Endpoint Detection and Response (EDR) in Healthcare: Mitigating Threats on Critical Devices
Bibliographic record
Abstract
Purpose: This paper aims to identify the strategies for designing, implementing, and evaluating EDR in the safety of mission-critical medical devices and workstations in healthcare environments. Methodology: The exercise involved installing EDR elements throughout a sample of health organization’s endpoints and using bots to stage select cyber threats. This way, the methodology provides controlled exposure to real-life attack scenarios to assess the detection, response time and impact on the system. Findings: Endpoint Detection and Response (EDR) solutions are gradually rising as preventive security measures in response to such new-age threats. With these characteristics, EDR programs are a more advanced form of AV tools as they provide endpoints with real-time monitoring, context-aware detection, automated action, and investigation across numerous phases. The given study depicts how EDR platforms make dwell time low, detect advanced threats in real time, and isolate the affected devices to prevent disruptions in healthcare facilities. Unique Contribution to Theory, Practice and Policy: The study pleas for the systematic integration of EDR into the healthcare cybersecurity frameworks as a cornerstone to the security of the healthcare system and the patient.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".