Medical Things Research Articles

Fast encryption of color medical videos for Internet of Medical Things

Fast encryption of color medical videos for Internet of Medical Things

A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT

AbstractInternet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

Homomorphic multi-party computation for Internet of Medical Things

Homomorphic multi-party computation for Internet of Medical Things

A DATA DRIVEN APPROACH THROUGH IOMT BASED PATIENT HEALTHCARE MONITORING SYSTEM

The current circumstances underscore the pressing need to accelerate the adoption of Internet of Things platforms in realm of the medical field to improve the health of mankind. Given the present challenges, I propose the development of an Advancing healthcare monitoring system built on the foundation of Internet of Medical Things (IOMT). Advancing patients healthcare IOMT system serves as a communication conduit between patients and doctors, facilitated by the IoMT platform. This platform utilizes various medical sensors connected to a server through technologies such as WiFi and Bluetooth. The data collected and stored in the server is then leveraged to analyse patients' health conditions, guiding subsequent treatment protocols. In this proposed system, the microcontroller serves as an intermediary interface between the sensors and the server. This cutting-edge technology enables remote patient monitoring, eliminating the need for physical presence. Within the prototype, patients' summaries and information is supplied using an online platform. Through the adoption of Advancing Healthcare through Data-Driven IOMT Patient Monitoring, lives can be safeguarded from critical conditions, underscoring the vital importance of embracing such advanced technologies in healthcare.

An enhanced and verifiable lightweight authentication protocol for securing the Internet of Medical Things (IoMT) based on CP-ABE encryption

An enhanced and verifiable lightweight authentication protocol for securing the Internet of Medical Things (IoMT) based on CP-ABE encryption

An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform

It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things

Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.

Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization

Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies.

Toward the Internet of Medical Things for Real-Time Health Monitoring Over Wi-Fi

Toward the Internet of Medical Things for Real-Time Health Monitoring Over Wi-Fi

Data Inspection and Access Control for 5G Edge Computing-Enabled Internet of Medical Things

Data Inspection and Access Control for 5G Edge Computing-Enabled Internet of Medical Things

Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity

<p>The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.</p>

Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework

Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that can help break down the barriers that prevent individuals from seeking the support they need for their mental well-being. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.

Research on Internet of Medical Things: Systematic Review, Research Trends and Challenges

Introduction: Remote data exchange operations in healthcare are observed, consulted, monitored and treated by the Internet of Medical Things (IoMT). It is an extension of the Internet of Things (IoT). Method: At the growing stage of IoT, IoMT is speedily drawing researchers’ interest due to its extensive use in healthcare systems. Smaller and lower-priced wireless devices with various communication protocols have led to the formation of IoMT. Healthcare data is exchanged through wireless communication with IoMT. The margining of IoMT and healthcare can yield multiple benefits in terms of: better quality of life, care services and developing solution/s at low cost. In this article, a systematic literature review has been conducted on IoMT. Results: Authors have thoroughly investigated the different versions of healthcare 1.0, 2.0, 3.0 and 4.0 as proposed by the healthcare industry. Furthermore, the taxonomy of IoMT has been designed and compared with existing surveys. Conclusion: This survey is unique and stands different from the point of view of existing surveys. It supports the future of IoMT researchers to bring new insight to their researches.

Secured mutual wireless communication using real and imaginary-valued artificial neuronal synchronization and attack detection

This research presents a cutting-edge security framework that integrates optimal Intrusion Detection System (IDS) and Artificial Neural Networks (ANNs)-based key exchange methods to enhance the reliability of mutual wireless communication in Internet of Medical Things (IoMT) networks. The advent of IoMT technology has brought about a significant transformation in patient care and healthcare operations. It allows for real-time monitoring and diagnosis to be conducted remotely. However, the protection of sensitive medical data has become a crucial issue that has to be addressed to maintain privacy and security. IoMT devices, which have limited processing capabilities, are especially susceptible to cyberattacks, therefore requiring the implementation of new security measures. The proposed methodology offers numerous advantages. By utilizing patient sensor data and analyzing network traffic, the suggested solution surpasses current methods in identifying and preventing network threats with increased precision. This research showcases the better effectiveness of Deep Learning (DL) models in identifying intrusions in IoMT settings, by examining 17 Machine Learning (ML) and 6 DL models. The suggested methodology relies on using a neural sequence of ANNs that consist of both real and imaginary-valued components. This approach enables reciprocal learning and synchronization, which in turn facilitates safe key distribution among IoMT devices. This novel method not only guarantees strong key exchange mechanisms but also enhances the overall security of IoMT networks. This paper presents a complete solution to solve the difficulties of protecting wireless communication in IoMT contexts. This approach performs better than comparable methods in the literature.

CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT

The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting CICIoMT2024 dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.

Multisource Data Framework for Prehospital Emergency Triage in Real-Time IoMT-Based Telemedicine Systems

Background and ObjectiveThe Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). MethodsThe study’s methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. ResultsThe RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. ConclusionsThe efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.

Enhancing Health Risk Prediction in Internet of Medical Things: Leveraging Association Rule Mining

Enhancing Health Risk Prediction in Internet of Medical Things: Leveraging Association Rule Mining