  Stealthy false data injection attacks on cyber-physical systems (CPSs)
introduce erroneous measurement information to on-board sensors with the
purpose to degrade system performance. An intelligent attacker is able to
leverage knowledge of the system model and noise characteristics to alter
sensor measurements while remaining undetected. To achieve this objective, the
stealthy attack sequence is designed such that the detector performs similarly
in the attacked and attack-free cases. Consequently, an attacker that wants to
remain hidden will leave behind traces of inconsistent behavior, contradicting
the system model. To deal with this problem, we propose a runtime monitor to
find these inconsistencies in sensor measurements by monitoring for serial
inconsistencies of the detection test measure. Specifically, we employ the
chi-square fault detection procedure to monitor the magnitude and signed
sequence of its chi-square test measure. We validate our approach with
simulations on an unmanned ground vehicle (UGV) under stealthy attacks and
compare the detection performance with various state-of-the-art anomaly
detectors.
