Fortifying Critical Infrastructures: Secure Data Management with Edge Computing

Technological advancements are transforming the healthcare sector, bringing solutions such as IoT and artificial intelligence, which enable faster diagnostics, near-real-time monitoring, and personalized care. However, challenges such as scalability, security, privacy, and interoperability still persist. This work proposes a distributed data management architecture for a Smart Healthcare environment, which integrates edge-fog-cloud computing, IoT, containers, and blockchain, and Apache Kafka for near real-time data streaming. The adoption of edge computing enables decentralized processing closer to data sources, reducing latency and improving responsiveness, while fog computing facilitates intermediate data aggregation and pre-processing, enhancing scalability and efficiency before reaching the cloud. The solution uses the Hyperledger Fabric blockchain network from the Amazon AWS cloud provider and the InterPlanetary File System (IPFS) content-addressable distributed storage system. The architecture is evaluated against a baseline solution, ensuring the integrity and auditability of information, and demonstrating the feasibility of its application in medical systems. The results demonstrate that the proposed approach significantly improves data access time, enhances system reliability, and security. The findings suggest that combining edge-fog-cloud layers is essential for scalable and secure healthcare data management, optimizing computational resources while ensuring data integrity and auditability. Furthermore, this research highlights future directions for Smart Healthcare, emphasizing emerging trends and opportunities to enhance interoperability and distributed technologies in digital health systems.

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

Critical infrastructure must withstand cyber and physical assaults in today's interconnected world. This study explores innovative strategies to shield these systems from cyber-physical threats. As networks become more connected, cyberattacks grow more sophisticated, making it imperative to safeguard sectors like transportation, energy, water, and information. The paper analyzes the present and future threat landscape, identifying vulnerabilities within critical infrastructures and proposing targeted mitigation strategies to enhance security. The research leverages AI and machine learning to develop detection tools that identify and predict cyber-physical attacks, enhancing response times and preventive measures. Additionally, resilience engineering and architecture are crucial in fortifying infrastructure, enabling it to withstand and recover from attacks while maintaining essential functions. The study also reviews legislation and policies surrounding infrastructure protection, pinpointing shortcomings and recommending improvements to bolster national security. Effective countermeasures against cyber-physical threats require collaboration and information sharing among government bodies, industry stakeholders, and educational institutions. These partnerships facilitate the exchange of knowledge, best practices, and tools to address threats efficiently. Furthermore, the paper highlights the importance of training programs that equip professionals with skills to integrate cyber and physical security defenses. Exploring the integration of blockchain, IoT, and autonomous technologies could further enhance the resilience of critical systems. These technologies foster secure communications and automated responses to incidents. Lastly, community awareness initiatives play a vital role in preparing for and mitigating cyber-physical attacks, ensuring that public readiness and resilience are maintained. This comprehensive approach aims to fortify critical infrastructure against evolving cyber threats, ensuring continuous operation and security.

Critical Infrastructure (CI) is the term used to describe assets that are of utmost importance, or in other words, essential in the functioning of an environment. Societies depend on their critical infrastructure in order to maintain and continuously improve on their standards of living. As these systems increasingly depend on cyber-technology, their security on networked environments become crucial. The modern smart grid has become part of CI through the enhancement it provides to regular electricity grid infrastructure. The application of biologically inspired systems to ensure security in aspects such as intrusion detection, access control, anomaly detection and others has become a widely researched field in when dealing with networked computing systems. This paper discusses the importance of CI, the application of artificial immune systems in the protection of CI information systems and proposes a new model for smart grid protection using biologically inspired concepts.

