From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
For behavioral sensing researchers, this work establishes encrypted network traffic as a viable passive sensing modality for longitudinal behavioral monitoring, though the findings are incremental as they apply existing methods to a new data modality.
This paper demonstrates that encrypted smartphone network traffic can passively capture behavioral patterns related to sleep, stress, and loneliness, using a transformer model with per-user adapters and sparse autoencoders to extract interpretable features. The results show that stress is linked to stable between-person differences, loneliness to within-person variation, and sleep disturbance to both, with learned representations capturing dynamics not visible in predefined features.
Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartphone network traffic -- a ubiquitous, always-on, passive sensing modality -- can passively capture behavioral patterns related to sleep, stress, and loneliness. We model shared behavioral structure using a transformer backbone with per-user adapters, allowing the model to represent both typical individual behavior and deviations from it. To make these representations interpretable, we apply a sparse autoencoder to extract behavioral features corresponding to distinct patterns of activity. We relate these features to sleep disturbance, stress, and loneliness using generalized estimating equations with Mundlak decomposition, separating between-person differences from within-person changes over time. We find that the three outcomes reflect distinct temporal structures: stress is primarily associated with stable between-person differences, loneliness with within-person variation, and sleep disturbance with a combination of both. Notably, these within-person dynamics are not captured by predefined network-traffic features, demonstrating the value of learned representations for longitudinal behavioral sensing. These results establish encrypted network traffic as a viable passive sensing modality, revealing interpretable behavioral dynamics -- particularly deviations from an individual's baseline -- that are not visible in raw traffic features.