  The learning data requirements are analyzed for the construction of stealth
attacks in state estimation. In particular, the training data set is used to
compute a sample covariance matrix that results in a random matrix with a
Wishart distribution. The ergodic attack performance is defined as the average
attack performance obtained by taking the expectation with respect to the
distribution of the training data set. The impact of the training data size on
the ergodic attack performance is characterized by proposing an upper bound for
the performance. Simulations on the IEEE 30-Bus test system show that the
proposed bound is tight in practical settings.
