Abstract
Introducing IoT systems to healthcare applications has made it possible to remotely monitor patients’ information and provide proper diagnostics whenever needed. However, providing high-security features that guarantee the correctness and confidentiality of patients’ data is a significant challenge. Any alteration to the data could affect the patients’ treatment, leading to human casualties in emergency conditions. Due to the high dimensionality and prominent dynamicity of the data involved in such systems, machine learning has the promise to provide an effective solution when it comes to intrusion detection. However, most of the available healthcare intrusion detection systems either use network flow metrics or patients’ biometric data to build their datasets. This paper aims to show that combining both network and biometric metrics as features performs better than using only one of the two types of features. We have built a real-time Enhanced Healthcare Monitoring System (EHMS) testbed that monitors the patients’ biometrics and collects network flow metrics. The monitored data is sent to a remote server for further diagnostic and treatment decisions. Man-in-the-middle cyber-attacks have been used, and a dataset of more than 16 thousand records of normal and attack healthcare data has been created. The system then applies different machine learning methods for training and testing the dataset against these attacks. Results prove that the performance has improved by 7% to 25% in some cases, and this shows the robustness of the proposed system in providing proper intrusion detection.
Highlights
Recent revolutionary advances in the construction of the Internet of Things (IoT) systems have made it possible to design healthcare monitoring systems using low power and low-cost sensors
DATA PREPROCESSING In any Machine Learning (ML) application, preprocessing the data is an essential step since the ML method results are as good as the data used
The traffic flow metrics and biometrics are first preprocessed using the following steps: 1. Splitting data into train and test datasets: To correctly measure the performance of the ML models, we split the dataset into training and testing datasets with a distribution of 80% and 20%, respectively
Summary
Recent revolutionary advances in the construction of the Internet of Things (IoT) systems have made it possible to design healthcare monitoring systems using low power and low-cost sensors. These sensors have been used widely in recent years to facilitate remote monitoring of patients, alleviating the need for the physical presence of doctors in the field. The innovation of smart decisionmaking techniques can enable early treatments resulting in favorable health outcomes and potentially saving lives in the community. To achieve such goals, continuous monitoring of the vital signs of community residents, which can be captured through wearable sensors, is required.
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