An Optimal Differential Evolution Based XGB Classifier for IoMT malware classification

Abstract

In the world we live in today, massive amounts of data are transferred in a matter of seconds. Internet of Medical Things (IoMT) is a technology that enables the health parameters of patients to be collected by medical devices and transmitted through Internet to a remote server for analysis by medical experts. This highly sensitive data can be affected by malware which causes threats to human lives. In this scenario, the application of Artificial Intelligent techniques have high impact on the analysis of malignancy in the health parameters. Boosting algorithms are very efficient in the classification of data. This paper proposes an EXtreme Gradient Boosting algorithm (XGBoost) for the detection of malware present in the data. The hyperparameters of the XGB algorithm are optimised using an intelligent evolutionary technique named as Differential Evolution (DE) . The experiment is conducted on a WUSTL EHMS 2020 Dataset for Internet of Medical Things (IoMT) CyberSecurity dataset and produced an accuracy of 97.39% after hyperparameter optimisation. The DE optimised XGB Classifier performed well in the detection of malware with regard to accuracy and speed.