On the Stealth of Unbounded Attacks Under Non-Negative-Kernel Feedback

TL;DR

This paper analyzes the stealth properties of unbounded false data injection attacks in linear time-varying control systems with non-negative kernel feedback, showing conditions under which attacks remain undetectable or untraceable.

Contribution

It provides theoretical conditions for the stealth and untraceability of polynomial FDIA signals in systems with non-negative kernels, using Volterra integral equations.

Findings

Polynomial FDIA signals of degree ≤ q are ε-stealthy.

FDIA signals of degree < q are untraceable.

Results are derived using linear Volterra integral equations.

Abstract

The stealth of false data injection attacks (FDIAs) against feedback sensors in linear time-varying (LTV) control systems is investigated. In that regard, the following notions of stealth are pursued: For some finite $\epsilon > 0$, i) an FDIA is deemed $\epsilon$-stealthy if the deviation it produces in the signal that is monitored by the anomaly detector remains $\epsilon$-bounded for all time, and ii) the $\epsilon$-stealthy FDIA is further classified as untraceable if the bounded deviation dissipates over time (asymptotically). For LTV systems that contain a chain of $q \geq 1$ integrators and feedback controllers with non-negative impulse-response kernels, it is proved that polynomial (in time) FDIA signals of degree $a$ - growing unbounded over time - will remain i) $\epsilon$-stealthy, for some finite $\epsilon > 0$, if $a \leq q$, and ii) untraceable, if $a < q$. These results are obtained using the theory of linear Volterra integral equations.