A Survey of Defensive Deception: Approaches Using Game Theory and Machine Learning

Abstract

Defensive deception is a promising approach for cyber defense. Via defensive deception, a defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding some of its resources. Although defensive deception is garnering increasing research attention, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey 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.