  Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have
demonstrated the capacity to efficiently build a prototype of "normal" cyber
behaviors in order to detect cyber threats' activity with greater accuracy than
traditional rule-based IDS. Because these are largely black boxes, their
acceptance requires proof of robustness to stealthy adversaries. Since it is
impossible to build a baseline from activity completely clean of that of
malicious cyber actors (outside of controlled experiments), the training data
for deployed models will be poisoned with examples of activity that analysts
would want to be alerted about. We train an autoencoder-based anomaly detection
system on network activity with various proportions of malicious activity mixed
in and demonstrate that they are robust to this sort of poisoning.
