Continuous authentication by electrocardiogram data
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".