Endpoint Security for Healthcare Devices: Protecting Patient Data on Windows and Samsung Assets
Bibliographic record
Abstract
Healthcare institutions in the digital age actively implement advanced technologies together with connected devices to achieve better healthcare services and operational process improvements.The transition to advanced healthcare technologies increases cybersecurity difficulties, especially with regard to endpoint defense systems.The Windows-based workstations and Samsung medical and mobile devices that serve as endpoint devices face rising attacks from cyber threats that attempt to access patient records.This research paper examines endpoint defense strategies that target healthcare facilities with special attention to Samsung devices and Windows operating systems.The paper examines healthcare system-specific vulnerabilities and then addresses threat pathways while reviewing present solutions before providing an all-encompassing endpoint security approach.The framework implements safe device parameters, encrypted data storage, biometric security features, and partitioned network infrastructure that combines with persistent threat detection protocols.The security framework meets compliance requirements, including HIPAA and GDPR standards, to protect health-related patient information.Our approach combines security layers with AI threat detection, mobile/fixed asset-specific policies, and scheduled risk evaluation.Our framework substantially improves endpoint resilience against ransomware, phishing attacks, data exfiltration, and insider threats through simulation and empirical tests.The research paper concludes with remarks on upcoming study guidelines and the necessity of developing an active security mindset within healthcare.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".