Search Results (65)

In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance. Full article

Combining a large collection of patient data and advanced technology, healthcare organizations can excel in medical research and increase the quality of patient care. At the same time, health records present serious privacy and security challenges because they are confidential and can be breached through networks. Even traditional methods with federated learning are used to share data, patient information might still be at risk of interference while updating the model. This paper proposes the Privacy-Preserving Federated Learning with Homomorphic Encryption (PPFLHE) framework, which strongly supports secure cooperation in healthcare and at the same time providing symmetric privacy protection among participating institutions. Everyone in the collaboration used the same EfficientNet-B0 architecture and training conditions and keeping the model symmetrical throughout the network to achieve a balanced learning process and fairness. All the institutions used CKKS encryption symmetrically for their models to keep data concealed and stop any attempts at inference. Our federated learning process uses FedAvg on the server to symmetrically aggregate encrypted model updates and decrease any delays in our server communication. We attained a classification accuracy of 83.19% and 81.27% when using the APTOS 2019 Blindness Detection dataset and MosMedData CT scan dataset, respectively. Such findings confirm that the PPFLHE framework is generalizable among the broad range of medical imaging methods. In this way, patient data are kept secure while encouraging medical research and treatment to move forward, helping healthcare systems cooperate more effectively. Full article

AI data governance is a crucial framework for ensuring that data are utilized in the lifecycle of large language model (LLM) activity, from the development process to the end-to-end testing process, model validation, secure deployment, and operations. This requires the data to be managed responsibly, confidentially, securely, and ethically. The main objective of data governance is to implement a robust and intelligent data governance framework for LLMs, which tends to impact data quality management, the fine-tuning of model performance, biases, data privacy laws, security protocols, ethical AI practices, and regulatory compliance processes in LLMs. Effective data governance steps are important for minimizing data breach activity, enhancing data security, ensuring compliance and regulations, mitigating bias, and establishing clear policies and guidelines. This paper covers the foundation of AI data governance, key components, types of data governance, best practices, case studies, challenges, and future directions of data governance in LLMs. Additionally, we conduct a comprehensive detailed analysis of data governance and how efficient the integration of AI data governance must be for LLMs to gain a trustable approach for the end user. Finally, we provide deeper insights into the comprehensive exploration of the relevance of the data governance framework to the current landscape of LLMs in the healthcare, pharmaceutical, finance, supply chain management, and cybersecurity sectors and address the essential roles to take advantage of the approach of data governance frameworks and their effectiveness and limitations. Full article

Recently, Internet of Drones (IoD) applications have grown in various fields, including the military, healthcare, smart agriculture, and traffic monitoring. Drones are equipped with computation resources, communication units, and embedded systems that allow them to sense, collect, and deliver data in real-time through public communication channels. However, this fact introduces the risk of attack on data transmitted over unsecured public channels. Addressing several security threats is crucial to ensuring the secure operation of IoD networks. Robust authentication protocols play a vital role in establishing secure processes in the IoD environment. However, designing efficient and lightweight authentication solutions is a complex task due to the unique characteristics of the IoD and the limitations of drones in terms of their communication and computational capabilities. There is a need to review the role of authentication processes in controlling security threats in the IoD due to the increasing complexity and frequency of security breaches. This review will present the primary issues and future path directions for authentication schemes in the IoD and provide a framework for relevant existing schemes to facilitate future research into the IoD. Consequently, in this paper, we review the literature to highlight the research conducted in this area of the IoD. This study reviews several existing methods for authenticating entities in the IoD environment. Moreover, this study discusses security requirements and highlights several challenges encountered with the authentication schemes used in the IoD. The findings of this paper suggest future directions for research to consider in order for this domain to continue to evolve. Full article

The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems. Full article

The rapid integration of the Internet of Medical Things (IoMT) is transforming healthcare through real-time monitoring, AI-driven diagnostics, and remote treatment. However, the growing reliance on IoMT devices, such as robotic surgical systems, life-support equipment, and wearable health monitors, has expanded the attack surface, exposing healthcare systems to cybersecurity risks like data breaches, device manipulation, and potentially life-threatening disruptions. While 6G networks offer significant benefits for healthcare, such as ultra-low latency, extensive connectivity, and AI-native capabilities, as highlighted in the ITU 6G (IMT-2030) framework, they are expected to introduce new and potentially more severe security challenges. These advancements put critical medical systems at greater risk, highlighting the need for more robust security measures. This study leverages AI techniques to systematically identify security vulnerabilities within 6G-enabled healthcare environments. Additionally, the proposed approach strengthens AI-driven security through use of multiple XAI techniques cross-validated against each other. Drawing on the insights provided by XAI, we tailor our mitigation strategies to the ITU-defined 6G usage scenarios, with a focus on their applicability to medical IoT networks. We propose that these strategies will effectively address potential vulnerabilities and enhance the security of medical systems leveraging IoT and 6G networks. Full article