The digital transformation of healthcare, driven by the Internet of Medical Things (IoMT), has intensified the need for secure, efficient and interoperable medical data management. This study develops and evaluates a secure system that combines blockchain technology, AES encryption, and zlib compression to enable decentralized, tamper-proof sharing of electronic health records (EHRs) while optimizing storage and ensuring data integrity. The system was implemented as a Flask-based web application and tested using diverse medical file types (TXT, PDF, DICOM, JPG/PNG), measuring compression ratios, encryption/decryption speed, and blockchain hash performance. The results demonstrated that text files achieved the highest compression ratio (~6.4), while encryption and decryption times remained fast and scaled linearly with file size. Blockchain hashing proved efficient (5–13 ms), highlighting the system’s suitability for real-time healthcare environments. The integrated approach effectively addressed core issues in current healthcare data systems such as inefficient storage, poor interoperability, and data security vulnerabilities, especially in managing structured and semi-structured clinical data. It can be concluded from the study that blockchain-based systems can significantly improve the security, scalability, and reliability of healthcare data exchange. The system ensures confidentiality, authenticity, and integrity of patient records while promoting seamless data access among authorized entities. Despite its effectiveness, challenges such as processing overhead and blockchain scalability were noted, suggesting a need for further optimization. Recommendations include the adoption of blockchain-integrated data sharing platforms by healthcare institutions to enhance traceability and trust. File compression should be prioritized for large textual records to reduce bandwidth and storage costs. Future improvements may incorporate biometric authentication for enhanced access control. Additionally, collaboration between government agencies, hospitals, and tech providers is essential to establish regulatory standards for blockchain deployment in health data management.

Introduction. In order to ensure the sustainable functioning of critical infrastructures (CI), in conditions of the growth of various types of threats, security systems (SB) for various purposes, for example, technical SB, should be created on them. As such systems, first of all, there are security alarm systems, access control and some others. These systems are usually integral parts of the overall physical protection of CI, so they play an important role in ensuring the security of CI. One of the most important SB on the CI is the identification and access control system. In recent years, specialists in these systems have paid closer attention to the issues of ensuring the security of checkpoints for vehicles. It is known that they can be one of the most vulnerable places in the system of physical protection of the CI. Based on this, the task of improving identification and access control systems, including the vehicle, is currently an urgent scientific and practical task, and therefore require further study. Aims and Purposes. The purpose of this study is to develop a simplified conceptual design of the wireless identification and access control system of the vehicle on the CI. Methods. To achieve this goal, the methods of system analysis, elements of the theory of radio engineering, as well as methods of analysis, description, generalization, results of research works on the subject of research were used. Results and discussion. The existing systems of wireless identification and access control on CI are considered on the example of wireless identification and access control systems of the vehicle. Some ways of improving the safety and reliability of the operation of these systems are outlined. The analysis of ways to improve the systems of wireless identification and access control on CI is carried out on the example of wireless identification and access control systems of the vehicle. A simplified conceptual design of the wireless identification and access control system of the vehicle on CI has been developed. The main primary provisions of the system under development related to the circuit engineering stage of conceptual design are formulated. Conclusions. As a result of the conducted research, a simplified conceptual design of the wireless identification and access control system of the vehicle on CI has been developed. According to the authors, the developed simplified conceptual project, with appropriate adaptation, can be applied in practice for the purposes of ensuring the physical protection of CI. Keywords: critical infrastructure; identification systems; access control; wireless data transmission systems; conceptual project.

The Internet of Things (IoT) is transforming the way physical systems interact with the digital world by enabling seamless communication between devices, sensors, and networks. Through real-time data collection, processing, and intelligent decision-making, IoT has found applications across diverse domains including smart homes, smart cities, healthcare, manufacturing, agriculture, transportation, and logistics. This chapter presents an overview of IoT fundamentals, highlighting its key components such as sensors, actuators, connectivity options, embedded gateways, systems, cloud platforms, and data centers. It discusses how IoT solutions enhance efficiency, productivity, and user experience while supporting sustainable practices. Practical use cases illustrate the technology’s potential to improve quality of life, optimize resources, and drive innovation. Alongside these opportunities, the chapter addresses major challenges such as data security, privacy, interoperability, scalability, and power management. Emerging trends, including the integration of artificial intelligence, machine learning, edge computing, and 5G connectivity, are explored as pathways to strengthen IoT performance and expand its capabilities. The discussion concludes with an outlook on IoT’s future role in creating intelligent, connected, and environmentally responsible systems that can significantly impact both industry and society.

