The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight Long Short-Term Memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.

翻译：物联网（IoT）设备在医疗领域的集成，通过实现实时监测、个性化治疗和高效数据管理，彻底改变了患者护理模式。然而，这一技术进步也带来了重大的安全风险，尤其是在敏感医疗数据的机密性、完整性和可用性方面。传统安全措施往往不足以应对物联网环境带来的独特挑战，例如异构性、资源限制以及实时处理需求。为应对这些挑战，我们提出了一个全面的三阶段安全框架，旨在增强物联网医疗系统的安全性和可靠性。在第一阶段，该框架采用基于信誉的信任评估机制评估物联网设备的可靠性，该机制结合了设备行为分析与链下数据存储，以确保可扩展性。第二阶段将区块链技术与轻量级工作量证明机制相结合，确保数据的不可篡改性、通信安全性及对未授权访问的抵抗能力。第三阶段采用轻量级长短期记忆（LSTM）模型进行异常检测与分类，实现对网络威胁的实时识别。仿真结果表明，所提框架在精确率、准确率和召回率上提升了2%，攻击检测率提高了5%，误报率降低了3%，性能优于现有方法。这些改进突显了该框架在保持可扩展性和实时性能的同时，解决关键安全问题的能力。