  We continue to develop our neural network (NN) based forecasting approach to
anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial
control system (ICS) testbed dataset. We propose genetic algorithms (GA) to
find the best NN architecture for a given dataset, using the NAB metric to
assess the quality of different architectures. The drawbacks of the F1-metric
are analyzed. Several techniques are proposed to improve the quality of AD:
exponentially weighted smoothing, mean p-powered error measure, individual
error weight for each variable, disjoint prediction windows. Based on the
techniques used, an approach to anomaly interpretation is introduced.
