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<p>The increasing adoption of the Internet of Medical Things (IoMT) has raised critical security challenges, necessitating robust encryption techniques to safeguard sensitive healthcare data. However, existing security models often suffer from high computational overhead, inefficiency in handling large-scale IoMT data and vulnerability to cyber threats. To address these challenges, this paper proposes a novel ESHA-256_GBGO security framework, integrating Enhanced Secure Hash Algorithm-256 (ESHA-256) with the Golden Butterfly Optimization (GBGO) algorithm for improved encryption and performance optimization. The proposed approach enhances data integrity, encryption efficiency and computational speed while ensuring minimal processing overhead. The framework is implemented and evaluated on a real-world IoMT dataset measuring key performance indicators such as encryption efficiency, processing time, throughput and computational overhead. Experimental results demonstrate that the proposed ESHA-256_GBGO model achieves 98.76% encryption efficiency, reduces computational overhead by 27.4% and enhances security robustness compared to conventional methods. These findings validate the effectiveness of ESHA-256_GBGO in securing IoMT networks making it a scalable and efficient solution for real-time healthcare applications.</p>

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ESHA-256_GBGO: a high-performance and optimized security framework for internet of medical thing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月20日

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  • G. Murugan,
  • M. Chinnadurai

摘要

The increasing adoption of the Internet of Medical Things (IoMT) has raised critical security challenges, necessitating robust encryption techniques to safeguard sensitive healthcare data. However, existing security models often suffer from high computational overhead, inefficiency in handling large-scale IoMT data and vulnerability to cyber threats. To address these challenges, this paper proposes a novel ESHA-256_GBGO security framework, integrating Enhanced Secure Hash Algorithm-256 (ESHA-256) with the Golden Butterfly Optimization (GBGO) algorithm for improved encryption and performance optimization. The proposed approach enhances data integrity, encryption efficiency and computational speed while ensuring minimal processing overhead. The framework is implemented and evaluated on a real-world IoMT dataset measuring key performance indicators such as encryption efficiency, processing time, throughput and computational overhead. Experimental results demonstrate that the proposed ESHA-256_GBGO model achieves 98.76% encryption efficiency, reduces computational overhead by 27.4% and enhances security robustness compared to conventional methods. These findings validate the effectiveness of ESHA-256_GBGO in securing IoMT networks making it a scalable and efficient solution for real-time healthcare applications.