  The problem of verifying whether a multi-component system has anomalies or
not is addressed. Each component can be probed over time in a data-driven
manner to obtain noisy observations that indicate whether the selected
component is anomalous or not. The aim is to minimize the probability of
incorrectly declaring the system to be free of anomalies while ensuring that
the probability of correctly declaring it to be safe is sufficiently large.
This problem is modeled as an active hypothesis testing problem in the
Neyman-Pearson setting. Component-selection and inference strategies are
designed and analyzed in the non-asymptotic regime. For a specific class of
homogeneous problems, stronger (with respect to prior work) non-asymptotic
converse and achievability bounds are provided.
