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From Spectral Theorem to Statistical Independence with Application to System Identification
In the light of these findings we set the stage for non-asymptotic error analysis in estimation of state transition matrix $A$ via least squares regression on observed trajectory by showing that element-wise error is essentially a variant of well-know Littlewood-Offord problem.
Vulnerability Analysis of Nonlinear Control Systems to Stealthy False Data Injection Attacks
When the attacker has complete knowledge about the system, we show that if the closed loop system is incrementally exponentially stable while the open loop plant is incrementally unstable, then the system is vulnerable to stealthy yet impactful attacks on sensors.
Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits
Specifically, we consider a general setup with a nonlinear affine physical plant controlled with a perception-based controller that maps both the physical (e. g., IMUs) and perceptual (e. g., camera) sensing to the control input; the system is also equipped with a statistical or learning-based anomaly detector (AD).
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).
