Fundamental Stealthiness-Distortion Tradeoffs in Dynamical Systems under Injection Attacks: A Power Spectral Analysis
This research addresses the problem of understanding the limits of stealthy attacks and their impact on dynamical systems, which is important for system designers and security analysts.
This paper analyzes the fundamental stealthiness-distortion tradeoffs of linear Gaussian dynamical systems under data injection attacks, using Kullback-Leibler (KL) divergence as the stealthiness measure. It provides explicit power spectral formulas characterizing these tradeoffs and the properties of worst-case attacks.
In this paper, we analyze the fundamental stealthiness-distortion tradeoffs of linear Gaussian dynamical systems under data injection attacks using a power spectral analysis, whereas the Kullback-Leibler (KL) divergence is employed as the stealthiness measure. Particularly, we obtain explicit formulas in terms of power spectra that characterize analytically the stealthiness-distortion tradeoffs as well as the properties of the worst-case attacks. Furthermore, it is seen in general that the attacker only needs to know the input-output behaviors of the systems in order to carry out the worst-case attacks.