  This work focuses on the use of deep learning for vulnerability analysis of
cyber-physical systems (CPS). Specifically, we consider a control architecture
widely used in CPS (e.g., robotics), where the low-level control is based on
e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate
analyzing the impact potential sensing attacks could have, our objective is to
develop learning-enabled attack generators capable of designing stealthy
attacks that maximally degrade system operation. We show how such problem can
be cast within a learning-based grey-box framework where parts of the runtime
information are known to the attacker, and introduce two models based on
feed-forward neural networks (FNN); both models are trained offline, using a
cost function that combines the attack effects on the estimation error and the
residual signal used for anomaly detection, so that the trained models are
capable of recursively generating such effective sensor attacks in real-time.
The effectiveness of the proposed methods is illustrated on several case
studies.
