Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model
Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model
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发表时间：
2020-02
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通讯作者：
Henger Li;Wenxian Shen;Zizhan Zheng
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作者：
Henger Li;Wenxian Shen;Zizhan Zheng
移动目标防御已经成为保护易受攻击系统免受持续和隐形攻击的关键范例。为了保护系统，防御者主动更改系统配置，以限制安全漏洞暴露给潜在的攻击者。在这样做的过程中，防御者为攻击者创造了不对称的不确定性和复杂性，使他们更难危及系统。在实践中，防御者每次迁移系统配置都会产生切换成本。切换成本通常取决于当前配置和后续配置。此外，不同的系统配置通常需要不同的攻击者利用和攻击的时间量。因此，防守者必须同时决定系统配置的最佳顺序和切换的最佳时机。在本文中，我们提出了一个马尔可夫Stackelberg博弈框架，以精确地描述防御者的空间和时间的决策，面对先进的攻击者。我们引入了一个相对值迭代算法，计算防御者的最佳移动目标防御策略。通过对实际问题的实证分析，验证了马尔可夫-斯塔克伯格博弈模型在时空移动目标防御中的优势。
Moving target defense has emerged as a critical paradigm of protecting a vulnerable system against persistent and stealthy attacks. To protect a system, a defender proactively changes the system configurations to limit the exposure of security vulnerabilities to potential attackers. In doing so, the defender creates asymmetric uncertainty and complexity for the attackers, making it much harder for them to compromise the system. In practice, the defender incurs a switching cost for each migration of the system configurations. The switching cost usually depends on both the current configuration and the following configuration. Besides, different system configurations typically require a different amount of time for an attacker to exploit and attack. Therefore, a defender must simultaneously decide both the optimal sequences of system configurations and the optimal timing for switching. In this paper, we propose a Markov Stackelberg Game framework to precisely characterize the defender's spatial and temporal decision-making in the face of advanced attackers. We introduce a relative value iteration algorithm that computes the defender's optimal moving target defense strategies. Empirical evaluation on real-world problems demonstrates the advantages of the Markov Stackelberg game model for spatial-temporal moving target defense.