  False Data Injection (FDI) attacks against powersystem state estimation are a
growing concern for operators.Previously, most works on FDI attacks have been
performedunder the assumption of the attacker having full knowledge ofthe
underlying system without clear justification. In this paper, wedevelop a
topology-learning-aided FDI attack that allows stealthycyber-attacks against AC
power system state estimation withoutprior knowledge of system information. The
attack combinestopology learning technique, based only on branch and bus
powerflows, and attacker-side pseudo-residual assessment to performstealthy FDI
attacks with high confidence. This paper, for thefirst time, demonstrates how
quickly the attacker can developfull-knowledge of the grid topology and
parameters and validatesthe full knowledge assumptions in the previous work.
