  A nearest neighbor-based detection scheme against load redistribution attacks
is presented. The detector is designed to scale from small to very large
systems while guaranteeing consistent detection performance. Extensive testing
is performed on a realistic, large scale system to evaluate the performance of
the proposed detector against a wide range of attacks, from simple random noise
attacks to sophisticated load redistribution attacks. The detection capability
is analyzed against different attack parameters to evaluate its sensitivity.
Finally, a statistical test that leverages the proposed detection algorithm is
introduced to identify which loads are likely to have been maliciously
modified, thus, localizing the attack subgraph. This test is based on ascribing
to each load a risk measure (probability of being attacked) and then computing
the best posterior likelihood that minimizes log-loss.
