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<p>IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats.</p>

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

  • 期刊论文

  • 发表日期：

    2025年1月15日

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  • Yemineni Sowjanya,
  • S. Gopalakrishnan,
  • R. Dinesh Kumar

摘要

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats.