Blockchain-based Ownership Management for Medical IoT (MIoT) Devices

Purpose This study aims to examine firmware vulnerabilities in medical Internet of Things (IoT) devices, focusing on their implications for healthcare policy, management and patient safety. The research aims to provide insights into developing effective mitigation strategies and enhancing regulatory frameworks to improve device security in the rapidly advancing field of medical IoT. This study underscores the critical need for identifying and addressing vulnerabilities to safeguard patient data and maintain the integrity of healthcare systems. Design/methodology/approach The research employs a qualitative multi-case analysis of firmware vulnerabilities in medical IoT devices, including insulin pumps, pacemakers and diagnostic equipment. Cases were selected based on criteria such as device diversity, severity of vulnerabilities and their impact on patient safety, as cross-referenced with the Common Vulnerabilities Exposure database. The analysis considers factors like device age, manufacturer practices, firmware update frequency, regulatory compliance and usage context to derive insights into policy and management challenges. Findings The study identifies key factors contributing to firmware vulnerabilities in medical IoT devices and examines their implications for healthcare management and policy. It provides recommendations for manufacturers and healthcare providers to enhance device security and outlines the need for robust regulatory measures. In addition, the research highlights gaps in the healthcare technology workforce and supply chain, advocating for stronger policies and management practices to mitigate risks. Originality/value This research contributes to the literature by linking technical vulnerabilities in medical IoT devices to broader policy and management concerns. The findings underscore the necessity for enhanced regulatory frameworks, proactive management strategies and industry-wide standards to ensure the security and reliability of medical IoT devices, ultimately protecting patient safety and data integrity.

The security of Internet of Medical Things (IoMT) devices is crucial for ensuring the integrity and reliability of patients’ medical data. These devices, operating over TCP and ICMP protocols, are highly susceptible to cyberattacks. While machine learning models can detect these attacks with acceptable accuracy, their operational mechanisms remain unclear, leaving the decision-making process of the models undefined. Moreover, the accuracy and training time of machine learning models is more questionable when the datasets has large number of sparse features and class imbalances. This study introduces an interpretable feature selection technique designed to enhance intrusion detection in IoMT by reducing redundant features and improving model efficiency. Random Forest-based explainable AI model provides transparency in attack classification and better decision-making. The simulated results employing the CICIoMT2024 dataset demonstrate that the proposed method significantly improves detection performance, with the Random Forest model achieving 99% accuracy, outperforming XGBoost (98%), Decision Tree (97%), and Support Vector (98%), while ensuring explainability through SHAP-based feature analysis. Thus, the simulation outcomes reveal the key contributing factors for various cyberattacks on IoMT, facilitating enhanced security measures and real-time monitoring. The proposed approach boosts detection accuracy and interpretability, making it highly suitable for real-world IoMT security applications.

The traditional healthcare systems are confronted with new issues as the number of patients keeps rising. The design of the Internet of Medical Things (IoMT) addresses this problem along with improving the healthcare system’s accuracy, dependability, productivity, and efficacy. IoMT devices are subjected to network interruption and denial of service assaults as a result of these attacks. IoMT may be viewed as an improvement and expenditure in order to respond to patient requirements more effectively and efficiently. An enormous number of medical devices are introduced into the healthcare system, which makes it a challenging task to identify traditional methods of attack. Deep learning is an approach to design models of optimal security based on actual device data. Using the N-BaIoT dataset, botnet attacks are our main focus against various IoMT devices that come in a variety of shapes and sizes and deep learning models that are based on it are constructed, for each gadget type. The findings of the experiment demonstrate that incorporating features decreases training time and model complexity while increasing bot detection rates. Our model delivers greater precision and accuracy when compared to other neural network approaches. There is a need to handle security in order to assure user confidence, encourage wide acceptance, and realize the benefits of IoMT. These security issues highlight an increasing trend of cyber-assaults on system infrastructure, as well as system inherent weaknesses inside the environment.

The rapid development of Internet of Things (IoT) technology has significantly impacted various market sectors. According to Li et al (2024), an estimated 75 billion devices will be on the market in 2025. The healthcare industry is a target to improve patient care and ease healthcare provider burdens. Chronic respiratory disease is likely to benefit from their inclusion, with 545 million people worldwide recorded to suffer in patients using these devices can track their dosage, while healthcare providers can improve medication administration and monitor respiratory health (Soriano et al, 2020). The growing prevalence of IoT devices, including intelligent inhalers like the Propeller Health System Smart Inhaler, Breather Fit, and Lookee O2 Ring, underscores the increasing importance of network connectivity and software development. While IoT medical devices offer numerous benefits, they also come with security vulnerabilities that can expose patient data to cyber-attacks. It’s crucial to prioritize security measures in developing and deploying IoT medical devices, especially in personalized health monitoring systems for individuals with respiratory conditions. Addressing the security gaps and vulnerabilities in IoT devices is essential to ensure patient data’s safety and privacy. Efforts are underway to assess the security risks associated with intelligent inhalers and respiratory medical devices by understanding usability behaviour and technological elements to identify and address vulnerabilities effectively. This work analyses usability behaviour and technical vulnerabilities, emphasizing the confidentiality of information gained from Smart Inhalers. It then extrapolates to interrogate potential vulnerabilities with Implantable Medical Devices (IMDs). Our work explores the tensions in device development through the intersection of IoT technology and respiratory health, particularly in the context of intelligent inhalers and other breathing medical devices, calling for integrating robust security measures into the development and deployment of IoT devices to safeguard patient data and ensure the secure functioning of these critical healthcare technologies.

The Internet of Medical Things (IoMT) is a collection of medical equipment and software that can help patients get better care. The purpose of this study is to improve the security of data collected through remote health monitoring of patients utilizing Constrained Application Protocol (CoAP). Asymmetric cryptography techniques may be used to assure the security of such sensor networks. For communication between different IoMT devices and a remote server, the safe CoAP is compatible with the Datagram Transport Layer Security (DTLS) protocol for creating a secure session using existing algorithms such as Lightweight Establishment of Secure Session. The DTLS layer of CoAP, in contrast, has shortcomings in key control, session establishment, and multicast message exchange. As a consequence, for IoMT communication, the creation of an efficient protocol for safe CoAP session establishment is needed. Thus, to solve the existing problems related to key management and multicast security in CoAP, we have proposed an efficient and secure communication technique to establish a secure session key between IoMT devices and distant servers using lightweight Energy-Efficient and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). The advantage of using E2SCEC2 over other identification methods such as Rivest-Shamir-Adleman (RSA) is its compact key size, which allows it to use a smaller key size. This article also compares these algorithms on parameters such as time spent generating keys, signature generation, and verification of E2SCEC2 and RSA algorithms, as well as energy consumption and radio duty cycle, to see if they are compatible in constrained environments.

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model’s accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system’s efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10).

With ubiquitous computing and penetration of high-speed data networks, the Internet of Medical Things (IoMT) has found widespread application. Digital healthcare helps medical professionals monitor patients and provide services remotely. With the increased adoption of IoMT comes an increased risk profile. Private and confidential medical data is gathered across various IoMT devices and transmitted to medical servers. Privacy breach or unauthorized access to personal medical data has far-reaching consequences. However, heterogeneity, limited computational resources, and lack of standardization in authentication schemes prevent a robust IoMT security framework. This paper introduces a secure lightweight authentication and authorization scheme. The use of the Physical Unclonable Function (PUF) reduces pressure on computational resources and establishes the authenticity of the IoMT. The use of OAuth 2.0 open standard for authorization allows interoperability between different vendors. The resilience of the model to impersonation and replay attacks is analyzed.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Security and privacy are becoming increasingly difficult to preserve in today’s fast-growing data world. We use the Internet of Medical Things (IoMT) to make quick diagnoses and get results. IoMT devices measure the human body’s functioning in various parameters and collect, process, and store data in cloud servers. There is a public channel used to collect data from humans and send it to the cloud, which is highly unreliable and vulnerable to attacks. Another risk is storing data in a single centralized system, which can be vulnerable to a single-point failure. Blockchain is being used for secure data storage in a decentralized manner to avoid single-point failure. We propose a secure IoMT-based data collection method for patients and storing the data on the blockchain in accordance with general data protection regulation (GDPR). In the proposed system, IoMT devices send data to the cloud via the patient’s personal digital assistant (PDA), and the cloud server transacts data on the blockchain. We propose a miner selection algorithm to avoid bias in the blockchain. We simulate various attacks on open channels between IoMT devices and the cloud servers. This scheme is implemented on a custom Python-based blockchain. IoT simulation is performed using the Bevywise IoT simulator and the message queuing telemetry transport (MQTT) simulator. The security protocol is analyzed using Scyther.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

