Secure Operation Research Articles

THE ROLE OF CLOUD COMPUTING IN SCALING E-COMMERCE BUSINESSES

In the rapidly evolving digital landscape, e-commerce has emerged as a cornerstone of global trade, necessitating robust, scalable solutions to accommodate increasing consumer demands. Cloud computing has become integral to this transformation, offering e-commerce businesses the flexibility and resources needed to scale effectively. This paper explores the multifaceted role of cloud computing in enhancing the scalability of e-commerce enterprises, addressing both its benefits and challenges. Cloud computing, defined as the delivery of computing services over the internet, encompasses various models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). These models provide e-commerce businesses with on-demand access to computing resources, allowing for rapid scaling in response to fluctuating market conditions. The inherent flexibility of cloud solutions enables companies to allocate resources dynamically, which is crucial during peak shopping seasons, such as Black Friday or Cyber Monday. This elasticity not only optimizes costs but also enhances the overall customer experience by ensuring high performance and reliability. The adoption of cloud computing can significantly mitigate common challenges faced by e-commerce businesses, including infrastructure limitations and performance bottlenecks. By leveraging cloud services, businesses can achieve load balancing, disaster recovery, and enhanced data security, thereby maintaining operational continuity and protecting sensitive customer information. Furthermore, the compliance features offered by major cloud providers assist businesses in adhering to regulatory requirements, fostering consumer trust. Case studies of prominent e-commerce platforms like Amazon and Shopify illustrate the successful implementation of cloud computing strategies. These companies have demonstrated substantial growth metrics, attributing their scalability to the strategic deployment of cloud technologies. For instance, Amazon’s use of its own cloud service, AWS, has allowed it to seamlessly manage millions of transactions daily while maintaining a robust infrastructure that can adapt to changing demands. Such examples provide valuable insights into best practices and the potential for innovation in cloud-driven e-commerce environments. Despite the myriad benefits, the transition to cloud computing is not without challenges. Businesses must consider potential drawbacks, such as dependency on internet connectivity, concerns regarding data privacy, and the complexity of managing cloud resources. To address these issues, organizations are advised to conduct thorough assessments of their specific needs and to carefully select cloud service providers that align with their operational goals and security requirements. Looking forward, the integration of emerging technologies, such as artificial intelligence and machine learning, with cloud computing is set to revolutionize e-commerce scalability further. These advancements will enable more personalized customer experiences, enhanced data analytics capabilities, and greater operational efficiency. In conclusion, cloud computing serves as a vital enabler for scaling e-commerce businesses, providing them with the necessary tools to navigate the complexities of the digital marketplace. As the e-commerce landscape continues to evolve, the strategic adoption of cloud solutions will remain essential for businesses seeking to maintain competitiveness and drive growth. KEYWORDS : E-Commerce, Cloud Computing, Scalability, Infrastructure As A Service (Iaas), Platform As A Service (Paas), Software As A Service (Saas), On-Demand Resources, Dynamic Resource Allocation, Load Balancing, Disaster Recovery, Data Security, Compliance, Consumer Trust, Amazon Web Services (AWS), Shopify, Peak Shopping Seasons, Black Friday, Cyber Monday, Performance Optimization, Internet Connectivity, Data Privacy, Artificial Intelligence, Machine Learning, Data Analytics, Operational Efficiency, Digital Marketplace, Strategic Deployment, Innovation, Regulatory Requirements, Customer Experience.

IOT IN CONTAINER SHIPPING INDUSTRY: APPLICATIONS AND EXAMPLES OF SOLUTIONS

The study analyzes the participation of technologies related to the development of the Internet of Things (IoT) network concept in the intermodal container transportation system. Since the beginning of the XXI century, IoT-related technologies have been dynamically spreading in almost all areas of international business, and this has changed the understanding of cargo owners about the quality of transportation services, among other things. Since transparency in monitoring the location of high-value goods is extremely important for transport companies, cargo owners, and all other stakeholders, and the intermodal transportation system is extremely complex due to the number and diversity of communications, the need to use IoT is obvious. As analysts predict that the introduction of IoT technologies in containerization will become widespread in the coming years, a study to assess the level of research on the introduction of such technologies and implementation in the practical activities of transport enterprises is relevant. The aim of the proposed review is to determine the degree of IoT implementation in the container transportation industry, areas of research on the topic, and identify factors that may impede the predicted rapid development of IoT in containerization. The proposed study includes an analysis of scientific publications related to the use of the Internet of Things in the field of international container transportation, identifies the main priority areas of such publications, establishes the areas of current implementation of IoT technology and determines the prospects for its use. Since the solutions that can potentially be obtained with the help of IoT are quite heterogeneous, the literature analysis is organized by grouping such publications into the following areas: container monitoring and tracking, enhancing supply chain transparency and visibility, optimization of operational efficiency, security and risk management, environmental impact and sustainability, challenges and barriers. The paper provides examples of online platforms and applications currently offered by major container operators that are related to this technology.

