BDLT-IoMT—a novel architecture: SVM machine learning for robust and secure data processing in Internet of Medical Things with blockchain cybersecurity

BDLT-IoMT—a novel architecture: SVM machine learning for robust and secure data processing in Internet of Medical Things with blockchain cybersecurity

연구 분야: Cryptography

학회: The Journal of Supercomputing

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.