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.

翻译：防御性欺骗是一种有希望的网络防御方法。 防御性欺骗是防御性欺骗,捍卫者可以预见攻击者的行动;它可以误导或引诱攻击者,或者隐藏真正的资源。 尽管防御性欺骗在研究界越来越受欢迎,但在各种问题环境中,都没有对其关键组成部分、基本原则及其权衡进行系统调查。本调查文件侧重于以游戏理论和机器学习为核心的防御性欺骗研究,因为这些研究是被广泛用于防御性欺骗的人工智能方法的突出组合。本文提出了先前工作中的深刻见解、教训和局限性。最后,它概述了一些研究方向,以解决当前防御性欺骗研究中的重大差距。