Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
It provides a comprehensive review for researchers and practitioners in cybersecurity, but is incremental as it synthesizes existing work without new empirical results.
This survey paper systematically investigates defensive deception in cyber defense, focusing on game theory and machine learning approaches, and outlines research directions to address current gaps.
Defensive deception is a promising approach for cyber defense. Via defensive deception, the defender can anticipate attacker actions; it can mislead or lure attacker, or hide real resources. 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.