arXiv：cs.LG· Rameen Mahmood, Omar El Shahawy, Souptik Barua, Zachary Beattie, Jeffrey Kaye, Xuhai "Orson'' Xu, Chao-Yi Wu, Danny Yuxing Huang·· 4 小时前AI 评分51
arXiv 论文：将加密网络流量解读为纵向行为信号
From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
AI 导读
arXiv 论文（arXiv:2605.01616，已被 ACM IMWUT 录用）验证加密手机网络流量可作为被动感知模态，捕捉睡眠、压力和孤独相关行为模式。方法采用 Transformer 骨干加每用户适配器，并用稀疏自编码器提取可解释行为特征，结合 Mundlak 分解的广义估计方程区分人际差异与个体内变化。
正文
Abstract: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.
| Comments: | 38 pages, 8 figures. Accepted to Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 10, No. 4 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Networking and Internet Architecture (cs.NI) |
| ACM classes: | C.2.3; I.2.6; J.3 |
| Cite as: | arXiv:2605.01616 [cs.LG] |
| (or arXiv:2605.01616v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.01616 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1145/3857944
DOI(s) linking to related resources |
Submission history
From: Rameen Mahmood [view email]
[v1]
Sat, 2 May 2026 21:40:07 UTC (355 KB)
[v2]
Fri, 5 Jun 2026 22:55:35 UTC (355 KB)
[v3]
Tue, 6 Oct 2026 21:21:39 UTC (436 KB)
来源：arXiv：cs.LG · arxiv.org