X-IoMT: A Cross-Layer IoMT Dataset for Anomaly Detection With FGSM- and PGD-Based Adversarial Augmentation

With the growing adoption of the Internet of Medical Things (IoMT), concerns around security, reliability, and anomaly detection across heterogeneous system layers are increasing. The essence of contemporary anomaly detection lies in Machine Learning (ML), training models on datasets for the desired functionality. Nonetheless, currently available datasets are mostly insufficiently supported with multi-layer analysis, as they usually target a single architectural layer or a narrow attack type. To close this gap, we offer X-IoMT, a publicly available dataset designed to enable the research of anomaly detection in IoMT environments. In the Internet of Things (IoT) architecture, the X-IoMT data embodies anomalies from the perception, network, and application layers. Besides, it incorporates adversarial perturbations by Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) under both L2 and L∞ norms with extra adversarial augmentation: both on detection performance under common attacks and on resilience against adversarially manipulated samples. Thus, X-IoMT could be an important tool for evaluating and benchmarking reliable anomaly detection models in IoMT scenarios with cross-layer attack coverage and extra adversarial augmentation. This paper contributes with the presentation of the dataset, inherently aiming to support the advancement of more reliable and secure IoMT systems.