从加密智能手机网络流量中学习行为信号
From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
AI总结：
本文利用基于Transformer的模型从加密网络流量中学习行为表征，结合用户特定适配器，并通过稀疏表示和广义估计方程分析，发现压力、孤独感和睡眠障碍分别与个体间差异、个体内波动及两者组合相关，且学习到的表征优于传统手工特征。
摘要：
人类行为难以在大规模下连续测量，然而日常活动和幸福感的痕迹可能反映在与个人设备的交互中。我们研究加密的智能手机网络流量是否可以作为被动感知信号，用于检测与睡眠障碍、压力和孤独感相关的行为状态。为了捕捉群体层面的模式和个体特定的行为，我们采用基于Transformer的模型，该模型带有用户特定的适配器，学习网络活动的表征，同时考虑个人基线及其偏差。为了提高可解释性，我们进一步使用稀疏表示学习分析这些表征，以识别与不同活动模式相关的潜在行为特征。我们使用带有Mundlak分解的广义估计方程将所得特征与睡眠障碍、压力和孤独感联系起来，从而能够区分稳定的个体间差异和随时间变化的个体内变化。我们的分析揭示了这三种结果具有不同的时间动态：压力主要与持续的个体间变异相关，孤独感与个体内波动更密切相关，而睡眠障碍则反映了两者的结合。重要的是，这些个体内行为信号无法通过传统的手工网络流量特征恢复，这突显了学习表征在纵向行为建模中的优势。总体而言，我们的发现表明加密网络流量包含可解释的行为信息，并能够支持被动、可扩展的行为动态监测，特别是相对于个体典型活动模式的变化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