SharePoint migration has emerged as a crucial transformation process for enterprises striving to modernize their digital infrastructure. Organizations moving from legacy on-premises SharePoint environments to SharePoint Online (M365) or adopting hybrid models must navigate a complex landscape of data management, governance, security, and user adoption challenges. The transition is not merely a technical upgrade but a strategic initiative requiring meticulous planning, execution, and risk mitigation. This paper delves into the key strategies that ensure a seamless SharePoint migration, including comprehensive pre-migration assessments, the selection of suitable migration approaches, robust governance frameworks, and leveraging advanced migration tools. Best practices such as phased rollouts, stakeholder engagement, metadata structuring, and automation-driven processes play a pivotal role in enhancing efficiency and minimizing disruption. Moreover, this research critically examines the persistent challenges faced by enterprises during migration, including large-scale data handling, metadata and permission retention, customization compatibility, compliance risks, and downtime mitigation. Addressing these obstacles requires a proactive approach that integrates industry best practices with adaptive solutions. Ensuring data integrity, security, and regulatory compliance remains at the core of successful SharePoint migration. The implementation of encryption mechanisms, access control policies, and compliance alignment with global standards such as GDPR, HIPAA, and FedRAMP is paramount. Furthermore, fostering user adoption through targeted training programs, automation tools like Power Automate, and intelligent search capabilities is vital to achieving long-term operational success. This study identifies significant gaps in existing research related to SharePoint migration, particularly in areas of AI-driven automation, hybrid model optimization, and governance best practices. It introduces a novel framework designed to streamline enterprise SharePoint migration, offering a structured roadmap that integrates cutting-edge technological advancements with organizational change management principles. By bridging these research gaps, this paper provides a comprehensive blueprint for enterprises aiming to maximize the benefits of SharePoint modernization while mitigating risks and ensuring sustainability in the evolving digital ecosystem.

An adaptive access control scheme based on trust degrees for edge computing

Cloud computing offers cost effective services on-demand which encourage critical infrastructure providers to consider migrating to the cloud. Critical infrastructures are considered as a backbone of modern societies such as power plants and water. Information in cloud computing is likely to be shared among different entities, which could have various degrees of sensitivity. This requires robust isolation and access control mechanisms. Although various access control models and policies have been developed, they cannot fulfil requirements for a cloud based access control system. The reason is that cloud computing has a diverse sets of security requirements and unique security challenges such as multi-tenant and heterogeneity of security policies, rules and domains. This thesis provides a detailed study of cloud computing security challenges and threats, which were used to identify security requirements for various critical infrastructure providers. We found that an access control system is a crucial security requirement for the surveyed critical infrastructure providers. Furthermore, the requirement analysis was used to propose a new criteria to evaluate access control systems for cloud computing. Moreover, this work presents a new cloud based access control model to meet the identified cloud access control requirements. The model does not only ensure the secure sharing of resources among potential untrusted tenants, but also has the capacity to support different access permissions for the same cloud user. Our focused in the proposed model is the lack of data isolation in lower levels (CPU caches), which could lead to bypass access control models to gain some sensitive information by using cache side-channel attacks. Therefore, the thesis investigates various real attack scenarios and the gaps in existing mitigation approaches. It presents a new Prime and Probe cache side-channel attack, which can give detailed information about addresses accessed by a virtual machine with no need for any information about cache sets accessed by the virtual machine. The design, implementation and evaluation of a proposed solution preventing cache side-channel attacks are also presented in the thesis. It is a new lightweight solution, which introduces very low overhead (less than 15,000 CPU cycles). It can be applied in any operating system and prevents cache side-channel attacks in cloud computing. The thesis also presents a new detecting cache side-channel attacks solution. It focuses on the infrastructure used to host cloud computing tenants by counting cache misses caused by a virtual machine. The detection solutions has 0% false negative and 15% false positive.

