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Abstract The Internet of Things (IoT) refers to the latest iteration of the Internet, enabling communication and interaction among interconnected items. It is thriving and permeating every aspect of our life, including school, home, automobiles, and healthcare. IoT serves as a valuable aid in healthcare and plays a crucial role in a wide range of applications for monitoring medical services. Various wireless body area network devices and sensors offer real-time health monitoring services. The data produced by sensor-based devices requires secrecy, reliability, and end-to-end security in order to provide secure transmission across public networks. The implementation of IoT in healthcare will pose significant risks if patient information is not securely managed during transmission across unsecured networks or while stored by administrators. Recent research introduced an inflatable and anonymity-preserving user identification mechanism for IoT-based healthcare. Nonetheless, this technique was susceptible to certain attacks. These attacks compromise both the patients' medical data and their personal information. This study presents a security framework designed for real-time health monitoring systems with the objective of guaranteeing data secrecy, reliability, and security. This paper proposes AEAD (authentication, encryption and anomaly detection) approach for secure IoT healthcare monitoring. The authentication protocol is proposed based on the hash and XoR with biometric cryptographic operation. The medical data is encrypted using XoR based encoding scheme. Next an efficient machine learning based algorithm is used to detect the anomaly medical data. Different evaluation metrics (computational cost, encryption time, decryption time, and accuracy) are used to analyze the performance of the AEAD approach. The practical tests and analysis have demonstrated the practicality and efficacy of the monitoring system. The results indicate that it is capable of efficiently analyzing large amounts of medical data and effectively predicting and mitigating security risks in IoT systems.
Song et al. (Wed,) studied this question.