DeepMedShield-XAI: An Explainable Deep Learning Framework for IoMT Security with PSO for Feature Optimization

The Internet of Medical Things (IoMT) is growing quickly, which has greatly increased the cybersecurity attacks on the healthcare systems. Security techniques used for preventing attacks can improve patient safety, which is most important. As most of the datasets generated by the IoMT have high dimensions, feature selection is needed for accurate identification of data, along with deployment in real-time with limited resources. Moreover, the most influential features need to be identified for intrusion detection. Thus, this paper proposes a novel explainable hybrid framework, DeepMedShield-XAI, using the particle swarm optimization (PSO) method for feature selection and classifying the selected features using deep learning algorithms. The highest performing model among the three deep learning models is the deep neural network (DNN) with 99.68% and 99.87% test accuracy on the CICIoMT2024 and IoMT_TrafficData datasets. The findings of explainable artificial intelligence (XAI) methods reveal that the CICIoMT2024 dataset relies on connection-level features like length, protocol type, and TCP flags, while the IoMT_TrafficData dataset uses flow-based attributes like flow length, byte counts, and packet speeds without a single feature dominating across attack types. The proposed DeepMedShield-XAI framework: the results indicate that cyberattacks and unauthorized access attempts can be detected early by DeepMedShield-XAI, which can substantially improve the security of IoMT devices. This drives research on lightweight, explainable, and real-time security frameworks to protect patient data and ensure healthcare system reliability.