Abstract: The Internet of Things (IoT) has gained significance over the past several years and is currently one of the most important technologies. The capacity to link everyday objects, such as home appliances, medical equipment, autos, and baby monitors, to the internet via embedded devices with a minimum of human interaction has made continuous communication between people, processes, and things feasible. IoT devices have established themselves in many sectors, of which electronic health is considered the most important. The IoT environment deals with many private and sensitive health data that must be kept safe from tampering or theft. If safety precautions are not implemented, these dangers and assaults against IoT devices in the health sector might completely destroy this industry. Detecting security threats to an IoT environment requires sophisticated technology; these attacks can be identified using machine learning (ML) techniques, which can also predict snooping behavior based on unidentified patterns. In this paper, it is proposed to apply five strategies to detect attacks in network traffic based on the NF-ToN-IoT dataset. The classifiers used are Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. These algorithms have been used instead of a centralized method to deliver compact security systems for IoT devices. The dataset was pre-processed to eliminate extraneous or missing data, and then a feature engineering approach was used to extract key features. The results obtained by applying each of the listed classifiers to a maximum classification accuracy of 98% achieved by the RF model showed our comparison to other work.

Haifa Khaled Alanazi, A. A. Abd El-Aziz and Hedi Hamdi, “Securing IoT Devices in e-Health using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140949

@article{Alanazi2023,
title = {Securing IoT Devices in e-Health using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140949},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140949},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Haifa Khaled Alanazi and A. A. Abd El-Aziz and Hedi Hamdi}
}