on real-time experimental data is used to obtain the best kinematics to maneuver the vehicle, and a 4-step strategy is uncovered that results in a 49% increase in maneuverability with respect to conventional 2-D kinematics. The proposed mechanism is shown to be effective for a wide range of fin geometries and stiffness values.

CGAN-based cyber deception framework against reconnaissance attacks in ICS

In recent years, Industrial Control Systems (ICSs) have faced increasing vulnerability to cyber attacks due to their integration with the Internet. Despite efforts to enhance cybersecurity, reconnaissance attacks remain a significant threat, prompting the need for innovative defensive strategies. This paper introduces a novel approach to strengthen the defensive capabilities of ICS networks against reconnaissance attacks using machine learning-driven cyber deception techniques. Leveraging Conditional Generative Adversarial Networks (CGANs), the proposed framework dynamically generates defensive network topologies to network shuffling and implement deception strategies, prioritizing system availability. Extensive simulations demonstrate the superior efficacy of the proposed framework in enhancing cybersecurity while minimizing computational overhead. By effectively mitigating reconnaissance attacks, this solution reinforces the resilience of ICS networks, safeguarding critical industrial infrastructure from evolving cyber threats. These findings underscore the significance of adopting machine learning-based cyber deception as a pragmatic security measure for protecting ICS networks in real-world industrial contexts.

Effects of Fly Ash and Graphene Oxide in Cement Mortar Considering the Local Recycled Material Context

Construction materials are at the forefront of global economic development as they provide the foundation for the infrastructure of other industries, with cementitious materials being predominantly used in construction projects. To promote sustainable development, alternative materials are added to cement mortar to increase durability and reduce emissions. In this regard, graphene oxide (GO) and fly ash (FA) are two alternative materials commonly used in cement mortar, which are readily available or are just the waste from other local material production. With different ratios, the amount of GO and FA can affect the properties of cement mortar positively or negatively. This study aims to determine the effects of GO and FA on cement mortar mixtures under material conditions. Research results show that 10 wt% FA and 0.036 wt% GO will give cement mortar the best physical and mechanical properties while ensuring other necessary properties, such as workability. When increasing FA to 30 wt% or GO to 0.05 wt%, the strength of the mortar mixture tends to decrease. Another issue is that the specific surface area of graphene is very high, which poses a significant challenge when uniform dispersion in the cement paste mixture is required. Polycarboxylate combined with a specific mixing sequence has demonstrated good dispersibility and high stability. Through this research, it is demonstrated that the addition of GO and FA has the potential for sustainable development of the construction industry by considering the contexts of the local recycled cementitious replacement materials.

Promoting sustainability in the solar industry: bibliometric and systematic analysis of alternatives for the end-of-life of photovoltaic modules

This study conducted bibliometric and systematic analyses of the literature to map the application of life cycle assessment studies on photovoltaic panels, focusing on end-of-life alternatives. Seventy-six articles addressing management strategies, treatment, recycling, and utilization of the panels were examined. The results revealed a significant increase in the number of publications over time, particularly in high-impact journals. Sixteen end-of-life alternatives for the panels were identified, including recycling practices, material recovery, and landfills. All alternatives were analyzed regarding environmental, social, and economic impacts. Understanding the impacts of different end-of-life methods for solar panels is key in supporting government management and the implementation of effective policies and regulations. Additionally, it is necessary to consider the environmental and social effects, as well as ensure the protection of workers involved in related activities and prevent soil, water, and air contamination caused by different types of alternatives, such as dissolution and incineration. To enhance the recycling and recovery of elements from solar panels, scientific advancements and improvements in industrial infrastructure are necessary along with the implementation of a regional logistics approach that minimizes emissions resulting from transportation. The bibliometric analysis indicates that 65% of the studies were published within the last 3 years, and one quarter of the trending keywords of this period is “recycling”. This is particularly relevant to show a major area for research in the solar industry. By means of the systematic analysis, this study compiles and synthetizes the most recent and relevant alternatives for managing end-of-life photovoltaic panels. The comprehensive overview of the impacts associated with these alternatives contributes to the literature and practice of sustainability in the energy sector, providing support for informed decisions directed towards implementation.

Enhancing telehealth services development in Pakistani healthcare sectors through examining various medical service quality characteristics.

The telehealth service increased attention both during and after the Covid-19 outbreak. Nevertheless, there is a dearth of research in developing countries, including Pakistan. Hence, the objective of this study was to examine telehealth service quality dimensions to promote the telehealth behavior intention and sustainable growth of telehealth in Pakistan. This study employed a cross-sectional descriptive design. Data were collected from doctors who were delivering telehealth services through a well-designed questionnaire. To examine the hypothesis of the study, we employed the Smart PLS structural equation modeling program, namely version 0.4. The study findings indicate that medical service quality, affordability, information quality, waiting time, and safety have a positive impact on the intention to engage in telehealth behavior. Furthermore, the adoption of telehealth behavior has a significant favorable effect on the actual utilization of telehealth services, which in turn has a highly good impact on sustainable development. The study determined that telehealth services effectively decrease the amount of time and money spent on travel, while still offering convenient access to healthcare. Furthermore, telehealth has the potential to revolutionize payment methods, infrastructure, and staffing in the healthcare industry. Implementing a well-structured telehealth service model can yield beneficial results for a nation and its regulatory efforts in the modern age of technology.

Role of Mathematics in the Development of Society" - A Perspective Study

A "society" is a group of individuals who have developed a long-term bond or an immense social gathering that is governed by the same government and cultural standards as the group in question. An economic, social, and/or industrial infrastructure is established as a result of the collaboration of people from many origins and cultures. Because man is a social animal, he and his fellow humans must collaborate in order to survive. A lot of group work helps with social skills. Social skills can be learned by working together on tasks with other people

Opportunities and Prospects of Developing Tourist Industry Infrastructure in the Republic of Altay on the Basis of State-Private Partnership

The research deals with planning and developing tourist industry in the Republic of Altay on the basis of mechanisms of state-private partnership oriented to building the necessary tourist infrastructure in the region. In the research the author analyzed trends in developing the system of academic views and regulatory control of stateprivate partnership in the Russian Federation and systematized key forms of direct and indirect participation of state in the face of bodies of state governance and regional levels in implementation of investment projects with cornerstone impact on social and economic and technological development of territories. The author checked tourist potential of the Republic of Altay and sufficiency of tourist infrastructure units in the region according to strategy of social and economic development of the region. Apart from that, the article identified technological and operation features of building and using different units of tourist infrastructure in the region and worked out recommendations aimed at application of concrete forms and methods of state-private partnership in order to ensure the complex development of tourist infrastructure in the Republic of Altay. Findings of the research can be used for elaboration and realization of the Republic of Altay policy aimed at tourism development in the region.