Abstract: The Internet of Medical Things (IOMT) provides solutions to healthcare problems through online computers. IOMT devices are linked with cloud platforms such as Amazon web services that store and analyze data. It helps to reduce the symptoms temporarily at the starting stage of the disease. This is also known as IoT (internet of things) health-care. It also includes remote patient monitoring and tracking the patient details and patient medication orders, the location of the hospital where the patient is admitted, the patient’s wearable m-health (mobile health) devices, all this information sent to caregivers. It helps to track vital and heart performance, monitor glucose, other body systems, activity, sleeping levels. healthcare aims to create an IoT is playing a central role in healthcare industry to improve the living conditions of individuals through suitable technological solutions. Clearly such advanced systems work by processing complex data continuously produced across a variety of different scenarios, such as physical and environmental signals. Never the less, data coming from all of these sensors can easily saturate the capacity of communication networks, which makes it necessary to design proper transmission process capable of preserving the reliability of the network at cost of the lower possible leak of information. Cardiovascular Disease is a disorder of the heart and blood vessels. which includes hypertension, coronary artery disease, congenital artery disease, heart attack and heart failure, peripheral vascular disease, vascular heart disease. CVD affects a large percentage of the population. Cardiovascular Disease is an overall term of condition that will influence the heart or veins and expansion in blood clumps. For the most part, the conditions that influence the heart like Heart assault or an unexpected blockage to the heart blood or oxygen supply the manifestations that are to find CVD resemble wickedness, weakness, or cold sweats commonly the danger of CVD increments as the age that beginnings about the age of 45 where 1 out of each 100 men foster indications of coronary illness going on like this the 55 above matured individuals the danger will be multiplied around 2.1 every 100 men. The danger of CVD may be expanded by smoking, Consumption of liquor or BP by decreasing the liquor, Tobacco, or eating new products of the soil we can forestall this CVD by doing medicine to diminish the low thickness of cholesterol and it further develops bloodstream by utilizing treatment like heart restoration can be effectively treated cardiovascular illness. Iomt gadgets are wearable they assist with realizing how the heart performing. Elderly individuals neglect to take their prescription it assists them with requiring some investment they took the drug. We collected this information by reading some journals and from different websites. CVD is a type of heart or any blood vessel disease. It is mainly caused because of high BP. we can prevent this disease by avoiding smoking and alcohol. It gives information about how to prevent the disease at the starting stage. CVD are among the most common serious illness affecting human health’s may be prevented or mitigated by early diagnosis and this may reduce mortality rates.

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn’t affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems.

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for ransomware, with the healthcare sector-particularly Internet of Medical Things (IoMT) devices-emerging as a critical target. Traditional cybersecurity solutions often fail to address the unique vulnerabilities and operational constraints of eHealth IoT environments, where ransomware attacks can result in severe consequences, including patient safety risks and operational disruptions. This paper introduces a novel hybrid detection framework that combines Long Short-Term Memory (LSTM) networks with Autoencoders to identify ransomware activity in IoMT devices. By leveraging the sequential data analysis capabilities of LSTMs and the anomaly detection strengths of Autoencoders, the proposed model effectively detects subtle and sophisticated ransomware behaviors while maintaining computational efficiency suitable for real-world deployment. The approach incorporates adaptive thresholding to accommodate dynamic IoMT environments and employs continuous learning to adapt to evolving ransomware tactics. Experimental results demonstrate that the hybrid model outperforms conventional detection methods in both accuracy and sensitivity, achieving robust real-time detection with minimal false positives. These findings lay the groundwork for future research into resilient, AI-driven security solutions for industrial and medical IoT applications

Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020, there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection over multiple protocols (e.g. Bluetooth, MQTT, CoAP, ZigBEE, etc.) the interpretation, as well as the harmonization of the data format that derive from the existing huge amounts of heterogeneous IoT medical devices. In this respect, this study aims at proposing an advanced Home Gateway architecture that offers a unique data collection module, supporting direct data acquisition over multiple protocols (i.e.BLE, MQTT) and indirect data retrieval from cloud health services (i.e. GoogleFit). Moreover, the solution propose a mechanism to automatically convert the original data format, carried over BLE, in HL7 FHIR by exploiting device capabilities semantic annotation implemented by means of FHIR resource as well. The adoption of such annotation enables the dynamic plug of new sensors within the instrumented environment without the need to stop and adapt the gateway. This simplifies the dynamic devices landscape customization requested by the several telemedicine applications contexts (e.g. CVD, Diabetes) and demonstrate, for the first time, a concrete example of using the FHIR standard not only (as usual) for health resources representation and storage but also as instrument to enable seamless integration of IoT devices. The proposed solution also relies on mobile phone technology which is widely adopted aiming at reducing any obstacle for a larger adoption.

Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection over multiple protocols (e.g, Bluetooth, MQTT, CoAP, ZigBEE etc.) the interpretation, as well as the harmonization of the data format that derive from the existing huge amounts of heterogeneous IoT medical devices. In this respect this study aims at proposing an advanced Home Gateway architecture that offers a unique data collection module, supporting direct data acquisition over multiple protocols (i.e. BLE, MQTT) and indirect data retrieval from cloud health services (i.e, GoogleFit). Moreover the solution propose a mechanism to automatically convert the original data format, carried over BLE, in HL7 FHIR by exploiting device capabilities semantic annotation implemented by means of FHIR resource as well. The adoption of such annotation enables the dynamic plug of new sensors within the instrumented environment without the need to stop and adapt the gateway. This simplifies the dynamic devices landscape customization requested by the several telemedicine applications contexts (e.g. CVD, Diabetes) and demonstrate, for the first time, a concrete example of using the FHIR standard not only (as usual) for health resources representation and storage but also as instrument to enable seamless integration of IoT devices. The proposed solution also relies on mobile phone technology which is widely adopted aiming at reducing any obstacle for a larger adoption.