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20172026
most citedTuning Windowed Chi-Squared Detectors for Sensor Attacks

26 citations · 54 across the 40 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

eess.SY2022★ 2 cited

Plug-and-Play Secondary Control for Safety of LTI Systems under Attacks

Yankai Lin, Michelle S. Chong, Carlos Murguia

We consider the problem of controller design for linear time-invariant cyber-physical systems (CPSs) controlled via networks. Specifically, we adopt the set-up that a controller ha…

cs.CR2022

Immersion and Invariance-based Coding for Privacy in Remote Anomaly Detection

Haleh Hayati, Nathan van de Wouw, Carlos Murguia

We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup.…

eess.SY2022

Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design

Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1

This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated inte…

eess.SY2022★ 2 cited

Privacy-Preserving Anomaly Detection in Stochastic Dynamical Systems: Synthesis of Optimal Gaussian Mechanisms

Haleh Hayati, Carlos Murguia, Nathan van de Wouw

We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy. We consider the problem setting in which a remo…

eess.SY2022

Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction

Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1

In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems. The method consists in augmenting the system dynamics with an approximated ul…

cs.LG2022

Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption

Haleh Hayati, Carlos Murguia, Nathan van de Wouw

Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their da…