  We exploit recent results in quantifying the robustness of neural networks to
input variations to construct and tune a model-based anomaly detector, where
the data-driven estimator model is provided by an autoregressive neural
network. In tuning, we specifically provide upper bounds on the rate of false
alarms expected under normal operation. To accomplish this, we provide a theory
extension to allow for the propagation of multiple confidence ellipsoids
through a neural network. The ellipsoid that bounds the output of the neural
network under the input variation informs the sensitivity - and thus the
threshold tuning - of the detector. We demonstrate this approach on a linear
and nonlinear dynamical system.
