Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
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by
Mu Zhu, Ahmed H. Anwar, Zelin Wan, Jin-Hee Cho, Charles Kamhoua, Munindar P. Singh
2021
Abstract
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.
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