  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 Bayesian model, 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.
