Anomaly Detection in Partially Observed Traffic Networks
Anomaly Detection in Partially Observed Traffic Networks
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DOI：
10.1109/tsp.2019.2892026
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发表时间：
2019-03-15
影响因子：
5.4
通讯作者：
Hero, Alfred O., III
中科院分区：
文献类型：
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作者：
Hou, Elizabeth;Yilmaz, Yasin;Hero, Alfred O., III
本文讨论的问题，检测异常活动的交通网络中的网络是不直接观察。如果知道网络中节点到节点的流量应该是什么，任何与此基线显著不同的活动都将被视为异常。我们提出了一个贝叶斯层次模型估计的流量率和检测网络中的异常变化。该模型的概率性质允许我们执行统计拟合优度测试，以检测与基线网络的显著偏差。我们发现，由于层次贝叶斯模型的结构更加明确，即使EM算法估计的经验模型被错误指定，这样的测试也表现良好。我们将我们的模型应用于模拟和真实的数据集，以证明其上级性能优于现有的替代品。
This paper addresses the problem of detecting anomalous activity in traffic networks where the network is not directly observed. Given knowledge of what the node-to-node traffic in a network should be, any activity that differs significantly from this baseline would be considered anomalous. We propose a Bayesian hierarchical model for estimating the traffic rates and detecting anomalous changes in the network. The probabilistic nature of the model allows us to perform statistical goodness-of-fit tests to detect significant deviations from a baseline network. We show that due to the more defined structure of the hierarchical Bayesianmodel, such tests perform well even when the empirical models estimated by the EM algorithm are misspecified. We apply our model to both simulated and real datasets to demonstrate its superior performance over existing alternatives.