Detection of Malicious Traffic in IoMT Environment Using Intelligent XGboost Approach

Abstract

The paradigm of computing has completely altered as a result of the development of communication and Information technology. The Internet of Things (IoT) is a type of communication environment made up of web-enabled devices that can communicate, gather, assess, and send data across the network without the need for human participation. The IoT idea is widely established in numerous fields of application, and it has also been used in the medical industry. The Internet of Medical Things (IoMT) refers to the combination of IoT with medical technology. Even though IoMT applications have facilitated real time monitoring in the healthcare sector, it suffers from several security and privacy attacks. The presence of such security and privacy attacks can cause the alteration or disclosure of sensitive data or sometimes authorized users cannot access the data also. Hence, it is crucial to secure the IoMT environment from such malicious attacks. Therefore, this study aims in assessing harmful traffic in IoMT environment. In this research, XGBoost classification algorithm is proposed to classify malicious traffic in an IoMT environment. Further, the study has also implemented state-of-art machine learning algorithms such as DT (Decision Tree), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), NB (Naive Bayes), MLP (Multilayer Perceptron), SGD (Stochastic Gradient Descent) to test the efficacy of the proposed model. The experimental outcomes indicate that the proposed XGBoost algorithm outperformed other conventional machine learning approaches in terms of predicting malicious traffic in an IoMT environment, with a 100-percentage accuracy rate. Furthermore, the XGBoost algorithm exhibits better performance in terms of precision, Fl-score, recall, and AUC as compared with traditional approaches.