Endpoint Security for Healthcare Devices: Protecting Patient Data on Windows and Samsung Assets
Bibliographic record
Abstract
Purpose: This work also involves conducting an assessment and improvement of endpoint defense initiatives in healthcare facilities, especially when it comes to cybersecurity issues with Windows types of workstations and Samsung medical/mobile devices. It touches on the growing danger of cyberattacks on patient records and the healthcare system caused by digitalization in the healthcare industry. Methodology: The study presents a mixed-methods research design, where a complete vulnerability landscape, threat vectors and endpoint security products within healthcare settings will be reviewed. It invokes both simulation-based testing and empirical analysis to check the efficacy of a suggested multi-layered endpoint security architecture that is industry-specific to Windows and Samsung devices. Findings: The suggested security model that incorporates the methods of encrypted storage, biometrics authentication, partitioned networking structure, and AI-based threat persistence identification augments ransomware, phishing, data misappropriation, and insider attacks considerably. The solution is then based on complying with the most important data protection standards, such as HIPAA and GDPR and shows a significant increase in endpoint resilience in simulations and practice tests. A unique contribution to theory, practice, and policy: This study provides a new, flexible, next-generation endpoint defence model with healthcare systems in mind. It enhances cybersecurity practice proficiency by combining AI threat detection, asset-specific policies, and risk assessment conducted on a regular basis. The policy states that regulatory guidelines should be enhanced to require enhanced endpoint protection and achievement of a proactively focused cybersecurity culture at healthcare institutions.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".