SafetilBERT: an efficient and explained LLM for IoMT attacks classification
Bibliographic record
Abstract
The rapid adoption of Internet of Medical Things (IoMT) devices has improved patient care but introduces vulnerabilities and exposure to cyber threats, particularly denial-of-service (DoS) attacks. This paper explores the potential of large language models (LLMs) for detecting and classifying IoMT network attacks, emphasizing explainability techniques to address the black-box nature of these models. Using the CICIoMT2024 dataset, we introduce SafetilBERT, a fine-tuned DistilBERT model specialized in IoMT cybersecurity. SafetilBERT achieves state-of-the-art performance scoring $96.94 \%$, significantly outperforming BERT and RoBERTa, particularly in DoS detection. Explainability methods such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive explanations (SHAP), and attention visualization were used to interpret key features influencing model predictions. Our findings show that SafetilBERT is efficient and adaptable to network data, particularly from packet capture files (PCAP). Furthermore, its interpretability paves the way for robust IoMT cybersecurity solutions applicable in real-world scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".