Soft Voting for Anomaly Detection in Internet of Medical Things

In this paper, we propose an approach for anomaly detection in Internet of Medical Things based on the soft voting between the most accurate machine learning algorithms. We compare 12 machines learning and 5 deep learning algorithms for anomaly detection to identify the top 3. We apply these algorithms over public annotated dataset with network and physiological parameters. The soft voting predicts the anomaly based on the predicted probability by each individual model from the selected 3. We also compare the performance of the 17 algorithms before and after dimensionality reduction using two different techniques, and we found that soft voting using CatBoost, XGBoost and LightGBM outperforms hard voting and other algorithms, where it achieves a detection accuracy of 97.45% and a false alarm rate of 2%.