Cyberattacks Detection in IoMT using Machine Learning Techniques

Abstract

Information and Communication Technology (ICT) has changed the computing paradigm. Various new channels for communication are created through these developments, and the Internet of Things (IoT) is one of them. Internet of Medical Things (IoMT) is a part of IoT in which medical devices are connected through a network. IoMT has resolved many traditional health-related problems and has some security concerns. This article uses three Machine Learning algorithms, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), to detect cyberattacks. Machine Learning models are best for performing cyberattack detection. Proposed Machine Learning models are evaluated on the WUSTL EHMS 2020 dataset, which consists of main in-themiddle, data injection, and spoofing attacks. The evaluation of the result analysis shows that the proposed Machine Learning models outperformed existing techniques.