Abushark Yoosef B, Hassan Shabbir, Khan Asif Irshad
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Computer Science, Aligarh Muslim University, Aligarh 202001, Uttar Pradesh, India.
Sensors (Basel). 2025 Jan 25;25(3):731. doi: 10.3390/s25030731.
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission.
物联网（IoT）连接各种医疗设备，实现远程监控，这可以改善患者的治疗效果，并帮助医疗服务提供者为患者提供准确的诊断和更好的服务。然而，基于物联网的医疗管理系统在数据安全方面面临重大挑战，例如维护保密性、完整性和可用性（CIA）三元组以及确保数据传输安全。本文提出了一种基于灰狼优化和国际数据加密算法的新型AdaBoost支持向量机（基于ASVM的GWO-IDEA），以保护物联网支持的医疗系统中的医疗数据安全。这项工作的主要目标是防止可能的网络攻击、未经授权的访问以及对这类医疗系统安全的篡改。所提出的方案在传输医疗数据之前对其进行编码，保护其免受未经授权的访问和其他网络漏洞的影响。该方案用Python实现，并使用基于Kaggle的公共医疗数据集对其效率进行了评估。在有效安全参数（如保密率和吞吐量）的背景下，用现有策略对模型/方案的性能进行了评估。使用所建议的方法时，数据传输过程得到了改善，在数据传输期间实现了97.86%的高吞吐量、98.45%的提高的资源利用率和93.45%的高效率。
