Trustworthy Intrusion Detection in E-Healthcare Systems

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review

Background

The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision.

Purpose

This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security.

Methods

This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out.

Results

Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives.

Conclusion

The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.

Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

Ensemble deep learning for brain tumor detection

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.