CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT
Bibliographic record
Abstract
The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting CICIoMT2024 dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".