Completely Stealthy FDI Attack Against State Estimation in Networked Control Systems
Yifa Liu, Long Cheng
Abstract
An effective false data injection attack should cause enough damage to the system while avoiding being spotted by detection methods. This brief proposes an extremely stealthy attack strategy, which can make the residual almost always unchanged to invalidate both the widely used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula> detector and the advanced summation detector and can eventually cause unbound state deviations. Compared with the existing attack methods, the proposed attack strategy can reduce the sum of cumulative residual increments by 85.39% while causing 9817001 times of the state deviations in the numerical simulations.