Anomaly Score Preserving Data Poisoning Attacks on Data-Driven Controller Design
This paper investigates stealthy data poisoning attacks against data-driven controller synthesis and develops an attack generation method. During data collection, an attacker can sequentially manipulate state measurements, while the defender attempts to detect such manipulation using a residual-based anomaly score based on the Mahalanobis distance. Under suitable assumptions on the defender's prior knowledge, we characterize the class of time-varying affine transformations that preserve the anomaly score for arbitrary data and parameterize this class by the orthogonal group. Based on this characterization, we formulate attack design under the stealthiness condition as an optimization problem over the orthogonal group and propose a Riemannian gradient method. Numerical experiments demonstrate that the proposed attack can degrade closed-loop performance while remaining stealthy.