Risk assessment of cyber-attacks on telemetry-enabled cardiac implantable electronic devices (CIED)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cardiac implantable electronic devices (CIED) are vulnerable to radio frequency (RF) cyber-attacks. Besides, CIED communicate with medical equipment whose telemetry capabilities and IP connectivity are creating new entry points that may be used by attackers. Therefore, it remains crucial to perform a cybersecurity risk assessment of CIED and the systems they rely on to determine the gravity of threats, address the riskiest ones on a priority basis, and develop effective risk management plans. In this study, we carry out such risk assessment according to the ISO/IEC 27005 standard and the NIST SP 800-30 guide. We employed a threat-oriented analytical approach and divided the analysis into three parts, an actor-based analysis to determine the impact of the attacks, a scenario-based analysis to measure the probability of occurrence of threats, and a combined analysis to identify the riskiest attack outcomes. The results show that vulnerabilities on the RF interface of CIED represent an acceptable risk, whereas the network and Internet connectivity of the systems they rely on represent an important potential risk. Further analysis reveals that the damages of these cyber-attacks could spread further to affect manufacturers through intellectual property theft or physicians by affecting their reputation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it