An Intrusion Detection Model for Internet of Medical Things Using BDA-DAN2 Model

The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) where medical devices communicate with one another to share sensitive data. The integration of medical devices into the IoT has greatly assisted the development of the IoMT. These advancements facilitate effective communication and providing care for patients in the healthcare sector. However, they also face specific security and privacy concerns, such as malware attacks and denial of service (DoS) attacks. To overcome this problem, intrusion detection systems (IDS) are introduced, specifically employing deep learning (DL) methodologies. This study proposes a deep learning-based binary dragonfly algorithm (BDA) with a dynamic architecture for artificial neural networks2 (DAN2) model for implementing a robust and accurate IDS in IoMT. The IDS has the following stages: collection of data, preprocessing, selection of features, and classification. The IoMT dataset is employed to train the model to get improved outcomes. The standard scalar technique is used for the data preprocessing process. The BDA algorithm is used for feature selection (FS) of the preprocessed data. The DAN2 model is implemented to classify the selected data and to improve the classification accuracy. The dataset was further divided for training and testing of the model. The performance of the BDA-DAN2 model is assessed utilizing the evaluation parameters of accuracy, recall, precision, and F1-score. The BDA-DAN2 model demonstrates superior performance with 99.12% accuracy, 99.28% precision, 99.40% recall, and 98.56% F1-score during training, and 98.92% accuracy, 98.50% precision, 98.68% recall, and 97.90% F1-score during testing. Experiments confirmed that the binary dragonfly algorithm with the DAN2 (BDA-DAN2) model has the highest accuracy compared to the existing models.