Recent research in cybersecurity has begun to develop active defense strategies using game‐theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance‐based learning cognitive model, built in ACT‐R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.

中文翻译：

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使用认知模型实现网络防御的个性化欺骗信号。

网络安全方面的最新研究已开始通过对有限防御的分配进行博弈论优化并结合欺骗性信号来开发主动防御策略。这些算法假设人类行为合理。但是，在旨在模拟内部攻击场景的在线游戏中，人类的行为表明，扮演攻击者角色的人类的攻击次数要比在完全理性的情况下预测的次数要多得多。我们描述了一个基于实例的学习认知模型，该模型内置于ACT-R中，可以准确地预测人类的表现和游戏中的偏见。为了提高防御能力，我们提出了一种自适应的信号传递方法，该方法使用认知模型实时跟踪个人的经历。我们讨论了针对个性化防御的这种自适应信令方法的结果和含义。