Drafts by Spencer Hallyburton
This work focuses on the use of deep learning for vulnerability analysis of cyberphysical systems... more This work focuses on the use of deep learning for vulnerability analysis of cyberphysical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., in 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 only parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN) and recurrent neural networks (RNN). Both models are trained offline, using a cost function that combines the attack impact on the estimation error (and thus control) and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating effective yet stealthy sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.
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Drafts by Spencer Hallyburton