Abstract:
This paper considers a deflection-based detector for random attacks compromising the inputs of a network system, using measurements from nodes non-collocated with the inp...Show MoreMetadata
Abstract:
This paper considers a deflection-based detector for random attacks compromising the inputs of a network system, using measurements from nodes non-collocated with the input nodes. We derive the decision rule of our deflection-based detector, and characterize its performance as a function of the edge weights, attack and noise statistics, and the locations of input and output nodes. In the asymptotic measurement regime, we show that the detector's performance is governed by the singular values of the system's transfer function matrix. Finally, for a given input and output node locations, we numerically solve an optimization problem to find the optimal network edge weights that maximize the detector's performance. Numerical examples are presented to validate the theoretical results.
Published in: 2021 American Control Conference (ACC)
Date of Conference: 25-28 May 2021
Date Added to IEEE Xplore: 28 July 2021
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