  When traditional pole-dynamics attacks (TPDAs) are implemented with nominal
models, model mismatch between exact and nominal models often affects their
stealthiness, or even makes the stealthiness lost. To solve this problem, our
current paper presents a novel stealthy measurement-aided pole-dynamics attacks
(MAPDAs) method with model mismatch. Firstly, the limitations of TPDAs using
exact models are revealed, where exact models help ensure the stealthiness of
TPDAs but model mismatch severely influences its stealthiness. Secondly, to
handle model mismatch, the proposed MAPDAs method is designed by using a model
reference adaptive control strategy, which can keep the stealthiness. Moreover,
it is easier to implement as only the measurements are needed in comparison
with the existing methods requiring both the measurements and control inputs.
Thirdly, the performance of the proposed MAPDAs method is explored using
convergence of multivariate measurements, and MAPDAs with model mismatch have
the same stealthiness and similar destructiveness as TPDAs. Specifically,
MAPDAs with adaptive gains will remain stealthy at an acceptable detection
threshold till destructiveness occurs. Finally, experimental results from a
networked inverted pendulum system confirm the feasibility and effectiveness of
the proposed method.
