Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

• URL: http://arxiv.org/abs/2101.10121v1
• Date: Thu, 21 Jan 2021 21:55:43 GMT
• Title: Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
• Authors: Mu Zhu, Ahmed H. Anwar, Zelin Wan, Jin-Hee Cho, Charles Kamhoua, and Munindar P. Singh
• Abstract summary: This paper focuses on defensive deception research centered on game theory and machine learning. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.
• Score: 13.624968742674143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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