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  • Open Access

    ARTICLE

    Attribute-Based Encryption for Secure Access Control in Personal Health Records

    Dakshnamoorthy Manivannan*

    Computer Systems Science and Engineering, Vol.49, pp. 533-555, 2025, DOI:10.32604/csse.2025.072267 - 08 December 2025

    Abstract Attribute-based Encryption (ABE) enhances the confidentiality of Electronic Health Records (EHR) (also known as Personal Health Records (PHR)) by binding access rights not to individual identities, but to user attribute sets such as roles, specialties, or certifications. This data-centric cryptographic paradigm enables highly fine-grained, policy-driven access control, minimizing the need for identity management and supporting scalable multi-user scenarios. This paper presents a comprehensive and critical survey of ABE schemes developed specifically for EHR/PHR systems over the past decade. It explores the evolution of these schemes, analyzing their design principles, strengths, limitations, and the level of More >

  • Open Access

    ARTICLE

    Future-Proofing CIA Triad with Authentication for Healthcare: Integrating Hybrid Architecture of ML & DL with IDPS for Robust IoMT Security

    Saad Awadh Alanazi1, Fahad Ahmad2,3,*

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025

    Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >

  • Open Access

    REVIEW

    Intrusion Detection in Internet of Medical Things Using Digital Twins—A Review

    Tony Thomas*, Ravi Prakash, Soumya Pal

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

    Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

  • Open Access

    ARTICLE

    SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things

    Mourad Benmalek1,*,#,*, Abdessamed Seddiki2,#, Kamel-Dine Haouam1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1157-1184, 2025, DOI:10.32604/cmes.2025.062841 - 11 April 2025

    Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >

  • Open Access

    ARTICLE

    LSBSP: A Lightweight Sharding Method of Blockchain Based on State Pruning for Efficient Data Sharing in IoMT

    Guoqiong Liao1,3, Yinxiang Lei1,2,*, Yufang Xie1, Neal N. Xiong4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3309-3335, 2025, DOI:10.32604/cmc.2024.060077 - 17 February 2025

    Abstract As the Internet of Medical Things (IoMT) continues to expand, smart health-monitoring devices generate vast amounts of valuable data while simultaneously raising critical security and privacy challenges. Blockchain technology presents a promising avenue to address these concerns due to its inherent decentralization and security features. However, scalability remains a persistent hurdle, particularly for IoMT applications that involve large-scale networks and resource-constrained devices. This paper introduces a novel lightweight sharding method tailored to the unique demands of IoMT data sharing. Our approach enhances state bootstrapping efficiency and reduces operational overhead by utilizing a dual-chain structure comprising… More >

  • Open Access

    ARTICLE

    Transforming Healthcare: AI-NLP Fusion Framework for Precision Decision-Making and Personalized Care Optimization in the Era of IoMT

    Soha Rawas1, Cerine Tafran1, Duaa AlSaeed2, Nadia Al-Ghreimil2,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4575-4601, 2024, DOI:10.32604/cmc.2024.055307 - 19 December 2024

    Abstract In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the… More >

  • Open Access

    ARTICLE

    Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

    Attiya Khan1, Muhammad Rizwan2, Ovidiu Bagdasar2,3, Abdulatif Alabdulatif4,*, Sulaiman Alamro4, Abdullah Alnajim5

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2121-2141, 2024, DOI:10.32604/cmes.2024.054380 - 31 October 2024

    Abstract The Internet of Medical Things (IoMT) is an emerging technology that combines the Internet of Things (IoT) into the healthcare sector, which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs. As IoMT devices become more scalable, Smart Healthcare Systems (SHS) have become increasingly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security. An IDS monitors systems or networks for suspicious activities or potential threats, safeguarding internal networks. This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets. We propose More >

  • Open Access

    REVIEW

    IoMT-Based Healthcare Systems: A Review

    Tahir Abbas1,*, Ali Haider Khan2, Khadija Kanwal3, Ali Daud4,*, Muhammad Irfan5, Amal Bukhari6, Riad Alharbey6

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 871-895, 2024, DOI:10.32604/csse.2024.049026 - 17 July 2024

    Abstract The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The More >

  • Open Access

    ARTICLE

    Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

    Jingjing Nie1,*, Zhenhua Chen2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4763-4779, 2024, DOI:10.32604/cmc.2024.051181 - 20 June 2024

    Abstract The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold… More >

  • Open Access

    ARTICLE

    Intrusion Detection and Prevention Model for Blockchain Based IoMT Applications

    Jameel Almalki*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 131-152, 2024, DOI:10.32604/csse.2023.038085 - 26 January 2024

    Abstract The recent global pandemic has resulted in growth in the medical and healthcare sectors. Applications used in these domains have become more advanced and digitally integrated. Sensor-based Internet of Things (IoT) devices are increasing in healthcare and medical units. The emerging trend with the use of IoT devices in medical healthcare is termed as Internet of Medical Things (IoMT). The instruments used in these healthcare units comprise various sensors that can record patient body observations. These recorded observations are streamed across Internet-based channels to be stored and analyzed in centralized servers. Patient diagnostics are performed… More >

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