  Defensive deception is a promising approach for cyberdefense. Although
defensive deception is increasingly popular in the research community, there
has not been a systematic investigation of its key components, the underlying
principles, and its tradeoffs in various problem settings. This survey paper
focuses on defensive deception research centered on game theory and machine
learning, since these are prominent families of artificial intelligence
approaches that are widely employed in defensive deception. This paper brings
forth insights, lessons, and limitations from prior work. It closes with an
outline of some research directions to tackle major gaps in current defensive
deception research.
