具有转换代价的移动目标防御有效策略学习
Learning Effective Strategies for Moving Target Defense with Switching Costs
摘要：
移动目标防御（MTD）已经成为各种安全应用中的关键技术，因为它剥夺了攻击者利用系统漏洞进行侦察的能力。然而，大多数现有的研究在该领域假设不切实际的访问信息的攻击者的动机和/或行动时，制定MTD战略。许多现有的方法还假设关于系统的漏洞以及攻击者如何利用这些漏洞中的每一个的完整知识。在这项工作中，我们的目标是创建算法，生成有效的移动目标防御策略，不依赖于攻击者的先验知识。我们的工作假设防御者接收关于其自身奖励的信息的唯一方式是通过在重复的游戏设置中与攻击者的交互。根据可以从相互作用中获得的信息量，我们设计了两种不同的算法，使用多臂强盗制定，以确定有效的策略。然后，我们使用从国家漏洞数据库中挖掘的数据来评估我们的算法，以展示它们与最先进技术的性能相匹配，尽管使用的信息量要少得多。
英文摘要：
Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker's motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
