Deep Learning-Assisted Security and Privacy Provisioning in the Internet of Medical Things Systems: A Survey on Recent Advances
Bibliographic record
Abstract
Internet of Medical Things (IoMT) are a kind of Internet of Things (IoT) systems which are used in the healthcare domain. Nowadays, there are an abundance of wearable smart devices, either commercial or clinical, which can be used to collect vital signs and transmit the collected data to remote servers for further analysis. Remote patient monitoring, smart diagnostics, and autonomous control of chronic diseases are examples of different healthcare services that can be provided by these systems at a lower cost and higher efficiency compared to traditional healthcare settings. However, as data related to patients’ health status and treatment history, transmitted in these systems, are highly confidential and private, security and privacy concerns in their widespread adoption may arise. Deap Learning (DL) algorithms with their ability in extracting knowledge from big data generated in these systems can be leveraged to design smart security mechanisms. In this survey study, the recent literature on the DL-assisted security and privacy provisioning frameworks in IoMT systems are categorized and summarized with respect to their main contributions. Finally, some possible future directions are introduced to assist interested researchers to continue research in this domain.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".