Artificial Intelligence for Anomaly Detection in IoMTs
Mickael Mohammed, Osman Salem, Ahmed Mehaoua · 2023
The exponential development and widespread emergence of the Internet of Medical Things (IoMT) have led to a growing need for effective anomaly detection techniques to ensure the reliability and security of healthcare systems. This article provides a review of existing machine learning and deep learning algorithms for anomaly detection in IoMT, followed by the presentation of a novel approach combining ARIMA for predicting health parameter values and a decision tree for anomaly detection. This hybrid approach aims to improve the accuracy and efficiency of anomaly detection in IoMT by leveraging both time series models and the discriminative features of decision trees. The preliminary results of this approach are presented and discussed, highlighting its potential to enhance early detection of anomalies in IoMT and contribute to safer and more reliable healthcare.