Background: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. Objective: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. Methods: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. Results: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. Conclusions: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare. Full article

Internet of Medical Things (IoMT) is a rapidly evolving field with the potential to bring significant changes to healthcare. While several surveys have examined the structure and operation of these systems, critical aspects such as interoperability, sustainability, security, runtime self-adaptation, and configurability are sometimes overlooked. Interoperability is essential for integrating data from various devices and platforms to provide a comprehensive view of a patient’s health. Sustainability addresses the environmental impact of IoMT technologies, crucial in the context of green computing. Security ensures the protection of sensitive patient data from breaches and manipulation. Runtime self-adaptation allows systems to adjust to changing patient conditions and environments. Configurability enables IoMT frameworks to monitor diverse patient conditions and manage different treatment paths. This article reviews current techniques addressing these aspects and highlights areas requiring further research. Full article

The secure and efficient storage and sharing of medical images have become increasingly important due to rising security threats and performance limitations in existing healthcare systems. Centralized systems struggle to provide adequate privacy, rapid access, and reliable storage for sensitive medical images. This paper proposes a decentralized medical image-sharing framework to address these issues by integrating blockchain technology, the InterPlanetary File System (IPFS), and edge computing. Blockchain technology enforces secure patient-centric access control through smart contracts that enable patients to directly manage their data-sharing permissions. The IPFS provides decentralized and scalable storage for medical images and effectively resolves the storage limitations associated with blockchain. Edge computing enhances system responsiveness by significantly reducing latency through local data processing to ensure timely medical image access. Robust security is ensured by using elliptic curve cryptography (ECC) for secure key management and the Advanced Encryption Standard (AES) for encrypting medical images to protect against unauthorized access and data breaches. Additionally, the system includes real-time monitoring to promptly detect and respond to unauthorized access attempts to ensure continuous protection against potential security threats. System results demonstrate that the proposed framework achieves lower latency, higher throughput, and improved security compared to traditional centralized storage solutions, which makes our system suitable for practical deployment in modern healthcare settings. Full article

The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats. Full article

Artificial intelligence (AI) has significantly driven advancement in the healthcare field by enabling the integration of highly advanced algorithms to improve diagnostics, patient surveillance, and treatment planning. Nonetheless, dependence on sensitive health data and automated decision-making exposes such systems to escalating risks of privacy breaches and is under rigorous regulatory oversight. In particular, the EU AI Act classifies AI uses pertaining to healthcare as “high-risk”, thus requiring the application of strict provisions related to transparency, safety, and privacy. This paper presents a comprehensive overview of the diverse privacy attacks that can target machine learning (ML)-based healthcare systems, including data-centric and model-centric attacks. We then propose a novel privacy-preserving architecture that integrates federated learning with secure computation protocols to minimally expose data while ensuring strong model performance. We outline an ongoing monitoring mechanism compliant with EU AI Act specifications and GDPR standards to further improve trust and compliance. We further elaborate on an independent adaptive algorithm that automatically tunes the level of cryptographic protection based on contextual factors like risk severity, computational capacity, and regulatory environment. This research aims to serve as a blueprint for designing trustworthy, high-risk AI systems in healthcare under emerging regulations by providing an in-depth review of ML-specific privacy threats and proposing a holistic technical solution. Full article

This study proposes a Zero Trust security framework for 6G-enabled Cyber-Physical Systems (CPS), integrating Adaptive Access Control (AAC), end-to-end encryption, and blockchain to enhance security, scalability, and real-time threat detection. As 6G networks facilitate massive device connectivity and low-latency communication, traditional perimeter-based security models are inadequate against evolving cyber threats such as Man-in-the-Middle (MITM) attacks, Distributed Denial-of-Service (DDoS), and data breaches. Zero Trust security eliminates implicit trust by enforcing continuous authentication, strict access control, and real-time anomaly detection to mitigate potential threats dynamically. The proposed framework leverages blockchain technology to ensure tamper-proof data integrity and decentralized authentication, preventing unauthorized modifications to CPS data. Additionally, AI-driven anomaly detection identifies suspicious behavior in real time, optimizing security response mechanisms and reducing false positives. Experimental evaluations demonstrate a 40% reduction in MITM attack success rates, 5.8% improvement in authentication efficiency, and 63.5% lower latency compared to traditional security methods. The framework also achieves high scalability and energy efficiency, maintaining consistent throughput and response times across large-scale CPS deployments. These findings underscore the transformative potential of Zero Trust security in 6G-enabled CPS, particularly in mission-critical applications such as healthcare, smart infrastructure, and industrial automation. By integrating blockchain-based authentication, AI-powered threat detection, and adaptive access control, this research presents a scalable and resource-efficient solution for securing next-generation CPS architectures. Future work will explore quantum-safe cryptography and federated learning to further enhance security, ensuring long-term resilience in highly dynamic network environments. Full article

