Continuous authentication by electrocardiogram data
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
Authentication is the process of verifying the claimed identity of a user. Traditional authentication systems suffer from vulnerabilities that can break the security of the system. An example of such vulnerabilities is Replay Attack: An attacker can use a pre-saved password or an authentication credential to log into the system. Another issue with existing authentication systems is that the authentication process is done only at the beginning of a session: once the user is authenticated in the system, her identity is assumed to remain the same during the lifetime of the session. In real world, an attacker can masquerade as a legitimate user by physically controlling an authenticated machine. Therefore, there is a need to continuously monitor the user to determine if the user who is using the computer is the same person that logged onto the system. In this paper, we present a framework for continuous authentication of the user based on the electrocardiogram data collected from the user's heart signal. The electrocardiogram (ECG) data is used as a soft biometric to continuously authenticate the identity of the user; Experimental results demonstrate that electrocardiogram biometric trait can guarantee the safety of the system from illegal access.
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 | 0.000 | 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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