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Data-driven techniques are used in cyber-physical systems (CPS) for controlling autonomous vehicles, handling demand responses for energy management, and modeling human physiology for medical devices. These data-driven techniques extract models from training data, where their performance is often analyzed with respect to random errors in the training data. However, if the training data is maliciously altered by attackers, the effect of these attacks on the learning algorithms underpinningdoi:10.1145/3055004.3055006 dblp:conf/iccps/ParkWL17 fatcat:xnfm7uytbfayzd5gllft4bcgfa