Who bears the tax burden? The withholding tax policy and persistent informality in artisanal small-scale mining in Ghana

ABSTRACT This article examines the withholding tax policy and the challenges of taxing ASM activities in Ghana. Based on qualitative interviews with operators and stakeholders, the article reveals that the mobile and uncertain nature of operations makes it difficult for state institutions to govern and generate domestic revenue from the sector. Assessment of the withholding tax policy reveals how taxation without ‘actual’ formalization indirectly legitimizes illegal activities. Formalization efforts must prioritize the livelihood security of operators over revenue generation concerns. State accountability and a working social contract are critical to domestic revenue mobilization in developing countries.

On Collaboration and Automation in the Context of Threat Detection and Response with Privacy-Preserving Features

Organizations and their security operation centers often struggle to detect and respond effectively to an extensive quantity of ever-evolving cyberattacks. While collaboration, such as threat intelligence sharing between security teams, and response automation are often discussed in the cybersecurity community, issues like data sensitivity and confidence in detection may hinder their adoption. This work investigates the potentials and challenges of collaboration and automation to enhance incident response processes. We propose a reference architecture for data sharing in threat detection and response, aiming to boost collaborative and automated efforts across organizations while also considering privacy-preserving features. To address these challenges and potentials, we discuss how such a framework could enhance current response processes within and between organizations, validated with results in local attack detection, incident response, and data sharing.

Automatic Vehicle Security System with ZigBee Keyless entry over CAN Network

The proposed work includes integration of ZigBee Keyless entry system with CAN in vehicles. ZigBee offers wireless Keyless access, while CAN serves as the vehicle's communication backbone. This allows security estimate averse to unauthorized access users advanced features while ensuring robust protection. through meticulous examination, it underscores the feasibility and benefits of this integration, heralding a new era of sophisticated, secure vehicle access and operation in the automotive industry. through careful analysis, it demonstrates that this integration is both feasible and advantageous, so ushering in a new era of sophisticated and secure vehicle access and operation within automotive industry.

Short-Term Prediction of the Solar Photovoltaic Power Output Using Nonlinear Autoregressive Exogenous Inputs and Artificial Neural Network Techniques Under Different Weather Conditions

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R2) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN.

A blockchain consensus mechanism for real-time regulation of renewable energy power systems

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems.

Infrastructure and Tools for Testing the Vulnerability of Control Systems to Cyberattacks: A Coal Mine Industrial Facility Case

Testing the vulnerability of information systems to cyberattacks is essential to ensure the operational security of organizations and industrial processes. In particular, it is essential to ensure the resilience of industrial processes, as a possible cyberattack can lead to process malfunctions and even process shutdowns, which can lead to substantial economic losses. The possibility of various attacks, e.g., ransomware, phishing, or advanced persistent threats (APTs), requires the evaluation of the effectiveness of cyberattack detection and incident response mechanisms. In industry, it is often impossible to carry out this type of test without risking system disruption, making it difficult to assess the true effectiveness of security features. This article discusses the issues concerned with testing the cyber resilience of a system operating in a real coal mine. First, this work briefly presents the hardware and software architecture used in the coal mine. Secondly, it describes the problem of replicating a real system in the laboratory and the necessary tools and methods used to implement a resilient system architecture. Finally, the scenarios of cyberattacks are detailed, and the obtained results are discussed.

A hybrid dynamic graph neural network framework for real-time anomaly detection

ABSTRACT The timely and robust detection of anomalies is essential for resilient and secure operations of critical water infrastructures against operational faults or malicious actions. However, real-world systems exhibit diverse and evolving spatiotemporal relationships among their components, posing an intricate challenge in anomaly detection task. This study proposes a hybrid dynamic graph neural network (GNN) that jointly maps long- and short-term spatiotemporal relationships in multivariate data streams. Those relationships are encoded via a hybrid graph, comprising an optimally learned static subgraph for persistent structural relationships and a complementary dynamic subgraph for dynamically shifting relationships. Additionally, an attention mechanism captures time-varying relational importances and shifts the model's focus towards significant relationships, while minimising contributions of less importance to the final outputs. The proposed architecture is showcased through a synthetic case study of a water distribution system with multivariate data streams from both on-site and soft sensors, and shows strong detection and localisation accuracy against all scenarios of operational faults and malicious actions explored. A comparative analysis with an equivalent static graph model indicates that the addition of the hybrid dynamic component enhances detection accuracy and reduces false alarm rates through more robust characterisation of behaviours, thus allowing actionable insights for more resilient and secure operations.

