  This work considers the problem of designing an attack strategy on remote
state estimation under the condition of strict stealthiness and
$\epsilon$-stealthiness of the attack. An attacker is assumed to be able to
launch a linear attack to modify sensor data. A metric based on
Kullback-Leibler divergence is adopted to quantify the stealthiness of the
attack. We propose a generalized linear attack based on past attack signals and
the latest innovation. We prove that the proposed approach can obtain an attack
that can cause more estimation performance loss than linear attack strategies
recently studied in the literature. The result thus provides a bound on the
tradeoff between available information and attack performance, which is useful
in the development of mitigation strategies. Finally, some numerical examples
are given to evaluate the performance of the proposed strategy.
