An anomaly detection based approach for continuous authentication with smartwatch inertial sensors
Why this work is in the frame
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Bibliographic record
Abstract
Conventional authentication methods protect unattended devices when they are logged out; however, logged-in devices left unattended are vulnerable to unauthorized access. Inactivity timeouts help mitigate this threat; however, long timeouts increase susceptibility to attack, whereas short timeouts hurt usability. In contrast, continuous authentication mitigates this threat by continuously and non-intrusively verifying whether a device is being used by the user who initially logged in. If verified, the user remains logged in; otherwise, the user is logged out. We design and evaluate a comprehensive data processing pipeline for smartwatch-based continuous authentication using inertial sensor data. We use a Siamese convolutional neural network to learn and extract discriminative features, and one-class classifiers to determine if a user is the account owner. We compare our learned features with handpicked features proposed in prior work; we show that our learned features achieve better equal-error rates (EER) compared to the handpicked features, particularly for shorter-duration time-series windows. We find that learned features are a promising approach to more quickly and accurately detect unauthorized use of devices. This work thus contributes to making smartwatch-based continuous authentication more secure and usable.
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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 | 0.000 |
| Open science | 0.001 | 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