通过建模停留点与行程，实现无标签下人类移动行为的异常检测。
Uncertainty-aware Human Mobility Modeling and Anomaly Detection
- 将GPS数据转为停留点与行程序列，用序列模型处理。
- 在工业级数据上，异常检测AUCROC提升3%~15%。
- 融合数据与模型不确定性，适合高噪声真实场景使用。
基于大规模人类代理的时序GPS坐标，如何在无标注数据情况下有效检测异常行为（如恶意行为）？人类移动与轨迹建模已广泛研究，但对复杂输入的处理能力与性能-效率权衡各异。本文将原始GPS数据建模为一系列停留点事件，每个事件包含时空特征，并考虑停留点间的行程（通勤）。该建模方式使我们可利用现代序列模型进行无监督训练与异常检测。提出模型USTAD（Uncertainty-aware Spatio-Temporal Anomaly Detection）同时引入数据不确定性（aleatoric）以捕捉个体行为固有的随机性，以及模型不确定性（epistemic）以应对多样行为下的数据稀疏问题。二者结合构建鲁棒损失函数，并支持不确定性感知的异常评分决策。大量实验表明，在工业级数据上，USTAD相比基线模型异常检测的AUCROC提升3%~15%。
原文摘要 · Abstract (English)
Given the temporal GPS coordinates from a large set of human agents, how can we model their mobility behavior toward effective anomaly (e.g. bad-actor or malicious behavior) detection without any labeled data? Human mobility and trajectory modeling have been extensively studied, showcasing varying abilities to manage complex inputs and balance performance-efficiency trade-offs. In this work, we formulate anomaly detection in complex human behavior by modeling raw GPS data as a sequence of stay-point events, each characterized by spatio-temporal features, along with trips (i.e. commute) between the stay-points. Our problem formulation allows us to leverage modern sequence models for unsupervised training and anomaly detection. Notably, we equip our proposed model USTAD (for Uncertainty-aware Spatio-Temporal Anomaly Detection) with aleatoric (i.e. data) uncertainty estimation to account for inherent stochasticity in certain individuals' behavior, as well as epistemic (i.e. model) uncertainty to handle data sparsity under a large variety of human behaviors. Together, aleatoric and epistemic uncertainties unlock a robust loss function as well as uncertainty-aware decision-making in anomaly scoring. Extensive experiments shows that USTAD improves anomaly detection AUCROC by 3\%-15\% over baselines in industry-scale data.
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