Towards Explainable AI in Continuous Smartphone Authentication: Leveraging CNN, BiLSTM, and Attention Techniques
Why this work is in the frame
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Bibliographic record
Abstract
Smartphones contain a significant amount of critical user information. Without proper security measures, this information can be at a risk of privacy leakage from unattended devices. To address this issue, we propose a deep learning-based continuous authentication mechanism using a combination of CNN, BiLSTM and attention. In the proposed continuous authentication model, the CNNs effectively learn spatial features from raw sensor data, the BiLSTMs effectively capture the temporal features of user behaviour patterns while the attention mechanism underscores important features. The proposed model achieves 98.8% accuracy and a 3.7% Equal Error Rate (EER) on the Extrasensory dataset, outperforming existing state-of-the-art continuous authentication models. To further enhance model transparency, we apply Local Interpretable Model-agnostic Explanations (LIME) within the Explainable AI (XAI) framework. LIME emphasizes the features that most influence each authentication decision. This interpretability not only builds trust in the proposed model but also supports better understanding of how specific user behaviors contribute to authentication outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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