The integration of Internet of Medical Devices (IoMD) in healthcare 4.0 enables real-time patient monitoring but introduces severe cybersecurity risks across sensor, network, and application layers. Traditional security models fail to address multi-layer threats such as device tampering, Man-in-the-Middle (MITM), Denial-of-Service (DoS), Eavesdropping, and insider attacks in a unified manner. This work proposes a blockchain (BC)-based IoMD security framework leveraging AES-256 encryption for confidentiality, SHA-256 hashing for integrity, and Practical Byzantine Fault Tolerance (PBFT) consensus for decentralized trust. The framework incorporates cryptographic firmware verification, BC-based device fingerprinting, payload hashing, and smart contract-based access control. The experiments demonstrate that the proposed system improves security effectiveness by up to 96% at the sensor layer, reduces unauthorized access by 93%, and mitigates MITM, DoS, and Eavesdropping attacks by 88-94%. Latency remains within real-time bounds (115-190 ms), while throughput scales up to 155 TPS. Encryption performance analysis confirms AES-256 as the most reliable option with a 99% success rate, despite moderate latency overhead. Comparative benchmarking reveals that the proposed system outperforms PoW and PoS-based models by achieving 60.3% lower gas costs, 35-42% lower latency, and broader attack coverage (covering five threat categories). These results validate the proposed framework’s effectiveness, scalability, and resilience for secure, decentralized, and performance-efficient IoMD deployments.

Mobile-edge computing (MEC) is a new architecture that provides services to the edge of networks. The software and hardware platforms are positioned at the network edge close to end-users. Emerging developments in MEC can be used for healthcare applications, such as remote patient monitoring, diagnosis, and treatment purposes. The remote access to the data can arise security and privacy issues. Unauthorized access or data leakage can hamper the complete security of the system. This makes the system inconvenient, untrusted, less suitable, and vulnerable. This article is aimed to propose a security framework for the privacy preservation of patient data in a MEC environment where the services are accessed at the network edge. A lightweight cryptographic technique is proposed by including a chaotic map and a DNA sequence of organisms for encryption of electronic health records (EHRs). The identity privacy will be maintained by using anonymous authentication. The framework’s performance is evaluated with memory usage and encryption time and found satisfactory results.

The images generated by smart cameras and sensors in an Industrial Internet of Things (IIoT) ecosystem are at significant risk when transmitted over a public network due to the dynamicity and open nature of the IIoT environment. Encryption is a viable method for safeguarding IIoT digital images. This article addresses an image security framework using chaotic maps and DNA cryptography. The proposed algorithm uses a tent, circle, Chebyshev, and 3-D logistic map in a multilevel fashion to generate three keys. These keys are used for row-column rotation of subblocks, determine the rule to perform DNA encoding–decoding on the subblocks, and generate a key image on which DNA XOR operation is performed to get the encrypted image. The proposed scheme’s result analysis illustrates that the average NPCR (99.6566%), UACI (33.4588%), Entropy (7.9971), and larger key-space of$ 10^{195}$are better than the existing schemes and are resistant to different attacks.

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.

In the contemporary digital era, access control is one of the security issues in the modern Electronic Healthcare System (EHS). In particular, the E-Health Cloud (EHC) needed a secure and reliable access control to access the EHC resources. In the past, several attempts have been made to provide secure and reliable access control (AC) to the EHS. But, due to lack of trust and dynamic nature of EHC, the model will suffer from different types of attacks and threats. The provided Access Control Models (ACMs) does not provide complete security to EHS. So, in this paper, we have proposed a Secure Access Control Model (SACM) for E-Health Cloud. The proposed model dynamically calculates the trust degree of the users based on their behavior. The computed trust degree will be used for adjusting the access view of the user. The access view is controlled with the help of access control rule set. We also verified our proposed rule set by using CPN Tools.