Continuous Behavioral Authentication via Multi-Expert BERT Log Analysis for Secure Data Sharing

Abstract:Continuous authentication for mobile and zero-trust systems requires nonintrusive evidence confirming the enrolled user-device context remains valid after initial login. This paper presents a BERT log analysis framework for continuous behavioral authentication using Android system logs. The proposed pipeline parses logcat streams into event templates and dynamic variables, pre-trains a domain-adapted BERT encoder on Android log syntax, and fine-tunes three expert models for network/device identity, battery-transition timing, and Wi-Fi topology. The expert confidence scores are fused through a log-space transformation and a 5-nearest-neighbor distance classifier to generate a normality score that is provided to a Policy Decision Point (PDP) for risk-aware access control. Experiments on normal traces, controlled anomaly injections, and benign Wi-Fi perturbations indicate that multi-expert BERT log analysis can detect semantic, battery-timing, and topology deviations in the evaluated setting while maintaining sub-1% False Positive Rate (FPR). The results suggest that Android system logs are a practical sensor-free signal for continuous authentication and user-device context assurance.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2606.21900 [cs.CR]

(or arXiv:2606.21900v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.21900 Focus to learn more arXiv-issued DOI via DataCite

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