Technological Intervention to Improve Tower Crane Safety through the Development of Anti-Collision Devices

The rise of extensive construction initiatives has led to an increased utilization of tower cranes, which are vital for managing materials at considerable elevations. Nevertheless, as the density of cranes in close quarters escalates, collisions have become a critical safety issue. This manuscript delineates the conceptualization and execution of an anti-collision apparatus for tower cranes, with an emphasis on facilitating secure operations within congested construction environments. The proposed framework employs sophisticated technologies, including GPS, sensors, and instantaneous data transmission, to oversee crane locations and issue collision alerts. Through the integration of real-time data analysis and automated control, the system contributes to preventing crane collisions, thereby safeguarding personnel and minimizing project interruptions

Digital Transformation in Energy Sector: Cybersecurity Challenges and Implications

Digital transformation in energy sector organizations has huge benefits but also exposes them to cybersecurity challenges. In this paper, we carried out a systematic literature review on cybersecurity challenges and issues in the energy domain. Energy-associated assets are very critical for any nation and cyber-attacks on these critical infrastructures can result in strategic, financial, and human losses. We investigated research papers published between 2019 and 2024 and categorized our work into three domains: oil and gas sector, the electricity sector, and the nuclear energy sector. Our study highlights that there is a need for more research in this important area to improve the security of critical infrastructures in the energy sector. We have outlined research directions for the scientific community to further strengthen the body of knowledge. This work is important for researchers to identify key areas to explore as well as for policymakers in energy sector organizations to improve their security operations by understanding the associated implications of cybersecurity.

An Ensemble Framework Augmenting Surveillance Cameras for Detecting Intruder Clusters as Potential Mobs

<p>Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a Machine Learning (ML) 3-tiers Ensemble framework, which integrates Gray Level Co-occurrence Matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.</p>

Application of monarch butterfly optimization algorithm for solving optimal power flow

This paper proposes a highly flexible, robust, and efficient constraint-handling approach for the solution of the optimal power flow (OPF) problem and this solution lies in the ability to solve the power system problem and avoid the mathematical traps. Centralized control of the power system has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives, generation cost minimization, power loss minimization, and enhancement of voltage stability index. These three objectives are solved separately by considering the evolutionary-based monarch butterfly optimization (MBO) algorithm. This MBO algorithm is validated on the IEEE 30 bus network and the obtained results are compared with differential evolution, particle swarm optimization, genetic algorithm, and Jaya algorithm. The obtained results reveal that among the various optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies.

Roadway portal and self-moving hydraulic support for rockburst prevention in coal mine and its application

As coal mining depths increase, rockbursts pose a significant threat to the safety of coal mine operations. Addressing the challenge of safely and steadily advancing excavation faces amid rockburst hazards, this study assesses the current state of reinforcement support technology for rockburst-prone roadways. It scrutinizes the concept of roadway anti-rockburst hydraulic support, support design, and the integration with the surrounding rock mass. The research delineates key directions for enhancing support systems, including the mechanical mechanisms of anti-rockburst hydraulic support, integrated roadway and support design, the synergistic pressure-equalizing effect of primary supports and bolts(cables), and the development of robotic systems for roadway anti-rockburst hydraulic support reinforcement. We propose an integrated design approach that holistically addresses both primary and reinforcement support in rockburst-prone roadways. To tackle the challenge of handling hydraulic supports in confined spaces, we developed a gantry hydraulic support system capable of self-movement with mechanical arm assistance for anti-rockburst hydraulic support. Field trials demonstrate that this system enables cyclical support advancement and retraction, automating and intelligently managing the underground advanced support sector. This innovation significantly reduces worker numbers and labor intensity, enhancing operational safety and efficiency. Furthermore, the system facilitates permanent support via bolt drilling under hydraulic protection, effectively mitigating the risks associated with unstable temporary roadway support and unsafe working conditions without proper roof coverage, thereby ensuring the secure and efficient operation of mining face.

Fluid mechanics of Na-Zn liquid metal batteries

Liquid metal batteries have been introduced as promising option to address the needs for new energy storage technologies. Currently, batteries based on sodium and zinc are under development and a favorable option due to their high theoretical cell potential, readily abundant materials, and cost-advantages. Nevertheless, they face the problem of self-discharge, which makes it inevitable to understand fluid dynamics in the whole cell. Motivated by that, several types of fluid mechanic instabilities in Na-Zn liquid metal batteries are identified and discussed here. On the one hand they can jeopardize secure operation, but on the other hand they can also improve mixing and increase the cell efficiency. In doing so, realistic cell as well as operation parameters are included and dimensionless numbers for identifying critical conditions are presented. The phenomena with highest significance for the discussed batteries are solutal convection, swirling flow, electrocapillary Marangoni convection, and droplet formation. Still, many open research questions remain and we aim at motivating researchers to dig deeper into some of these topics to contribute to an improved cell design and performance.

