IoMT-TrafficData — Internet of Medical Things IDS Benchmark [2.7 GB]
Catalog Summary
"Internet of Medical Things IDS dataset with BLE, IP packet, and IP flow captures. CSV and pickle formats, 2.7 GB, for ML-based healthcare IoT intrusion detection."
Catalog Notes
Overview
IoMT-TrafficData is a public dataset for benchmarking intrusion detection in Internet of Medical Things environments. It was created to support machine-learning models that improve the security of medical devices and protect patient data in IoT and IoMT networks.
The dataset was produced from a scenario using IoT and IoMT devices to simulate real-world attacks. The Zenodo record provides both capture data and processed datasets, allowing researchers to work at multiple analysis levels.
The ZIP package is organised into captures and datasets. Captures are grouped by BLE and IP-based analysis, while datasets are grouped into BLE, IP-based packet, and IP-based flow datasets. Data is provided in CSV and pickle formats.
Column Schema
| Column | Description |
|---|---|
| btle.advertising_header | BLE advertising packet header. |
| btle.advertising_header.length | Length field in the BLE advertising header. |
| btle.crc.incorrect | Indicator for incorrect BLE CRC values. |
| frame.cap_len | Frame length stored in the capture file. |
| frame.len | Frame length on the wire. |
| fwd_pkts_per_sec | Forward packets per second in IP-based flow features. |
| bwd_pkts_per_sec | Backward packets per second in IP-based flow features. |
| flow_pkts_per_sec | Total packets per second for the flow. |
| fwd_header_size | Forward header bytes. |
| bwd_header_size | Backward header bytes. |
| active | Flow active duration. |
Key Statistics
- Total Records: Multiple BLE, IP-based packet, and IP-based flow datasets
- Features: BLE, packet-level, and flow-level feature sets
- File Format: CSV, pickle, packet captures
- File Size: 2.7 GB
- Time Period: Published 2023; associated article DOI published in 2024
Use Cases
- Healthcare IoT intrusion detection
- BLE and IP-based anomaly detection
- Packet-level versus flow-level IDS benchmarking
- Machine-learning security evaluation for medical devices
Source & Attribution
Created by José Areia, Ivo Afonso Bispo, Leonel Santos, and Rogério Luís Costa at Politécnico de Leiria. The dataset is published on Zenodo with DOI 10.5281/zenodo.8116338 and the associated article is 'IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things', DOI 10.1109/ACCESS.2024.3437214.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Zenodo.
Preview on ZenodoCite This Dataset
Areia, J., Bispo, I. A., Santos, L., & Costa, R. L. (2023). IoMT-TrafficData: A Dataset for Benchmarking Intrusion Detection in IoMT. [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.8116338
Source metadata: Zenodo (2023) · DOI: 10.5281/zenodo.8116338
Indexed by IoTDataset.com on Jun 02, 2026
Review the Source Record
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