  State estimators are crucial components of anomaly detectors that are used to
monitor cyber-physical systems. Many frequently used state estimators are
susceptible to model risk as they rely critically on the availability of an
accurate state-space model. Modeling errors make it more difficult to
distinguish whether deviations from expected behavior are due to anomalies or
simply a lack of knowledge about the system dynamics. In this research, we
account for model uncertainty through a multiplicative noise framework.
Specifically, we propose two different state estimators in this setting to
hedge against the model uncertainty risk namely, 1) multiplicative noise LQG,
and 2) Wasserstein distributionally robust Kalman filter. The size of the
residual from either estimator can then be compared against a threshold to
detect anomalies. Finally, the proposed detectors are validated using numerical
simulations. Extension of state-of-the-art anomaly detection in cyber-physical
systems to handle model uncertainty represents the main novel contribution of
the present work.
