An anomaly detection based approach for continuous authentication with smartwatch inertial sensors
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".