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.

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防御性欺骗调查：使用博弈论和机器学习的方法

防御性欺骗是一种很有前途的网络防御方法。通过防御欺骗，防御者可以通过误导或引诱攻击者或隐藏其某些资源来预测和防止攻击。尽管防御性欺骗引起了越来越多的研究关注，但尚未对其关键组成部分、基本原理及其在各种问题环境中的权衡进行系统调查。本次调查侧重于以博弈论和机器学习为中心的防御性欺骗研究，因为这些是广泛用于防御性欺骗的人工智能方法的主要系列。本文提出了先前工作的见解、教训和局限性。最后概述了一些研究方向，以解决当前防御性欺骗研究中的主要差距。