  We develop an anomaly-detection method when systematic anomalies, possibly
statistically very similar to genuine inputs, are affecting control systems at
the input and/or output stages. The method allows anomaly-free inputs (i.e.,
those before contamination) to originate from a wide class of random sequences,
thus opening up possibilities for diverse applications. To illustrate how the
method works on data, and how to interpret its results and make decisions, we
analyze several actual time series, which are originally non-stationary but in
the process of analysis are converted into stationary. As a further
illustration, we provide a controlled experiment with anomaly-free inputs
following an ARMA time series model under various contamination scenarios.
