标题： 通过攻击来学习防御（反之亦然）：网络安全游戏中的学习迁移 显示英文标题

标题： Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning in Cybersecurity Games

摘要： 设计考虑人类决策中的认知偏差的网络防御系统已在提高对抗人类攻击者的表现方面显示出显著的成功。 然而，这一领域的大部分关注集中在对人类攻击者偏差的相对简单的描述上，而对于对手行为或如何通过干扰攻击者的行为来改进防御了解甚少。 在本工作中，我们提出了一种受基于实例的学习理论、心智理论和学习迁移的认知能力启发的人类决策模型。 该模型通过从安全场景中的两个角色（防御者和攻击者）中学习，并对对手的信念、意图和行动进行预测来发挥作用。 与那些试图在不考虑人类偏差的情况下表现最佳的替代方法相比，所提出的模型能够更好地防御来自各种对手的攻击。 此外，通过显式建模人类的学习迁移，所提出的模型在对抗各种类人行为时表现更好，而学习迁移尚未应用于网络防御场景。 模拟实验的结果表明了受认知启发的在攻击和防御角色中训练的代理模型的潜在实用性，以及这些见解可能如何用于现实世界的网络安全。 显示英文摘要

摘要： Designing cyber defense systems to account for cognitive biases in human decision making has demonstrated significant success in improving performance against human attackers. However, much of the attention in this area has focused on relatively simple accounts of biases in human attackers, and little is known about adversarial behavior or how defenses could be improved by disrupting attacker's behavior. In this work, we present a novel model of human decision-making inspired by the cognitive faculties of Instance-Based Learning Theory, Theory of Mind, and Transfer of Learning. This model functions by learning from both roles in a security scenario: defender and attacker, and by making predictions of the opponent's beliefs, intentions, and actions. The proposed model can better defend against attacks from a wide range of opponents compared to alternatives that attempt to perform optimally without accounting for human biases. Additionally, the proposed model performs better against a range of human-like behavior by explicitly modeling human transfer of learning, which has not yet been applied to cyber defense scenarios. Results from simulation experiments demonstrate the potential usefulness of cognitively inspired models of agents trained in attack and defense roles and how these insights could potentially be used in real-world cybersecurity.