Security of Broadcast Authentication for Cloud-Enabled Wireless Medical Sensor Devices in 5G Networks
Bibliographic record
Abstract
Wireless Body Area Network (WBAN) has become one of the fastest growing technologies nowadays. There are some characteristic limitations in WBAN, especially when it comes to health-related applications that are used to monitor human bodies. To overcome and mitigate theses limitations in WBAN, cloud computing technology can be combined with the WBAN as a solution. We can classify the WBAN sensors in the cloud-based WBAN into i) nodes that monitor the human body and ii) WBAN actuators that take action upon the order commands from the medical staff. The biggest concern is the security of the medical commands to the WBAN actuators because if they are altered or tampered with, there can be serious consequences. Therefore, authentication plays an important role in securing cloud-based WBANs. In this article, we explore the security and privacy issues of Wireless Body Area Network combined with Mobile Cloud Computing (wMCC) with 5G mobile networks and investigate public-key based security solutions. At first, the paper presents a detailed description of wMCC architecture, discussing its main advantages and limitations. The main features of 5G mobile network are then presented, focusing on the advancement it may provide if integrated with wMCC systems. We further investigate the security issues of wMCC with 5G mobile networks while emphasizing the challenges that face this system in healthcare applications. The authentication techniques in wMCC are then classified and discussed with the feasibility of deploying practical solutions. Finally, we outline the main challenges and metrics of an ideal authentication protocols to be used in wMCC with 5G. The metrics are helpful for researchers in this field to evaluate, analyze, and compare the authentication protocols to decide the suitable application for each protocol.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".