The increasing complexity of cloud computing has led enterprises to adopt multi-cloud and hybrid cloud strategies to enhance performance, security, and operational resilience while mitigating vendor lock-in risks. These architectures enable organizations to distribute workloads across multiple cloud providers and integrate on-premises infrastructure with cloud environments for greater flexibility and regulatory compliance. This paper examines the key drivers behind multi-cloud and hybrid cloud adoption, including business continuity, regulatory requirements, workload portability, and cost optimization. It analyzes hybrid cloud integration patterns, inter-cloud networking models, and security challenges, while also exploring best practices for cloud interoperability, governance, and workload orchestration across AWS, Azure, and Google Cloud. Real-world case studies highlight cloud-agnostic strategies, Kubernetes-based orchestration, and automated security policy enforcement in multi-cloud environments. The paper also discusses AI-driven cloud optimization, zero-trust security frameworks, and edge computing in hybrid cloud deployments, providing insights for cloud architects, DevOps teams, and IT practitioners designing scalable, secure, and cost-efficient multi-cloud ecosystems.

This article explores the role of IT audits in strengthening cybersecurity for Bulk Electric Systems (BES). Focusing on compliance with NERC Critical Infrastructure Protection (CIP) standards, it highlights how IT audits enhance access management practices, mitigate risks, and safeguard sensitive data. Through actionable insights, such as improving access controls, permissions, and authentication methods, IT audits help organizations protect critical infrastructure from unauthorized access and evolving cyber threats.

With the scale and complexity of healthcare data expanding more than ever, the need for efficient, secure, and intelligent data management systems is more important than ever. This paper provides a comprehensive overview of traditional and contemporary medical data management methods. The paper begins with traditional relational database management methods and provides an overview of these classical methods and discusses some advantages of relational databases such as structured data integrity and consistency. It then reviews emerging methods such as NoSQL databases, cloud computing methods, and AI-enabled systems that address issues associated with relational listing methods, including weaknesses handling unstructured data with structured schemas, managing scalability secondary to growth, and enabling advanced analytics based on querying and ingest. It also separately reviews hybrid approaches and forward-looking technologies, including blockchain, federated learning, edge computing, and semantic knowledge graphs that are largely shaping the next generation of healthcare data demography. It then contrasts the core features, trade-offs and challenges of implementing each system, and outlines current trends and for future guidance objectives for design of medical data systems. The aim is to provide useful references for practitioners in the design and selection of adaptable systems for the broad applicability of clinical needs.

With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading.

Critical infrastructures support essential services across various sectors, including finance, energy, healthcare, and public administration. However, their increasing exposure to the internet, combined with insecure communication protocols, weak authentication mechanisms, and misconfigurations, makes them prime targets for cyber threats. To address these risks, this paper presents a novel cybersecurity methodology tailored for Industrial Control Systems (ICS), leveraging the Shodan search engine for systematic vulnerability identification. The proposed approach integrates six key phases: IP discovery (1), DNS verification (2), port, protocol and service identification (3), operating system profiling (4), web application analysis (5), and comprehensive risk assessment using a customized risk matrix (6). This study contributes by (1) conducting a systematic literature review to identify gaps in existing ICS security frameworks, (2) developing a structured methodology for risk assessment across different ICS lifecycle phases, (3) applying the methodology to a real-world case study to validate its effectiveness, and (4) demonstrating its capability to systematically identify vulnerabilities, quantify risks, and provide actionable security insights. The results reveal critical security gaps, such as outdated software and unencrypted communication channels, emphasizing the need for improved security controls. By quantifying the attack surface and assessing risk levels, the proposed model enables stakeholders to prioritize remediation efforts effectively. The findings highlight the urgent need for proactive security measures in ICS environments, reinforcing the importance of continuous monitoring and risk mitigation strategies to enhance the resilience of critical infrastructures against evolving cyber threats.