Background. Continuous behavioral authentication (keystroke dynamics, touch/swipe, motion sensors) verifies identity without extra actions. However, models degrade under device, session and activity shifts, are sensitive to noise and often require significant labeling. As passwordless logins spread, demand rises for post-login risk control and for models that are robust, compute-efficient and stable in the wild. Objective. To develop and empirically study a compact CNN-Transformer hybrid with lightweight self-supervised masked time-series autoencoding (MAE-style) for mobile behavioral biometrics on the HMOG and WISDM datasets. Methods. A 1D-CNN front end extracts local cues from smartphone motion signals, while a Transformer encoder captures longer-range dependencies. We use masked reconstruction on unlabeled HMOG sessions for self-supervised pretraining under a limited computational budget and then fine-tune the same hybrid architecture for user identification. We evaluate three hybrid variants on HMOG (trained from scratch, with masked pretraining, and with masked pretraining plus CORAL domain adaptation) and three models on WISDM (a Transformer baseline, a hybrid trained from scratch and a hybrid initialized from the HMOG-pretrained weights). Performance is measured using user-level mean and median Equal Error Rate (EER) and AUC. Results. On HMOG, the hybrid model trained from scratch achieves the best user-level metrics (EER 21.51% mean, 18.63% median; AUC 0.854 mean, 0.905 median), while the lightweight MAE and CORAL variants do not yet surpass this baseline. On WISDM, the hybrid model substantially outperforms a pure Transformer baseline (EER 9.41% vs 51.25% mean; AUC 0.902 vs 0.488 mean), and cross-dataset initialization from the HMOG MAE-pretrained weights provides an additional improvement (EER 8.42% mean, 2.07% median; AUC 0.907 mean, 0.959 median). Conclusions. The results indicate that a compact CNN-Transformer hybrid is effective for sensor-based mobile behavioral biometrics and that even lightweight masked pretraining can be helpful for cross-dataset transfer. At the same time, the benefits of MAE and CORAL on HMOG depend strongly on the pretraining budget and masking configuration, suggesting that further tuning is needed to fully exploit self-supervised pretraining in this setting.
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