This comprehensive study provides an in-depth examination of the Internet of Things (IoT), which refers to the interconnection of multiple devices through various wireless protocols that facilitate data transfer and improve operational intelligence. IoT is widely used in numerous fields, including urban infrastructure, domestic settings, transportation systems, military operations, healthcare, and agriculture. However, with its growing prevalence comes a significant increase in security risks across multiple layers, such as hardware, software, cloud infrastructure, and networks. This study categorizes these vulnerabilities and explores how adversaries can exploit weaknesses to compromise IoT systems. In doing so, it highlights the risks associated with unauthorized access, data breaches, and system manipulation, all of which pose a direct threat to confidentiality, integrity, and availability. To address these concerns, this paper examines various mitigation strategies that aim to enhance IoT security by reducing attack surfaces, improving authentication methods, and securing communication protocols. By systematically analyzing existing vulnerabilities and countermeasures, this research contributes to the ongoing effort to fortify IoT devices and infrastructure against current and emerging threats. Through this study, we seek to advance the discussion on securing IoT environments while emphasizing the importance of proactive security measures in this rapidly evolving landscape. Full article

Background/Objectives: Brain tumors are among the most aggressive diseases, significantly contributing to human mortality. Typically, the classification of brain tumors is performed through a biopsy, which is often delayed until brain surgery is necessary. An automated image classification technique is crucial for accelerating diagnosis, reducing the need for invasive procedures and minimizing the risk of manual diagnostic errors being made by radiologists. Additionally, the security of sensitive MRI images remains a major concern, with robust encryption methods required to protect patient data from unauthorized access and breaches in Medical Internet of Things (MIoT) systems. Methods: This study proposes a secure and automated MRI image classification system that integrates chaotic and Arnold encryption techniques with hybrid deep learning models using VGG16 and a deep neural network (DNN). The methodology ensures MRI image confidentiality while enabling the accurate classification of brain tumors and not compromising performance. Results: The proposed system demonstrated a high classification performance under both encryption scenarios. For chaotic encryption, it achieved an accuracy of 93.75%, precision of 94.38%, recall of 93.75%, and an F-score of 93.67%. For Arnold encryption, the model attained an accuracy of 94.1%, precision of 96.9%, recall of 94.1%, and an F-score of 96.6%. These results indicate that encrypted images can still be effectively classified, ensuring both security and diagnostic accuracy. Conclusions: The proposed hybrid deep learning approach provides a secure, accurate, and efficient solution for brain tumor detection in MIoT-based healthcare applications. By encrypting MRI images before classification, the system ensures patient data confidentiality while maintaining high diagnostic performance. This approach can empower radiologists and healthcare professionals worldwide, enabling early and secure brain tumor diagnosis without the need for invasive procedures. Full article

The COVID-19 pandemic exposed critical vulnerabilities in global healthcare systems, particularly in data security and interoperability. This paper introduces the blockHealthSecure Framework, which integrates blockchain technology with advanced cybersecurity measures to address these weaknesses and build resilient post-pandemic healthcare systems. Blockchain’s decentralized and immutable architecture enhances the accuracy, transparency, and protection of electronic medical records (EMRs) and sensitive healthcare data. Additionally, it facilitates seamless and secure data sharing among healthcare providers, addressing long-standing interoperability challenges. This study explores the challenges and benefits of blockchain integration in healthcare, with a focus on regulatory and ethical considerations such as HIPAA and GDPR compliance. Key contributions include detailed case studies and examples that demonstrate blockchain’s ability to mitigate risks like ransomware, insider threats, and data breaches. This framework’s design leverages smart contracts, cryptographic hashing, and zero-trust architecture to ensure secure data management and proactive threat mitigation. The findings emphasize the framework’s potential to enhance data security, improve system adaptability, and support regulatory compliance in the face of evolving healthcare challenges. By bridging existing gaps in healthcare cybersecurity, the blockHealthSecure Framework offers a scalable, future-proof solution for safeguarding health outcomes and preparing for global health crises. Full article