  Automated adversary emulation is becoming an indispensable tool of network
security operators in testing and evaluating their cyber defenses. At the same
time, it has exposed how quickly adversaries can propagate through the network.
While research has greatly progressed on quality decoy generation to fool human
adversaries, we may need different strategies to slow computer agents. In this
paper, we show that decoy generation can slow an automated agent's decision
process, but that the degree to which it is inhibited is greatly dependent on
the types of objects used. This points to the need to explicitly evaluate decoy
generation and placement strategies against fast moving, automated adversaries.
