  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 to use the multiplicative noise LQG based compensator
in this setting to hedge against the model uncertainty risk. The size of the
residual from the estimator can then be compared against a threshold to detect
anomalies. Finally, the proposed detector is 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.
