This deposit is a machine-learning-ready, tabular release of unified Internet of Medical Things (IoMT) network traffic. It combines two public PCAP-derived benchmarks—CICIoMT2024 and IoMT-TrafficData—into one aligned window-level feature table produced with MIOTTA-NPT (W = 100 packets per row). Each record contains 58 numeric window statistics plus seven provenance/label fields. Labels are provided at three harmonised granularities: binary (Benign vs. Attack), six classes (Benign, DDoS, DoS, MQTT, Reconnaissance, Spoofing; MQTT-related attack names are collapsed into a single MQTT category), and 26 fine-grained sub-types. Rows retain source provenance (dataset, file, original attack metadata). The table is split into stratified train/validation/test files (70/15/15 at the six-class level; 8,011,534 rows in total). Feature values in the CSVs are z-scores from a StandardScaler fitted on the training split only. For exact downstream preprocessing and external evaluation on new PCAPs processed through MIOTTA-NPT, the publication bundles scaler.joblib (fitted StandardScaler) and metadata.joblib (feature column order, split row counts, harmonisation scheme tag, near-zero-variance QA list) from the same export run as the CSVs. Without the saved scaler, consumers can still train or analyse models on the provided CSVs as-is, but cannot faithfully re-apply the identical scaling transform to newly extracted raw feature matrices.

Other funding agency: INCIBE CARISMATICA Chair of Cybersecurity (partial support)