  This paper describes the use of neural networks to enhance simulations for
subsequent training of anomaly-detection systems. Simulations can provide edge
conditions for anomaly detection which may be sparse or non-existent in
real-world data. Simulations suffer, however, by producing data that is "too
clean" resulting in anomaly detection systems that cannot transition from
simulated data to actual conditions. Our approach enhances simulations using
neural networks trained on real-world data to create outputs that are more
realistic and variable than traditional simulations.
