Detecting Ransomware within Real Healthcare Medical Records Adopting Internet of Medical Things using Machine and Deep Learning Techniques

Abstract

The Internet of Medical Things was immensely implemented in healthcare systems during the covid 19 pandemic to enhance the patient's circumstances remotely in critical care units while keeping the medical staff safe from being infected. However, Healthcare systems were severely affected by ransomware attacks that may override data or lock systems from caregivers' access. In this work, after obtaining the required approval, we have got a real medical dataset from actual critical care units. For the sake of research, a portion of data was used, transformed, and manifested using laboratory-made payload ransomware and successfully labeled. The detection mechanism adopted supervised machine learning techniques of K Nearest Neighbor, Support Vector Machine, Decision Trees, Random Forest, and Logistic Regression in contrast with deep learning technique of Artificial Neural Network. The methods of KNN, SVM, and DT successfully detected ransomware's signature with an accuracy of 100%. However, ANN detected the signature with an accuracy of 99.9%. The results of this work were validated using precision, recall, and f1 score metrics.