ADS-B Communication Security: Assessing the Performance of Format-Preserving Lightweight Block Ciphers

In this work, we document an investigation regarding the vulnerabilities of ADS-B (automatic dependent surveillance-broadcast) communication, considering the risks to operational security. The ADS-B technology is a communication protocol to transmit surveillance information, that is, data related to position, altimetry, and speed, from the aircraft's avionics, an evolution to radars. We present an encryption solution for this communication using format-preserving encryption implemented in a microcontroller-embedded system. We also evaluate the use of lightweight symmetric block ciphers for better computational performance. When used as a pseudorandom function, we observe that such ciphers maintain a high entropy output value with low computational cost --- up to sixteen times faster for similar entropy values. Finally, the computational performance obtained with the proposed solution is analyzed by processing real-time data from aircraft in the landing and takeoff phase at the international airport of Recife/Guararapes - Gilberto Freyre, based in Recife/PE, Brazil.

AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions

AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide AI implementation in security operations. By addressing the increasing sophistication of cyber threats, AI-driven approaches aim to create a proactive, dynamic cybersecurity posture that can keep up with evolving cyber adversaries.

Proactive monitoring and security in cloud infrastructure: leveraging tools like Prometheus, Grafana, and HashiCorp Vault for Robust DevOps Practices

This study investigates proactive monitoring and security within cloud infrastructure, focusing on implementing tools like Prometheus, Grafana, and HashiCorp Vault to enhance operational resilience and data security. As cloud adoption grows, so does the complexity of maintaining secure systems amidst ever-evolving threats and regulatory pressures. By integrating these tools within DevOps workflows, organizations can achieve real-time monitoring, visualization, and secure access management, significantly strengthening their cloud environments. A systematic literature review was conducted, examining each tool's unique capabilities, the limitations they address, and the potential for combined functionality to offer a layered security approach. The findings reveal that while these tools are invaluable for preemptively detecting threats and managing access, they require continuous refinement to meet scalability, compatibility, and compliance demands. This study concludes that a proactive, integrative approach utilizing Prometheus for data collection and alerting, Grafana for visualization, and HashiCorp Vault for secret management fosters robust cloud security practices. Recommended strategies include adopting zero-trust architectures, utilizing automated compliance tools, and integrating sustainability principles in cloud management to ensure both security and operational efficiency. Ultimately, this study emphasizes the need for adaptive security frameworks that evolve alongside technological advancements, positioning organizations to safeguard their digital assets and respond to emerging threats with agility and confidence.

Automation in Cloud-Based DevOps: A Guide to CI/CD Pipelines and Infrastructure as Code (IaC) with Terraform and Jenkins

This study offers a comprehensive analysis of automation in cloud-based DevOps, focusing on the role of Continuous Integration/Continuous Delivery (CI/CD) pipelines and Infrastructure as Code (IaC) in streamlining software development processes. Employing tools like Jenkins and Terraform, the research aims to demonstrate how automation can significantly enhance operational efficiency, scalability, and security in cloud deployments. Through a detailed examination of CI/CD components and their integration with IaC, this paper identifies key findings, including the reduction of manual errors, improved deployment consistency across environments, and enhanced security through DevSecOps practices. The study further explores challenges such as configuration complexity and compliance, proposing best practices like proactive monitoring, encrypted secrets management, and version control to mitigate these issues. Conclusively, the research recommends the adoption of AI-driven analytics and robust security frameworks to optimize cloud-based CI/CD automation. This work not only highlights current methodologies but also anticipates future trends, providing a strategic roadmap for organizations aiming to leverage DevOps automation effectively.

Development of Bank Payment Systems in the Digital Economy of the Republic of Moldova

Payment systems represent an integral part of the banking industry. In the modern world, convenience is essential and because of the increasing requirements of people towards reduced costs, faster processing period, increased security, and effortless operation process, digital banking is becoming more and more popular among population, constituting already a significant component of their lives, what can be explained with 65.3% of customers using online platforms for financial management and transactions. Credit cards, e-cash, internet, mobile, QR code, and e-check payments are few examples of payment methods that a closely associated with people's daily lives nowadays. The efficient and secure operation of payment systems is essential not only for each individual separately, but also for the economy's financial system overall performance. Thus, using robust payment systems is essential to maintain and enhance financial stability. In recent years, there has also been a significant breakthrough in the design of payment systems, especially with regard to the development and widespread adoption of systems that use real-time gross settlement. The object of the study is the development of bank payment systems in the context of digital economy in the Republic of Moldova. The purpose of the research is the analysis of digital payment systems in the banks of the RM, pursuing the activities taken for the sustainable implementation of innovations in the field at national level, assuming the international practice.